Have you updated SPM with the latest updates?
I just used your condition.mat file with some random files and it
created a design without a problem.
Best Regards, Donald McLaren
================D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
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On Wed, Feb 9, 2011 at 9:16 AM, Tatiana Usnich
<[log in to unmask]> wrote:
> Dear SPMlers,
>
> We also have the same problem. Starting to run fMRI model specification in SPM8 we are asked for a vector of onsets for one of the conditions and durations even though the conditions are specified in a .mat file.
>
> Conditions file is attached. Id really appreciate your help.
>
> Tatiana
>
========================================================================Date: Wed, 9 Feb 2011 16:53:43 +0000
Reply-To: Scott Hayes <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Scott Hayes <[log in to unmask]>
Subject: Research Assistant and Postdoctoral Fellowship Positions at VA
Boston
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We anticipate an opening for a full-time research assistant to begin in June, 2011. We are seeking applicants with previous experiences with behavioral testing and neuroimaging. The RA will assist with two primary projects, one focusing on aging and memory, and the other focusing on the elucidation of neural correlates of Posttraumatic Stress Disorder and Traumatic Brain Injury.
Mieke Verfaellie and David Salat also have an opening for a postdoctoral fellow examining the neural correlates of blast-induced TBI.
Additional details for both positions are available here: http://www.bu.edu/brainlab/job-openings/
========================================================================Date: Wed, 9 Feb 2011 17:13:52 +0000
Reply-To: Scott Hayes <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Scott Hayes <[log in to unmask]>
Subject: Re: Research Assistant and Postdoctoral Fellowship Positions at
VA Boston
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Full description of RA position:
Preliminary Announcement for Neuroimaging Research Assistant Position
National Center for Posttraumatic Stress Disorder (Boston)
The Behavioral Science Division of the National Center for PTSD,
located at the VA Boston Healthcare System, anticipates an opening for
a research assistant who will work on projects related to neuroimaging
of posttraumatic stress disorder and memory disorders.
Responsibilities for this position include: Collecting behavioral and
neuroimaging data from healthy and clinical subjects, entering data
and managing databases, performing statistical analyses, and assisting
in the preparation of grants, manuscripts, and professional
presentations. Applicants should have a B.A./B.S. (concentration in
Cognitive Neuroscience/Psychology is preferred), experience working in
an imaging laboratory and running functional magnetic resonance
imaging (fMRI) experiments, proficiency with UNIX and MATLAB, FSL,
and/or SPM, and the maturity and interpersonal sensitivity necessary
for working in a clinical setting.
Openings are expected for June 2011. Salary and benefits are highly
competitive with similar positions at institutions in the Boston area.
A two year commitment typically is required. Official job postings and
formal application instructions are expected to appear in early March
2011, but potential candidates are encouraged to submit the following
materials prior to formal application: 1) a letter describing their
interest in a position and related career goals, 2) a resume, 3)
a current transcript, and 4) at least two letters of recommendation.
Materials can be sent electronically or by mail to:
Andrea Carney
National Center for PTSD (116B-2)
VA Boston Healthcare System
150 South Huntington Ave.
Boston, MA 02130-4817
[log in to unmask]
Further information can be obtained via e-mail or by phone at (857)
364-4143, or visit the websites:
http://www.bu.edu/traumamemorylab/
http://www.bu.edu/brainlab/
========================================================================Date: Wed, 9 Feb 2011 17:54:43 +0000
Reply-To: Cyril Pernet <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Cyril Pernet <[log in to unmask]>
Subject: spm_preproc
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Dear SPMers, John
silly question to get my transformation matrices after segmenting ...
once I run spm_prep2sn, what function shall I call to write down on the
disk the transformation matrices - couldn't find out ..
cheers
cyril
--
The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.
========================================================================Date: Wed, 9 Feb 2011 18:10:44 +0000
Reply-To: Mohamed Seghier <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Mohamed Seghier <[log in to unmask]>
Subject: Re: spm_preproc
Comments: To: Cyril Pernet <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Hi Cyril,
Let's say R = spm_preproc('your anatomical image');
just run PO = spm_prep2sn(R) ;
and then, all the variables that you need are:
VG = PO.VG ;
VF = PO.VF ;
Tr = PO.Tr ;
Affine = PO.Affine ;
flags = PO.flags ;
You can save them as an '*_seg_sn.mat' if your wish...
I hope this helps,
Mohamed
On 09/02/2011 17:54, Cyril Pernet wrote:
> Dear SPMers, John
>
> silly question to get my transformation matrices after segmenting ...
> once I run spm_prep2sn, what function shall I call to write down on
> the disk the transformation matrices - couldn't find out ..
>
> cheers
> cyril
>
>
>
========================================================================Date: Wed, 9 Feb 2011 11:41:10 -0800
Reply-To: kambiz rakhshan <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: kambiz rakhshan <[log in to unmask]>
Subject: Vbm8
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Dear experts, I have done a vbm analysis on 100 subjects with age range between 25-80. I used vbm8 toolbox and modulated non-linear only option to modulate the normalizad data. My specific question was to find relative volumetric loss that were not induced by total gray matter. I have two question now: 1-should one include TIV as a covariate while using non-linear modulate option? 2- in my analysis, i didn't include TIV as cov but instead age as a cov variable. Negative correlation with age reflects almost whole the brain with a very strong t value( P<0.0001,FWE).what could be the reason for that? I basically used all the default options for preprocessing. Additionally,i have removed all the potential outliers that seem to have an artifact. Any comment will be highly appreciate. Best.
Kami
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<table cellspacing="0" cellpadding="0" border="0"><tr><td valign="top" style="font: inherit;"><div>Dear experts, I have done a vbm analysis on 100 subjects with age range between 25-80. I used vbm8 toolbox and modulated non-linear only option to modulate the normalizad data. My specific question was to find relative volumetric loss that were not induced by total gray matter. I have two question now: 1-should one include TIV as a covariate while using non-linear modulate option? 2- in my analysis, i didn't include TIV as cov but instead age as a cov variable. Negative correlation with age reflects almost whole the brain with a very strong t value( P<0.0001,FWE).what could be the reason for that? I basically used all the default options for preprocessing. Additionally,i have removed all the potential outliers that seem to have an artifact.
Any comment will be highly appreciate. Best. Kami</div></td></tr></table><br>
--0-1928328450-1297280470=:17947--
========================================================================Date: Wed, 9 Feb 2011 16:34:28 -0500
Reply-To: justine dupont <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: justine dupont <[log in to unmask]>
Subject: Re: non isotropic voxels for DARTEL anatomical template?
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Dear John and other experts
I have inherited a nice T1 dataset but the voxels are anisotropic (-0.97 x 0.97 x 1). I want to do a VBM study using these data, however according to a very recent post by John on the list, DARTEL is not appropriate for anisotropic voxels. So my question is... is it OK to use traditional VBM method (i.e. not using DARTEL) for such data?
thank you very much :)
Justine
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Dear John and other experts <div><br></div><div>I have inherited a nice T1 dataset but the voxels are anisotropic (-0.97 x 0.97 x 1). I want to do a VBM study using these data, however according to a very recent post by John on the list, DARTEL is not appropriate for anisotropic voxels. So my question is... is it OK to use traditional VBM method (i.e. not using DARTEL) for such data?</div><div><br></div><div>thank you very much :)</div><div><br></div><div>Justine</div><div><br></div><div><br></div>
</body>
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========================================================================Date: Wed, 9 Feb 2011 17:37:58 -0500
Reply-To: =?ISO-8859-1?Q?Véronique_Paradis?= <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: =?ISO-8859-1?Q?Véronique_Paradis?= <[log in to unmask]>
Subject: negative beta values
In-Reply-To: <[log in to unmask]>
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Bonjour our dear SPMers
We have the following non-intuitive problem which can be easily stated:
1 -After making a T test on a simple condition (a beta value greater than
0) we get the coordinates of the activated regions for a given spherical
region of interest.
2- We thus expect the mean beta value within those ROI to be positive.
3- We extract the beta values themselves (from the beta and SPM files in the
corresponding statistical result directory) of all the voxels in the ROIs
4- When we compute means and stdev from them, we sometimes get negative mean
beta values which are statistically significant when compared to their
stdev.
5- How does it make sense to have both positive and negative beta values
considering that we used the same spherical ROI in each cases?.
6- The other point is that when we plot the beta value for a given ROI using
SPM we always get positive and much larger values.
How can we make sense of all of this?
No doubt that other readers have met the same problem before.
Can you shed any light on this, we are still groping in the dark...
Merci
Pierre and Véronique
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<div class="gmail_quote">
<div>Bonjour our dear SPMers</div>
<div> </div>
<div>We have the following non-intuitive problem which can be easily stated:</div>
<div> </div>
<div>1 -After making a T test on a simple condition (a beta value greater than 0) we get the coordinates of the activated regions for a given spherical region of interest.</div>
<div>2- We thus expect the mean beta value within those ROI to be positive.</div>
<div>3- We extract the beta values themselves (from the beta and SPM files in the corresponding statistical result directory) of all the voxels in the ROIs</div>
<div>4- When we compute means and stdev from them, we sometimes get negative mean beta values which are statistically significant when compared to their stdev.</div>
<div>5- How does it make sense to have both positive and negative beta values considering that we used the same spherical ROI in each cases?.</div>
<div>6- The other point is that when we plot the beta value for a given ROI using SPM we always get positive and much larger values.</div>
<div>How can we make sense of all of this?</div>
<div> </div>
<div>No doubt that other readers have met the same problem before.</div>
<div>Can you shed any light on this, we are still groping in the dark...</div>
<div> </div>
<div>Merci </div>
<div> </div>
<div>Pierre and Véronique</div>
<div> </div>
<div> </div></div><br>
--0016e656b59ae16c2b049be11db9--
========================================================================Date: Wed, 9 Feb 2011 17:50:58 -0500
Reply-To: Jonathan Peelle <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Jonathan Peelle <[log in to unmask]>
Subject: Re: Vbm8
Comments: To: kambiz rakhshan <[log in to unmask]>
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Dear Kami,
Dear experts, I have done a vbm analysis on 100 subjects with age range
> between 25-80. I used vbm8 toolbox and modulated non-linear only option to
> modulate the normalizad data. My specific question was to find relative
> volumetric loss that were not induced by total gray matter. I have two
> question now: 1-should one include TIV as a covariate while using non-linear
> modulate option? 2- in my analysis, i didn't include TIV as cov but instead
> age as a cov variable. Negative correlation with age reflects almost whole
> the brain with a very strong t value( P<0.0001,FWE).what could be the reason
> for that? I basically used all the default options for preprocessing.
> Additionally,i have removed all the potential outliers that seem to have an
> artifact. Any comment will be highly appreciate. Best. Kami
>
1) I have not used the VBM8 toolbox so will let others comment on the
specifics. My understanding is that most people who do the non-linear
modulate option would not include TIV in the statistical model, under the
assumption that the affine scaling component accurately reflects differences
in TIV (Buckner et al., 2004). At the same time, if you have a sufficient
number of subjects, I don't think there is any downside to including it, as
there may be residual variance you can explain (i.e. affine scaling is
probably not *perfectly* correlated with TIV). (I suppose one negative
would be that if tissue class segmentation provides a poor estimate of TIV,
or one that included systematic biases, than including this in the model
might decrease sensitivity to effects of interest...). I would be very
interested to hear comments from others on this approach.
The most common approach when not using the VBM8 toolbox would be to
modulate images (for both linear + nonlinear components) and simply try to
address differences in TIV in the statistical model (Barnes et al., 2010).
2) There are multiple types of global effects which may affect your data in
different ways. One of course is TIV; another is total gray matter (TGM).
If you look at, for example, Good et al. (2001; Fig 3) you can see that
these two measures don't always correlate. That is, generally, if you have
a bigger head (TIV) you may have a bigger brain (TGM). But in the case of
aging, there is also age-related GM loss, so as you age TGM decreases, but
your skull doesn't shrink (thus, TIV doesn't necessarily change). (Of
course, in cross-sectional studies there may be systematic effects on TIV
due to nutrition or health care etc., but I'm leaving those aside for now.)
Given that there are reliable decreases in GM in most areas of the brain
with age, your findings are sensible. If you want to identify areas that
change differently than TGM, you will need to somehow account for TGM in
your analysis. An easy of doing this is to include TGM in your statistical
model as a covariate (although this is not always appropriate; it really
depends on what you are interested in).
Hope this helps,
Jonathan
Barnes J, Ridgway GR, Bartlett J, Henley SMD, Lehmann M, Hobbs N, Clarkson
MJ, MacManus DG, Ourselin S, Fox NC (Head size, age and gender adjustment in
MRI studies: a necessary nuisance? NeuroImage 53:1244-1255.2010).
Buckner RL, Head D, Parker J, Fotenos AF, Marcus D, Morris JC, Snyder AZ (A
unified approach for morphometric and functional data analysis in young,
old, and demented adutls using automated atlas-based head size
normalization: reliability and validation against manual measurement of
total intracranial volume. NeuroImage 23:724-738.2004).
Good CD, Johnsrude IS, Ashburner J, Henson RNA, Friston KJ, Frackowiak RSJ
(A voxel-based morphometric study of ageing in 465 normal adult human
brains. NeuroImage 14:21-36.2001).
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Dear Kami,<br><br><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;"><table cellspacing="0" cellpadding="0" border="0"><tbody><tr><td valign="top" style="font:inherit">
<div>Dear experts, I have done a vbm analysis on 100 subjects with age range between 25-80. I used vbm8 toolbox and modulated non-linear only option to modulate the normalizad data. My specific question was to find relative volumetric loss that were not induced by total gray matter. I have two question now: 1-should one include TIV as a covariate while using non-linear modulate option? 2- in my analysis, i didn't include TIV as cov but instead age as a cov variable. Negative correlation with age reflects almost whole the brain with a very strong t value( P<0.0001,FWE).what could be the reason for that? I basically used all the default options for preprocessing. Additionally,i have removed all the potential outliers that seem to have an artifact.
Any comment will be highly appreciate. Best. Kami</div></td></tr></tbody></table></blockquote><div><br>
</div><div><br></div><div>1) I have not used the VBM8 toolbox so will let others comment on the specifics. My understanding is that most people who do the non-linear modulate option would not include TIV in the statistical model, under the assumption that the affine scaling component accurately reflects differences in TIV (Buckner et al., 2004). At the same time, if you have a sufficient number of subjects, I don't think there is any downside to including it, as there may be residual variance you can explain (i.e. affine scaling is probably not *perfectly* correlated with TIV). (I suppose one negative would be that if tissue class segmentation provides a poor estimate of TIV, or one that included systematic biases, than including this in the model might decrease sensitivity to effects of interest...). I would be very interested to hear comments from others on this approach. </div>
<div><br></div><div>The most common approach when not using the VBM8 toolbox would be to modulate images (for both linear + nonlinear components) and simply try to address differences in TIV in the statistical model (Barnes et al., 2010). </div>
</div><div><br></div><div><br></div>2) There are multiple types of global effects which may affect your data in different ways. One of course is TIV; another is total gray matter (TGM). If you look at, for example, Good et al. (2001; Fig 3) you can see that these two measures don't always correlate. That is, generally, if you have a bigger head (TIV) you may have a bigger brain (TGM). But in the case of aging, there is also age-related GM loss, so as you age TGM decreases, but your skull doesn't shrink (thus, TIV doesn't necessarily change). (Of course, in cross-sectional studies there may be systematic effects on TIV due to nutrition or health care etc., but I'm leaving those aside for now.) Given that there are reliable decreases in GM in most areas of the brain with age, your findings are sensible. If you want to identify areas that change differently than TGM, you will need to somehow account for TGM in your analysis. An easy of doing this is to include TGM in your statistical model as a covariate (although this is not always appropriate; it really depends on what you are interested in).<div>
<br></div><div>Hope this helps,</div><div><br></div><div>Jonathan<br><div><br></div><div><br></div><div><br></div><div><p style="margin: 0.0px 0.0px 0.0px 36.0px; text-indent: -36.0px; font: 12.0px Helvetica">Barnes J, Ridgway GR, Bartlett J, Henley SMD, Lehmann M, Hobbs N, Clarkson MJ, MacManus DG, Ourselin S, Fox NC (Head size, age and gender adjustment in MRI studies: a necessary nuisance? NeuroImage 53:1244-1255.2010).</p>
<p style="margin: 0.0px 0.0px 0.0px 36.0px; text-indent: -36.0px; font: 12.0px Helvetica"><br></p><p style="margin: 0.0px 0.0px 0.0px 36.0px; text-indent: -36.0px; font: 12.0px Helvetica">Buckner RL, Head D, Parker J, Fotenos AF, Marcus D, Morris JC, Snyder AZ (A unified approach for morphometric and functional data analysis in young, old, and demented adutls using automated atlas-based head size normalization: reliability and validation against manual measurement of total intracranial volume. NeuroImage 23:724-738.2004).</p>
<p style="margin: 0.0px 0.0px 0.0px 36.0px; text-indent: -36.0px; font: 12.0px Helvetica"><br></p></div><div><p style="margin: 0.0px 0.0px 0.0px 36.0px; text-indent: -36.0px; font: 12.0px Helvetica">Good CD, Johnsrude IS, Ashburner J, Henson RNA, Friston KJ, Frackowiak RSJ (A voxel-based morphometric study of ageing in 465 normal adult human brains. NeuroImage 14:21-36.2001).</p>
</div><div><br></div></div>
--00163630ed1965d720049be14c09--
========================================================================Date: Wed, 9 Feb 2011 22:03:53 -0500
Reply-To: Jonathan Peelle <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Jonathan Peelle <[log in to unmask]>
Subject: Re: negative beta values
Comments: To: =?iso-8859-1?Q?Véronique_Paradis?= <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Dear Véronique and Pierre,
Please see inline responses below. I think that the answer to your question depends on the details of your analysis, so if you cannot figure this out, it would be helpful to know a bit more about the analysis you've done.
> 1 -After making a T test on a simple condition (a beta value greater than 0) we get the coordinates of the activated regions for a given spherical region of interest.
> 2- We thus expect the mean beta value within those ROI to be positive.
I understand you to mean that you did a whole-brain analysis in SPM, identified some clusters that were significant, and then wanted to extract the parameter estimates (contrast values) for these regions.
You are correct to think that if voxels show a positive effect in a t-test (e.g., a [1] contrast over a condition), that these same voxels should show positive beta values for that condition. I suspect the difference may arise in the details of how you constructed your ROIs. For example, if you create spherical ROIs around peak coordinates, there could be voxels within that sphere that do not show a significant effect in the t-test; the bigger the sphere, the more likely this is to be true. If you want to just look at the voxels that show a significant effect in your t-test, you can build such an ROI using MarsBar (http://marsbar.sourceforge.net/)—that is, an ROI equivalent to an SPM cluster. So, it would be helpful to know the specifics of your analysis (single subject or group design, for example), and the details of how you constructed the ROIs.
> 3- We extract the beta values themselves (from the beta and SPM files in the corresponding statistical result directory) of all the voxels in the ROIs
Again, it would help to know the specifics—what files you extracted the data from, and the procedure you used to extract the values.
> 4- When we compute means and stdev from them, we sometimes get negative mean beta values which are statistically significant when compared to their stdev.
> 5- How does it make sense to have both positive and negative beta values considering that we used the same spherical ROI in each cases?.
(I'm not sure what cases you mean here, so I may have misunderstood what you are trying to do...)
> 6- The other point is that when we plot the beta value for a given ROI using SPM we always get positive and much larger values.
It would be good to verify how you are plotting the beta value for an ROI in SPM. However, as indeed you would expect the beta values to be positive, it might suggest that whatever values are extracted by SPM in this way are accurate, whereas the alternate approach (that results in the negative beta values) is not accurate (or at least, not what you want to do).
> How can we make sense of all of this?
It's seldom easy! ;-) Good luck.
Best regards,
Jonathan
========================================================================Date: Thu, 10 Feb 2011 08:43:13 +0100
Reply-To: Marko Wilke <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Marko Wilke <[log in to unmask]>
Subject: Re: spm_preproc
Comments: To: Mohamed Seghier <[log in to unmask]>
Comments: cc: cyril pernet <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Hi Cyril, Mohamed,
> Let's say R = spm_preproc('your anatomical image');
> just run PO = spm_prep2sn(R) ;
> and then, all the variables that you need are:
> VG = PO.VG ;
> VF = PO.VF ;
> Tr = PO.Tr ;
> Affine = PO.Affine ;
> flags = PO.flags ;
just to add another 2 cents: prep2sn is multifunctional, so if you call
it without an output argument, it will save the segmentation parameters
appropriately, as in
% segment dataset, need to specify options beforehand
results = spm_preproc(ana,opts_seg);
% process generated spatial normalization parameters
[po,pin] = spm_prep2sn(results);
% save parameters
spm_prep2sn(results);
% write out segmented data (better specify these options, too :)
spm_preproc_write(po,opts_write);
This saved me time and some headache, hope it does for you, too.
Cheers,
Marko
--
____________________________________________________
PD Dr. med. Marko Wilke
Facharzt für Kinder- und Jugendmedizin
Leiter, Experimentelle Pädiatrische Neurobildgebung
Universitäts-Kinderklinik
Abt. III (Neuropädiatrie)
Marko Wilke, MD, PhD
Pediatrician
Head, Experimental Pediatric Neuroimaging
University Children's Hospital
Dept. III (Pediatric Neurology)
Hoppe-Seyler-Str. 1
D - 72076 Tübingen, Germany
Tel. +49 7071 29-83416
Fax +49 7071 29-5473
[log in to unmask]
http://www.medizin.uni-tuebingen.de/kinder/epn
____________________________________________________
========================================================================Date: Thu, 10 Feb 2011 07:58:14 +0000
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Subject: Re: spm_preproc
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------------=_1297324745-21276-186--
========================================================================Date: Thu, 10 Feb 2011 09:57:38 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: non isotropic voxels for DARTEL anatomical template?
Comments: To: justine dupont <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
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It is the size of the voxels in the "imported" images that I was
referring to. The actual Dartel registration is usually done using
"imported" data, which are rigidly aligned versions of the GM and WM,
downsampled to 1.5 mm x 1.5 mm x 1.5 mm. Your original voxel sizes
should be fine.
Best regards,
-John
On 9 February 2011 21:34, justine dupont <[log in to unmask]> wrote:
> Dear John and other experts
> I have inherited a nice T1 dataset but the voxels are anisotropic (-0.97 x
> 0.97 x 1). I want to do a VBM study using these data, however according to
> a very recent post by John on the list, DARTEL is not appropriate
> for anisotropic voxels. So my question is... is it OK to use
> traditional VBM method (i.e. not using DARTEL) for such data?
> thank you very much :)
> Justine
>
>
========================================================================Date: Thu, 10 Feb 2011 12:07:03 +0100
Reply-To: marco tettamanti <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: marco tettamanti <[log in to unmask]>
Subject: DCM: mutual interactions between networks
MIME-Version: 1.0
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Message-ID: <[log in to unmask]>
Dear all,
I don't think that this question has been addressed before:
I would like to set up a DCM analysis with fMRI data to investigate the
following issue: we have identified an activation in a very well known network
of 4 brain regions (A,B,C,D), and we believe that, in our task, this network can
be gated - depending on which one of 2 different input modalities is involved -
by the activity of two other distinct networks, each including 3 brain regions
(E,F,G, or H,I,J).
That is, we would like to assess how network EFG interacts with network ABCD in
modality 1 and how network HIJ interacts with network ABDC in modality 2, in the
context of a sort of 2x2 (Modality x network-network interaction) factorial design.
I presume that one could address this, by simply treating each brain region
independently, and for instance by using BMS to test whether the preferred model
fits the expected interaction patterns, but I think that there may be problems
with the relatively large number of regions (>8).
I also presume that the question could be approached by non-linear DCM, even
though I think that non-linear DCM has been only used in the context of the
influence of one single region on a system of interconnected regions, and not in
the context of network-network interactions.
Can anyone suggest a more direct way to approach this issue?
Thank you and all the best,
Marco
--
Marco Tettamanti, Ph.D.
San Raffaele Scientific Institute
Via Olgettina 58
I-20132 Milano, Italy
Tel. ++39-02-26434888
Fax ++39-02-26434892
Email: [log in to unmask]
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========================================================================Date: Thu, 10 Feb 2011 11:14:52 +0000
Reply-To: "Pettersson-Yeo, William" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "Pettersson-Yeo, William" <[log in to unmask]>
Subject: New Segmentation Out of Memory Error
Content-Type: multipart/alternative;
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--_000_699C0D407EF04E2B8781F618FECB9D2Ckclacuk_
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Dear SPMers,
I use a MacBookPro with a 4GB hard drive and 2.53 GHz processor.
When I try and run the 'New Segment' function in SPM8, I get the 'Out of Memory' error message detailed below.
The structural files I'm trying to segment are approx. 80mb (bigger than what I usually work with)
I have tried re-installing spm8, and have also tried changing the defaults.stats.maxmem = 2^26 to 2^31 but without success.
Can anyone help?
Many thanks
William
(Dept.of Psychosis, Institute of Psychiatry, KCL)
ERROR MESSAGE:
------------------------------------------------------------------------
Running job #1
------------------------------------------------------------------------
Running 'New Segment'
Failed 'New Segment'
Error using ==> unknown
Out of memory. Type HELP MEMORY for your options.
In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_write8.m" (v3172), function "affind" at line 428.
In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_write8.m" (v3172), function "spm_preproc_write8" at line 241.
In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_run.m" (v3859), function "run_job" at line 112.
In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_run.m" (v3859), function "spm_preproc_run" at line 27.
In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/tbx_cfg_preproc8.m" (v3910), function "spm_local_preproc_run" at line 353.
The following modules did not run:
Failed: New Segment
??? Error using ==> spm_input at 1470
Input object cleared whilst waiting for response: Bailing out!
Error in ==> spm_dcm_ui at 88
selected = spm_input(str,1,'m',Actions);
??? Error using ==> spm_dcm_ui;
Error using ==> spm_input at 1470
Input object cleared whilst waiting for response: Bailing out!
??? Error while evaluating uicontrol Callback
--_000_699C0D407EF04E2B8781F618FECB9D2Ckclacuk_
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<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Dear SPMers,<div><br></div><div>I use a MacBookPro with a 4GB hard drive and 2.53 GHz processor.</div><div>When I try and run the 'New Segment' function in SPM8, I get the 'Out of Memory' error message detailed below.</div><div><br></div><div>The structural files I'm trying to segment are approx. 80mb (bigger than what I usually work with)</div><div><br></div><div>I have tried re-installing spm8, and have also tried changing the <span class="Apple-style-span" style="font-family: monospace; font-size: 13px; white-space: pre-wrap; ">defaults.stats.maxmem = 2^26 to </span><span class="Apple-style-span" style="font-family: monospace; font-size: 13px; white-space: pre-wrap; ">2^31 but without success. </span></div><div><span class="Apple-style-span" style="font-family: monospace; font-size: 13px; white-space: pre-wrap; "><br></span></div><div><span class="Apple-style-span" style="font-family: monospace; font-size: 13px; white-space: pre-wrap; ">Can anyone help?</span></div><div><span class="Apple-style-span" style="font-family: monospace; font-size: 13px; white-space: pre-wrap; "><br></span></div><div><span class="Apple-style-span" style="font-family: monospace; font-size: 13px; white-space: pre-wrap; ">Many thanks</span></div><div><span class="Apple-style-span" style="font-family: monospace; font-size: 13px; white-space: pre-wrap; ">William</span></div><div><span class="Apple-style-span" style="font-family: monospace; font-size: 13px; white-space: pre-wrap; ">(Dept.of Psychosis, Institute of Psychiatry, KCL)</span></div><div><br></div><div><br></div><div>ERROR MESSAGE:</div><div><div>------------------------------------------------------------------------</div><div>Running job #1</div><div>------------------------------------------------------------------------</div><div>Running 'New Segment'</div><div>Failed 'New Segment'</div><div>Error using ==> unknown</div><div>Out of memory. Type HELP MEMORY for your options.</div><div>In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_write8.m" (v3172), function "affind" at line 428.</div><div>In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_write8.m" (v3172), function "spm_preproc_write8" at line 241.</div><div>In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_run.m" (v3859), function "run_job" at line 112.</div><div>In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_run.m" (v3859), function "spm_preproc_run" at line 27.</div><div>In file "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/tbx_cfg_preproc8.m" (v3910), function "spm_local_preproc_run" at line 353.</div><div><br></div><div>The following modules did not run:</div><div>Failed: New Segment</div><div><br></div><div>??? Error using ==> spm_input at 1470</div><div>Input object cleared whilst waiting for response: Bailing out!</div><div><br></div><div>Error in ==> spm_dcm_ui at 88</div><div> selected = spm_input(str,1,'m',Actions);</div><div><br></div><div>??? Error using ==> spm_dcm_ui;</div><div>Error using ==> spm_input at 1470</div><div>Input object cleared whilst waiting for response: Bailing out!</div><div><br></div><div>??? Error while evaluating uicontrol Callback</div></div><div><br></div></body></html>
--_000_699C0D407EF04E2B8781F618FECB9D2Ckclacuk_--
========================================================================Date: Thu, 10 Feb 2011 11:35:59 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: New Segmentation Out of Memory Error
Comments: To: "Pettersson-Yeo, William" <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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The stats.maxmem setting won't have any effect here, as it only
determines how much data is read at a time when doing the stats. I
think you may simply be reaching the limit of how much a 32 bit
computer can hold in memory at once for one process. This is only
(2^32-1)/1024^2 Mbytes, so once you have a few deformation fields and
stuff in memory, then this is easily reached. 64 bit computers don't
have this limit.
I'm pretty sure that much of your image is not actually brain, so
maybe you could crop the data to make the file a bit smaller.
Best regards,
-John
On 10 February 2011 11:14, Pettersson-Yeo, William
<[log in to unmask]> wrote:
> Dear SPMers,
> I use a MacBookPro with a 4GB hard drive and 2.53 GHz processor.
> When I try and run the 'New Segment' function in SPM8, I get the 'Out of
> Memory' error message detailed below.
> The structural files I'm trying to segment are approx. 80mb (bigger than
> what I usually work with)
> I have tried re-installing spm8, and have also tried changing
> the defaults.stats.maxmem = 2^26 to 2^31 but without success.
> Can anyone help?
> Many thanks
> William
> (Dept.of Psychosis, Institute of Psychiatry, KCL)
>
> ERROR MESSAGE:
> ------------------------------------------------------------------------
> Running job #1
> ------------------------------------------------------------------------
> Running 'New Segment'
> Failed 'New Segment'
> Error using ==> unknown
> Out of memory. Type HELP MEMORY for your options.
> In file
> "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_write8.m"
> (v3172), function "affind" at line 428.
> In file
> "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_write8.m"
> (v3172), function "spm_preproc_write8" at line 241.
> In file
> "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_run.m"
> (v3859), function "run_job" at line 112.
> In file
> "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/spm_preproc_run.m"
> (v3859), function "spm_preproc_run" at line 27.
> In file
> "/Users/williampettersson-yeo/Documents/spm8/toolbox/Seg/tbx_cfg_preproc8.m"
> (v3910), function "spm_local_preproc_run" at line 353.
> The following modules did not run:
> Failed: New Segment
> ??? Error using ==> spm_input at 1470
> Input object cleared whilst waiting for response: Bailing out!
> Error in ==> spm_dcm_ui at 88
> selected = spm_input(str,1,'m',Actions);
> ??? Error using ==> spm_dcm_ui;
> Error using ==> spm_input at 1470
> Input object cleared whilst waiting for response: Bailing out!
> ??? Error while evaluating uicontrol Callback
>
========================================================================Date: Thu, 10 Feb 2011 13:52:05 +0100
Reply-To: Tatiana Usnich <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Tatiana Usnich <[log in to unmask]>
Subject: Re: Question regarding the fMRI model specification
Comments: To: "MCLAREN, Donald" <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Thank you!
There was in fact one empty cell in other conditions.mat files, that
didn't work. That was most likely causing the problem.
We restructured the .mat file and it works now.
Best regards,
Tatiana
> (1) You probably have less data than time in your conditions.mat (this
> would make it appear that no events and it might ask for conditions;
> how did you specify the time in the design and how many time points
> are in each run?
>
> (2) Have you updated SPM with the latest updates?
>
> I just used your condition.mat file with some random files and it
> created a design without a problem.
>
> Best Regards, Donald McLaren
> ================> D.G. McLaren, Ph.D.
> Postdoctoral Research Fellow, GRECC, Bedford VA
> Research Fellow, Department of Neurology, Massachusetts General
> Hospital and Harvard Medical School
> Office: (773) 406-2464
> ====================> This e-mail contains CONFIDENTIAL INFORMATION which may contain
> PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
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> named above. If the reader of the e-mail is not the intended recipient
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>
>
>
> On Wed, Feb 9, 2011 at 9:16 AM, Tatiana Usnich
> <[log in to unmask]> wrote:
>> Dear SPMlers,
>>
>> We also have the same problem. Starting to run fMRI model specification
>> in SPM8 we are asked for a vector of onsets for one of the conditions
>> and durations even though the conditions are specified in a .mat file.
>>
>> Conditions file is attached. Id really appreciate your help.
>>
>> Tatiana
>>
>
========================================================================Date: Thu, 10 Feb 2011 12:53:29 +0000
Reply-To: Meng-Chuan Lai <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Meng-Chuan Lai <[log in to unmask]>
Subject: registration error under DARTEL and suggested smoothness
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Dear John and SPM users.
I would like to inquire if there is any available data showing the average registration error by DARTEL?
I am currently running VBM using DARTEL-processed modulated images. I found that in group comparison, the smaller extra smooth kernel I put on the modulated images (e.g. 8mm --> 4mm --> 2mm --> 1mm --> no extra smooth), the more significant clusters identified using topological FDR. Note that even with no extra smoothing, the effective smoothness of the images is about 3.8mm (voxel size is 1mm isotropic). I am not sure what smoothness should I go for so would like choose it according to the registration error by DARTEL (in mm), if there's any data available. Any idea or suggestion regarding an appropriate smoothness for DARTEL-processed modulated images with 1mm isotropic voxels when running VBM?
Thanks so much.
Meng-Chuan Lai
========================================================================Date: Thu, 10 Feb 2011 13:36:30 +0000
Reply-To: Neal Hinvest <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Neal Hinvest <[log in to unmask]>
Subject: PhD studentship opportunity at the University of Bath,
UK (Department of Psychology)
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The Department of Psychology at the University of Bath, U.K., is
inviting applications for a PhD studentship. The PhD is for a fixed
3-year term and is fully funded (all tuition fees paid plus stipend).
The successful candidate will start in October 2011. The project
investigates internet and video-game addiction. The project is
supervised by Dr. Neal Hinvest.
The Department of Psychology is highly research active. The Departments
has three main areas of research stength; Cognition, Affective Science
and Technology Labs (CASTL), Health Psychology and Social and Cultural
Psychology. For more details see http://www.bath.ac.uk/psychology/research.
The successful candidate will have a 1st or 2:1 and will have relevant
experience in psychology. Research experience or advanced training (e.g.
Masters level degree) would be advantageous but not necessary.
Experience with statistical software packages (e.g. SPSS) would be
beneficial.
The deadline for applications is the 15th April. Send applications to
Dr. Neal Hinvest, Department of Psychology, University of Bath, Bath,
BA2 7AY, U.K. Electronic applications are preferred and can be sent to
[log in to unmask]
--
Dr. Neal Hinvest
Lecturer
Department of Psychology
University of Bath
Bath
BA2 7AY
========================================================================Date: Thu, 10 Feb 2011 15:36:47 +0100
Reply-To: Cornelia Hummel <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Cornelia Hummel <[log in to unmask]>
Subject: matlab path
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Dear experts,
I encounterd the following problem:
I installed SPM8, ran matlab and SPM with the respetive path set, then moved
the entire SPM folder to another site, changed the path in matlab, and now,
when running segment, got the error message that the file "(previous
folder)\spm\tpm\grey.nii" does not exist. I double checked the path setting,
and cannot find why matlab still tries to read from the path that no longer
exists rather than the correctly set one. (The realign and coregister
procedures ran without problems.)
Can anybody please help me?
Thanks in advance,
Cornelia
--
Cornelia Hummel
Voglerstraße 14
01277 Dresden
--00235444776d4c0b65049bee85bf
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Dear experts,<br>I encounterd the following problem:<br>I installed
SPM8, ran matlab and SPM with the respetive path set, then moved the
entire SPM folder to another site, changed the path in matlab, and now,
when running segment, got the error message that the file "(previous
folder)\spm\tpm\grey.nii" does not exist. I double checked the path
setting, and cannot find why matlab still tries to read from the path
that no longer exists rather than the correctly set one. (The realign
and coregister procedures ran without problems.)<br>
Can anybody please help me?<br>Thanks in advance,<br>Cornelia<br clear="all"><br clear="all"><br>-- <br>Cornelia Hummel<br>Voglerstraße 14<br>01277 Dresden<br>
--00235444776d4c0b65049bee85bf--
========================================================================Date: Thu, 10 Feb 2011 16:05:04 +0000
Reply-To: William Pettersson-Yeo <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: William Pettersson-Yeo <[log in to unmask]>
Subject: Re: New Segmentation Out of Memory Error
Comments: To: John Ashburner <[log in to unmask]>
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Thanks John.
It turns out the problem was due to the version of Matlab I was running.
Matlab2007 was only running 32bit, whilst the 2010 version is 64.
Having set up with Matlab2010, the new segmentation with DARTEL import now runs without any problem, without me having to alter/crop any of the images.
All the best
William
========================================================================Date: Thu, 10 Feb 2011 16:47:53 +0000
Reply-To: Joachim Gross <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Joachim Gross <[log in to unmask]>
Subject: Chair Position Glasgow
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UNIVERSITY of GLASGOW
College of Medical, Veterinary and Life Sciences
Institute of Neuroscience and Psychology (INP)
Faculty Position in Formal Models of Cortical Networks
- may be appointed at Lecturer/Senior Lecturer/Reader or Professor level
Ref: M00075
Salary: Lecturer/Senior Lecturer - £31,798 - £52,556 per annum
Reader/Professor – negotiable – depends on experience
Applications are invited for a faculty position (up to chair level) in
Formal Models of Cortical Networks from individuals with an outstanding
research record. The post is designed to complement existing research
strengths in the Institute for Neuroscience and Psychology and to develop
a programme for studying and describing networks across the different
Centres of the Institute.
The Post-holder will provide research leadership in the area of cortical
networks and networks in general (connectivity, information theory, graph
theory). The Post-holder will be expected to raise external funds to
support their research programme, to attract postdoctoral research staff
and PhD students, and to contribute to research-led teaching, especially
via the development of specialised content for masters and postgraduate
degrees.
Candidates for this post will have an exceptional international research
profile, including a track record of high impact publications and
substantial research funding, in the areas of network/connectivity
analysis, information theory or graph theory.
Informal enquiries may be made to Philippe Schyns (Director of Institute,
+44 141 330 4937, [log in to unmask]) or Joachim Gross (Acting
Director of Centre for Cognitive Neuroimaging (CCNi), +44 141 330 3947,
[log in to unmask]).
Apply online at www.glasgow.ac.uk/jobs
Closing date: 12 April 2011.
========================================================================Date: Thu, 10 Feb 2011 16:59:04 +0000
Reply-To: Vy Dinh <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vy Dinh <[log in to unmask]>
Subject: SPM8 VOI Extraction: Adjusting for effects of no interest
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Dear all,
Excuse me for not getting straight to the question, but I found it difficult to formulate my question otherwise. First of all, according to responses on the forum (links below), "adjusting for effects of (no) interest" removes the variances introduced by confounds in the 1st-level design matrix. SPM does this by mean-centering the data based on the betas obtained from columns that are zeroed in the F-contrast. (adjusted Y = global raw data = [sum (beta x effects of no interest)]); Therefore, if I have a design matrix with 3 conditions, motion parameters, and 1 regressor of no interest and I specified an F-contrast of
1 0 0 < right side padded with 2 zeroes (1 for regressor, 1 for constant)
0 1 0
0 0 1
.. the F-contrast will be padded with 2 zeroes as well and these columns will be accounted for during VOI extraction with adjustment (please correct me if I'm wrong).
Motion parameters have often been used as an example of a regressor of no interest in these posts, but HOW DOES SPM'S ADJUSTMENT OPTION INTERPRET A COLUMN FOR DEWEIGHTING A "BAD" SCAN (WHERE BAD SCANS ARE SPECIFIED AS 1S)? Are the data associated with these scans also not included in the VOI extraction? Answers to these questions would help us understand why we are seeing slight differences between results using adjusted and non-adjusted data that do not have any confounds in the first model. Motion was addressed by adjusting the data with ArtRepair Toolbox prior to estimation. If the deweighting columns should have no effect on the VOI extraction, then mathematically speaking, the results should be the same. However, they are not.
Also, some of our subjects had zero instances of 1 condition, so we had to define this condition as a zero in the Effects Of Interest F-contrast. In these cases, would specifying adjust or not adjust make a difference?
Thanks,
Vy
Here are the links to the listserv responses regarding adjusting for regressors of no interest:
https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=SPM;664c9b8.03; https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=SPM;f695a1.1004; https://www.jiscmail.ac.uk/cgi-bin/webadmin?A2=SPM;9945e539.03)
========================================================================Date: Thu, 10 Feb 2011 11:05:48 -0600
Reply-To: Darren Gitelman <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Darren Gitelman <[log in to unmask]>
Subject: Re: DCM: mutual interactions between networks
Comments: To: marco tettamanti <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Marco
On Thu, Feb 10, 2011 at 5:07 AM, marco tettamanti
<[log in to unmask]> wrote:
> Dear all,
> I don't think that this question has been addressed before:
>
> I would like to set up a DCM analysis with fMRI data to investigate the
> following issue: we have identified an activation in a very well known
> network of 4 brain regions (A,B,C,D), and we believe that, in our task, this
> network can be gated - depending on which one of 2 different input
> modalities is involved - by the activity of two other distinct networks,
> each including 3 brain regions (E,F,G, or H,I,J).
> That is, we would like to assess how network EFG interacts with network ABCD
> in modality 1 and how network HIJ interacts with network ABDC in modality 2,
> in the context of a sort of 2x2 (Modality x network-network interaction)
> factorial design.
>
> I presume that one could address this, by simply treating each brain region
> independently, and for instance by using BMS to test whether the preferred
> model fits the expected interaction patterns, but I think that there may be
> problems with the relatively large number of regions (>8).
You can deal with this by setting up the DCM file from the command
line. If you set up a simpler DCM using the GUI you will see what
fields you need. The 8 region limitation is mostly due to the gui (and
memory).
> I also presume that the question could be approached by non-linear DCM, even
> though I think that non-linear DCM has been only used in the context of the
> influence of one single region on a system of interconnected regions, and
> not in the context of network-network interactions.
I'm not sure how you could use a non-linear DCM in this scenario. You
can't really setup a small DCM network as an input, per se. The two
"subnetworks" would just be connected into the larger 4 region network
perhaps through a common area. This would be through an intrinsic
connection. DCM only accepts as inputs the condition-type vectors that
it extracts from an SPM.mat file. You would also need other actual
inputs into each of the subnetworks in order to drive overall system
activity. Non-linear DCM's can be used to modulate an intrinsic
connection, so perhaps you are thinking of using the 3 region
subnetworks to modulate an external input into the 4 region network?
However, I don't think you can modulate an input as DCM is currently
set up, and you would still need to drive the subnetworks in some way.
Darren
>
> Can anyone suggest a more direct way to approach this issue?
>
> Thank you and all the best,
> Marco
>
> --
> Marco Tettamanti, Ph.D.
> San Raffaele Scientific Institute
> Via Olgettina 58
> I-20132 Milano, Italy
> Tel. ++39-02-26434888
> Fax ++39-02-26434892
> Email: [log in to unmask]
>
> -----------------------------------------------------------------------------------
> SOSTIENI ANCHE TU LA RICERCA DEL SAN RAFFAELE.
> NON C'E' CURA SENZA RICERCA.
> Per donazioni: ccp 42437681 intestato a Fondazione Arete' Onlus del San
> Raffaele.
> Per informazioni: tel. 02.2643.4461 - www.sanraffaele.org
>
--
Darren Gitelman, MD
Northwestern University
710 N. Lake Shore Dr., 1122
Chicago, IL 60611
Ph: (312) 908-8614
Fax: (312) 908-5073
========================================================================Date: Thu, 10 Feb 2011 17:29:41 +0000
Reply-To: Tom Johnstone <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Tom Johnstone <[log in to unmask]>
Subject: University of Reading International Studentship
Comments: To: "[log in to unmask] [log in to unmask]" <[log in to unmask]>,
[log in to unmask]
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Opportunity for PhD Studentship at the Centre for Integrative
Neuroscience & Neurodynamics (CINN).
The Faculties of Sciences & Life Sciences at the University of Reading
are jointly holding a studentship competition for International
Students (defined as non-EU/EEA). We encourage applications from
candidates wishing to complete a PhD in the Cognitive, Affective or
Social Neurosciences at the CINN.
Each studentship will provide a subsistence grant (normal UK research
council rate) as well as the University tuition fee (full-time
international at Reading) for 3 Academic Years.
Candidates should possess a 1st class Honours degree or equivalent in
Psychology, Neuroscience or a related discipline. An MSc with
Distinction or progress towards such an MSc would be an advantage.
Applications will be judged in a 2-stage procedure. In the first step,
the School of Psychology & Clinical Language Sciences will select the
strongest 2 submissions to the school. These will then be submitted to
the Faculty Deans, who will select the highest quality candidates who
have strong research plans for a topic compatible with the
University's Research Strategy.
The Centre for Integrative Neuroscience and Neurodynamics (
http://www.reading.ac.uk/cinn/ ) occupies a dedicated wing in the
School of Psychology & CLS building at the University of Reading. It
houses a 3T Siemens Trio research-dedicated MRI scanner and
high-density EEG laboratory. These facilities are complemented by high
resolution stimulus display systems with integrated high-speed eye
tracking, MRI compatible EEG and TMS systems. Research at CINN
involves the neural mechanisms underpinning complex cognitive and
affective behaviours, with particular strengths in affective
neuroscience and the neural mechanisms of perception and action.
Interested candidates should send an email with a brief statement of
research interests and a CV, including details of nationality to:
Tom Johnstone
Centre for Integrative Neuroscience & Neurodynamics
School of Psychology and CLS
[log in to unmask]
http://www.personal.reading.ac.uk/~sxs07itj/index.html
Closing date: Friday the 25th of February.
========================================================================Date: Thu, 10 Feb 2011 18:12:34 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: registration error under DARTEL and suggested smoothness
Comments: To: Meng-Chuan Lai <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Without knowing the ground truth (which probably does not exist
anyway), it is difficult to say what the average registration error
is. The closest we have are comparisons of various registration
algorithms in terms of how well they can propagate manually defined
regions. I suspect that the manually defined regions used for the
validations may not be so accurate, but they will probably be accurate
enough to compare among models.
There is a validation of what was in the SPM5 implementation (with the
original Unified Segmentation) within:
Evaluation of 14 nonlinear deformation algorithms applied to human
brain MRI registration Original Research Article
NeuroImage, Volume 46, Issue 3, 1 July 2009, Pages 786-802
Arno Klein, Jesper Andersson, Babak A. Ardekani, John Ashburner, Brian
Avants, Ming-Chang Chiang, Gary E. Christensen, D. Louis Collins,
James Gee, Pierre Hellier, Joo Hyun Song, Mark Jenkinson, Claude
Lepage, Daniel Rueckert, Paul Thompson, Tom Vercauteren, Roger P.
Woods, J. John Mann, Ramin V. Parsey
For a validation of the SPM8 implementation (with the New Segment), see:
Diffeomorphic registration using geodesic shooting and Gauss–Newton
optimisation
NeuroImage, In Press, Corrected Proof, Available online 7 January 2011
John Ashburner, Karl J. Friston
Best regards,
-John
On 10 February 2011 12:53, Meng-Chuan Lai <[log in to unmask]> wrote:
> Dear John and SPM users.
>
> I would like to inquire if there is any available data showing the average registration error by DARTEL?
>
> I am currently running VBM using DARTEL-processed modulated images. I found that in group comparison, the smaller extra smooth kernel I put on the modulated images (e.g. 8mm --> 4mm --> 2mm --> 1mm --> no extra smooth), the more significant clusters identified using topological FDR. Note that even with no extra smoothing, the effective smoothness of the images is about 3.8mm (voxel size is 1mm isotropic). I am not sure what smoothness should I go for so would like choose it according to the registration error by DARTEL (in mm), if there's any data available. Any idea or suggestion regarding an appropriate smoothness for DARTEL-processed modulated images with 1mm isotropic voxels when running VBM?
>
> Thanks so much.
>
> Meng-Chuan Lai
>
========================================================================Date: Thu, 10 Feb 2011 18:17:56 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: matlab path
Comments: To: Cornelia Hummel <[log in to unmask]>
In-Reply-To: <AANLkTió[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <AANLkTi=Mr-02RkNw\[log in to unmask]>
Are you trying to run a batch file? If so, the location of the tissue
probability maps will be defined by that file.
Best regards,
-John
On 10 February 2011 14:36, Cornelia Hummel <[log in to unmask]> wrote:
> Dear experts,
> I encounterd the following problem:
> I installed SPM8, ran matlab and SPM with the respetive path set, then moved
> the entire SPM folder to another site, changed the path in matlab, and now,
> when running segment, got the error message that the file "(previous
> folder)\spm\tpm\grey.nii" does not exist. I double checked the path setting,
> and cannot find why matlab still tries to read from the path that no longer
> exists rather than the correctly set one. (The realign and coregister
> procedures ran without problems.)
> Can anybody please help me?
> Thanks in advance,
> Cornelia
>
>
> --
> Cornelia Hummel
> Voglerstraße 14
> 01277 Dresden
>
========================================================================Date: Thu, 10 Feb 2011 13:27:21 -0500
Reply-To: Giulia Dormal <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Giulia Dormal <[log in to unmask]>
Subject: SPM8 fMRI model specification
MIME-Version: 1.0
Content-Type: multipart/mixed; boundaryMessage-ID: <[log in to unmask]>
--00504502c86877f759049bf1bb7b
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--00504502c86877f751049bf1bb79
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Dear SPMers,
I am trying to run an fMRI model specification batch (in attached), for a
single run of a block design containing 3 conditions + baseline. I have
specified the different conditions in seconds. When I run the batch, I
systematically get this error message in matlab window:
Failed 'fMRI model specification'
Cell contents reference from a non-cell array object.
In file "C:\Program Files\spm8\spm8\spm_Ce.m" (v3527), function "spm_Ce" at
line 54.
In file "C:\Program Files\spm8\spm8\spm_fmri_spm_ui.m" (v3691),
function "spm_fmri_spm_ui" at line 345.
In file "C:\Program Files\spm8\spm8\config\spm_run_fmri_spec.m"(v3757),
function "spm_run_fmri_spec" at line 301.
I would very much appreciate your help!
Giulia
---------- Forwarded message ----------
From: Giulia Dormal <[log in to unmask]>
Date: 2011/2/10
Subject:
To: "giulia.dormal" <[log in to unmask]>
--
Giulia Dormal, PhD Student
IPSY/IoNS - Université Catholique de Louvain
CERNEC - Université de Montréal
(001) 514 343 61 11 #1715
--
Giulia Dormal, PhD Student
IPSY/IoNS - Université Catholique de Louvain
CERNEC - Université de Montréal
(001) 514 343 61 11 #1715
--00504502c86877f751049bf1bb79
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Dear SPMers,<br><br>I am trying to run an fMRI model specification batch (in attached), for a single run of a block design containing 3 conditions + baseline. I have specified the different conditions in seconds. When I run the batch, I systematically get this error message in matlab window:<br>
Failed 'fMRI model specification'<br>
Cell contents reference from a non-cell array object.<br>
In file "C:\Program Files\spm8\spm8\spm_Ce.m" (v3527), function "spm_Ce" at line 54.<br>
In file "C:\Program Files\spm8\spm8\spm_fmri_spm_ui.m" (v3691),<br><div>
function "spm_fmri_spm_ui" at line 345.<br>
In file "C:\Program Files\spm8\spm8\config\spm_run_fmri_spec.m"(v3757), function "spm_run_fmri_spec" at line 301.<br>
<br>I would very much appreciate your help!<br><br>Giulia<br><br></div><br><br><div class="gmail_quote">---------- Forwarded message ----------<br>From: <b class="gmail_sendername">Giulia Dormal</b> <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span><br>
Date: 2011/2/10<br>Subject: <br>To: "giulia.dormal" <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>><br><br><br><font color="#888888">--<br>
Giulia Dormal, PhD Student<br>
IPSY/IoNS - Université Catholique de Louvain<br>
CERNEC - Université de Montréal<br>
(001) 514 343 61 11 #1715<br>
</font></div><br><br clear="all"><br>-- <br><font color="#000099">Giulia Dormal, PhD Student</font><div><font color="#000099">IPSY/IoNS - Université Catholique de Louvain</font></div><div><font color="#000099">CERNEC - Université de Montréal</font></div>
<div><font color="#000099">(001) 514 343 61 11 #1715</font></div><div><font color="#000099"><br></font></div><br>
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========================================================================Date: Thu, 10 Feb 2011 19:47:50 +0100
Reply-To: maaike rive <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: maaike rive <[log in to unmask]>
Subject: regressors of no interest in first level model
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <AANLkTikcmrTCAFmEHuSGjAMJe5WrknmT-gHJD3ï[log in to unmask]>
--0016364ef8feb97f88049bf20402
Content-Type: text/plain; charset=ISO-8859-1
Hi all,
I hope you can help me with a question regarding modelling regressors of no
interest in a first level model.
I assumed it is better to model all time intervals, including the periods of
certain ''conditions'' you are not interested in (for example, an
instruction period before a specific event), to reduce variance in the
signal during the conditions that you will use for further analysis that is
actually caused by conditions of no interest.
However, now I was told that in doing so, one would reduce the degree
of orthogonality of each regressor with respect to the constant regressor.
Now I do not know what is the best option: model every condition or only the
ones you will use for further analysis. I hope someone can advise me on
this!
Thanks,
Maaike
--0016364ef8feb97f88049bf20402
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<div>Hi all,</div>
<div> </div>
<div>I hope you can help me with a question regarding modelling regressors of no interest in a first level model.</div>
<div>I assumed it is better to model all time intervals, including the periods of certain ''conditions'' you are not interested in (for example, an instruction period before a specific event), to reduce variance in the signal during the conditions that you will use for further analysis that is actually caused by conditions of no interest.</div>
<div> </div>
<div>However, now I was told that in doing so, one would reduce the degree of orthogonality of each regressor with respect to the constant regressor.</div>
<div> </div>
<div>Now I do not know what is the best option: model every condition or only the ones you will use for further analysis. I hope someone can advise me on this!</div>
<div> </div>
<div>Thanks,</div>
<div> </div>
<div>Maaike</div>
<div> </div>
<div> </div>
<div> </div>
--0016364ef8feb97f88049bf20402--
========================================================================Date: Thu, 10 Feb 2011 14:13:51 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: regressors of no interest in first level model
Comments: To: maaike rive <[log in to unmask]>
In-Reply-To: <AANLkTikcmrTCAFmEHuSGjAMJe5WrknmT-gHJD3ï[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
If you know that the activity is changing from baseline, then you
should model it if you can. Otherwise, the condition of interest will
be with respective to the constant+instruction. The problem can arise
though, that the instruction is too close to the condition of interest
or is not properly randomized, then it becomes hard to estimate the
effects of each.
For example, if the instruction is for 1s followed by your condition,
then its hard to separate the two events, especially if the
instruction is always the same before that type of condition. Its not
the orthogonality with the constant, its the lack of
orthogonality/collinearity of each of the condtions that becomes an
issue.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
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immediately notify the sender via telephone at (773) 406-2464 or
email.
On Thu, Feb 10, 2011 at 1:47 PM, maaike rive <[log in to unmask]> wrote:
> Hi all,
>
> I hope you can help me with a question regarding modelling regressors of no
> interest in a first level model.
> I assumed it is better to model all time intervals, including the periods of
> certain ''conditions'' you are not interested in (for example, an
> instruction period before a specific event), to reduce variance in the
> signal during the conditions that you will use for further analysis that is
> actually caused by conditions of no interest.
>
> However, now I was told that in doing so, one would reduce the degree
> of orthogonality of each regressor with respect to the constant regressor.
>
> Now I do not know what is the best option: model every condition or only the
> ones you will use for further analysis. I hope someone can advise me on
> this!
>
> Thanks,
>
> Maaike
>
>
>
========================================================================Date: Thu, 10 Feb 2011 19:38:43 +0000
Reply-To: Meng-Chuan Lai <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Meng-Chuan Lai <[log in to unmask]>
Subject: Re: registration error under DARTEL and suggested smoothness
Comments: To: John Ashburner <[log in to unmask]>
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain; charset="UTF-8"
Message-ID: <[log in to unmask]>
Dear John,
Thank you for the very valuable information.
With these in mind, practically, if to run VBM and we expect to find regional differences in small areas in the brain such as hypothalamus, ventral striatum or amygdala, what would be the lowest effective smoothness for DARTEL-processed modulated images with 1mm isotropic voxels, that the registration error should have been taken care of by such smoothness?
Thank you!
best regards,
Meng-chuan Lai
========================================================================Date: Thu, 10 Feb 2011 20:42:47 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: registration error under DARTEL and suggested smoothness
Comments: To: Meng-Chuan Lai <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
These regions tend to be pretty well aligned, so you probably need a
bit less than you would for the cortex. However, you may need to use
some smoothing to suppress some of the aliasing effects that occur.
These depend on the resolution of your original scans. At a rough
guess, I'd suggest that somewhere between about 4mm and 8mm could be
reasonable, although I don't have any evidence to back this up.
Best regards,
-John
On 10 February 2011 19:38, Meng-Chuan Lai <[log in to unmask]> wrote:
> Dear John,
>
> Thank you for the very valuable information.
>
> With these in mind, practically, if to run VBM and we expect to find regional differences in small areas in the brain such as hypothalamus, ventral striatum or amygdala, what would be the lowest effective smoothness for DARTEL-processed modulated images with 1mm isotropic voxels, that the registration error should have been taken care of by such smoothness?
>
> Thank you!
>
> best regards,
> Meng-chuan Lai
>
========================================================================Date: Thu, 10 Feb 2011 16:31:01 -0500
Reply-To: "Claire (Jung Eun) Han" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "Claire (Jung Eun) Han" <[log in to unmask]>
Subject: Question regarding FIR and contrasts
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <AANLkTimVLVP6zAXuj6b_hzNAYŠ[log in to unmask]>
--000e0cd4cd4455ea25049bf44c95
Content-Type: text/plain; charset=ISO-8859-1
Hello,
There are two conditions A and B I would like to compare.
In fMRI model specification, I chose to use the Finite Impulse Response such
that my design has 9 three-second-bins for each condition.
I would like to compare the two conditions in each time bin...is it possible
to do this at once? Which of the t- or f-test should I be using and what
should I put for contrasts?
Thanks a lot,
Claire
--000e0cd4cd4455ea25049bf44c95
Content-Type: text/html; charset=ISO-8859-1
Hello,<br><br>There are two conditions A and B I would like to compare.<br><br>In fMRI model specification, I chose to use the Finite Impulse Response such that my design has 9 three-second-bins for each condition.<br><br>
I would like to compare the two conditions in each time bin...is it possible to do this at once? Which of the t- or f-test should I be using and what should I put for contrasts? <br><br>Thanks a lot,<br>Claire <br>
--000e0cd4cd4455ea25049bf44c95--
========================================================================Date: Thu, 10 Feb 2011 21:39:29 +0000
Reply-To: Meng-Chuan Lai <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Meng-Chuan Lai <[log in to unmask]>
Subject: Re: registration error under DARTEL and suggested smoothness
Comments: To: John Ashburner <[log in to unmask]>
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain; charset="UTF-8"
Message-ID: <[log in to unmask]>
Hi John,
Thanks so much. For the 4-8mm you suggested, do you mean the smoothing applied with FWHM or the effective smoothness in the data itself?
I have tried a 2mm kernel which gives an effective smoothness of 5.5mm in the data itself, and it seems to pick up quite a few reasonable regions. Would you suspect this to be valid?
Thanks so much for the prompt answers!
best,
Meng-chuan
========================================================================Date: Thu, 10 Feb 2011 17:31:16 -0500
Reply-To: Zipporah Nussbaum <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Zipporah Nussbaum <[log in to unmask]>
Subject: Postdoctoral Fellowship in Cognitive Neuroscience of Memory
MIME-Version: 1.0
Content-Type: text/plain; charset=windows-1252; format=flowed
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
The Rotman Research Institute of Baycrest, affiliated with the
University of Toronto is offering a postdoctoral fellowship in cognitive
neuroscience of memory in the laboratory of Dr. Bradley Buchsbaum. The
post-doctoral fellow will collaborate in CIHR-funded research
investigating the neural basis of human memory, especially short-term
and episodic memory. Projects will involve the use of fMRI and MEG to
study how memories are stored and represented in the brain, using both
univariate and multivariate “pattern recognition” methods. Candidates
should have interest and expertise in some of the following topics:
- Cognitive neuroscience of working memory
- Aging and memory
- Functional neuroimaging (fMRI and MEG)
- Multivariate pattern recognition applied to neuroimaging data
The Rotman Institute is fully equipped for cognitive neuroscience
research, with a _3 T MRI, _151 -channel CTF MEG, several EEG systems.
We seek a candidate with a strong background in memory research, good
computing skills, and experience with functional neuroimaging.
Toronto is consistently ranked as one of the most livable cities in the
world, as well as the most multicultural. It is an excellent place to
work for those interested in cross-linguistic research, as native
speaker populations can be found for dozens of world languages.
Applicants should have a recent Ph.D. or M.D. degree, and the potential
for successfully obtaining external funding. The postdoctoral position
carries a term of 2 years and is potentially renewable. Bursaries are in
line with the fellowship scales of the Canadian Institutes of Health
Research (CIHR) and include an allowance for travel and research
expenses. A minimum of 80% of each fellow’s time will be devoted to
research and related activities. Start date is negotiable, but ideally
in the spring or summer of 2011. Applicants should submit a C.V. and
relevant reprints, together with a cover letter describing current
research interests and future research goals, and also arrange to have
three letters of reference sent independently to:
Bradley Buchsbaum, Ph.D.
Rotman Research Institute, Baycrest
3560 Bathurst Street, Toronto, Ontario, M6A 2E1, Canada
[log in to unmask] <mailto:[log in to unmask]>
For more information on the institute, see
http://www.rotman-baycrest.on.ca/
The Rotman Research Institute welcomes applications from all qualified
individuals, including members of visible groups, minorities, women,
aboriginal persons, and persons with disabilities. All qualified
candidates are encouraged to apply; however, Canadians and permanent
residents will be given priority.
========================================================================Date: Thu, 10 Feb 2011 22:58:01 +0000
Reply-To: Courtney Kastner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Courtney Kastner <[log in to unmask]>
Subject: Extracting ROIs in SPM8
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I'm using SPM8 and its functions to analyze data collected from a test with two conditions of rotation and baseline in math-gifted and average students. The end goal of my project is to extract regions of interest from the fMRI data. Is it absolutely necessary to create a design matrix and define a contrast, or is there another easier but still effective way to do this?
========================================================================Date: Fri, 11 Feb 2011 01:17:50 +0000
Reply-To: Matt Johnson <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Matt Johnson <[log in to unmask]>
Subject: Standard error calculation in spm_contrasts.m - r4010
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Hi SPMers. We have been doing some comparisons between different fMRI data processing methods recently, including sanity checks on the stats returned by SPM vs FSL vs done by hand.
While evaluating a T-contrast (on a simple second-level one-sample T-test model), I noticed that the T-statistics returned by SPM8 r4010 were different than those returned by the equivalent FSL model or by simply running a T-test in the Matlab command window (with FSL, Matlab, and earlier SPMs being identical, within reasonable rounding error).
Upon further investigation, I see that the difference arises from line 178 of spm_contrasts.m, where the parameter estimate is divided by standard error to yield the T-statistic. The line is now:
Z=cB./(SE+exp(-8)*max(SE));
whereas previously it did not contain the (+exp(-8)*max(SE)) bit. I believe this is the change referred to thus in the release notes: "Problems with using SPM to test for between-subject effects in contrasts based on source reconstructed MEEG data have been finessed by lower bounding the standard error of a t-test that forces very low variance and effect-size voxels to shrink to 0. This is hidden (in spm_contrasts.m) because it will not affect normal SPMs (where the scaling of the data is roughly uniform over the image)."
While the difference induced by this change is small, I think it may not be entirely inconsequential for some users. For instance, in my sample analysis, it changes the max T in the results from a "true" value of 18.37 to a value of 17.91. For smaller Ts, the change is less drastic, but it still equates to about 35 fewer voxels in the output passing an uncorrected p<.001 threshold, or about 600 fewer passing an uncorrected p<.05 threshold. This probably makes no difference for most analyses, but it might be a bit more than rounding error for those who are sticklers about precision.
Of course in our local copy we can just change that line back if we want our SPM Ts to agree with the others, but I thought I'd pass this along just as an FYI to others, and/or in case the folks at the FIL might want to tweak the line in question a bit further (e.g., to only apply the standard error correction when it is needed).
Cheers,
Matt
========================================================================Date: Fri, 11 Feb 2011 11:00:10 +0100
Reply-To: marco tettamanti <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: marco tettamanti <[log in to unmask]>
Subject: Re: DCM: mutual interactions between networks
Comments: To: Darren Gitelman <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Dear Darren,
thank you for your useful comments!
On 02/10/2011 06:05 PM, Darren Gitelman wrote:
> You can deal with this by setting up the DCM file from the command
> line. If you set up a simpler DCM using the GUI you will see what
> fields you need. The 8 region limitation is mostly due to the gui (and
> memory).
I was not aware of this possibility and it is good to know. On the other hand, I
would like to stick to the "relevant model space" principle as much as possible,
whereby I am much more interested in network-network than in within-networks
dynamics. But as you are pointing out, it may not be possible to study such
modulations with DCM, at least in its current implementation.
> I'm not sure how you could use a non-linear DCM in this scenario. You
> can't really setup a small DCM network as an input, per se. The two
> "subnetworks" would just be connected into the larger 4 region network
> perhaps through a common area. This would be through an intrinsic
> connection. DCM only accepts as inputs the condition-type vectors that
> it extracts from an SPM.mat file. You would also need other actual
> inputs into each of the subnetworks in order to drive overall system
> activity. Non-linear DCM's can be used to modulate an intrinsic
> connection, so perhaps you are thinking of using the 3 region
> subnetworks to modulate an external input into the 4 region network?
> However, I don't think you can modulate an input as DCM is currently
> set up, and you would still need to drive the subnetworks in some way.
One possibility I was wondering about is whether it would be possible to
constrain the within-network interactions, by modelling each netwtork as a
single VOI, i.e. extracting one VOI for network 1 based on a mask including the
activations of areas A,B,C,D, one VOI for network 2 including areas E,F,G and
one VOI for network 3 including areas H,I,J, and then study modality-dependent
bilinear modulations between networks in a simple model including these three VOIs.
Does this make any sense? Or do such multi-regional VOIs violate the biophysical
assumptions or any other assumptions in DCM?
Thank you again,
Marco
--
Marco Tettamanti, Ph.D.
San Raffaele Scientific Institute
Via Olgettina 58
I-20132 Milano, Italy
Tel. ++39-02-26434888
Fax ++39-02-26434892
Email: [log in to unmask]
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========================================================================Date: Fri, 11 Feb 2011 11:34:07 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: registration error under DARTEL and suggested smoothness
Comments: To: Meng-Chuan Lai <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Message-ID: <AANLkTi=igFpfNnk_XxUhe+6YOMbnMê[log in to unmask]>
It's really a matter of empiricism (although I don't actually know how
to check these things properly using the SPM analysis framework), but
my best guess is that between 4 and 8 mm of additional smoothing would
work reasonably well. As I say, that is just a guess, and I have no
idea at all really.
Best regards,
-John
On 10 February 2011 21:39, Meng-Chuan Lai <[log in to unmask]> wrote:
> Hi John,
>
> Thanks so much. For the 4-8mm you suggested, do you mean the smoothing applied with FWHM or the effective smoothness in the data itself?
>
> I have tried a 2mm kernel which gives an effective smoothness of 5.5mm in the data itself, and it seems to pick up quite a few reasonable regions. Would you suspect this to be valid?
>
> Thanks so much for the prompt answers!
>
> best,
> Meng-chuan
>
>
========================================================================Date: Fri, 11 Feb 2011 13:30:57 +0100
Reply-To: Marko Wilke <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Marko Wilke <[log in to unmask]>
Subject: Re: matlab path
Comments: To: John Ashburner <[log in to unmask]>
In-Reply-To: <AANLkTi=Mr-02RkNw\[log in to unmask]>
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Message-ID: <[log in to unmask]>
Hello,
just to mention another instance where this bugged me some time ago: if
you segmented data on another machine (or before moving spm), the
location of the priors used is hardcoded in the resulting seg_sn-files.
Therefore, if you want to use them to normalize an image later-on, this
may also result in the error you mentioned. You can check that if you
load the file and check
po.VG(1)
and see which GM prior that refers to.
Hope this helps,
Marko
John Ashburner wrote:
> Are you trying to run a batch file? If so, the location of the tissue
> probability maps will be defined by that file.
>
> Best regards,
> -John
>
> On 10 February 2011 14:36, Cornelia Hummel<[log in to unmask]> wrote:
>> Dear experts,
>> I encounterd the following problem:
>> I installed SPM8, ran matlab and SPM with the respetive path set, then moved
>> the entire SPM folder to another site, changed the path in matlab, and now,
>> when running segment, got the error message that the file "(previous
>> folder)\spm\tpm\grey.nii" does not exist. I double checked the path setting,
>> and cannot find why matlab still tries to read from the path that no longer
>> exists rather than the correctly set one. (The realign and coregister
>> procedures ran without problems.)
>> Can anybody please help me?
>> Thanks in advance,
>> Cornelia
>>
>>
>> --
>> Cornelia Hummel
>> Voglerstraße 14
>> 01277 Dresden
>>
>
--
____________________________________________________
PD Dr. med. Marko Wilke
Facharzt für Kinder- und Jugendmedizin
Leiter, Experimentelle Pädiatrische Neurobildgebung
Universitäts-Kinderklinik
Abt. III (Neuropädiatrie)
Marko Wilke, MD, PhD
Pediatrician
Head, Experimental Pediatric Neuroimaging
University Children's Hospital
Dept. III (Pediatric Neurology)
Hoppe-Seyler-Str. 1
D - 72076 Tübingen, Germany
Tel. +49 7071 29-83416
Fax +49 7071 29-5473
[log in to unmask]
http://www.medizin.uni-tuebingen.de/kinder/epn
____________________________________________________
========================================================================Date: Fri, 11 Feb 2011 14:21:51 +0000
Reply-To: Lena Palaniyappan <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Lena Palaniyappan <[log in to unmask]>
Subject: VBM ROI Marsbar query
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<TITLE>VBM ROI Marsbar query</TITLE>
</HEAD>
<BODY>
<FONT FACE="Calibri, Verdana, Helvetica, Arial"><SPAN STYLE='font-size:11pt'>Hi all<BR>
<BR>
I am trying to calculate the volume of grey matter within a ROI defined using Marsbar (originally the binary ROI was obtained from the ‘activated’ cluster when doing a VBM analysis) in a set of 90 scans. When I extract the mean ‘time series’ value using Marsbar for each image using standard marsbar functions, I presume what I get in structural scans is the Mean VBM ‘density’. If I need the volume, I should multiply the density by total number of voxels within the mask (the mask is 1X1X1 isotropic). Is this a correct assumption?<BR>
<BR>
I undertook a ‘modulated VBM’ - hope the assumptions still hold true<BR>
<BR>
Cheers<BR>
Lena<BR>
</SPAN></FONT>
<br/>
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========================================================================Date: Fri, 11 Feb 2011 09:51:12 -0500
Reply-To: Jonathan Peelle <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Jonathan Peelle <[log in to unmask]>
Subject: Re: VBM ROI Marsbar query
In-Reply-To: <[log in to unmask]>
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Hi Lena
> I am trying to calculate the volume of grey matter within a ROI defined using Marsbar (originally the binary ROI was obtained from the ‘activated’ cluster when doing a VBM analysis) in a set of 90 scans. When I extract the mean ‘time series’ value using Marsbar for each image using standard marsbar functions, I presume what I get in structural scans is the Mean VBM ‘density’. If I need the volume, I should multiply the density by total number of voxels within the mask (the mask is 1X1X1 isotropic). Is this a correct assumption?
In the modulated images, each voxel value basically represents the gray matter volume for a voxel. So, typically, this is what you would use—for example, the average values in an ROI would represent average gray matter volume within that ROI.
The density/volume issue can be a bit tricky and relates to the modulated/unmodulated analyses, but in most cases the modulated analysis (as you've done) is what people want.
Hope this helps!
Jonathan
========================================================================Date: Fri, 11 Feb 2011 09:25:34 -0800
Reply-To: Hoameng Ung <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Hoameng Ung <[log in to unmask]>
Subject: Research Assistant position at Stanford University
MIME-Version: 1.0
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--20cf3054a48fc6ab4c049c04fe03
Content-Type: text/plain; charset=ISO-8859-1
*Stanford University Social Science Research Assistant*
Job ID: 41512
Job Location: School of Medicine
Job Category : Social Science Research
Salary : 1A3
Date Posted : Feb 9, 2011
The Stanford Systems Neuroscience and Pain Lab is recruiting a full-time
Social Science Research Assistant (SSRA) to assist with ongoing and new NIH
funded research related to neuroimaging and pain. These studies involve
structural and functional imaging in healthy individuals and patients with
chronic pain, functional imaging of the cervical spinal cord, and using fMRI
as a biofeedback tool (real-time fMRI neurofeedback) for pain modulation.
The research assistant will work directly with postdoctoral research
fellows, principal investigators, and fellow research assistants in a large,
collaborative environment. This position will span multiple projects,
requiring the applicant to be comfortable working in a variety of settings,
prioritizing goals, and working with multiple lab members.
The SSRA will assist neuroimaging research, operation of MRI scanner and
other study equipment, and data pre-processing and analysis. Assistance at
the scanner will include operating scanner and stimuli computers, assisting
with participant set-up, and transferring and organizing data. The SSRA will
also assist with implementation and maintenance of analysis software and
serve as technical support for lab personnel regarding software and
analyses. Familiarity with SPM (preferred), AFNI, FSL neuroimaging analysis
programs is preferred. Some programming experience (eg Matlab) is preferred.
The SSRA will be responsible for maintenance of psychophysical testing
equipment and communication with technical support as necessary. Must be
self-motivated, independent, and able to work in a fast-paced, changing
environment. Applicant may also be responsible for some recruitment,
scheduling, and participant procedures. The applicant must have strong
interpersonal skills, be organized and detail-oriented, and able to work
independently with minimal supervision.
Required:
Proficient with MS office, including Word and Excel. Ability to multi-task
and work independently. Strong organizational skills. Familiarity with
neuroscience and computer programming. A two year college degree or
equivalent experience.
Desired:
Previous experience working with MRI scanners, MRI and fMRI processing, and
neuroimaging analysis highly desirable. Knowledge of programming (eg Matlab)
and statistical analyses. Comfort with multiple operating systems, including
Linux. Previous experience with software packages such as SPM, FSL, and
AFNI. A four-year degree desired.
--20cf3054a48fc6ab4c049c04fe03
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<p class="MsoNormal"><b>Stanford University Social Science Research Assistant</b></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Times New Roman","serif"">Job
ID: 41512 </span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Times New Roman","serif"">Job
Location: School of Medicine </span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Times New Roman","serif"">Job
Category : Social Science Research </span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Times New Roman","serif"">Salary : 1A3
</span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Times New Roman","serif"">Date
Posted : Feb 9, 2011</span></p>
<p class="MsoNormal"> </p>
<p class="MsoNormal">The Stanford Systems Neuroscience and Pain Lab is recruiting
a full-time Social Science Research Assistant (SSRA) to assist with ongoing and
new NIH funded research related to neuroimaging and pain. These studies involve
structural and functional imaging in healthy individuals and patients with
chronic pain, functional imaging of the cervical spinal cord, and using fMRI as
a biofeedback tool (real-time fMRI neurofeedback) for pain modulation. The
research assistant will work directly with postdoctoral research fellows,
principal investigators, and fellow research assistants in a large,
collaborative environment. This position will span multiple projects, requiring
the applicant to be comfortable working in a variety of settings, prioritizing
goals, and working with multiple lab members.<br>
<br>
The SSRA will assist neuroimaging research, operation of MRI scanner and other
study equipment, and data pre-processing and analysis. Assistance at the
scanner will include operating scanner and stimuli computers, assisting with
participant set-up, and transferring and organizing data. The SSRA will also
assist with implementation and maintenance of analysis software and serve as
technical support for lab personnel regarding software and analyses.
Familiarity with SPM (preferred), AFNI, FSL neuroimaging analysis programs is
preferred. Some programming experience (eg Matlab) is preferred. The SSRA will
be responsible for maintenance of psychophysical testing equipment and
communication with technical support as necessary. Must be self-motivated,
independent, and able to work in a fast-paced, changing environment. Applicant
may also be responsible for some recruitment, scheduling, and participant
procedures. The applicant must have strong interpersonal skills, be organized
and detail-oriented, and able to work independently with minimal supervision. <br>
<br>
Required: <br>
Proficient with MS office, including Word and Excel. Ability to multi-task and
work independently. Strong organizational skills. Familiarity with neuroscience
and computer programming. A two year college degree or equivalent experience.<br>
<br>
Desired: <br>
Previous experience working with MRI scanners, MRI and fMRI processing, and
neuroimaging analysis highly desirable. Knowledge of programming (eg Matlab)
and statistical analyses. Comfort with multiple operating systems, including
Linux. Previous experience with software packages such as SPM, FSL, and AFNI. A
four-year degree desired.</p>
<p class="MsoNormal"> </p>
--20cf3054a48fc6ab4c049c04fe03--
========================================================================Date: Fri, 11 Feb 2011 10:13:49 -0800
Reply-To: "Jiang, Zhiguo" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "Jiang, Zhiguo" <[log in to unmask]>
Subject: naive quesiton about mapping between images in different space
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In spm_reslice.m , it states
% Assuming that image1 has a transformation matrix M1, and image2 has a
% transformation matrix M2, the mapping from image1 to image2 is: M2\M1
% (ie. from the coordinate system of image1 into millimeters, followed
% by a mapping from millimeters into the space of image2).
So to map a x0,y0,z0 in image1 space to image2, isn't the composite matrix
M1\M2 instead of M2\M1... ?
Beause we need to bring x0,y0,z0 to mm then do an inverse of M2 to image 2 ...
Not sure where I got it wrong...
Thanks,
Tony
------------------------------------------------------------------------------------------------------------------------------
[cid:image001.jpg@01CBC9D4.606E1D30]
Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles
Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,CA 90095
Tel: (310)206-1423|Email: [log in to unmask]
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<p class="MsoNormal">In spm_reslice.m , it states <o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";
color:black">% Assuming that image1 has a transformation matrix M1, and image2 has a<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";
color:black">% transformation matrix M2, the mapping from image1 to image2 is: M2\M1<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";
color:black">% (ie. from the coordinate system of image1 into millimeters, followed<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";
color:black">% by a mapping from millimeters into the space of image2).<o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">So to map a x0,y0,z0 in image1 space to image2, isn’t the composite matrix<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">M1\M2 instead of M2\M1… ?<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Beause we need to bring x0,y0,z0 to mm then do an inverse of M2 to image 2 …
<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Not sure where I got it wrong…<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Thanks,<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Tony<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">------------------------------------------------------------------------------------------------------------------------------<o:p></o:p></p>
<p class="MsoNormal"><img width="448" height="48" id="Picture_x0020_1" src="cid:image001.jpg@01CBC9D4.606E1D30" alt="logo"><o:p></o:p></p>
<p class="MsoNormal">Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles<br>
Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,CA 90095<o:p></o:p></p>
<p class="MsoNormal">Tel: (310)206-1423|Email: [log in to unmask]<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
</div>
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--_004_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D05EMGMB6admedct_--
========================================================================Date: Fri, 11 Feb 2011 11:10:54 -0800
Reply-To: "Jiang, Zhiguo" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "Jiang, Zhiguo" <[log in to unmask]>
Subject: Ignore my previous question
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--_004_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D20EMGMB6admedct_
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I mistakenly read '\' as '/'
Tony
------------------------------------------------------------------------------------------------------------------------------
[cid:image001.jpg@01CBC9DC.596C77E0]
Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles
Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,CA 90095
Tel: (310)206-1423|Email: [log in to unmask]
________________________________
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<p class="MsoNormal">I mistakenly read ‘\’ as ‘/’<o:p></o:p></p>
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<p class="MsoNormal">Tony<o:p></o:p></p>
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<p class="MsoNormal">------------------------------------------------------------------------------------------------------------------------------<o:p></o:p></p>
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<p class="MsoNormal">Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles<br>
Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,CA 90095<o:p></o:p></p>
<p class="MsoNormal">Tel: (310)206-1423|Email: [log in to unmask]<o:p></o:p></p>
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========================================================================Date: Fri, 11 Feb 2011 19:56:43 +0000
Reply-To: Hauke Hillebrandt <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Hauke Hillebrandt <[log in to unmask]>
Subject: Modelling time derivative: valid to use t-contrasts?
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Dear SPM Users,
in my fMRI model specification I model the time derivative and then I use t-contrasts on the 1st and 2nd level of my analysis to get the activation maps. Now I read in the SPM manual on page 66 that this might not be okay:
"The informed basis set requires an SPMF for inference. T-contrasts over just the canonical are
perfectly valid but assume constant delay/dispersion."
am I right that this means that I have to use f-contrasts exclusively on the 1st and 2nd level of my analysis (+the flexible factorial design option) if I model the time derivative and that I cannot use t-tests at all in this case?
Best wishes,
Hauke
========================================================================Date: Fri, 11 Feb 2011 16:07:58 -0500
Reply-To: Jason Steffener <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Jason Steffener <[log in to unmask]>
Subject: Re: Modelling time derivative: valid to use t-contrasts?
Comments: To: Hauke Hillebrandt <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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--001636c5b6bbba64f8049c081780
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Dear Hauke,
The use of a t-test across the two regressors means that you are testing a
specific relationship between the two regressors.
e.g your design is:
[canonical derivative]
and your contrast is:
[1 1]
Then you are "assuming" that the canonical and derivative have equal weight.
The F-test allows you to test any arbitrary relationship between the
canonical and derivative regressors.
I hope this helps,
Jason.
On Fri, Feb 11, 2011 at 2:56 PM, Hauke Hillebrandt <
[log in to unmask]> wrote:
> Dear SPM Users,
>
> in my fMRI model specification I model the time derivative and then I use
> t-contrasts on the 1st and 2nd level of my analysis to get the activation
> maps. Now I read in the SPM manual on page 66 that this might not be okay:
>
> "The informed basis set requires an SPMF for inference. T-contrasts over
> just the canonical are
> perfectly valid but assume constant delay/dispersion."
>
> am I right that this means that I have to use f-contrasts exclusively on
> the 1st and 2nd level of my analysis (+the flexible factorial design option)
> if I model the time derivative and that I cannot use t-tests at all in this
> case?
>
> Best wishes,
>
> Hauke
>
--
Jason Steffener, Ph.D.
Department of Neurology
Columbia University
http://www.cogneurosci.org/steffener.html
--001636c5b6bbba64f8049c081780
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Dear Hauke,<br>The use of a t-test across the two regressors means that you are testing a specific relationship between the two regressors.<br>e.g your design is:<br>[canonical derivative]<br>and your contrast is:<br>[1 1]<br>
Then you are "assuming" that the canonical and derivative have equal weight.<br>The F-test allows you to test any arbitrary relationship between the canonical and derivative regressors.<br><br><br>I hope this helps,<br>
Jason.<br><br><br><div class="gmail_quote">On Fri, Feb 11, 2011 at 2:56 PM, Hauke Hillebrandt <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Dear SPM Users,<br>
<br>
in my fMRI model specification I model the time derivative and then I use t-contrasts on the 1st and 2nd level of my analysis to get the activation maps. Now I read in the SPM manual on page 66 that this might not be okay:<br>
<br>
"The informed basis set requires an SPMF for inference. T-contrasts over just the canonical are<br>
perfectly valid but assume constant delay/dispersion."<br>
<br>
am I right that this means that I have to use f-contrasts exclusively on the 1st and 2nd level of my analysis (+the flexible factorial design option) if I model the time derivative and that I cannot use t-tests at all in this case?<br>
<br>
Best wishes,<br>
<font color="#888888"><br>
Hauke<br>
</font></blockquote></div><br><br clear="all"><br>-- <br>Jason Steffener, Ph.D.<br>Department of Neurology<br>Columbia University<br><a href="http://www.cogneurosci.org/steffener.html" target="_blank">http://www.cogneurosci.org/steffener.html</a><br>
--001636c5b6bbba64f8049c081780--
========================================================================Date: Fri, 11 Feb 2011 14:03:33 -0800
Reply-To: Manish Dalwani <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Manish Dalwani <[log in to unmask]>
Subject: Why should I use SPM8 vs SPM5?
MIME-Version: 1.0
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Hello SPM'ers,
Can experts please advice on why I should switch from SPM8 from SPM5?
Regards,Manish DalwaniSr. PRADept. of PsychiatryUniversity of Colorado
--0-466683208-1297461813=:75525
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<table cellspacing="0" cellpadding="0" border="0" ><tr><td valign="top" style="font: inherit;">Hello SPM'ers, <div><br></div><div>Can experts please advice on why I should switch from SPM8 from SPM5?</div><div><br></div><div>Regards,</div><div>Manish Dalwani</div><div>Sr. PRA</div><div>Dept. of Psychiatry</div><div>University of Colorado</div></td></tr></table><br>
--0-466683208-1297461813=:75525--
========================================================================Date: Fri, 11 Feb 2011 14:33:49 -0800
Reply-To: Michael T Rubens <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Michael T Rubens <[log in to unmask]>
Subject: Re: Modelling time derivative: valid to use t-contrasts?
Comments: To: Hauke Hillebrandt <[log in to unmask]>
In-Reply-To: <AANLkTi=fZEoCEBvnu9aVGRX+GpFdM0ü[log in to unmask]>
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I think this is a good question Hauke. If I understand you correctly, you'd
want to do a comparison of 2 conditions, each with an informed basis set.
i.e.,
[A-hrf A-tderiv B-hrf B-tderiv]
[ 1 1 -1 -1 ]
to my understanding that makes sense, but i'd like to hear other opinions.
Cheers,
Michael
On Fri, Feb 11, 2011 at 1:07 PM, Jason Steffener <[log in to unmask]>wrote:
> Dear Hauke,
> The use of a t-test across the two regressors means that you are testing a
> specific relationship between the two regressors.
> e.g your design is:
> [canonical derivative]
> and your contrast is:
> [1 1]
> Then you are "assuming" that the canonical and derivative have equal
> weight.
> The F-test allows you to test any arbitrary relationship between the
> canonical and derivative regressors.
>
>
> I hope this helps,
> Jason.
>
>
>
> On Fri, Feb 11, 2011 at 2:56 PM, Hauke Hillebrandt <
> [log in to unmask]> wrote:
>
>> Dear SPM Users,
>>
>> in my fMRI model specification I model the time derivative and then I use
>> t-contrasts on the 1st and 2nd level of my analysis to get the activation
>> maps. Now I read in the SPM manual on page 66 that this might not be okay:
>>
>> "The informed basis set requires an SPMF for inference. T-contrasts over
>> just the canonical are
>> perfectly valid but assume constant delay/dispersion."
>>
>> am I right that this means that I have to use f-contrasts exclusively on
>> the 1st and 2nd level of my analysis (+the flexible factorial design option)
>> if I model the time derivative and that I cannot use t-tests at all in this
>> case?
>>
>> Best wishes,
>>
>> Hauke
>>
>
>
>
> --
> Jason Steffener, Ph.D.
> Department of Neurology
> Columbia University
> http://www.cogneurosci.org/steffener.html
>
--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco
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I think this is a good question Hauke. If I understand you correctly, you'd want to do a comparison of 2 conditions, each with an informed basis set. i.e., <br><br>[A-hrf A-tderiv B-hrf B-tderiv]<br><br>[  1       1        -1      -1  ]<br>
<br>to my understanding that makes sense, but i'd like to hear other opinions.<br><br>Cheers,<br>Michael<br><br><div class="gmail_quote">On Fri, Feb 11, 2011 at 1:07 PM, Jason Steffener <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">Dear Hauke,<br>The use of a t-test across the two regressors means that you are testing a specific relationship between the two regressors.<br>
e.g your design is:<br>[canonical derivative]<br>and your contrast is:<br>[1 1]<br>
Then you are "assuming" that the canonical and derivative have equal weight.<br>The F-test allows you to test any arbitrary relationship between the canonical and derivative regressors.<br><br><br>I hope this helps,<br>
Jason.<div><div></div><div class="h5"><br><br><br><div class="gmail_quote">On Fri, Feb 11, 2011 at 2:56 PM, Hauke Hillebrandt <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Dear SPM Users,<br>
<br>
in my fMRI model specification I model the time derivative and then I use t-contrasts on the 1st and 2nd level of my analysis to get the activation maps. Now I read in the SPM manual on page 66 that this might not be okay:<br>
<br>
"The informed basis set requires an SPMF for inference. T-contrasts over just the canonical are<br>
perfectly valid but assume constant delay/dispersion."<br>
<br>
am I right that this means that I have to use f-contrasts exclusively on the 1st and 2nd level of my analysis (+the flexible factorial design option) if I model the time derivative and that I cannot use t-tests at all in this case?<br>
<br>
Best wishes,<br>
<font color="#888888"><br>
Hauke<br>
</font></blockquote></div><br><br clear="all"><br></div></div><font color="#888888">-- <br>Jason Steffener, Ph.D.<br>Department of Neurology<br>Columbia University<br><a href="http://www.cogneurosci.org/steffener.html" target="_blank">http://www.cogneurosci.org/steffener.html</a><br>
</font></blockquote></div><br><br clear="all"><br>-- <br>Research Associate<br>Gazzaley Lab<br>Department of Neurology<br>University of California, San Francisco<br>
--20cf30549f638ad175049c094c9f--
========================================================================Date: Fri, 11 Feb 2011 18:44:08 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: Why should I use SPM8 vs SPM5?
Comments: To: Manish Dalwani <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Switching from 5 to 8 as newer algorithms and bug fixes are employed.
The newer algorithms address concerns of the field such as
registration accuracy. Also, only 8 is really supported.
Can't think of any reason to go back to 5.
On Friday, February 11, 2011, Manish Dalwani <[log in to unmask]> wrote:
> Hello SPM'ers,
> Can experts please advice on why I should switch from SPM8 from SPM5?
> Regards,Manish DalwaniSr. PRADept. of PsychiatryUniversity of Colorado
>
--
Best Regards, Donald McLaren
================D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Office: (773) 406-2464
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========================================================================Date: Fri, 11 Feb 2011 20:52:23 -0500
Reply-To: Jonathan Peelle <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Jonathan Peelle <[log in to unmask]>
Subject: Re: Modelling time derivative: valid to use t-contrasts?
In-Reply-To: <[log in to unmask]>
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What we usually want to know when doing fMRI analyses is whether a region is "active" or not. When modeling with just a canonical HRF, this is fairly simple, as a positive parameter estimate (beta value) means that there was a response that matched the canonical HRF, which we can fairly safely interpret as "activation" (i.e., increase in signal that is time-locked to some stimulus category at about the time we expect).
The tricky thing about interpreting any other type of basis set is how it relates to "activation" (assuming this is what we're interested in). In general, the effect of the derivatives is really only interpretable in relation to a reliable positive loading on the canonical HRF. I.e., if you have an HRF-like shape, the derivatives can tell you about how the observed response differs from canonical. But you could imagine a situation in which you have a significant weighting on a derivative, but not on the canonical. In these situations it's quite difficult to interpret the result.
Getting back to the point at hand, if you model conditions A and B with
[Ahrf Atemp_deriv Bhrf Btemp_deriv]
then the contrast
[1 1 0 0]
will give you the effect of the *average* of the canonical and temporal derivative. But this is sort of a meaningless measure; it would give the same result for something that is really well-explained by the canonical only, really well-explained by the temporal derivative only, or explained by equal contributions from the two. The most straightforward way to assess "activation" would be to just look at the canonical HRF:
[1 0 0 0]
Or, if you want to look for "informed" effects, use an F test:
[1 0 0 0
0 1 0 0]
see Chapter 30 in the SPM manual, for example (Face Group Data).
For comparing across groups, the same logic holds: the most straightforward way to assess "activation" would be to just compare the canonical HRFs:
[1 0 -1 0]
and if you wanted to compare more, you could use an F test:
[1 0 -1 0
0 1 0 -1]
but you still run into non-straightforward interpretations, because a significant F value doesn't tell you (a) in which direction the effect is, or (b) whether the difference is on the canonical HRF or the derivative.
The following papers are very helpful—surely moreso than my attempt above. :)
Henson, R.N.A., Price, C.J., Rugg, M.D., Turner, R., Friston, K.J., 2002. Detecting latency differences in event-related BOLD responses: Application to words versus nonwords and initial versus repeated face presentations. NeuroImage 15, 83-97.
Calhoun, V.D., Stevens, M.C., Pearlson, G.D., Kiehl, K.A., 2004. fMRI analysis with the general linear model: removal of latency-induced amplitude bias by incorporation of hemodynamic derivative terms. NeuroImage 22, 252-257.
Hope this helps,
Jonathan
--
Dr. Jonathan Peelle
Department of Neurology
University of Pennsylvania
3 West Gates
3400 Spruce Street
Philadelphia, PA 19104
USA
http://jonathanpeelle.net/
On Feb 11, 2011, at 5:33 PM, Michael T Rubens wrote:
> I think this is a good question Hauke. If I understand you correctly, you'd want to do a comparison of 2 conditions, each with an informed basis set. i.e.,
>
> [A-hrf A-tderiv B-hrf B-tderiv]
>
> [ 1 1 -1 -1 ]
>
> to my understanding that makes sense, but i'd like to hear other opinions.
>
> Cheers,
> Michael
>
> On Fri, Feb 11, 2011 at 1:07 PM, Jason Steffener <[log in to unmask]> wrote:
> Dear Hauke,
> The use of a t-test across the two regressors means that you are testing a specific relationship between the two regressors.
> e.g your design is:
> [canonical derivative]
> and your contrast is:
> [1 1]
> Then you are "assuming" that the canonical and derivative have equal weight.
> The F-test allows you to test any arbitrary relationship between the canonical and derivative regressors.
>
>
> I hope this helps,
> Jason.
>
>
>
> On Fri, Feb 11, 2011 at 2:56 PM, Hauke Hillebrandt <[log in to unmask]> wrote:
> Dear SPM Users,
>
> in my fMRI model specification I model the time derivative and then I use t-contrasts on the 1st and 2nd level of my analysis to get the activation maps. Now I read in the SPM manual on page 66 that this might not be okay:
>
> "The informed basis set requires an SPMF for inference. T-contrasts over just the canonical are
> perfectly valid but assume constant delay/dispersion."
>
> am I right that this means that I have to use f-contrasts exclusively on the 1st and 2nd level of my analysis (+the flexible factorial design option) if I model the time derivative and that I cannot use t-tests at all in this case?
>
> Best wishes,
>
> Hauke
>
========================================================================Date: Sat, 12 Feb 2011 11:20:32 +0800
Reply-To: Xinian Zuo <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Xinian Zuo <[log in to unmask]>
Subject: Opening positions
Comments: To: fsl <[log in to unmask]>, [log in to unmask]
Comments: cc: ZANG Yu-Feng <[log in to unmask]>, Xuchu Weng <[log in to unmask]>
MIME-Version: 1.0
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Message-ID: <[log in to unmask]>
--20cf305644d323d10c049c0d4c9c
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Opening positions (postdoc, research assistant, or professorship) for
collaborative projects from the Center for Human Brain Research, Hangzhou
Normal University (Professor WENG Xuchu, [log in to unmask]), Key Laboratory
of Cognitive Neuroscience and Learning, Beijing Normal University (Professor
ZANG Yufeng, [log in to unmask]), and Institute of Psychology, Chinese Academy
of Science (Professor ZUO Xi-Nian, [log in to unmask]). Any applicant with
relevant neuroimaging background is welcome, including neuroscience,
medicine, psychology, biology, physics, mathematics, computer and
information science, and so on. Salary and compensation are almost the best
in China.
Please send your CV to any of the three e-mail addresses listed above if you
are interested in these jobs.
--
Xi-Nian Zuo, Ph.D of Applied Mathematics (http://lfcd.psych.ac.cn)
--20cf305644d323d10c049c0d4c9c
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Opening positions (postdoc, research assistant, or professorship) for collaborative projects from the Center for Human Brain Research, Hangzhou Normal University (Professor WENG Xuchu, <a href="mailto:[log in to unmask]">[log in to unmask]</a>), Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University (Professor ZANG Yufeng, <a href="mailto:[log in to unmask]">[log in to unmask]</a>), and Institute of Psychology, Chinese Academy of Science (Professor ZUO Xi-Nian, <a href="mailto:[log in to unmask]">[log in to unmask]</a>). Any applicant with relevant neuroimaging background is welcome, including neuroscience, medicine, psychology, biology, physics, mathematics, computer and information science, and so on. Salary and compensation are almost the best in China.<br>
<br>Please send your CV to any of the three e-mail addresses listed above if you are interested in these jobs.<br>-- <br>Xi-Nian Zuo, Ph.D of Applied Mathematics (<a href="http://lfcd.psych.ac.cn">http://lfcd.psych.ac.cn</a>) <br>
<br><br><br>
--20cf305644d323d10c049c0d4c9c--
========================================================================Date: Fri, 11 Feb 2011 22:57:17 -0500
Reply-To: hekmatyar <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: hekmatyar <[log in to unmask]>
Subject: Re: coregistration problem
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Hi All
I try to co register mean functional (EPI) to anatomical (T2 weighted) using
spm99, it does coregister, but not good, kind of distorted and did not do
properly. When I tried to use spm8, after co-registration, it puts one of my
coronal view into saggital plane, it completely screws up. I even tried
mutual information selection, it did not work.I am wondering that what might
be causing the orientation swap. Please let me know.
Thanks
Khan Hekmatyar
BioImaging Research Center
University of Georgia
Athens, GA-30606
========================================================================Date: Sat, 12 Feb 2011 11:54:42 +0100
Reply-To: Lin Wang <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Lin Wang <[log in to unmask]>
Subject: Re: 'NaN' in cluster level p value
In-Reply-To: <[log in to unmask]>
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Dear all,
I realized that I incorrectly took the SPM[T] values for the 2nd-level
statistical analysis, which leads to the NaNs.
Now I know I should use the contrast images for the group analysis. But I
still don't get it why the use of SPM[T] values will give me NaNs in the
statistical results...
Best,
Lin
2011/2/7 Lin Wang <[log in to unmask]>
> Dear all,
>
> I got the same problem after running the 2nd-level statistical analysis.
> The cluster-level p values are NaNs, and I get the following warning when I
> click "whole brain" to show all the clusters:
>
> Warning: Returning NaN for out of range arguments
> > In spm_Pcdf at 88
> In spm_P_RF at 106
> In spm_P at 48
> In spm_list at 487
>
> I searched the documents, but I couldn't find a clear answer for it. Could
> anyone help me with it?
>
> Thanks very much.
>
> Best,
> Lin
>
>
--0016e644c79c5d9991049c13a4c9
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Dear all,<br><br>I realized that I incorrectly took the SPM[T] values for the 2nd-level statistical analysis, which leads to the NaNs.<br><br>Now I know I should use the contrast images for the group analysis. But I still don't get it why the use of SPM[T] values will give me NaNs in the statistical results...<br>
<br>Best,<br>Lin<br><br><div class="gmail_quote">2011/2/7 Lin Wang <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span><br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
Dear all,<br>
<br>
I got the same problem after running the 2nd-level statistical analysis. The cluster-level p values are NaNs, and I get the following warning when I click "whole brain" to show all the clusters:<br>
<br>
Warning: Returning NaN for out of range arguments<br>
> In spm_Pcdf at 88<br>
In spm_P_RF at 106<br>
In spm_P at 48<br>
In spm_list at 487<br>
<br>
I searched the documents, but I couldn't find a clear answer for it. Could anyone help me with it?<br>
<br>
Thanks very much.<br>
<br>
Best,<br>
<font color="#888888">Lin<br>
<br>
</font></blockquote></div><br>
--0016e644c79c5d9991049c13a4c9--
========================================================================Date: Sat, 12 Feb 2011 17:43:10 +0000
Reply-To: "Stephen J. Fromm" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "Stephen J. Fromm" <[log in to unmask]>
Subject: spm_api_bmc.m: typo in help comments?
Mime-Version: 1.0
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The beginning of SPM8: spm_api_bmc.m reads:
function out=spm_api_bmc(F,N,exp_r,xp,family)
% API to select and compare DCMs using Bayesian model comparison
% FORMAT out=spm_api_bmc(F,N,alpha,exp_r,xp)
%
% INPUT:
% F - Matrix/Vector of log model evidences
% N - vector of model names
% alpha - vector of model probabilities
% exp_r - expectation of the posterior p(r|y)
% xp - exceedance probabilities
%
% OUTPUT:
% out - conditional probability of DCMs (when using fixed effect method)
%__________________________________________________________________________
% Copyright (C) 2008 Wellcome Trust Centre for Neuroimaging
The actual function prototype and the one in the help under FORMAT don't seem to match.
========================================================================Date: Sat, 12 Feb 2011 17:47:13 +0000
Reply-To: "Stephen J. Fromm" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "Stephen J. Fromm" <[log in to unmask]>
Subject: DCM theory: def'n of exceedance prob.?
Mime-Version: 1.0
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Message-ID: <[log in to unmask]>
Equation (16) of Stephan et al., "Bayesian model selection for group studies" (2009, NeuroImage) is:
--------------------------------------------------
For all j in {1, 2, ..., K | j=/=k}:
phi_k = p(r_k > r_j | y; alpha).
--------------------------------------------------
This doesn't make sense to me as written. I would assume it's something like
--------------------------------------------------
phi_k = p(r_k > r_j for all j=/=k | y; alpha).
--------------------------------------------------
========================================================================Date: Sun, 13 Feb 2011 04:27:28 +0000
Reply-To: Yang Hu <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Yang Hu <[log in to unmask]>
Subject: about ROI signal value
Mime-Version: 1.0
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Dear Experts,
Based on whole-brain analysis via SPM8 (1st- and 2nd-level analysis), I want to extract signal value from atlas-based ROIs (e.g. amygdala) in four experimental conditions, compared with rest condition respectively, by AFNI rather than MarsBar. Specific steps are listed as follows:
1. Mask generation: by WFU pickatlas, an file of ROI in amygdala (named as "amy.nii") was generated
2. File conversion: by AFNI command "3dcopy", the "amy.nii" was converted to "amy+orig.BRIK/HEAD", which could be recognized by AFNI
3. ROI calculation: by AFNI command "3dmaskave", the average value within the ROI mask (amygdala) of each subject (sbj**) in certain condition (con_000*) could be obtained; Specifically, the full command is like that "3dmaskave -quiet -mask amy+orig sbj**_con_000*.hdr". The file "sbj**_con_000*.hdr" was generated after 1st-level analysis via SPM8.
So I just wonder the meaning of ROI value obtained through those steps mentioned above. Whether does the value mean percent signal change (PSC), contrast value (CV), or parameter estimates (PE) ? And what's the difference between the three terms (namely PSC, CV, PE) always used in ROI analysis?
Thanks a lot for your help and comment!
Yang Hu
ICN, ECNU, Shanghai, China
========================================================================Date: Sun, 13 Feb 2011 09:40:33 +0000
Reply-To: William Sohn <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: William Sohn <[log in to unmask]>
Subject: MEG data conversion
Mime-Version: 1.0
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Dear SPM community,
I have some MEG data and I want to run analysis using SPM. The data format is a bin cue format but I can export data into a text format. Data is arranged where channels are in columns.
I was wondering how I can convert this into a format where I can read in SPM.
Thank you,
-William Sohn
========================================================================Date: Sun, 13 Feb 2011 17:59:34 +0800
Reply-To: Brahim HAMADICHAREF <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Brahim HAMADICHAREF <[log in to unmask]>
Subject: Re: MEG data conversion
Comments: To: [log in to unmask]
In-Reply-To: <[log in to unmask]>
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Maybe by using biosig to import the data and save it into a standardand known format by SPM ... GDF for e.g. although I am not sure whatSPM8 recognize ...
Brahim
> Date: Sun, 13 Feb 2011 09:40:33 +0000
> From: [log in to unmask]
> Subject: [SPM] MEG data conversion
> To: [log in to unmask]
>
> Dear SPM community,
>
> I have some MEG data and I want to run analysis using SPM. The data format is a bin cue format but I can export data into a text format. Data is arranged where channels are in columns.
>
> I was wondering how I can convert this into a format where I can read in SPM.
>
> Thank you,
>
> -William Sohn
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Maybe by using biosig to import the data and save it into a standard<div>and known format by SPM ... GDF for e.g. although I am not sure what</div><div>SPM8 recognize ... <br><div><br>Brahim <br><br><br>> Date: Sun, 13 Feb 2011 09:40:33 +0000<br>> From: [log in to unmask]<br>> Subject: [SPM] MEG data conversion<br>> To: [log in to unmask]<br>> <br>> Dear SPM community,<br>> <br>> I have some MEG data and I want to run analysis using SPM. The data format is a bin cue format but I can export data into a text format. Data is arranged where channels are in columns.<br>> <br>> I was wondering how I can convert this into a format where I can read in SPM.<br>> <br>> Thank you,<br>> <br>> -William Sohn<br></div></div> </body>
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========================================================================Date: Sun, 13 Feb 2011 11:45:19 +0000
Reply-To: Vladimir Litvak <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Litvak <[log in to unmask]>
Subject: Re: MEG data conversion
Comments: To: William Sohn <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
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Dear William,
In SPM manual you can find 'Converting arbitrary data' section which
explains what you should do. There is also an example script mentioned
there. This will be sufficient to convert your channel data. However,
you also need your sensor locations and for this you will need the
data in their original format. For some analyses you might manage
without, but it depends on what exactly you want to do.
Best,
Vladimir
On Sun, Feb 13, 2011 at 9:40 AM, William Sohn <[log in to unmask]> wrote:
> Dear SPM community,
>
> I have some MEG data and I want to run analysis using SPM. The data format is a bin cue format but I can export data into a text format. Data is arranged where channels are in columns.
>
> I was wondering how I can convert this into a format where I can read in SPM.
>
> Thank you,
>
> -William Sohn
>
========================================================================Date: Sun, 13 Feb 2011 12:00:23 +0000
Reply-To: Viridiana Mazzola <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Viridiana Mazzola <[log in to unmask]>
Subject: extract ROI at the edge of the cerebellum
Mime-Version: 1.0
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Dear all,
I am trying to extract a ROI at the edge of the cerebellum following the Brett's FAQ instructions.
1 . I rename the ROI my_roi.mat, then I write in matlab the command:
roi_filename = 'my_roi.mat';
my_roi = maroi(roi_filename);
my_roi = spm_hold(my_roi, 0); % set NN resampling
saveroi(my_roi, roi_filename);
2. then I rerun the extraction of my_roi, but this error appears:
Fetching data : 1/1 Error in ==> maroi.getdata at 30
if nargin < 2
??? Output argument "vXYZ" (and maybe others) not assigned during call to
"[log in to unmask] (getdata)".
Error in ==> maroi.get_marsy at 76
[y vals vXYZ mat] = getdata(o, VY);
Error in ==> marsbar at 985
marsY = get_marsy(o{:}, VY, sumfunc, 'v');
Error in ==> spm at 964
evalin('base',CBs{v-1})
??? Error while evaluating uicontrol Callback
=> Where am I wrong?
Thank you for your attention!
Viridiana
NeuroLab
IPRA
========================================================================Date: Sun, 13 Feb 2011 12:42:14 +0000
Reply-To: Klaas Enno Stephan <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Klaas Enno Stephan <[log in to unmask]>
Subject: Re: DCM theory: def'n of exceedance prob.?
Comments: To: "Stephen J. Fromm" <[log in to unmask]>
MIME-Version: 1.0
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Dear Stephen,
I am sorry if my mathematical notation confused you (the convention I grew up
with is that existential and universal quantifiers precede set-theoretic
statements). The equation means exactly what you express in your
reformulation.
Best wishes,
Klaas
________________________________
Von: Stephen J. Fromm <[log in to unmask]>
An: [log in to unmask]
Gesendet: Samstag, den 12. Februar 2011, 18:47:13 Uhr
Betreff: [SPM] DCM theory: def'n of exceedance prob.?
Equation (16) of Stephan et al., "Bayesian model selection for group studies"
(2009, NeuroImage) is:
--------------------------------------------------
For all j in {1, 2, ..., K | j=/=k}:
phi_k = p(r_k > r_j | y; alpha).
--------------------------------------------------
This doesn't make sense to me as written. I would assume it's something like
--------------------------------------------------
phi_k = p(r_k > r_j for all j=/=k | y; alpha).
--------------------------------------------------
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<html><head><style type="text/css"><!-- DIV {margin:0px;} --></style></head><body><div style="font-family:times new roman,new york,times,serif;font-size:12pt"><meta http-equiv="x-dns-prefetch-control" content="off"><div style="font-family: times new roman,new york,times,serif; font-size: 12pt;"><div>Dear Stephen,<br><br>I am sorry if my mathematical notation confused you (the convention I grew up with is that existential and universal quantifiers precede set-theoretic statements). The equation means exactly what you express in your reformulation. <br><br>Best wishes,<br>Klaas<br><br></div><div style="font-family: times new roman,new york,times,serif; font-size: 12pt;"><br><div style="font-family: arial,helvetica,sans-serif; font-size: 10pt;"><font face="Tahoma" size="2"><hr size="1"><b><span style="font-weight: bold;">Von:</span></b> Stephen J. Fromm <[log in to unmask]><br><b><span style="font-weight: bold;">An:</span></b>
[log in to unmask]<br><b><span style="font-weight: bold;">Gesendet:</span></b> Samstag, den 12. Februar 2011, 18:47:13 Uhr<br><b><span style="font-weight: bold;">Betreff:</span></b> [SPM] DCM theory: def'n of exceedance prob.?<br></font><br>Equation (16) of Stephan et al.,
"Bayesian model selection for group studies" (2009, NeuroImage) is:<br><br>--------------------------------------------------<br><br>For all j in {1, 2, ..., K | j=/=k}:<br><br>phi_k = p(r_k > r_j | y; alpha).<br><br>--------------------------------------------------<br><br>This doesn't make sense to me as written. I would assume it's something like<br><br>--------------------------------------------------<br><br>phi_k = p(r_k > r_j for all j=/=k | y; alpha).<br><br>--------------------------------------------------<br></div></div>
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========================================================================Date: Sun, 13 Feb 2011 08:38:39 -0500
Reply-To: "Fromm, Stephen (NIH/NIMH) [C]" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "Fromm, Stephen (NIH/NIMH) [C]" <[log in to unmask]>
Subject: Re: DCM theory: def'n of exceedance prob.?
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Klaas,
Thanks for your timely response! Very interesting paper.
Cheers,
Stephen J. Fromm, PhD
Contractor, NIMH/MAP
(301) 451--9265
________________________________
From: Klaas Enno Stephan [[log in to unmask]]
Sent: Sunday, February 13, 2011 7:42 AM
To: Fromm, Stephen (NIH/NIMH) [C]; [log in to unmask]
Subject: RE: [SPM] DCM theory: def'n of exceedance prob.?
Dear Stephen,
I am sorry if my mathematical notation confused you (the convention I grew up with is that existential and universal quantifiers precede set-theoretic statements). The equation means exactly what you express in your reformulation.
Best wishes,
Klaas
________________________________
Von: Stephen J. Fromm <[log in to unmask]>
An: [log in to unmask]
Gesendet: Samstag, den 12. Februar 2011, 18:47:13 Uhr
Betreff: [SPM] DCM theory: def'n of exceedance prob.?
Equation (16) of Stephan et al., "Bayesian model selection for group studies" (2009, NeuroImage) is:
--------------------------------------------------
For all j in {1, 2, ..., K | j=/=k}:
phi_k = p(r_k > r_j | y; alpha).
--------------------------------------------------
This doesn't make sense to me as written. I would assume it's something like
--------------------------------------------------
phi_k = p(r_k > r_j for all j=/=k | y; alpha).
--------------------------------------------------
========================================================================Date: Sun, 13 Feb 2011 14:39:21 +0000
Reply-To: Klaas Enno Stephan <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Klaas Enno Stephan <[log in to unmask]>
Subject: Re: DCM theory: def'n of exceedance prob.?
Comments: To: "Fromm, Stephen (NIH/NIMH) [C]" <[log in to unmask]>
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Thank you, Stephen.
To prevent any further confusion, let me point out that there is a typo in Eq.
A.3 in the appendix. The log's are missing for the first two terms on the right
hand side, i.e. the equation should read:
KL[q(theta)|p(theta|m)] = 0.5*log(det(C_theta)) - 0.5*log(det(C_theta|y)) + ...
There is an erratum at this address:
http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WNP-4WNWRT0-1&_user=10&_coverDate=10%2F15%2F2009&_rdoc=1&_fmt=high&_orig=browse&_origin=browse&_zone=rslt_list_item&_cdi=6968&_sort=d&_docanchor=&view=c&_ct=1&_refLink=Y&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=9790004e41680cc8dbdadb633ac5d207&searchtype=a
Best wishes,
Klaas
________________________________
Von: "Fromm, Stephen (NIH/NIMH) [C]" <[log in to unmask]>
An: [log in to unmask]
Gesendet: Sonntag, den 13. Februar 2011, 14:38:39 Uhr
Betreff: Re: [SPM] DCM theory: def'n of exceedance prob.?
Klaas,
Thanks for your timely response! Very interesting paper.
Cheers,
Stephen J. Fromm, PhD
Contractor, NIMH/MAP
(301) 451--9265
________________________________
From: Klaas Enno Stephan [[log in to unmask]]
Sent: Sunday, February 13, 2011 7:42 AM
To: Fromm, Stephen (NIH/NIMH) [C]; [log in to unmask]
Subject: RE: [SPM] DCM theory: def'n of exceedance prob.?
Dear Stephen,
I am sorry if my mathematical notation confused you (the convention I grew up
with is that existential and universal quantifiers precede set-theoretic
statements). The equation means exactly what you express in your reformulation.
Best wishes,
Klaas
________________________________
Von: Stephen J. Fromm <[log in to unmask]>
An: [log in to unmask]
Gesendet: Samstag, den 12. Februar 2011, 18:47:13 Uhr
Betreff: [SPM] DCM theory: def'n of exceedance prob.?
Equation (16) of Stephan et al., "Bayesian model selection for group studies"
(2009, NeuroImage) is:
--------------------------------------------------
For all j in {1, 2, ..., K | j=/=k}:
phi_k = p(r_k > r_j | y; alpha).
--------------------------------------------------
This doesn't make sense to me as written. I would assume it's something like
--------------------------------------------------
phi_k = p(r_k > r_j for all j=/=k | y; alpha).
--------------------------------------------------
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<html><head><style type="text/css"><!-- DIV {margin:0px;} --></style></head><body><div style="font-family:times new roman,new york,times,serif;font-size:12pt"><div>Thank you, Stephen.<br><br>To prevent any further confusion, let me point out that there is a typo in Eq. A.3 in the appendix. The log's are missing for the first two terms on the right hand side, i.e. the equation should read:<br><br>KL[q(theta)|p(theta|m)] = 0.5*log(det(C_theta)) - 0.5*log(det(C_theta|y)) + ...<br><br>There is an erratum at this address:<br><span><a target="_blank"
href="http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WNP-4WNWRT0-1&_user=10&_coverDate=10%2F15%2F2009&_rdoc=1&_fmt=high&_orig=browse&_origin=browse&_zone=rslt_list_item&_cdi=6968&_sort=d&_docanchor=&view=c&_ct=1&_refLink=Y&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=9790004e41680cc8dbdadb633ac5d207&searchtype=a">http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6WNP-4WNWRT0-1&_user=10&_coverDate=10%2F15%2F2009&_rdoc=1&_fmt=high&_orig=browse&_origin=browse&_zone=rslt_list_item&_cdi=6968&_sort=d&_docanchor=&view=c&_ct=1&_refLink=Y&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=9790004e41680cc8dbdadb633ac5d207&searchtype=a</a></span><br><br>Best wishes,<br>Klaas<br><br><br></div><div style="font-family: times new roman,new york,times,serif; font-size:
12pt;"><br><div style="font-family: arial,helvetica,sans-serif; font-size: 10pt;"><font face="Tahoma" size="2"><hr size="1"><b><span style="font-weight: bold;">Von:</span></b> "Fromm, Stephen (NIH/NIMH) [C]" <[log in to unmask]><br><b><span style="font-weight: bold;">An:</span></b> [log in to unmask]<br><b><span style="font-weight: bold;">Gesendet:</span></b> Sonntag, den 13. Februar 2011, 14:38:39 Uhr<br><b><span style="font-weight: bold;">Betreff:</span></b> Re: [SPM] DCM theory: def'n of exceedance prob.?<br></font><br>Klaas,<br><br>Thanks for your timely response! Very interesting paper.<br><br>Cheers,<br><br>Stephen J. Fromm, PhD<br>Contractor, NIMH/MAP<br>(301) 451--9265<br><br>________________________________<br>From: Klaas Enno Stephan [<a ymailto="mailto:[log in to unmask]" href="mailto:[log in to unmask]">[log in to unmask]</a>]<br>Sent: Sunday, February 13, 2011 7:42 AM<br>To: Fromm, Stephen (NIH/NIMH) [C]; <a
ymailto="mailto:[log in to unmask]" href="mailto:[log in to unmask]">[log in to unmask]</a><br>Subject: RE: [SPM] DCM theory: def'n of exceedance prob.?<br><br>Dear Stephen,<br><br>I am sorry if my mathematical notation confused you (the convention I grew up with is that existential and universal quantifiers precede set-theoretic statements). The equation means exactly what you express in your reformulation.<br><br>Best wishes,<br>Klaas<br><br><br>________________________________<br>Von: Stephen J. Fromm <<a ymailto="mailto:[log in to unmask]" href="mailto:[log in to unmask]">[log in to unmask]</a>><br>An: <a ymailto="mailto:[log in to unmask]" href="mailto:[log in to unmask]">[log in to unmask]</a><br>Gesendet: Samstag, den 12. Februar 2011, 18:47:13 Uhr<br>Betreff: [SPM] DCM theory: def'n of exceedance prob.?<br><br>Equation (16) of Stephan et al., "Bayesian model selection for group studies" (2009, NeuroImage)
is:<br><br>--------------------------------------------------<br><br>For all j in {1, 2, ..., K | j=/=k}:<br><br>phi_k = p(r_k > r_j | y; alpha).<br><br>--------------------------------------------------<br><br>This doesn't make sense to me as written. I would assume it's something like<br><br>--------------------------------------------------<br><br>phi_k = p(r_k > r_j for all j=/=k | y; alpha).<br><br>--------------------------------------------------<br></div></div>
</div><br></body></html>
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========================================================================Date: Sun, 13 Feb 2011 15:34:23 -0500
Reply-To: Jason Steffener <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Jason Steffener <[log in to unmask]>
Subject: Re: Modelling time derivative: valid to use t-contrasts?
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Hello.
I completely agree with everything that Johnathan has said. I also want to
suggest reading my own paper. I combine many of the ideas behind the Henson
and Calhoun papers and provide a method for bringing the derivative info to
the group level. I hope this helps for at least providing another view of
some of the issues at hand. Also, all the steps discussed in the paper have
accompanying code which I provide a link to in the paper and below.
Steffener, J., Tabert, M., Reuben, A., Stern, Y.
Investigating hemodynamic response variability at the group level
using basis functions
(2010) NeuroImage, 49 (3), pp. 2113-2122.
http://cumc.columbia.edu/dept/sergievsky/cnd/steffener.html
Jason.
On Fri, Feb 11, 2011 at 8:52 PM, Jonathan Peelle <[log in to unmask]> wrote:
> What we usually want to know when doing fMRI analyses is whether a region
> is "active" or not. When modeling with just a canonical HRF, this is fairly
> simple, as a positive parameter estimate (beta value) means that there was a
> response that matched the canonical HRF, which we can fairly safely
> interpret as "activation" (i.e., increase in signal that is time-locked to
> some stimulus category at about the time we expect).
>
> The tricky thing about interpreting any other type of basis set is how it
> relates to "activation" (assuming this is what we're interested in). In
> general, the effect of the derivatives is really only interpretable in
> relation to a reliable positive loading on the canonical HRF. I.e., if you
> have an HRF-like shape, the derivatives can tell you about how the observed
> response differs from canonical. But you could imagine a situation in which
> you have a significant weighting on a derivative, but not on the canonical.
> In these situations it's quite difficult to interpret the result.
>
> Getting back to the point at hand, if you model conditions A and B with
>
> [Ahrf Atemp_deriv Bhrf Btemp_deriv]
>
> then the contrast
>
> [1 1 0 0]
>
> will give you the effect of the *average* of the canonical and temporal
> derivative. But this is sort of a meaningless measure; it would give the
> same result for something that is really well-explained by the canonical
> only, really well-explained by the temporal derivative only, or explained by
> equal contributions from the two. The most straightforward way to assess
> "activation" would be to just look at the canonical HRF:
>
> [1 0 0 0]
>
> Or, if you want to look for "informed" effects, use an F test:
>
> [1 0 0 0
> 0 1 0 0]
>
> see Chapter 30 in the SPM manual, for example (Face Group Data).
>
> For comparing across groups, the same logic holds: the most straightforward
> way to assess "activation" would be to just compare the canonical HRFs:
>
> [1 0 -1 0]
>
> and if you wanted to compare more, you could use an F test:
>
> [1 0 -1 0
> 0 1 0 -1]
>
> but you still run into non-straightforward interpretations, because a
> significant F value doesn't tell you (a) in which direction the effect is,
> or (b) whether the difference is on the canonical HRF or the derivative.
>
> The following papers are very helpful—surely moreso than my attempt above.
> :)
>
> Henson, R.N.A., Price, C.J., Rugg, M.D., Turner, R., Friston, K.J., 2002.
> Detecting latency differences in event-related BOLD responses: Application
> to words versus nonwords and initial versus repeated face presentations.
> NeuroImage 15, 83-97.
>
> Calhoun, V.D., Stevens, M.C., Pearlson, G.D., Kiehl, K.A., 2004. fMRI
> analysis with the general linear model: removal of latency-induced amplitude
> bias by incorporation of hemodynamic derivative terms. NeuroImage 22,
> 252-257.
>
>
> Hope this helps,
> Jonathan
>
> --
> Dr. Jonathan Peelle
> Department of Neurology
> University of Pennsylvania
> 3 West Gates
> 3400 Spruce Street
> Philadelphia, PA 19104
> USA
> http://jonathanpeelle.net/
>
>
> On Feb 11, 2011, at 5:33 PM, Michael T Rubens wrote:
>
> > I think this is a good question Hauke. If I understand you correctly,
> you'd want to do a comparison of 2 conditions, each with an informed basis
> set. i.e.,
> >
> > [A-hrf A-tderiv B-hrf B-tderiv]
> >
> > [ 1 1 -1 -1 ]
> >
> > to my understanding that makes sense, but i'd like to hear other
> opinions.
> >
> > Cheers,
> > Michael
> >
> > On Fri, Feb 11, 2011 at 1:07 PM, Jason Steffener <[log in to unmask]>
> wrote:
> > Dear Hauke,
> > The use of a t-test across the two regressors means that you are testing
> a specific relationship between the two regressors.
> > e.g your design is:
> > [canonical derivative]
> > and your contrast is:
> > [1 1]
> > Then you are "assuming" that the canonical and derivative have equal
> weight.
> > The F-test allows you to test any arbitrary relationship between the
> canonical and derivative regressors.
> >
> >
> > I hope this helps,
> > Jason.
> >
> >
> >
> > On Fri, Feb 11, 2011 at 2:56 PM, Hauke Hillebrandt <
> [log in to unmask]> wrote:
> > Dear SPM Users,
> >
> > in my fMRI model specification I model the time derivative and then I use
> t-contrasts on the 1st and 2nd level of my analysis to get the activation
> maps. Now I read in the SPM manual on page 66 that this might not be okay:
> >
> > "The informed basis set requires an SPMF for inference. T-contrasts over
> just the canonical are
> > perfectly valid but assume constant delay/dispersion."
> >
> > am I right that this means that I have to use f-contrasts exclusively on
> the 1st and 2nd level of my analysis (+the flexible factorial design option)
> if I model the time derivative and that I cannot use t-tests at all in this
> case?
> >
> > Best wishes,
> >
> > Hauke
> >
>
--
Jason Steffener, Ph.D.
Department of Neurology
Columbia University
http://www.cogneurosci.org/steffener.html
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Hello.<br>I completely agree with everything that Johnathan has said. I also want to suggest reading my own paper. I combine many of the ideas behind the Henson and Calhoun papers and provide a method for bringing the derivative info to the group level. I hope this helps for at least providing another view of some of the issues at hand. Also, all the steps discussed in the paper have accompanying code which I provide a link to in the paper and below.<br>
<br><pre>Steffener, J., Tabert, M., Reuben, A., Stern, Y.<br>Investigating hemodynamic response variability at the group level using basis functions<br>(2010) NeuroImage, 49 (3), pp. 2113-2122. <br><br><a href="http://cumc.columbia.edu/dept/sergievsky/cnd/steffener.html">http://cumc.columbia.edu/dept/sergievsky/cnd/steffener.html</a><br>
</pre><br>Jason.<br><br><br><div class="gmail_quote">On Fri, Feb 11, 2011 at 8:52 PM, Jonathan Peelle <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
What we usually want to know when doing fMRI analyses is whether a region is "active" or not. When modeling with just a canonical HRF, this is fairly simple, as a positive parameter estimate (beta value) means that there was a response that matched the canonical HRF, which we can fairly safely interpret as "activation" (i.e., increase in signal that is time-locked to some stimulus category at about the time we expect).<br>
<br>
The tricky thing about interpreting any other type of basis set is how it relates to "activation" (assuming this is what we're interested in). In general, the effect of the derivatives is really only interpretable in relation to a reliable positive loading on the canonical HRF. I.e., if you have an HRF-like shape, the derivatives can tell you about how the observed response differs from canonical. But you could imagine a situation in which you have a significant weighting on a derivative, but not on the canonical. In these situations it's quite difficult to interpret the result.<br>
<br>
Getting back to the point at hand, if you model conditions A and B with<br>
<br>
[Ahrf Atemp_deriv Bhrf Btemp_deriv]<br>
<br>
then the contrast<br>
<br>
[1 1 0 0]<br>
<br>
will give you the effect of the *average* of the canonical and temporal derivative. But this is sort of a meaningless measure; it would give the same result for something that is really well-explained by the canonical only, really well-explained by the temporal derivative only, or explained by equal contributions from the two. The most straightforward way to assess "activation" would be to just look at the canonical HRF:<br>
<br>
[1 0 0 0]<br>
<br>
Or, if you want to look for "informed" effects, use an F test:<br>
<br>
[1 0 0 0<br>
0 1 0 0]<br>
<br>
see Chapter 30 in the SPM manual, for example (Face Group Data).<br>
<br>
For comparing across groups, the same logic holds: the most straightforward way to assess "activation" would be to just compare the canonical HRFs:<br>
<br>
[1 0 -1 0]<br>
<br>
and if you wanted to compare more, you could use an F test:<br>
<br>
[1 0 -1 0<br>
0 1 0 -1]<br>
<br>
but you still run into non-straightforward interpretations, because a significant F value doesn't tell you (a) in which direction the effect is, or (b) whether the difference is on the canonical HRF or the derivative.<br>
<br>
The following papers are very helpful—surely moreso than my attempt above. :)<br>
<br>
Henson, R.N.A., Price, C.J., Rugg, M.D., Turner, R., Friston, K.J., 2002. Detecting latency differences in event-related BOLD responses: Application to words versus nonwords and initial versus repeated face presentations. NeuroImage 15, 83-97.<br>
<br>
Calhoun, V.D., Stevens, M.C., Pearlson, G.D., Kiehl, K.A., 2004. fMRI analysis with the general linear model: removal of latency-induced amplitude bias by incorporation of hemodynamic derivative terms. NeuroImage 22, 252-257.<br>
<br>
<br>
Hope this helps,<br>
Jonathan<br>
<br>
--<br>
Dr. Jonathan Peelle<br>
Department of Neurology<br>
University of Pennsylvania<br>
3 West Gates<br>
3400 Spruce Street<br>
Philadelphia, PA 19104<br>
USA<br>
<a href="http://jonathanpeelle.net/" target="_blank">http://jonathanpeelle.net/</a><br>
<div><div></div><div class="h5"><br>
<br>
On Feb 11, 2011, at 5:33 PM, Michael T Rubens wrote:<br>
<br>
> I think this is a good question Hauke. If I understand you correctly, you'd want to do a comparison of 2 conditions, each with an informed basis set. i.e.,<br>
><br>
> [A-hrf A-tderiv B-hrf B-tderiv]<br>
><br>
> [ 1 1 -1 -1 ]<br>
><br>
> to my understanding that makes sense, but i'd like to hear other opinions.<br>
><br>
> Cheers,<br>
> Michael<br>
><br>
> On Fri, Feb 11, 2011 at 1:07 PM, Jason Steffener <<a href="mailto:[log in to unmask]">[log in to unmask]</a>> wrote:<br>
> Dear Hauke,<br>
> The use of a t-test across the two regressors means that you are testing a specific relationship between the two regressors.<br>
> e.g your design is:<br>
> [canonical derivative]<br>
> and your contrast is:<br>
> [1 1]<br>
> Then you are "assuming" that the canonical and derivative have equal weight.<br>
> The F-test allows you to test any arbitrary relationship between the canonical and derivative regressors.<br>
><br>
><br>
> I hope this helps,<br>
> Jason.<br>
><br>
><br>
><br>
> On Fri, Feb 11, 2011 at 2:56 PM, Hauke Hillebrandt <<a href="mailto:[log in to unmask]">[log in to unmask]</a>> wrote:<br>
> Dear SPM Users,<br>
><br>
> in my fMRI model specification I model the time derivative and then I use t-contrasts on the 1st and 2nd level of my analysis to get the activation maps. Now I read in the SPM manual on page 66 that this might not be okay:<br>
><br>
> "The informed basis set requires an SPMF for inference. T-contrasts over just the canonical are<br>
> perfectly valid but assume constant delay/dispersion."<br>
><br>
> am I right that this means that I have to use f-contrasts exclusively on the 1st and 2nd level of my analysis (+the flexible factorial design option) if I model the time derivative and that I cannot use t-tests at all in this case?<br>
><br>
> Best wishes,<br>
><br>
> Hauke<br>
><br>
</div></div></blockquote></div><br><br clear="all"><br>-- <br>Jason Steffener, Ph.D.<br>Department of Neurology<br>Columbia University<br><a href="http://www.cogneurosci.org/steffener.html" target="_blank">http://www.cogneurosci.org/steffener.html</a><br>
--001636c5b6bb4e352c049c2fdb0c--
========================================================================Date: Sun, 13 Feb 2011 20:58:37 +0000
Reply-To: eleftherios garyfallidis
<[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: eleftherios garyfallidis
<[log in to unmask]>
Subject: First Official Dipy Release :-)
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=======================
Announcing Dipy 0.5.0
=======================
Dipy is a fast growing python toolbox for analysis of diffusion MR imaging.
Just from its first official release dipy provides a great amount of utilities to facilitate diffusion MRI research. Here are just a few of them.
- Reconstruction algorithms e.g. GQI, DTI
- Tractography generation algorithms e.g. EuDX
- Intelligent downsampling of tracks
- Ultra fast tractography clustering
- Resampling datasets with anisotropic voxels to isotropic
- Visualizing multiple brains simultaneously
- Finding track correspondence between different brains
- Warping tractographies into another space e.g. MNI space
- Reading many different file formats e.g. Trackvis or Nifti
- Dealing with huge tractographies without memory restrictions
- Playing with datasets interactively without storing on disk
* Website
http://www.dipy.org<http://www.dipy.org/>
* Docs
http://www.dipy.org/documentation.html
* Tutorials
http://www.dipy.org/examples_index.html
* Downloads
http://pypi.python.org/pypi/dipy<http://pypi.python.org/pypi/Bottleneck>
* Code
https://github.com/Garyfallidis/dipy
* Mailing List
http://mail.scipy.org/mailman/listinfo/nipy-devel
* Bugs/Issues/Requests
https://github.com/Garyfallidis/dipy/issues
Be happy to inform us for any problems or join in and contribute with your code.
Best wishes,
Dipy Developers
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<pre style="margin-top: 14pt; margin-bottom: 14pt;">=======================<br> Announcing Dipy 0.5.0<br>=======================<br><br>Dipy is a fast growing python toolbox for analysis of diffusion MR imaging. <br><br>Just from its first official release dipy provides a great amount of utilities to facilitate diffusion MRI research. Here are just a few of them.<br><br>- Reconstruction algorithms e.g. GQI, DTI <br>- Tractography generation algorithms e.g. EuDX<br>- Intelligent downsampling of tracks<br>- Ultra fast tractography clustering<br>- Resampling datasets with anisotropic voxels to isotropic<br><br>- Visualizing multiple brains simultaneously<br>- Finding track correspondence between different brains<br>- Warping tractographies into another space e.g. MNI space<br>- Reading many different file formats e.g. Trackvis or Nifti<br><br>- Dealing with huge tractographies without memory restrictions<br>- Playing with datasets interactively without storing on disk<br><br>* Website<br><a href="http://www.dipy.org/" target="_blank">http://www.dipy.org</a><br><br>* Docs<br><a href="http://www.dipy.org/documentation.html" target="_blank">http://www.dipy.org/documentation.html</a><br><br>* Tutorials<br><a href="http://www.dipy.org/examples_index.html" target="_blank">http://www.dipy.org/examples_index.html</a><br><br>* Downloads<br><a href="http://pypi.python.org/pypi/Bottleneck" target="_blank">http://pypi.python.org/pypi/dipy</a><br><br>* Code<br><a href="https://github.com/Garyfallidis/dipy" target="_blank">https://github.com/Garyfallidis/dipy</a><br><br>* Mailing List<br><a href="http://mail.scipy.org/mailman/listinfo/nipy-devel" target="_blank">http://mail.scipy.org/mailman/listinfo/nipy-devel</a><br><br>* Bugs/Issues/Requests<br><a href="https://github.com/Garyfallidis/dipy/issues" target="_blank">https://github.com/Garyfallidis/dipy/issues</a><br><br>Be happy to inform us for any problems or join in and contribute with your code.<br><br>Best wishes,<br>Dipy Developers<br></pre>
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========================================================================Date: Mon, 14 Feb 2011 17:28:08 +0800
Reply-To: Linling Li <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Linling Li <[log in to unmask]>
Subject: How to do the coregistration between the functional MRI images
with a high resolution anatomical head image
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--============================================================================Date: Mon, 14 Feb 2011 09:30:45 +0000
Reply-To: Martin Dietz <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Martin Dietz <[log in to unmask]>
Subject: M/EEG chantype
Mime-Version: 1.0
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Content-Type: text/plain; charset="UTF-8"
Message-ID: <[log in to unmask]>
Dear Vladimir & list,
I can successfully change the channel type using spm_eeg_prep. According to the manual chantype is able to do the same job. However chantype doesn't seem to allow any (0/1) flags ?
Best wishes
Martin
========================================================================Date: Mon, 14 Feb 2011 11:15:52 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: naive quesiton about mapping between images in different space
Comments: To: "Jiang, Zhiguo" <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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--0015175cb154c389eb049c3c2bf2
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Multiplying by M1 maps from voxels in image1 to mm space. Then multiplying
this by the inverse of M2 will map from mm space to voxels in image2.
Note that if we want to generate a resliced version of image1 that is
aligned with image2, we need the mapping from image2 to image1. This is
because reslicing an image usually involves scanning through the voxels
image that we wish to create, and then figuring out where the values should
be read from in the original.
Best regards,
-John
On 11 February 2011 18:13, Jiang, Zhiguo <[log in to unmask]>wrote:
> In spm_reslice.m , it states
>
>
>
> % Assuming that image1 has a transformation matrix M1, and image2 has a
>
> % transformation matrix M2, the mapping from image1 to image2 is: M2\M1
>
> % (ie. from the coordinate system of image1 into millimeters, followed
>
> % by a mapping from millimeters into the space of image2).
>
>
>
>
>
>
>
> So to map a x0,y0,z0 in image1 space to image2, isn’t the composite matrix
>
>
>
> M1\M2 instead of M2\M1… ?
>
>
>
> Beause we need to bring x0,y0,z0 to mm then do an inverse of M2 to image 2
> …
>
>
>
> Not sure where I got it wrong…
>
>
>
>
>
> Thanks,
>
>
>
> Tony
>
>
>
>
> ------------------------------------------------------------------------------------------------------------------------------
>
> [image: logo]
>
> Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles
> Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los
> Angeles,CA 90095
>
> Tel: (310)206-1423|Email: [log in to unmask]
>
>
>
>
>
>
>
> ------------------------------
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> information that is privileged and confidential. You, the recipient, are
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Multiplying by M1 maps from voxels in image1 to mm space. Then multiplying this by the inverse of M2 will map from mm space to voxels in image2.<div><br></div><div>Note that if we want to generate a resliced version of image1 that is aligned with image2, we need the mapping from image2 to image1. This is because reslicing an image usually involves scanning through the voxels image that we wish to create, and then figuring out where the values should be read from in the original.<br>
<br><div>Best regards,</div><div>-John</div><div><br><div class="gmail_quote">On 11 February 2011 18:13, Jiang, Zhiguo <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
<div lang="EN-US" link="blue" vlink="purple">
<div>
<p class="MsoNormal">In spm_reslice.m , it states </p>
<p class="MsoNormal"> </p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:black">% Assuming that image1 has a transformation matrix M1, and image2 has a</span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:black">% transformation matrix M2, the mapping from image1 to image2 is: M2\M1</span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:black">% (ie. from the coordinate system of image1 into millimeters, followed</span></p>
<p class="MsoNormal"><span style="font-size:10.0pt;font-family:"Courier New";color:black">% by a mapping from millimeters into the space of image2).</span></p>
<p class="MsoNormal"> </p>
<p class="MsoNormal"> </p>
<p class="MsoNormal"> </p>
<p class="MsoNormal">So to map a x0,y0,z0 in image1 space to image2, isn’t the composite matrix</p>
<p class="MsoNormal"> </p>
<p class="MsoNormal">M1\M2 instead of M2\M1… ?</p>
<p class="MsoNormal"> </p>
<p class="MsoNormal">Beause we need to bring x0,y0,z0 to mm then do an inverse of M2 to image 2 …
</p>
<p class="MsoNormal"> </p>
<p class="MsoNormal">Not sure where I got it wrong…</p>
<p class="MsoNormal"> </p>
<p class="MsoNormal"> </p>
<p class="MsoNormal">Thanks,</p>
<p class="MsoNormal"> </p>
<p class="MsoNormal">Tony</p>
<p class="MsoNormal"> </p>
<p class="MsoNormal">------------------------------------------------------------------------------------------------------------------------------</p>
<p class="MsoNormal"><img width="448" height="48" src="cid:image001.jpg@01CBC9D4.606E1D30" alt="logo"></p>
<p class="MsoNormal">Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles<br>
Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,CA 90095</p>
<p class="MsoNormal">Tel: (310)206-1423|Email: <a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a></p>
<p class="MsoNormal"> </p>
<p class="MsoNormal"> </p>
<p class="MsoNormal"> </p>
</div>
<br>
<hr>
<font face="Arial" color="Navy" size="2">IMPORTANT WARNING: This email (and any attachments) is only intended for the use of the person or entity to which it is addressed, and may contain information that is privileged and confidential. You, the recipient,
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</div>
</blockquote></div><br></div></div>
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--0015175cb154c389f2049c3c2bf3--
========================================================================Date: Mon, 14 Feb 2011 11:34:12 +0000
Reply-To: Vladimir Litvak <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Litvak <[log in to unmask]>
Subject: Re: M/EEG chantype
Comments: To: Martin Dietz <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Message-ID: <[log in to unmask]>
Dear Martin,
The syntax of chantype is something like:
D = chantype(D, 1:10, 'EEG');
I don't see where you expect 0/1 flags to be.
Vladimir
On Mon, Feb 14, 2011 at 9:30 AM, Martin Dietz <[log in to unmask]> wrote:
> Dear Vladimir & list,
>
> I can successfully change the channel type using spm_eeg_prep. According to the manual chantype is able to do the same job. However chantype doesn't seem to allow any (0/1) flags ?
>
> Best wishes
> Martin
>
========================================================================Date: Mon, 14 Feb 2011 12:56:17 +0100
Reply-To: Martin Jensen Dietz <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Martin Jensen Dietz <[log in to unmask]>
Subject: Re: M/EEG chantype
Comments: To: Vladimir Litvak <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
Content-Type: text/plain; charset="us-ascii"
Content-Transfer-Encoding: quoted-printable
MIME-Version: 1.0
Message-ID: <[log in to unmask]>
Hi Vladimir,
Thanks. I erroneously assumed a flag based on the example in section 12.7.4
Martin
On Feb 14, 2011, at 12:34 PM, Vladimir Litvak wrote:
> Dear Martin,
>
> The syntax of chantype is something like:
>
> D = chantype(D, 1:10, 'EEG');
>
> I don't see where you expect 0/1 flags to be.
>
> Vladimir
>
> On Mon, Feb 14, 2011 at 9:30 AM, Martin Dietz <[log in to unmask]> wrote:
>> Dear Vladimir & list,
>>
>> I can successfully change the channel type using spm_eeg_prep. According to the manual chantype is able to do the same job. However chantype doesn't seem to allow any (0/1) flags ?
>>
>> Best wishes
>> Martin
>>
========================================================================Date: Mon, 14 Feb 2011 12:29:18 +0000
Reply-To: Vladimir Litvak <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Litvak <[log in to unmask]>
Subject: Re: Question about lead field matrix
Comments: To: [log in to unmask]
Comments: cc: Robert Oostenveld <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
Dear Carsten
On Fri, Feb 11, 2011 at 11:56 PM, Carsten Stahlhut <[log in to unmask]> wrote:
> Dear Vladimir,
>
> I have approx three questions (or issues) that I hope you can help me out
> with.
>
> Do you know what the units are for the lead field matrix and the source
> estimates in SPM8?
These are two different questions.
> If you apply one of the EEG imaging methods (as I understood it) then the
> source estimates should be current density i.e. the unit should be something
> like A/m^2 or uA/mm^2 depending on the scaling of the EEG data and lead
> field matrix.
> And then the units of the lead field should be something like m^2 * Ohm ( =
> m^2 / S ).
In theory the forward computation done by the 'forward' toolbox should
be unit-blind so the units of the lead field should depend on units of
the head model (mm in SPM). In practice this might not be the case. We
recently found a difference in scaling between 'single shell' and
other MEG models. This will be fixed in the next update. There might
be something similar also for EEG. SPM is not sensitive to it but it's
still something worth looking into.
> Does this also holds for SPM8 or do there exist any type of scaling of the
> lead field matrix such as a normalization of the columns or something like
> that?
>
Yes, there are several scaling and normalization steps so the units of
the output images are not related to original physical units but are
normalize to the total power across sources and conditions.
> The second question is related to the lead field matrix as well. If I
> compare two different head models a BEM and 3-spheres model then they seem
> to be on two very different scales - is that really the case? Or am I
> missing an important step? Of course it is two very different head model
> assumptions so they should be quite different but I'm a bit surprised that
> there exists a scaling factor of 100 between the largest element in the BEM
> gain matrix and the 3-spheres gain matrix.
See above.
> What I just did was to load the mat-files with the gain-matrices and used
> the imagesc-function to show the amplitudes of the two lead field matrices.
>
> The 28th Jul 2010 you sent an answer to a post regarding head models and you
> here mentioned that FEM head models from SimBio might be an option in the
> very nice MEEGTool. I was wondering what the status is on integrating the
> SimBio FEM implementation with SPM, as I'm very interested to try out the
> FEM head models. I guess there is no reason that I will try to write the
> code my self if the option is almost done and will be part of the MEEGTool.
>
I am aware of some efforts to integrate SimBio support in the forward
toolbox. Even if this is done I don't expect it to become one of the
standard options in SPM because SimBio runs only on a particular Unix
platform (I'm not even sure it's LINUX) and I suspect has very lengthy
computing time. But if you compute a gain matrix for the canonical
mesh in SimBio you can just replace the mat file generated by SPM with
your file and it will be used by spm_eeg_invert. If there is
inconsistency in units, it shouldn't be a problem.
There is also a possibility to use another BEM implementation OpenMEEG
(http://www-sop.inria.fr/athena/software/OpenMEEG/). That's something
that works and I tested it last year, but found it impractical for
most users again because of difficulties with installation and long
computation time (about 24 h for normal mesh). The differences in the
lead fields with our BEM were only for a small number of vertices
close to the surface. But I can give you the glue code for this if you
want.
Best,
Vladimir
> Best,
> Carsten
>
>
>
========================================================================Date: Mon, 14 Feb 2011 12:44:58 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: How to do the coregistration between the functional MRI
images with a high resolution anatomical head image
Comments: To: Linling Li <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=GB2312
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
It's not really about right or wrong. The important thing is whether
it works for your data. This will depend on many factors, but the
amount of spatial distortion in your fMRI will be one of these.
Best regards,
-John
2011/2/14 Linling Li <[log in to unmask]>:
> Dear all,
>
>
>
> I have two kinds of anatomical images of each subject, one is regular T2
> image which has low resolution, and the other one is 3D T1 structural image
> (3DHead) which has very high resolution. And I do the preprocessing as
> follow:
>
>
>
> 1. Realignment(Estimate&Write) -> Data to input: all fMRI images;
>
>
>
> 2. Coregistration(Estimate) -> Reference Image: mean fMRI image
> produced after last step
>
> Source Image: T2 anatomical image
>
> So, the T2 anatomical image could be registered to functional images during
> this step;
>
>
>
> 3. Coregistration(Estimate) -> Reference Image: T2 anatomical image
>
> Source Image: 3DHead image
>
> Here, the 3DHead image could be registered to T2 image which has been
> already registered to functional images. Therefore, we have finished the
> coregistration between 3DHead image and functional images with the help of
> T2 anatomical image. Since the large resolution difference between3DHead
> image and functional image, We think this coregistration method is more
> proper;
>
>
>
> 4. Segmentation -> Data: 3DHead image
>
>
>
> 5. Normalise(write) -> Parameter file: 3DHead_seg_sn.mat
>
> Images to write: all the realigned functional images
>
>
>
> Would this be right?
>
>
>
> Any suggestion would be appreciated and thank you all in advance!
>
>
> 2011-02-14
> ________________________________
> ÀîÁÕÁá
>
> Linling Li
> Research Centre for Neural Engineering
> --------------------------------------
> Phone: +86-755-86392232
> Email: [log in to unmask]
> Shenzhen Institutes of Advanced Technology,
> Chinese Academy of Sciences.
========================================================================Date: Mon, 14 Feb 2011 15:38:19 +0100
Reply-To: workshop-networks <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: workshop-networks <[log in to unmask]>
Subject: workshop "networks in the human brain"
MIME-Version: 1.0
Content-Type: text/plain; charset=utf-8
Content-Transfer-Encoding: 7bit
Message-ID: <499626810.8000.1297694299153.JavaMail.root@zimbra>
Dear all,
please do not try to register anymore for our workshop
"networks in the human brain" to be held at the Max-Planck-
Institute in Leipzig, March 7-8. 2011.
We are booked up !
Thank you for your interest,
Gabriele Lohmann,
Dirk Goldhahn,
Robert Turner
========================================================================Date: Mon, 14 Feb 2011 09:44:47 -0500
Reply-To: justine dupont <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: justine dupont <[log in to unmask]>
Subject: dartel misregistration - or not?
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Dear experts
I have run a DARTEL analysis and comparing the DARTEL template and the regular T1 canonical brain I get the result attached. I was surprised that the two images are so different in shape. My sample is 45 young healthy adults, roughly equal numbers of males and females. Eyeballing the original scans there are no 'outliers' or anomalies. Likewise there were no obvious anomalies looking at the various stages of preprocessing.
So I am not sure whether something has gone wrong, or whether my sample, for some reason, had genuinely small brains, compared to the canonical T1, or perhaps whether there is some other explanation.
Thank you
Justine
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Dear experts<br><br>I have run a DARTEL analysis and comparing the DARTEL template and the regular T1 canonical brain I get the result attached. I was surprised that the two images are so different in shape. My sample is 45 young healthy adults, roughly equal numbers of males and females. Eyeballing the original scans there are no 'outliers' or anomalies. Likewise there were no obvious anomalies looking at the various stages of preprocessing.<br><br>So I am not sure whether something has gone wrong, or whether my sample, for some reason, had genuinely small brains, compared to the canonical T1, or perhaps whether there is some other explanation.<br><br>Thank you<br><br>Justine<br><br> </body>
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--_eb62bde8-154a-4a7d-9fd9-b2bc8bfd8e0e_--
========================================================================Date: Mon, 14 Feb 2011 09:56:02 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: dartel misregistration - or not?
Comments: To: justine dupont <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
DARTEL typically produces a smaller template compared to the T1
canonical brain in MNI. In SPM8, you should select the option to warp
to MNI space as well. If your using SPM5, there is a program under
resources at brainmap.wisc.edu to warp to MNI space.
If you selected the warp to MNI space, I am surprised with the misregistration.
On Monday, February 14, 2011, justine dupont <[log in to unmask]> wrote:
>
>
>
>
>
> Dear experts
>
> I have run a DARTEL analysis and comparing the DARTEL template and the regular T1 canonical brain I get the result attached. I was surprised that the two images are so different in shape. My sample is 45 young healthy adults, roughly equal numbers of males and females. Eyeballing the original scans there are no 'outliers' or anomalies. Likewise there were no obvious anomalies looking at the various stages of preprocessing.
>
> So I am not sure whether something has gone wrong, or whether my sample, for some reason, had genuinely small brains, compared to the canonical T1, or perhaps whether there is some other explanation.
>
> Thank you
>
> Justine
>
>
>
--
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Office: (773) 406-2464
=====================
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========================================================================Date: Mon, 14 Feb 2011 14:59:34 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: dartel misregistration - or not?
Comments: To: justine dupont <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
The single subject brain is just that of a single individual, who has
been affine aligned to MNI space. The Dartel template is the average
in both shape and size of your population of subjects. Unless those
subjects have extremely large brains, then it is unlikely that their
average will match with MNI space.
If you use the option to normalise to MNI space (in the Dartel toolbox
of SPM8), then an additional affine registration step is done, which
aligns your population average to MNI space. It only uses an affine
registration because the official MNI space has only been defined
using affine registered brains.
Best regards,
-John
On 14 February 2011 14:44, justine dupont <[log in to unmask]> wrote:
> Dear experts
>
> I have run a DARTEL analysis and comparing the DARTEL template and the
> regular T1 canonical brain I get the result attached. I was surprised that
> the two images are so different in shape. My sample is 45 young healthy
> adults, roughly equal numbers of males and females. Eyeballing the original
> scans there are no 'outliers' or anomalies. Likewise there were no obvious
> anomalies looking at the various stages of preprocessing.
>
> So I am not sure whether something has gone wrong, or whether my sample, for
> some reason, had genuinely small brains, compared to the canonical T1, or
> perhaps whether there is some other explanation.
>
> Thank you
>
> Justine
>
>
========================================================================Date: Mon, 14 Feb 2011 10:08:08 -0500
Reply-To: Hekmatyar <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Hekmatyar <[log in to unmask]>
Subject: Re: Orientation swap
In-Reply-To: <499626810.8000.1297694299153.JavaMail.root@zimbra>
MIME-Version: 1.0
Content-Type: multipart/mixed;
boundary="----=_NextPart_000_01BD_01CBCC2F.14974BE0"
Message-ID: <[log in to unmask]>
------=_NextPart_000_01BD_01CBCC2F.14974BE0
Content-Type: text/plain; charset="utf-8"
Content-Transfer-Encoding: quoted-printable
Hello All
Could you please suggest me why the orientation of the images were swapped after co-registraton (snapshot of coregistration)? Thanks
Hekmatyar
BIRC
UGA
------=_NextPart_000_01BD_01CBCC2F.14974BE0
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------=_NextPart_000_01BD_01CBCC2F.14974BE0--
========================================================================Date: Mon, 14 Feb 2011 15:18:52 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: Orientation swap
Comments: To: Hekmatyar <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Message-ID: <[log in to unmask]>
I'm not sure that any orientation has been swapped. The images are
simply displayed in a way that uses the orientation images in the
headers. If you show one of the images with eg the Display button,
then the orientation in which the image is displayed will be
determined by the orientation encoded in your image headers.
As these do not appear to be human brains, I would perhaps suggest
changing the values in the "Separation" option. When estimating the
parameters, the algorithm only looks at the image every few mm (to
save time). In the case of these images, every few mm is likely to
mean that most of the information is unused. I notice that the joint
histograms are very noisy. This should be fixed by using more of the
data.
Best regards,
-John
On 14 February 2011 15:08, Hekmatyar <[log in to unmask]> wrote:
> Hello All
> Could you please suggest me why the orientation of the images were swapped after co-registraton (snapshot of coregistration)? Thanks
>
> Hekmatyar
> BIRC
> UGA
>
>
========================================================================Date: Mon, 14 Feb 2011 10:11:58 -0500
Reply-To: Alexandre Gramfort <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Alexandre Gramfort <[log in to unmask]>
Subject: Re: Question about lead field matrix
Comments: To: Vladimir Litvak <[log in to unmask]>
Comments: cc: Sharing information on OpenMEEG
<[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <AANLkTi=tnRGj³[log in to unmask]>
Hi Vladimir,
> There is also a possibility to use another BEM implementation OpenMEEG
> (http://www-sop.inria.fr/athena/software/OpenMEEG/). That's something
> that works and I tested it last year, but found it impractical for
> most users again because of difficulties with installation
we're working on this. New windows 64, linux 64 and Mac OS X installers will
be available shortly with the 2.1 release that adds speed improvements
using the adjoint approach.
> and long
> computation time (about 24 h for normal mesh).
we will be presenting at HBM this year our improvements using the adjoint
approach that brings computation on the SPM canonical mesh to 1h40
on a Linux 64 with 8 cores machine (3h45 with non-adjoint method).
> The differences in the
> lead fields with our BEM were only for a small number of vertices
> close to the surface.
That is the whole point of the symmetric BEM. Give precision for dipoles
close to the inner skull (sources on top of gyri). Note that OpenMEEG
features also lead fields computation for EIT and Internal Potential
calculation (see. "Forward field computation with OpenMEEG"
Alexandre Gramfort, Théodore Papadopoulo, Emmanuel Olivi, and M. Clerc
to appear in the special issue:
"Academic Software Applications for Electromagnetic Brain Mapping
Using MEG and EEG")
Best,
Alex and the OpenMEEG developers.
--
Alexandre Gramfort, PhD
[log in to unmask]
Dept. of Radiology MGH Martinos Center / Harvard Medical School
http://www-sop.inria.fr/members/Alexandre.Gramfort/
========================================================================Date: Mon, 14 Feb 2011 11:31:56 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: about ROI signal value
Comments: To: Yang Hu <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
These are CV as they come from the contrasts. Contrasts are simply a
linear combination (addition or subtraction) of the PE or beta images.
PSC requires dividing by the constant term.
I always use the CV and I also use the above AFNI commands.
The one cautionary note is that AFNI/SPM have the L and R on opposite
sides, so you want to make sure your extracting from the correct
voxels. Additionally, you don't need step 2 as AFNI can read .nii
files.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
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On Sat, Feb 12, 2011 at 11:27 PM, Yang Hu <[log in to unmask]> wrote:
> Dear Experts,
>
> Based on whole-brain analysis via SPM8 (1st- and 2nd-level analysis), I want to extract signal value from atlas-based ROIs (e.g. amygdala) in four experimental conditions, compared with rest condition respectively, by AFNI rather than MarsBar. Specific steps are listed as follows:
>
> 1. Mask generation: by WFU pickatlas, an file of ROI in amygdala (named as "amy.nii") was generated
> 2. File conversion: by AFNI command "3dcopy", the "amy.nii" was converted to "amy+orig.BRIK/HEAD", which could be recognized by AFNI
> 3. ROI calculation: by AFNI command "3dmaskave", the average value within the ROI mask (amygdala) of each subject (sbj**) in certain condition (con_000*) could be obtained; Specifically, the full command is like that "3dmaskave -quiet -mask amy+orig sbj**_con_000*.hdr". The file "sbj**_con_000*.hdr" was generated after 1st-level analysis via SPM8.
>
> So I just wonder the meaning of ROI value obtained through those steps mentioned above. Whether does the value mean percent signal change (PSC), contrast value (CV), or parameter estimates (PE) ? And what's the difference between the three terms (namely PSC, CV, PE) always used in ROI analysis?
>
> Thanks a lot for your help and comment!
>
> Yang Hu
>
> ICN, ECNU, Shanghai, China
>
========================================================================Date: Mon, 14 Feb 2011 11:35:04 -0500
Reply-To: Hekmatyar <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Hekmatyar <[log in to unmask]>
Subject: Re: Orientation swap
Comments: To: John Ashburner <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/mixed;
boundary="----=_NextPart_000_023B_01CBCC3B.393DA870"
Message-ID: <[log in to unmask]>
------=_NextPart_000_023B_01CBCC3B.393DA870
Content-Type: text/plain; charset="us-ascii"
Content-Transfer-Encoding: 7bit
Thanks Dr. John
I will check that, The orientation of anatomical before coregistration is
same as orientation in the functional. I have attached some snapshots of
display and checkreg.
Those are from rat brains, I will try changing separation values as well.
Thanks
Hek
-----Original Message-----
From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On
Behalf Of John Ashburner
Sent: Monday, February 14, 2011 10:19 AM
To: [log in to unmask]
Subject: Re: [SPM] Orientation swap
I'm not sure that any orientation has been swapped. The images are
simply displayed in a way that uses the orientation images in the
headers. If you show one of the images with eg the Display button,
then the orientation in which the image is displayed will be
determined by the orientation encoded in your image headers.
As these do not appear to be human brains, I would perhaps suggest
changing the values in the "Separation" option. When estimating the
parameters, the algorithm only looks at the image every few mm (to
save time). In the case of these images, every few mm is likely to
mean that most of the information is unused. I notice that the joint
histograms are very noisy. This should be fixed by using more of the
data.
Best regards,
-John
On 14 February 2011 15:08, Hekmatyar <[log in to unmask]> wrote:
> Hello All
> Could you please suggest me why the orientation of the images were swapped
after co-registraton (snapshot of coregistration)? Thanks
>
> Hekmatyar
> BIRC
> UGA
>
>
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========================================================================Date: Mon, 14 Feb 2011 11:46:32 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: Orientation swap
Comments: To: Hekmatyar <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
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Message-ID: <AANLkTinObU5snG46TÊ[log in to unmask]>
A couple other things to check:
(1) That the voxel sizes are on the same scale between scans;
(2) Use a smaller smoothing kernel as well.
(3) Set the voxel size to the actual size, rather than multiplying by 10 or 20.
(4) Make sure the origin is the same in both images.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain
PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
and which is intended only for the use of the individual or entity
named above. If the reader of the e-mail is not the intended recipient
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immediately notify the sender via telephone at (773) 406-2464 or
email.
On Mon, Feb 14, 2011 at 11:35 AM, Hekmatyar <[log in to unmask]> wrote:
> Thanks Dr. John
> I will check that, The orientation of anatomical before coregistration is
> same as orientation in the functional. I have attached some snapshots of
> display and checkreg.
> Those are from rat brains, I will try changing separation values as well.
> Thanks
> Hek
>
> -----Original Message-----
> From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]] On
> Behalf Of John Ashburner
> Sent: Monday, February 14, 2011 10:19 AM
> To: [log in to unmask]
> Subject: Re: [SPM] Orientation swap
>
> I'm not sure that any orientation has been swapped. The images are
> simply displayed in a way that uses the orientation images in the
> headers. If you show one of the images with eg the Display button,
> then the orientation in which the image is displayed will be
> determined by the orientation encoded in your image headers.
>
> As these do not appear to be human brains, I would perhaps suggest
> changing the values in the "Separation" option. When estimating the
> parameters, the algorithm only looks at the image every few mm (to
> save time). In the case of these images, every few mm is likely to
> mean that most of the information is unused. I notice that the joint
> histograms are very noisy. This should be fixed by using more of the
> data.
>
> Best regards,
> -John
>
> On 14 February 2011 15:08, Hekmatyar <[log in to unmask]> wrote:
>> Hello All
>> Could you please suggest me why the orientation of the images were swapped
> after co-registraton (snapshot of coregistration)? Thanks
>>
>> Hekmatyar
>> BIRC
>> UGA
>>
>>
>
========================================================================Date: Mon, 14 Feb 2011 13:02:36 -0500
Reply-To: "Claire (Jung Eun) Han" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "Claire (Jung Eun) Han" <[log in to unmask]>
Subject: Putting a non-zero value for the time derivative
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
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Hello,
What does the result mean if I put a non-zero value for the time derivative
(e.g. putting '1' for both hrf and the time derivative)?
Thanks in advance,
Claire
--000e0cd2e6e258a66c049c41da0a
Content-Type: text/html; charset=ISO-8859-1
Hello,<br><br>What does the result mean if I put a non-zero value for the time derivative (e.g. putting '1' for both hrf and the time derivative)?<br><br>Thanks in advance,<br>Claire <br>
--000e0cd2e6e258a66c049c41da0a--
========================================================================Date: Mon, 14 Feb 2011 12:20:10 -0600
Reply-To: Steffie Tomson <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Steffie Tomson <[log in to unmask]>
Subject: Resizing mask
MIME-Version: 1.0
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Hi Everyone - probably a silly question, but how do I resize a mask? I
have masks from the AAL atlas that are 181x217x181 and I need them in
the 46x56x34 size. Conceptually it should be a simple interpolation,
but is there a function that does this?
Thanks again,
Steffie
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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link=blue vlink=purple><div class=WordSection1><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Cambria","serif"'>Hi Everyone – probably a silly question, but how do I resize a mask? I have masks from the AAL atlas that are 181x217x181 and I need them in the 46x56x34 size. Conceptually it should be a simple interpolation, but is there a function that does this?<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Cambria","serif"'><o:p> </o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Cambria","serif"'>Thanks again,<o:p></o:p></span></p><p class=MsoNormal><span style='font-size:12.0pt;font-family:"Cambria","serif"'>Steffie </span><span style='font-family:"Cambria","serif"'><o:p></o:p></span></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>
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========================================================================Date: Mon, 14 Feb 2011 18:37:57 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: Resizing mask
Comments: To: Steffie Tomson <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=windows-1252
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
You can reslice the mask to match some other image using either the
realign (reslice only) or coregister (reslice only) options.
Best regards,
-John
On 14 February 2011 18:20, Steffie Tomson <[log in to unmask]> wrote:
> Hi Everyone – probably a silly question, but how do I resize a mask? I have
> masks from the AAL atlas that are 181x217x181 and I need them in the
> 46x56x34 size. Conceptually it should be a simple interpolation, but is
> there a function that does this?
>
>
>
> Thanks again,
>
> Steffie
>
>
========================================================================Date: Mon, 14 Feb 2011 13:52:37 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: Putting a non-zero value for the time derivative
Comments: To: "Claire (Jung Eun) Han" <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Message-ID: <[log in to unmask]>
It means that you would average the weight of the HRF and the
derivative. This is completelly uninformative as you would get the
same answer for when the the time derivative is 0 and HRF is 1 or when
the canonical HRF is 0 and time derivative is 1, which are clearly
very different in their interpretation.
There been several posts this week on this topic.
Best Regards, Donald McLaren
================D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
====================This e-mail contains CONFIDENTIAL INFORMATION which may contain
PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
and which is intended only for the use of the individual or entity
named above. If the reader of the e-mail is not the intended recipient
or the employee or agent responsible for delivering it to the intended
recipient, you are hereby notified that you are in possession of
confidential and privileged information. Any unauthorized use,
disclosure, copying or the taking of any action in reliance on the
contents of this information is strictly prohibited and may be
unlawful. If you have received this e-mail unintentionally, please
immediately notify the sender via telephone at (773) 406-2464 or
email.
On Mon, Feb 14, 2011 at 1:02 PM, Claire (Jung Eun) Han
<[log in to unmask]> wrote:
> Hello,
>
> What does the result mean if I put a non-zero value for the time derivative
> (e.g. putting '1' for both hrf and the time derivative)?
>
> Thanks in advance,
> Claire
>
========================================================================Date: Mon, 14 Feb 2011 17:16:57 -0500
Reply-To: cliff <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: cliff <[log in to unmask]>
Subject: spm_create_vol &&& spm_write_vol
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--000e0cd5d004f9ac20049c456771
Content-Type: text/plain; charset=ISO-8859-1
Hi, all,
I thought before that "spm_create_vol " is used to create the ".hdr" files
of the images.
However, today I found that "spm_write_vol" can produce both of the ".hdr"
and ".img" files.
then, what is the "spm_create_vol " exactly used for?
Thanks~
--000e0cd5d004f9ac20049c456771
Content-Type: text/html; charset=ISO-8859-1
Hi, all,<br>I thought before that "spm_create_vol " is used to create the ".hdr" files of the images.<br>However, today I found that "spm_write_vol" can produce both of the ".hdr" and ".img" files.<br>
<br>then, what is the "spm_create_vol " exactly used for?<br><br>Thanks~<br>
--000e0cd5d004f9ac20049c456771--
========================================================================Date: Mon, 14 Feb 2011 22:32:05 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: spm_create_vol &&& spm_write_vol
Comments: To: cliff <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Message-ID: <[log in to unmask]>
spm_write_vol calls spm_create_vol. Sometimes, for example in the
fMRI stats, we may wish to create headers but only write out a slice
of data at a time (as there is not enough memory to hold all the
images).
best regards,
-John
On 14 February 2011 22:16, cliff <[log in to unmask]> wrote:
> Hi, all,
> I thought before that "spm_create_vol " is used to create the ".hdr" files
> of the images.
> However, today I found that "spm_write_vol" can produce both of the ".hdr"
> and ".img" files.
>
> then, what is the "spm_create_vol " exactly used for?
>
> Thanks~
>
========================================================================Date: Mon, 14 Feb 2011 17:20:56 -0800
Reply-To: Matthew Brett <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Matthew Brett <[log in to unmask]>
Subject: Re: extract ROI at the edge of the cerebellum
Comments: To: Viridiana Mazzola <[log in to unmask]>
Comments: cc: [log in to unmask]
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
Hi,
On Sun, Feb 13, 2011 at 4:00 AM, Viridiana Mazzola
<[log in to unmask]> wrote:
> Dear all,
> I am trying to extract a ROI at the edge of the cerebellum following the Brett's FAQ instructions.
>
> 1 . I rename the ROI my_roi.mat, then I write in matlab the command:
> roi_filename = 'my_roi.mat';
> my_roi = maroi(roi_filename);
> my_roi = spm_hold(my_roi, 0); % set NN resampling
> saveroi(my_roi, roi_filename);
>
> 2. then I rerun the extraction of my_roi, but this error appears:
> Fetching data : 1/1 Error in ==> maroi.getdata at 30
> if nargin < 2
> ??? Output argument "vXYZ" (and maybe others) not assigned during call to
Could you have a try of the fixes suggested here and let us know they work?
http://marsbar.sourceforge.net/faq.html#why-do-i-get-a-no-valid-data-for-roi-warning-when-extracting-data
The marsbar is the best list for these questions - I've Cc'ed it...
Best,
Matthew
========================================================================Date: Mon, 14 Feb 2011 20:25:30 -0500
Reply-To: MP <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: MP <[log in to unmask]>
Subject: Re: EEG significant clusters outside the MIP glass brain
Comments: To: [log in to unmask]
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/mixed; boundarye6ba476b13485c07049c480aa2
Message-ID: <[log in to unmask]>
--90e6ba476b13485c07049c480aa2
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Hello Vladimir,
I tried to use the inner skull mask as you suggested to avoid getting
significant cluster outside the glass brain, but it is drastically changing
the results. Attached the figures of results with and without applying the
mask. Could you please tell me if I am doing something wrong here?
P.S. I include the iskull mask as an implicit mask in the flexible factorial
design.
Thanks
- Muhammad
On Fri, Dec 31, 2010 at 12:03 AM, <[log in to unmask]> wrote:
> Dear Muhammad,
>
> Threshold masking will not necessarily work. It depends whether the values
> outside the brain are really low. You can use an explicit inner skull mask.
> There should be one in EEGTemplates as far as I remember or you can make
> your own.
>
> You can either re-run your stats or use small volume correction for inner
> skull but the latter will not fix the graphics.
>
> Best,
>
> Vladimir
>
> Sent from my BlackBerry® smartphone from orange
> ------------------------------
> *From: * MP <[log in to unmask]>
> *Sender: * "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
> *Date: *Thu, 30 Dec 2010 21:33:40 -0500
> *To: *<[log in to unmask]>
> *ReplyTo: * MP <[log in to unmask]>
> *Subject: *[SPM] EEG significant clusters outside the MIP glass brain
>
> Hello all,
>
> I am doing EEG analysis using SPM8 and while viewing the results, I see
> that some of the significant clusters appear outside the brain. I understand
> that it could be due to smoothing of the images, but is there a way to
> restrict these clusters inside the MIP? I think Threshold masking may be the
> way to go, but I am not sure what threshold value to use?
>
> If indeed threshold masking is the way to go, would I need to re-do all the
> analyses?
>
> Any help would be much appreciated.
>
> Thanks
> - Muhammad
>
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Hello Vladimir,<div><br></div><div>I tried to use the inner skull mask as you suggested to avoid getting significant cluster outside the glass brain, but it is drastically changing the results. Attached the figures of results with and without applying the mask. Could you please tell me if I am doing something wrong here?</div>
<div><br></div><div>P.S. I include the iskull mask as an implicit mask in the flexible factorial design.</div><div><br></div><div>Thanks</div><div>- Muhammad <br><br><div class="gmail_quote">On Fri, Dec 31, 2010 at 12:03 AM, <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"> Dear Muhammad,<br><br>Threshold masking will not necessarily work. It depends whether the values outside the brain are really low. You can use an explicit inner skull mask. There should be one in EEGTemplates as far as I remember or you can make your own.<br>
<br>You can either re-run your stats or use small volume correction for inner skull but the latter will not fix the graphics.<br><br>Best,<br><br>Vladimir<p>Sent from my BlackBerry® smartphone from orange</p><hr><div><b>From: </b> MP <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>>
</div><div><b>Sender: </b> "SPM (Statistical Parametric Mapping)" <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>>
</div><div><b>Date: </b>Thu, 30 Dec 2010 21:33:40 -0500</div><div><b>To: </b><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></div><div><b>ReplyTo: </b> MP <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>>
</div><div><b>Subject: </b>[SPM] EEG significant clusters outside the MIP glass brain</div><div><div></div><div><div><br></div>Hello all,<div><br></div><div>I am doing EEG analysis using SPM8 and while viewing the results, I see that some of the significant clusters appear outside the brain. I understand that it could be due to smoothing of the images, but is there a way to restrict these clusters inside the MIP? I think Threshold masking may be the way to go, but I am not sure what threshold value to use?</div>
<div><br></div><div>If indeed threshold masking is the way to go, would I need to re-do all the analyses?</div><div><br></div><div>Any help would be much appreciated.</div><div><br></div><div>Thanks</div><div>- Muhammad </div>
</div></div></blockquote></div><br></div>
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========================================================================Date: Mon, 14 Feb 2011 21:04:16 -0500
Reply-To: cliff <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: cliff <[log in to unmask]>
Subject: Re: spm_create_vol &&& spm_write_vol
Comments: To: John Ashburner <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--000e0cd5d004ec29fe049c48948b
Content-Type: text/plain; charset=ISO-8859-1
Thanks~~
Does that mean if I use "spm_create_vol" to create headers followed by
"spm_write_vol" (if it is necessary), then the headers created by
"spm_create_vol" will be overwrited?
On Mon, Feb 14, 2011 at 5:32 PM, John Ashburner <[log in to unmask]>wrote:
> spm_write_vol calls spm_create_vol. Sometimes, for example in the
> fMRI stats, we may wish to create headers but only write out a slice
> of data at a time (as there is not enough memory to hold all the
> images).
>
> best regards,
> -John
>
> On 14 February 2011 22:16, cliff <[log in to unmask]> wrote:
> > Hi, all,
> > I thought before that "spm_create_vol " is used to create the ".hdr"
> files
> > of the images.
> > However, today I found that "spm_write_vol" can produce both of the
> ".hdr"
> > and ".img" files.
> >
> > then, what is the "spm_create_vol " exactly used for?
> >
> > Thanks~
> >
>
--
Sincerely,
Senhua Zhu
--000e0cd5d004ec29fe049c48948b
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
<div>Thanks~~</div>
<div> </div>
<div>Does that mean if I use "spm_create_vol" to create headers followed by "spm_write_vol" (if it is necessary), then the headers created by "spm_create_vol" will be overwrited? <br><br></div>
<div class="gmail_quote">On Mon, Feb 14, 2011 at 5:32 PM, John Ashburner <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote style="BORDER-LEFT: #ccc 1px solid; MARGIN: 0px 0px 0px 0.8ex; PADDING-LEFT: 1ex" class="gmail_quote">spm_write_vol calls spm_create_vol. Sometimes, for example in the<br>fMRI stats, we may wish to create headers but only write out a slice<br>
of data at a time (as there is not enough memory to hold all the<br>images).<br><br>best regards,<br><font color="#888888">-John<br></font>
<div>
<div></div>
<div class="h5"><br>On 14 February 2011 22:16, cliff <<a href="mailto:[log in to unmask]">[log in to unmask]</a>> wrote:<br>> Hi, all,<br>> I thought before that "spm_create_vol " is used to create the ".hdr" files<br>
> of the images.<br>> However, today I found that "spm_write_vol" can produce both of the ".hdr"<br>> and ".img" files.<br>><br>> then, what is the "spm_create_vol " exactly used for?<br>
><br>> Thanks~<br>><br></div></div></blockquote></div><br><br clear="all"><br>-- <br>
<div>Sincerely,</div>
<div>Senhua Zhu<br><br></div><br>
--000e0cd5d004ec29fe049c48948b--
========================================================================Date: Mon, 14 Feb 2011 18:09:10 -0800
Reply-To: Michael T Rubens <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Michael T Rubens <[log in to unmask]>
Subject: Re: EEG significant clusters outside the MIP glass brain
Comments: To: MP <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--00248c6a5726382f06049c48a80a
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: quoted-printable
that is not right, the results should change due to the mask. Why don't you
just run the analysis and mask out the resulting stat map. You're obviously
not interested in anything off the brain (i.e., noise) anyway, so its valid.
Cheers,
Michael
On Mon, Feb 14, 2011 at 5:25 PM, MP <[log in to unmask]> wrote:
> Hello Vladimir,
>
> I tried to use the inner skull mask as you suggested to avoid getting
> significant cluster outside the glass brain, but it is drastically changing
> the results. Attached the figures of results with and without applying the
> mask. Could you please tell me if I am doing something wrong here?
>
> P.S. I include the iskull mask as an implicit mask in the flexible
> factorial design.
>
> Thanks
> - Muhammad
>
>
> On Fri, Dec 31, 2010 at 12:03 AM, <[log in to unmask]> wrote:
>
>> Dear Muhammad,
>>
>> Threshold masking will not necessarily work. It depends whether the values
>> outside the brain are really low. You can use an explicit inner skull mask.
>> There should be one in EEGTemplates as far as I remember or you can make
>> your own.
>>
>> You can either re-run your stats or use small volume correction for inner
>> skull but the latter will not fix the graphics.
>>
>> Best,
>>
>> Vladimir
>>
>> Sent from my BlackBerry® smartphone from orange
>> ------------------------------
>> *From: * MP <[log in to unmask]>
>> *Sender: * "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
>> *Date: *Thu, 30 Dec 2010 21:33:40 -0500
>> *To: *<[log in to unmask]>
>> *ReplyTo: * MP <[log in to unmask]>
>> *Subject: *[SPM] EEG significant clusters outside the MIP glass brain
>>
>> Hello all,
>>
>> I am doing EEG analysis using SPM8 and while viewing the results, I see
>> that some of the significant clusters appear outside the brain. I understand
>> that it could be due to smoothing of the images, but is there a way to
>> restrict these clusters inside the MIP? I think Threshold masking may be the
>> way to go, but I am not sure what threshold value to use?
>>
>> If indeed threshold masking is the way to go, would I need to re-do all
>> the analyses?
>>
>> Any help would be much appreciated.
>>
>> Thanks
>> - Muhammad
>>
>
>
--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco
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that is not right, the results should change due to the mask. Why don't you just run the analysis and mask out the resulting stat map. You're obviously not interested in anything off the brain (i.e., noise) anyway, so its valid.<br>
<br>Cheers, <br>Michael<br><br><div class="gmail_quote">On Mon, Feb 14, 2011 at 5:25 PM, MP <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
Hello Vladimir,<div><br></div><div>I tried to use the inner skull mask as you suggested to avoid getting significant cluster outside the glass brain, but it is drastically changing the results. Attached the figures of results with and without applying the mask. Could you please tell me if I am doing something wrong here?</div>
<div><br></div><div>P.S. I include the iskull mask as an implicit mask in the flexible factorial design.</div><div><br></div><div>Thanks</div><div>- Muhammad  <div><div></div><div class="h5"><br><br><div class="gmail_quote">
On Fri, Dec 31, 2010 at 12:03 AM, <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;"> Dear Muhammad,<br><br>Threshold masking will not necessarily work. It depends whether the values outside the brain are really low. You can use an explicit inner skull mask. There should be one in EEGTemplates as far as I remember or you can make your own.<br>
<br>You can either re-run your stats or use small volume correction for inner skull but the latter will not fix the graphics.<br><br>Best,<br><br>Vladimir<p>Sent from my BlackBerry® smartphone from orange</p><hr><div><b>From: </b> MP <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>>
</div><div><b>Sender: </b> "SPM (Statistical Parametric Mapping)" <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>>
</div><div><b>Date: </b>Thu, 30 Dec 2010 21:33:40 -0500</div><div><b>To: </b><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></div><div><b>ReplyTo: </b> MP <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>>
</div><div><b>Subject: </b>[SPM] EEG significant clusters outside the MIP glass brain</div><div><div></div><div><div><br></div>Hello all,<div><br></div><div>I am doing EEG analysis using SPM8 and while viewing the results, I see that some of the significant clusters appear outside the brain. I understand that it could be due to smoothing of the images, but is there a way to restrict these clusters inside the MIP? I think Threshold masking may be the way to go, but I am not sure what threshold value to use?</div>
<div><br></div><div>If indeed threshold masking is the way to go, would I need to re-do all the analyses?</div><div><br></div><div>Any help would be much appreciated.</div><div><br></div><div>Thanks</div><div>- Muhammad </div>
</div></div></blockquote></div><br></div></div></div>
</blockquote></div><br><br clear="all"><br>-- <br>Research Associate<br>Gazzaley Lab<br>Department of Neurology<br>University of California, San Francisco<br>
--00248c6a5726382f06049c48a80a--
========================================================================Date: Mon, 14 Feb 2011 21:27:10 -0500
Reply-To: MP <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: MP <[log in to unmask]>
Subject: Re: EEG significant clusters outside the MIP glass brain
Comments: To: Michael T Rubens <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Thanks Michael, but even then the results ARE very different when I use the
"inclusive" iskull.nii mask on stat map. See attached.
What do you think is going on?
On Mon, Feb 14, 2011 at 9:09 PM, Michael T Rubens <[log in to unmask]>wrote:
> that is not right, the results should change due to the mask. Why don't you
> just run the analysis and mask out the resulting stat map. You're obviously
> not interested in anything off the brain (i.e., noise) anyway, so its valid.
>
> Cheers,
> Michael
>
>
> On Mon, Feb 14, 2011 at 5:25 PM, MP <[log in to unmask]> wrote:
>
>> Hello Vladimir,
>>
>> I tried to use the inner skull mask as you suggested to avoid getting
>> significant cluster outside the glass brain, but it is drastically changing
>> the results. Attached the figures of results with and without applying the
>> mask. Could you please tell me if I am doing something wrong here?
>>
>> P.S. I include the iskull mask as an implicit mask in the flexible
>> factorial design.
>>
>> Thanks
>> - Muhammad
>>
>>
>> On Fri, Dec 31, 2010 at 12:03 AM, <[log in to unmask]> wrote:
>>
>>> Dear Muhammad,
>>>
>>> Threshold masking will not necessarily work. It depends whether the
>>> values outside the brain are really low. You can use an explicit inner skull
>>> mask. There should be one in EEGTemplates as far as I remember or you can
>>> make your own.
>>>
>>> You can either re-run your stats or use small volume correction for inner
>>> skull but the latter will not fix the graphics.
>>>
>>> Best,
>>>
>>> Vladimir
>>>
>>> Sent from my BlackBerry® smartphone from orange
>>> ------------------------------
>>> *From: * MP <[log in to unmask]>
>>> *Sender: * "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
>>> *Date: *Thu, 30 Dec 2010 21:33:40 -0500
>>> *To: *<[log in to unmask]>
>>> *ReplyTo: * MP <[log in to unmask]>
>>> *Subject: *[SPM] EEG significant clusters outside the MIP glass brain
>>>
>>> Hello all,
>>>
>>> I am doing EEG analysis using SPM8 and while viewing the results, I see
>>> that some of the significant clusters appear outside the brain. I understand
>>> that it could be due to smoothing of the images, but is there a way to
>>> restrict these clusters inside the MIP? I think Threshold masking may be the
>>> way to go, but I am not sure what threshold value to use?
>>>
>>> If indeed threshold masking is the way to go, would I need to re-do all
>>> the analyses?
>>>
>>> Any help would be much appreciated.
>>>
>>> Thanks
>>> - Muhammad
>>>
>>
>>
>
>
> --
> Research Associate
> Gazzaley Lab
> Department of Neurology
> University of California, San Francisco
>
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Thanks Michael, but even then the results ARE very different when I use the "inclusive" iskull.nii mask on stat map. See attached.<div><br></div><div>What do you think is going on?<br><br><div class="gmail_quote">
On Mon, Feb 14, 2011 at 9:09 PM, Michael T Rubens <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
that is not right, the results should change due to the mask. Why don't you just run the analysis and mask out the resulting stat map. You're obviously not interested in anything off the brain (i.e., noise) anyway, so its valid.<br>
<br>Cheers, <br><font color="#888888">Michael</font><div><div></div><div class="h5"><br><br><div class="gmail_quote">On Mon, Feb 14, 2011 at 5:25 PM, MP <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0pt 0pt 0pt 0.8ex;border-left:1px solid rgb(204, 204, 204);padding-left:1ex">
Hello Vladimir,<div><br></div><div>I tried to use the inner skull mask as you suggested to avoid getting significant cluster outside the glass brain, but it is drastically changing the results. Attached the figures of results with and without applying the mask. Could you please tell me if I am doing something wrong here?</div>
<div><br></div><div>P.S. I include the iskull mask as an implicit mask in the flexible factorial design.</div><div><br></div><div>Thanks</div><div>- Muhammad <div><div></div><div><br><br><div class="gmail_quote">
On Fri, Dec 31, 2010 at 12:03 AM, <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0pt 0pt 0pt 0.8ex;border-left:1px solid rgb(204, 204, 204);padding-left:1ex"> Dear Muhammad,<br><br>Threshold masking will not necessarily work. It depends whether the values outside the brain are really low. You can use an explicit inner skull mask. There should be one in EEGTemplates as far as I remember or you can make your own.<br>
<br>You can either re-run your stats or use small volume correction for inner skull but the latter will not fix the graphics.<br><br>Best,<br><br>Vladimir<p>Sent from my BlackBerry® smartphone from orange</p><hr><div><b>From: </b> MP <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>>
</div><div><b>Sender: </b> "SPM (Statistical Parametric Mapping)" <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>>
</div><div><b>Date: </b>Thu, 30 Dec 2010 21:33:40 -0500</div><div><b>To: </b><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></div><div><b>ReplyTo: </b> MP <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>>
</div><div><b>Subject: </b>[SPM] EEG significant clusters outside the MIP glass brain</div><div><div></div><div><div><br></div>Hello all,<div><br></div><div>I am doing EEG analysis using SPM8 and while viewing the results, I see that some of the significant clusters appear outside the brain. I understand that it could be due to smoothing of the images, but is there a way to restrict these clusters inside the MIP? I think Threshold masking may be the way to go, but I am not sure what threshold value to use?</div>
<div><br></div><div>If indeed threshold masking is the way to go, would I need to re-do all the analyses?</div><div><br></div><div>Any help would be much appreciated.</div><div><br></div><div>Thanks</div><div>- Muhammad </div>
</div></div></blockquote></div><br></div></div></div>
</blockquote></div><br><br clear="all"><br></div></div><div><div></div><div class="h5">-- <br>Research Associate<br>Gazzaley Lab<br>Department of Neurology<br>University of California, San Francisco<br>
</div></div></blockquote></div><br></div>
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========================================================================Date: Mon, 14 Feb 2011 18:39:53 -0800
Reply-To: Michael T Rubens <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Michael T Rubens <[log in to unmask]>
Subject: Re: EEG significant clusters outside the MIP glass brain
Comments: To: MP <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundary cf3054ac0b0f9a2b049c491673
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--20cf3054ac0b0f9a2b049c491673
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no idea, did you look at the mask to see what it looks like?
what kind of threshold/correction are you using? if you're using a cluster
based threshold like friston's cluster fdr then it could be that off brain
stuff is inflating clusters that don't pass correction with the mask.
Cheers,
Michael
On Mon, Feb 14, 2011 at 6:27 PM, MP <[log in to unmask]> wrote:
> Thanks Michael, but even then the results ARE very different when I use the
> "inclusive" iskull.nii mask on stat map. See attached.
>
> What do you think is going on?
>
> On Mon, Feb 14, 2011 at 9:09 PM, Michael T Rubens <[log in to unmask]>wrote:
>
>> that is not right, the results should change due to the mask. Why don't
>> you just run the analysis and mask out the resulting stat map. You're
>> obviously not interested in anything off the brain (i.e., noise) anyway, so
>> its valid.
>>
>> Cheers,
>> Michael
>>
>>
>> On Mon, Feb 14, 2011 at 5:25 PM, MP <[log in to unmask]> wrote:
>>
>>> Hello Vladimir,
>>>
>>> I tried to use the inner skull mask as you suggested to avoid getting
>>> significant cluster outside the glass brain, but it is drastically changing
>>> the results. Attached the figures of results with and without applying the
>>> mask. Could you please tell me if I am doing something wrong here?
>>>
>>> P.S. I include the iskull mask as an implicit mask in the flexible
>>> factorial design.
>>>
>>> Thanks
>>> - Muhammad
>>>
>>>
>>> On Fri, Dec 31, 2010 at 12:03 AM, <[log in to unmask]> wrote:
>>>
>>>> Dear Muhammad,
>>>>
>>>> Threshold masking will not necessarily work. It depends whether the
>>>> values outside the brain are really low. You can use an explicit inner skull
>>>> mask. There should be one in EEGTemplates as far as I remember or you can
>>>> make your own.
>>>>
>>>> You can either re-run your stats or use small volume correction for
>>>> inner skull but the latter will not fix the graphics.
>>>>
>>>> Best,
>>>>
>>>> Vladimir
>>>>
>>>> Sent from my BlackBerry® smartphone from orange
>>>> ------------------------------
>>>> *From: * MP <[log in to unmask]>
>>>> *Sender: * "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
>>>> *Date: *Thu, 30 Dec 2010 21:33:40 -0500
>>>> *To: *<[log in to unmask]>
>>>> *ReplyTo: * MP <[log in to unmask]>
>>>> *Subject: *[SPM] EEG significant clusters outside the MIP glass brain
>>>>
>>>> Hello all,
>>>>
>>>> I am doing EEG analysis using SPM8 and while viewing the results, I see
>>>> that some of the significant clusters appear outside the brain. I understand
>>>> that it could be due to smoothing of the images, but is there a way to
>>>> restrict these clusters inside the MIP? I think Threshold masking may be the
>>>> way to go, but I am not sure what threshold value to use?
>>>>
>>>> If indeed threshold masking is the way to go, would I need to re-do all
>>>> the analyses?
>>>>
>>>> Any help would be much appreciated.
>>>>
>>>> Thanks
>>>> - Muhammad
>>>>
>>>
>>>
>>
>>
>> --
>> Research Associate
>> Gazzaley Lab
>> Department of Neurology
>> University of California, San Francisco
>>
>
>
--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco
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no idea, did you look at the mask to see what it looks like? <br><br>what kind of threshold/correction are you using? if you're using a cluster based threshold like friston's cluster fdr then it could be that off brain stuff is inflating clusters that don't pass correction with the mask.<br>
<br>Cheers,<br>Michael<br><br><div class="gmail_quote">On Mon, Feb 14, 2011 at 6:27 PM, MP <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
Thanks Michael, but even then the results ARE very different when I use the "inclusive" iskull.nii  mask on stat map. See attached.<div><br></div><div>What do you think is going on?<br><br><div class="gmail_quote">
On Mon, Feb 14, 2011 at 9:09 PM, Michael T Rubens <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
that is not right, the results should change due to the mask. Why don't you just run the analysis and mask out the resulting stat map. You're obviously not interested in anything off the brain (i.e., noise) anyway, so its valid.<br>
<br>Cheers, <br><font color="#888888">Michael</font><div><div></div><div><br><br><div class="gmail_quote">On Mon, Feb 14, 2011 at 5:25 PM, MP <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
Hello Vladimir,<div><br></div><div>I tried to use the inner skull mask as you suggested to avoid getting significant cluster outside the glass brain, but it is drastically changing the results. Attached the figures of results with and without applying the mask. Could you please tell me if I am doing something wrong here?</div>
<div><br></div><div>P.S. I include the iskull mask as an implicit mask in the flexible factorial design.</div><div><br></div><div>Thanks</div><div>- Muhammad  <div><div></div><div><br><br><div class="gmail_quote">
On Fri, Dec 31, 2010 at 12:03 AM, <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;"> Dear Muhammad,<br><br>Threshold masking will not necessarily work. It depends whether the values outside the brain are really low. You can use an explicit inner skull mask. There should be one in EEGTemplates as far as I remember or you can make your own.<br>
<br>You can either re-run your stats or use small volume correction for inner skull but the latter will not fix the graphics.<br><br>Best,<br><br>Vladimir<p>Sent from my BlackBerry® smartphone from orange</p><hr><div><b>From: </b> MP <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>>
</div><div><b>Sender: </b> "SPM (Statistical Parametric Mapping)" <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>>
</div><div><b>Date: </b>Thu, 30 Dec 2010 21:33:40 -0500</div><div><b>To: </b><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></div><div><b>ReplyTo: </b> MP <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>>
</div><div><b>Subject: </b>[SPM] EEG significant clusters outside the MIP glass brain</div><div><div></div><div><div><br></div>Hello all,<div><br></div><div>I am doing EEG analysis using SPM8 and while viewing the results, I see that some of the significant clusters appear outside the brain. I understand that it could be due to smoothing of the images, but is there a way to restrict these clusters inside the MIP? I think Threshold masking may be the way to go, but I am not sure what threshold value to use?</div>
<div><br></div><div>If indeed threshold masking is the way to go, would I need to re-do all the analyses?</div><div><br></div><div>Any help would be much appreciated.</div><div><br></div><div>Thanks</div><div>- Muhammad </div>
</div></div></blockquote></div><br></div></div></div>
</blockquote></div><br><br clear="all"><br></div></div><div><div></div><div>-- <br>Research Associate<br>Gazzaley Lab<br>Department of Neurology<br>University of California, San Francisco<br>
</div></div></blockquote></div><br></div>
</blockquote></div><br><br clear="all"><br>-- <br>Research Associate<br>Gazzaley Lab<br>Department of Neurology<br>University of California, San Francisco<br>
--20cf3054ac0b0f9a2b049c491673--
========================================================================Date: Mon, 14 Feb 2011 21:52:53 -0500
Reply-To: MP <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: MP <[log in to unmask]>
Subject: Re: EEG significant clusters outside the MIP glass brain
Comments: To: Michael T Rubens <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundarye6ba476b13bf211e049c494270
Message-ID: <[log in to unmask]>
--90e6ba476b13bf211e049c494270
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hi Michael,
The mask looks fine to me. Could it be due to different dimensions of the
mask and the stat map?
- M
On Mon, Feb 14, 2011 at 9:39 PM, Michael T Rubens <[log in to unmask]>wrote:
> no idea, did you look at the mask to see what it looks like?
>
> what kind of threshold/correction are you using? if you're using a cluster
> based threshold like friston's cluster fdr then it could be that off brain
> stuff is inflating clusters that don't pass correction with the mask.
>
> Cheers,
> Michael
>
>
> On Mon, Feb 14, 2011 at 6:27 PM, MP <[log in to unmask]> wrote:
>
>> Thanks Michael, but even then the results ARE very different when I use
>> the "inclusive" iskull.nii mask on stat map. See attached.
>>
>> What do you think is going on?
>>
>> On Mon, Feb 14, 2011 at 9:09 PM, Michael T Rubens <[log in to unmask]>wrote:
>>
>>> that is not right, the results should change due to the mask. Why don't
>>> you just run the analysis and mask out the resulting stat map. You're
>>> obviously not interested in anything off the brain (i.e., noise) anyway, so
>>> its valid.
>>>
>>> Cheers,
>>> Michael
>>>
>>>
>>> On Mon, Feb 14, 2011 at 5:25 PM, MP <[log in to unmask]> wrote:
>>>
>>>> Hello Vladimir,
>>>>
>>>> I tried to use the inner skull mask as you suggested to avoid getting
>>>> significant cluster outside the glass brain, but it is drastically changing
>>>> the results. Attached the figures of results with and without applying the
>>>> mask. Could you please tell me if I am doing something wrong here?
>>>>
>>>> P.S. I include the iskull mask as an implicit mask in the flexible
>>>> factorial design.
>>>>
>>>> Thanks
>>>> - Muhammad
>>>>
>>>>
>>>> On Fri, Dec 31, 2010 at 12:03 AM, <[log in to unmask]> wrote:
>>>>
>>>>> Dear Muhammad,
>>>>>
>>>>> Threshold masking will not necessarily work. It depends whether the
>>>>> values outside the brain are really low. You can use an explicit inner skull
>>>>> mask. There should be one in EEGTemplates as far as I remember or you can
>>>>> make your own.
>>>>>
>>>>> You can either re-run your stats or use small volume correction for
>>>>> inner skull but the latter will not fix the graphics.
>>>>>
>>>>> Best,
>>>>>
>>>>> Vladimir
>>>>>
>>>>> Sent from my BlackBerry® smartphone from orange
>>>>> ------------------------------
>>>>> *From: * MP <[log in to unmask]>
>>>>> *Sender: * "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
>>>>>
>>>>> *Date: *Thu, 30 Dec 2010 21:33:40 -0500
>>>>> *To: *<[log in to unmask]>
>>>>> *ReplyTo: * MP <[log in to unmask]>
>>>>> *Subject: *[SPM] EEG significant clusters outside the MIP glass brain
>>>>>
>>>>> Hello all,
>>>>>
>>>>> I am doing EEG analysis using SPM8 and while viewing the results, I see
>>>>> that some of the significant clusters appear outside the brain. I understand
>>>>> that it could be due to smoothing of the images, but is there a way to
>>>>> restrict these clusters inside the MIP? I think Threshold masking may be the
>>>>> way to go, but I am not sure what threshold value to use?
>>>>>
>>>>> If indeed threshold masking is the way to go, would I need to re-do all
>>>>> the analyses?
>>>>>
>>>>> Any help would be much appreciated.
>>>>>
>>>>> Thanks
>>>>> - Muhammad
>>>>>
>>>>
>>>>
>>>
>>>
>>> --
>>> Research Associate
>>> Gazzaley Lab
>>> Department of Neurology
>>> University of California, San Francisco
>>>
>>
>>
>
>
> --
> Research Associate
> Gazzaley Lab
> Department of Neurology
> University of California, San Francisco
>
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hi Michael,<div><br></div><div>The mask looks fine to me. Could it be due to different dimensions of the mask and the stat map?</div><div><br></div><div>- M<br><br><div class="gmail_quote">On Mon, Feb 14, 2011 at 9:39 PM, Michael T Rubens <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">no idea, did you look at the mask to see what it looks like? <br><br>what kind of threshold/correction are you using? if you're using a cluster based threshold like friston's cluster fdr then it could be that off brain stuff is inflating clusters that don't pass correction with the mask.<br>
<br>Cheers,<br><font color="#888888">Michael</font><div><div></div><div class="h5"><br><br><div class="gmail_quote">On Mon, Feb 14, 2011 at 6:27 PM, MP <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0pt 0pt 0pt 0.8ex;border-left:1px solid rgb(204, 204, 204);padding-left:1ex">
Thanks Michael, but even then the results ARE very different when I use the "inclusive" iskull.nii mask on stat map. See attached.<div><br></div><div>What do you think is going on?<br><br><div class="gmail_quote">
On Mon, Feb 14, 2011 at 9:09 PM, Michael T Rubens <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0pt 0pt 0pt 0.8ex;border-left:1px solid rgb(204, 204, 204);padding-left:1ex">
that is not right, the results should change due to the mask. Why don't you just run the analysis and mask out the resulting stat map. You're obviously not interested in anything off the brain (i.e., noise) anyway, so its valid.<br>
<br>Cheers, <br><font color="#888888">Michael</font><div><div></div><div><br><br><div class="gmail_quote">On Mon, Feb 14, 2011 at 5:25 PM, MP <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0pt 0pt 0pt 0.8ex;border-left:1px solid rgb(204, 204, 204);padding-left:1ex">
Hello Vladimir,<div><br></div><div>I tried to use the inner skull mask as you suggested to avoid getting significant cluster outside the glass brain, but it is drastically changing the results. Attached the figures of results with and without applying the mask. Could you please tell me if I am doing something wrong here?</div>
<div><br></div><div>P.S. I include the iskull mask as an implicit mask in the flexible factorial design.</div><div><br></div><div>Thanks</div><div>- Muhammad <div><div></div><div><br><br><div class="gmail_quote">
On Fri, Dec 31, 2010 at 12:03 AM, <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0pt 0pt 0pt 0.8ex;border-left:1px solid rgb(204, 204, 204);padding-left:1ex"> Dear Muhammad,<br><br>Threshold masking will not necessarily work. It depends whether the values outside the brain are really low. You can use an explicit inner skull mask. There should be one in EEGTemplates as far as I remember or you can make your own.<br>
<br>You can either re-run your stats or use small volume correction for inner skull but the latter will not fix the graphics.<br><br>Best,<br><br>Vladimir<p>Sent from my BlackBerry® smartphone from orange</p><hr><div><b>From: </b> MP <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>>
</div><div><b>Sender: </b> "SPM (Statistical Parametric Mapping)" <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>>
</div><div><b>Date: </b>Thu, 30 Dec 2010 21:33:40 -0500</div><div><b>To: </b><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></div><div><b>ReplyTo: </b> MP <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>>
</div><div><b>Subject: </b>[SPM] EEG significant clusters outside the MIP glass brain</div><div><div></div><div><div><br></div>Hello all,<div><br></div><div>I am doing EEG analysis using SPM8 and while viewing the results, I see that some of the significant clusters appear outside the brain. I understand that it could be due to smoothing of the images, but is there a way to restrict these clusters inside the MIP? I think Threshold masking may be the way to go, but I am not sure what threshold value to use?</div>
<div><br></div><div>If indeed threshold masking is the way to go, would I need to re-do all the analyses?</div><div><br></div><div>Any help would be much appreciated.</div><div><br></div><div>Thanks</div><div>- Muhammad </div>
</div></div></blockquote></div><br></div></div></div>
</blockquote></div><br><br clear="all"><br></div></div><div><div></div><div>-- <br>Research Associate<br>Gazzaley Lab<br>Department of Neurology<br>University of California, San Francisco<br>
</div></div></blockquote></div><br></div>
</blockquote></div><br><br clear="all"><br>-- <br>Research Associate<br>Gazzaley Lab<br>Department of Neurology<br>University of California, San Francisco<br>
</div></div></blockquote></div><br></div>
--90e6ba476b13bf211e049c494270--
========================================================================Date: Tue, 15 Feb 2011 08:43:31 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: spm_create_vol &&& spm_write_vol
Comments: To: cliff <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
If spm_create_vol provides a different set of information about
headers from what was in spm_create_vol, then the header will contain
the most recent information.
Best regards,
-John
On 15 February 2011 02:04, cliff <[log in to unmask]> wrote:
> Thanks~~
>
> Does that mean if I use "spm_create_vol" to create headers followed by
> "spm_write_vol" (if it is necessary), then the headers created by
> "spm_create_vol" will be overwrited?
>
> On Mon, Feb 14, 2011 at 5:32 PM, John Ashburner <[log in to unmask]>
> wrote:
>>
>> spm_write_vol calls spm_create_vol. Sometimes, for example in the
>> fMRI stats, we may wish to create headers but only write out a slice
>> of data at a time (as there is not enough memory to hold all the
>> images).
>>
>> best regards,
>> -John
>>
>> On 14 February 2011 22:16, cliff <[log in to unmask]> wrote:
>> > Hi, all,
>> > I thought before that "spm_create_vol " is used to create the ".hdr"
>> > files
>> > of the images.
>> > However, today I found that "spm_write_vol" can produce both of the
>> > ".hdr"
>> > and ".img" files.
>> >
>> > then, what is the "spm_create_vol " exactly used for?
>> >
>> > Thanks~
>> >
>
>
>
> --
> Sincerely,
> Senhua Zhu
>
>
>
========================================================================Date: Tue, 15 Feb 2011 12:09:03 +0100
Reply-To: vgarcia <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: vgarcia <[log in to unmask]>
Subject: Paired t-test + Covariate
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1; format=flowed
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Message-ID: <[log in to unmask]>
Dear List,
Our design is a paired t-test (13 subjects) with a covariate (its value
is the same in both images of the same subject). We would like to test
the hypothesis of zero regression slope against the alternative of a
positive slope with the following contrast (value 1 in the column of the
covariate):
zeros(1,13) 1 0 0
But the contrast is invalid. What are we doing wrong?
Thanks a million
Verónica
========================================================================Date: Tue, 15 Feb 2011 11:20:54 +0000
Reply-To: Vladimir Litvak <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Litvak <[log in to unmask]>
Subject: Re: EEG significant clusters outside the MIP glass brain
Comments: To: MP <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
Dear Muhammad,
It looks like the analysis you are doing is at the sensor level, but
you use a brain mask. This does not make sense. In your original
message you said that you are getting clusters outside the brain so I
assumed that you are talking about source images. If you are doing
sensor level analysis and you smooth your images you can possibly get
clusters extending outside the scalp. To avoid that you can generate a
mask using Other/Mask Images. You should provide an original
unsmoothed image as input.
Best,
Vladimir
On Tue, Feb 15, 2011 at 2:52 AM, MP <[log in to unmask]> wrote:
> hi Michael,
> The mask looks fine to me. Could it be due to different dimensions of the
> mask and the stat map?
> - M
>
> On Mon, Feb 14, 2011 at 9:39 PM, Michael T Rubens <[log in to unmask]>
> wrote:
>>
>> no idea, did you look at the mask to see what it looks like?
>>
>> what kind of threshold/correction are you using? if you're using a cluster
>> based threshold like friston's cluster fdr then it could be that off brain
>> stuff is inflating clusters that don't pass correction with the mask.
>>
>> Cheers,
>> Michael
>>
>> On Mon, Feb 14, 2011 at 6:27 PM, MP <[log in to unmask]> wrote:
>>>
>>> Thanks Michael, but even then the results ARE very different when I use
>>> the "inclusive" iskull.nii mask on stat map. See attached.
>>> What do you think is going on?
>>>
>>> On Mon, Feb 14, 2011 at 9:09 PM, Michael T Rubens <[log in to unmask]>
>>> wrote:
>>>>
>>>> that is not right, the results should change due to the mask. Why don't
>>>> you just run the analysis and mask out the resulting stat map. You're
>>>> obviously not interested in anything off the brain (i.e., noise) anyway, so
>>>> its valid.
>>>>
>>>> Cheers,
>>>> Michael
>>>>
>>>> On Mon, Feb 14, 2011 at 5:25 PM, MP <[log in to unmask]> wrote:
>>>>>
>>>>> Hello Vladimir,
>>>>> I tried to use the inner skull mask as you suggested to avoid getting
>>>>> significant cluster outside the glass brain, but it is drastically changing
>>>>> the results. Attached the figures of results with and without applying the
>>>>> mask. Could you please tell me if I am doing something wrong here?
>>>>> P.S. I include the iskull mask as an implicit mask in the flexible
>>>>> factorial design.
>>>>> Thanks
>>>>> - Muhammad
>>>>>
>>>>> On Fri, Dec 31, 2010 at 12:03 AM, <[log in to unmask]> wrote:
>>>>>>
>>>>>> Dear Muhammad,
>>>>>>
>>>>>> Threshold masking will not necessarily work. It depends whether the
>>>>>> values outside the brain are really low. You can use an explicit inner skull
>>>>>> mask. There should be one in EEGTemplates as far as I remember or you can
>>>>>> make your own.
>>>>>>
>>>>>> You can either re-run your stats or use small volume correction for
>>>>>> inner skull but the latter will not fix the graphics.
>>>>>>
>>>>>> Best,
>>>>>>
>>>>>> Vladimir
>>>>>>
>>>>>> Sent from my BlackBerry® smartphone from orange
>>>>>>
>>>>>> ________________________________
>>>>>> From: MP <[log in to unmask]>
>>>>>> Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
>>>>>> Date: Thu, 30 Dec 2010 21:33:40 -0500
>>>>>> To: <[log in to unmask]>
>>>>>> ReplyTo: MP <[log in to unmask]>
>>>>>> Subject: [SPM] EEG significant clusters outside the MIP glass brain
>>>>>> Hello all,
>>>>>> I am doing EEG analysis using SPM8 and while viewing the results, I
>>>>>> see that some of the significant clusters appear outside the brain. I
>>>>>> understand that it could be due to smoothing of the images, but is there a
>>>>>> way to restrict these clusters inside the MIP? I think Threshold masking may
>>>>>> be the way to go, but I am not sure what threshold value to use?
>>>>>> If indeed threshold masking is the way to go, would I need to re-do
>>>>>> all the analyses?
>>>>>> Any help would be much appreciated.
>>>>>> Thanks
>>>>>> - Muhammad
>>>>
>>>>
>>>>
>>>> --
>>>> Research Associate
>>>> Gazzaley Lab
>>>> Department of Neurology
>>>> University of California, San Francisco
>>>
>>
>>
>>
>> --
>> Research Associate
>> Gazzaley Lab
>> Department of Neurology
>> University of California, San Francisco
>
>
========================================================================Date: Tue, 15 Feb 2011 11:29:30 +0000
Reply-To: Vladimir Litvak <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Litvak <[log in to unmask]>
Subject: Re: Question about lead field matrix
Comments: To: Carsten Stahlhut <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <AANLkTimh][log in to unmask]>
Dear Carsten,
I actually see now that the option to use OpenMEEG is already in the
code of the present version 4010. If you look in spm_eeg_inv_forward
around line 88, you'll find it there. It's just not in the GUI, which
is easy to fix by adding 'OpenMEEG BEM' to the list in
spm_eeg_inv_forward_ui. I'm sure Alexandre will be glad to help you
with installation. I suspect on LINUX it's more straightforward than
on Windows.
Best,
Vladimir
On Mon, Feb 14, 2011 at 9:58 PM, Carsten Stahlhut
<[log in to unmask]> wrote:
> Dear Vladimir,
>
> Thanks for clarifying the issue with the physical units - that confirmed a
> little suspicion on my that it would probably be better for me to leave out
> the units in some plots that I did of reconstructed sources.
>
> Yes, indeed the platform is also one my concerns - however, I think it
> should work with Linux.
> Regarding the computation time - my impression is that it is very fast
> compared to other FEM implementations and almost just as fast as computing
> BEM solutions. I guess the reason why I suddenly found the SimBio's FEM head
> model very appealing was that in one of their recent papers (see e.g. the
> attached paper page 1926), they claim that with a grid of 2 * 2 * 2 mm^3
> they can build a FEM with 71 electrodes in less than 22 min and less than 38
> min for a sensor configuration of 258 electrodes! That's quite an
> improvement.
> All of this is performed just on a Linux-workstation - Intel Core 2 Duo
> E6600 processor (2.4 GHz) with 4 GB memory.
>
> Thanks for bringing the OpenMEEG software to my attention. I think that's a
> really good alternative. Yes, that would be really great if I would send me
> your code for using the OpenMEEG BEM implementation.
>
> Best,
> Carsten
>
>
>
>
> 2011/2/14 Vladimir Litvak <[log in to unmask]>
>>
>> Dear Carsten
>>
>> On Fri, Feb 11, 2011 at 11:56 PM, Carsten Stahlhut <[log in to unmask]> wrote:
>> > Dear Vladimir,
>> >
>> > I have approx three questions (or issues) that I hope you can help me
>> > out
>> > with.
>> >
>> > Do you know what the units are for the lead field matrix and the source
>> > estimates in SPM8?
>>
>> These are two different questions.
>>
>> > If you apply one of the EEG imaging methods (as I understood it) then
>> > the
>> > source estimates should be current density i.e. the unit should be
>> > something
>> > like A/m^2 or uA/mm^2 depending on the scaling of the EEG data and lead
>> > field matrix.
>> > And then the units of the lead field should be something like m^2 * Ohm
>> > ( =
>> > m^2 / S ).
>>
>> In theory the forward computation done by the 'forward' toolbox should
>> be unit-blind so the units of the lead field should depend on units of
>> the head model (mm in SPM). In practice this might not be the case. We
>> recently found a difference in scaling between 'single shell' and
>> other MEG models. This will be fixed in the next update. There might
>> be something similar also for EEG. SPM is not sensitive to it but it's
>> still something worth looking into.
>>
>> > Does this also holds for SPM8 or do there exist any type of scaling of
>> > the
>> > lead field matrix such as a normalization of the columns or something
>> > like
>> > that?
>> >
>>
>> Yes, there are several scaling and normalization steps so the units of
>> the output images are not related to original physical units but are
>> normalize to the total power across sources and conditions.
>>
>> > The second question is related to the lead field matrix as well. If I
>> > compare two different head models a BEM and 3-spheres model then they
>> > seem
>> > to be on two very different scales - is that really the case? Or am I
>> > missing an important step? Of course it is two very different head model
>> > assumptions so they should be quite different but I'm a bit surprised
>> > that
>> > there exists a scaling factor of 100 between the largest element in the
>> > BEM
>> > gain matrix and the 3-spheres gain matrix.
>>
>> See above.
>>
>> > What I just did was to load the mat-files with the gain-matrices and
>> > used
>> > the imagesc-function to show the amplitudes of the two lead field
>> > matrices.
>> >
>> > The 28th Jul 2010 you sent an answer to a post regarding head models and
>> > you
>> > here mentioned that FEM head models from SimBio might be an option in
>> > the
>> > very nice MEEGTool. I was wondering what the status is on integrating
>> > the
>> > SimBio FEM implementation with SPM, as I'm very interested to try out
>> > the
>> > FEM head models. I guess there is no reason that I will try to write the
>> > code my self if the option is almost done and will be part of the
>> > MEEGTool.
>> >
>>
>> I am aware of some efforts to integrate SimBio support in the forward
>> toolbox. Even if this is done I don't expect it to become one of the
>> standard options in SPM because SimBio runs only on a particular Unix
>> platform (I'm not even sure it's LINUX) and I suspect has very lengthy
>> computing time. But if you compute a gain matrix for the canonical
>> mesh in SimBio you can just replace the mat file generated by SPM with
>> your file and it will be used by spm_eeg_invert. If there is
>> inconsistency in units, it shouldn't be a problem.
>>
>> There is also a possibility to use another BEM implementation OpenMEEG
>> (http://www-sop.inria.fr/athena/software/OpenMEEG/). That's something
>> that works and I tested it last year, but found it impractical for
>> most users again because of difficulties with installation and long
>> computation time (about 24 h for normal mesh). The differences in the
>> lead fields with our BEM were only for a small number of vertices
>> close to the surface. But I can give you the glue code for this if you
>> want.
>>
>> Best,
>>
>> Vladimir
>>
>>
>>
>>
>> > Best,
>> > Carsten
>> >
>> >
>> >
>
>
>
> --
> Carsten Stahlhut
> Section for Cognitive Systems
> Department of Informatics and Mathematical Modelling
>
> Richard Petersens Plads, Building 321
> Technical University of Denmark
> DK-2800 Kongens Lyngby, Denmark
>
========================================================================Date: Tue, 15 Feb 2011 12:59:06 +0000
Reply-To: "B. de Haan" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "B. de Haan" <[log in to unmask]>
Subject: Lesion analysis workshop Tuebingen 27th of May 2011
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain; charset="UTF-8"
Message-ID: <[log in to unmask]>
We would like to announce a lesion analysis workshop on the 27th of May 2011 in Tuebingen.
The workshop will provide an overview on the background, rationale and methods of modern lesion analysis as well as a demonstration of their practical application. At the end of the workshop, participants should be able to independently conduct modern lesion analyses.
Further information including program and registration form can be found on:
http://homepages.uni-tuebingen.de/karnath/LesionAnalysisWorkshop2011/LesionAnalysisWorkshopHome.html
========================================================================Date: Tue, 15 Feb 2011 14:07:20 +0000
Reply-To: Joseph Whittaker <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Joseph Whittaker <[log in to unmask]>
Subject: Normalization after New Segment
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--001636c59ab9c984c4049c52ae52
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Dear SPM experts,
I have some structural images that will not segment properly using the
default segment option in SPM8, however the New Segment option available in
the tools does work. The only problem is that it does not output a parameter
file that can be used in the normalization procedure. Has anyone found away
around this problem? Perhaps I could copy the relevant parameters from the
seg8.mat file (produced by New Segment) to the seg_sn.mat file (produced by
default Segment) as a sort of work around, or maybe just change the seg8.mat
file into an acceptable format. Any advice or information would be
appreciated. Thanks.
Regards
joe
--
Joseph Whittaker, M.Sc
Ph.D Student
G.803 Neuroscience and Psychiatry Unit
Stopford Building
University of Manchester
Oxford Road
Manchester, UK
Tel: 0161 275 1729
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<br clear="all">Dear SPM experts,<br><br>I have some structural images that will not segment properly using the default segment option in SPM8, however the New Segment option available in the tools does work. The only problem is that it does not output a parameter file that can be used in the normalization procedure. Has anyone found away around this problem? Perhaps I could copy the relevant parameters from the seg8.mat file (produced by New Segment) to the seg_sn.mat file (produced by default Segment) as a sort of work around, or maybe just change the seg8.mat file into an acceptable format. Any advice or information would be appreciated. Thanks.<br>
<br>Regards<br><br>joe<br>-- <br>Joseph Whittaker, M.Sc<br>Ph.D Student<br>G.803 Neuroscience and Psychiatry Unit<br>Stopford Building<br>University of Manchester<br>Oxford Road<br>Manchester, UK<br>Tel: 0161 275 1729<br>
--001636c59ab9c984c4049c52ae52--
========================================================================Date: Tue, 15 Feb 2011 14:42:35 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: Normalization after New Segment
Comments: To: Joseph Whittaker <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Message-ID: <AANLkTikTeUnJM-3PTpMUJGsx+r97ckß[log in to unmask]>
The new segment algorithm can be set up to generate a deformation
field, which can be used for spatially normalising images. You'll see
these options down the bottom of the new segment UI. The deformation
fields can be used to warp images via the Deformations Utility,
although it may not be so friendly for those who don't know what a
composition is.
Best regards,
-John
On 15 February 2011 14:07, Joseph Whittaker
<[log in to unmask]> wrote:
>
> Dear SPM experts,
>
> I have some structural images that will not segment properly using the
> default segment option in SPM8, however the New Segment option available in
> the tools does work. The only problem is that it does not output a parameter
> file that can be used in the normalization procedure. Has anyone found away
> around this problem? Perhaps I could copy the relevant parameters from the
> seg8.mat file (produced by New Segment) to the seg_sn.mat file (produced by
> default Segment) as a sort of work around, or maybe just change the seg8.mat
> file into an acceptable format. Any advice or information would be
> appreciated. Thanks.
>
> Regards
>
> joe
> --
> Joseph Whittaker, M.Sc
> Ph.D Student
> G.803 Neuroscience and Psychiatry Unit
> Stopford Building
> University of Manchester
> Oxford Road
> Manchester, UK
> Tel: 0161 275 1729
>
========================================================================Date: Tue, 15 Feb 2011 16:42:06 +0100
Reply-To: vgarcia <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: vgarcia <[log in to unmask]>
Subject: Re: Paired t-test + Covariate
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Message-ID: <[log in to unmask]>
This is a multi-part message in MIME format.
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Dear List
Our design matrix is the following one:
Thanks a lot,
Verónica
On 14 February 2011 15:08, vgarcia<[log in to unmask]> wrote:
> Dear List,
>
> Our design is a paired t-test (13 subjects) with a covariate (its value
> is the same in both images of the same subject). We would like to test
> the hypothesis of zero regression slope against the alternative of a
> positive slope with the following contrast (value 1 in the column of the
> covariate):
>
> zeros(1,13) 1 0 0
>
> But the contrast is invalid. What are we doing wrong?
>
> Thanks a million
>
> Verónica
>
>
>
>
--------------080802090806010109080809
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<pre wrap="">Dear List
Our design matrix is the following one:
<img src="cid:part1.01060407.04040807@mce.hggm.es" alt="">
Thanks a lot,
Verónica
On 14 February 2011 15:08, vgarcia <a class="moz-txt-link-rfc2396E"
href="mailto:[log in to unmask]"><[log in to unmask]></a> wrote:
</pre>
<blockquote cite="mid:[log in to unmask]" type="cite">Dear
List,
<br>
<br>
Our design is a paired t-test (13 subjects) with a covariate (its value
<br>
is the same in both images of the same subject). We would like to test
<br>
the hypothesis of zero regression slope against the alternative of a
<br>
positive slope with the following contrast (value 1 in the column of
the
<br>
covariate):
<br>
<br>
zeros(1,13) 1 0 0
<br>
<br>
But the contrast is invalid. What are we doing wrong?
<br>
<br>
Thanks a million
<br>
<br>
Verónica
<br>
<br>
<br>
<br>
<br>
</blockquote>
<br>
</body>
</html>
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--------------060905000103080704000403--
--------------080802090806010109080809--
========================================================================Date: Tue, 15 Feb 2011 10:55:03 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: Paired t-test + Covariate
Comments: To: vgarcia <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Veronica,
A couple of points:
(1) Conditions should be set to equal variance, you have them set to
unequal;
(2) This is not a valid contrast, however, there are two ways around this
depending on your question:
(a) You want to know where you covariate correlates to the average value of
condition1 and condition2; form a new contrast at the single subject level
for the average of condition1 and condition 2 (1/2 1/2) and then then use a
one-sample t-test with your covariate.
(b) You want to know where the covariate correlates with the difference in
condition 1 and condition2; form a new contrast at the single subject level
for condition1-condition2 (1 -1) and then then a one-sample t-test with your
covariate.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED
HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is
intended only for the use of the individual or entity named above. If the
reader of the e-mail is not the intended recipient or the employee or agent
responsible for delivering it to the intended recipient, you are hereby
notified that you are in possession of confidential and privileged
information. Any unauthorized use, disclosure, copying or the taking of any
action in reliance on the contents of this information is strictly
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unintentionally, please immediately notify the sender via telephone at (773)
406-2464 or email.
On Tue, Feb 15, 2011 at 10:42 AM, vgarcia <[log in to unmask]> wrote:
> Dear List
>
> Our design matrix is the following one:
>
>
>
> Thanks a lot,
>
> Verónica
>
>
> On 14 February 2011 15:08, vgarcia <[log in to unmask]> <[log in to unmask]> wrote:
>
> Dear List,
>
> Our design is a paired t-test (13 subjects) with a covariate (its value
> is the same in both images of the same subject). We would like to test
> the hypothesis of zero regression slope against the alternative of a
> positive slope with the following contrast (value 1 in the column of the
> covariate):
>
> zeros(1,13) 1 0 0
>
> But the contrast is invalid. What are we doing wrong?
>
> Thanks a million
>
> Verónica
>
>
>
>
>
>
--20cf3054a28506deda049c54307b
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Veronica,<br><br>A couple of points:<br>(1) Conditions should be set to equal variance, you have them set to unequal;<br>(2) This is not a valid contrast, however, there are two ways around this depending on your question:<br>
(a) You want to know where you covariate correlates to the average value of condition1 and condition2; form a new contrast at the single subject level for the average of condition1 and condition 2 (1/2 1/2) and then then use a one-sample t-test with your covariate.<br>
(b) You want to know where the covariate correlates with the difference in condition 1 and condition2; form a new contrast at the single subject level for condition1-condition2 (1 -1) and then then a one-sample t-test with your covariate.<br clear="all">
<br>Best Regards, Donald McLaren<br>=================<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, GRECC, Bedford VA<br>Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School<br>
Office: (773) 406-2464<br>=====================<br>This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email.<br>
<br><br><div class="gmail_quote">On Tue, Feb 15, 2011 at 10:42 AM, vgarcia <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<div bgcolor="#ffffff" text="#000000">
<pre>Dear List
Our design matrix is the following one:
<img src="cid:part1.01060407.04040807@mce.hggm.es" alt="">
Thanks a lot,
Verónica
On 14 February 2011 15:08, vgarcia <a href="mailto:[log in to unmask]" target="_blank"><[log in to unmask]></a> wrote:
</pre><div><div></div><div class="h5">
<blockquote type="cite">Dear
List,
<br>
<br>
Our design is a paired t-test (13 subjects) with a covariate (its value
<br>
is the same in both images of the same subject). We would like to test
<br>
the hypothesis of zero regression slope against the alternative of a
<br>
positive slope with the following contrast (value 1 in the column of
the
<br>
covariate):
<br>
<br>
zeros(1,13) 1 0 0
<br>
<br>
But the contrast is invalid. What are we doing wrong?
<br>
<br>
Thanks a million
<br>
<br>
Verónica
<br>
<br>
<br>
<br>
<br>
</blockquote>
<br>
</div></div></div>
</blockquote></div><br>
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--20cf3054a28506dee0049c54307c--
========================================================================Date: Tue, 15 Feb 2011 08:36:13 -0800
Reply-To: Jose Soares <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Jose Soares <[log in to unmask]>
Subject: fMRI-EEG Post-Doc Open Position
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Hi all,
there is an open position to fMRI-EEG,
http://www.eracareers.pt/opportunities/index.aspx?task=global&jobId=22187
Best,
Jose
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<table cellspacing="0" cellpadding="0" border="0" ><tr><td valign="top" style="font: inherit;">Hi all, <div><br></div><div>there is an open position to fMRI-EEG,</div><div><br></div><div><span class="Apple-style-span" style="font-family: arial, sans-serif; font-size: 15px; "><a href="http://www.eracareers.pt/opportunities/index.aspx?task=global&jobId=22187" target="_blank">http://www.eracareers.pt/<wbr>opportunities/index.aspx?task=<wbr>global&jobId=22187</a></span></div><div><br></div><div>Best, </div><div><br></div><div>Jose</div></td></tr></table><br>
--0-1499706008-1297787773=:89678--
========================================================================Date: Tue, 15 Feb 2011 09:03:39 -0800
Reply-To: "Jiang, Zhiguo" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "Jiang, Zhiguo" <[log in to unmask]>
Subject: AR(1)
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Can someone point me to some useful example/scripts/tutorial on how SPM take care of the serial correlations.
It seems to me that SPM uses 0.2 as a default AR coefficient. (but in Statistical Parametric Maping book, p. 122. It says SPM uses a 1/e as coefficient).
I am trying to figure out how to get the whitening matrix SPM uses without running SPM8 model specification. I tracked it down to
spm_Ce.m and spm_Q.m but I got stuck on these codes
Q = spm_Q(a,v);
dQda = spm_diff('spm_Q',a,v,1);
C{1} = Q - dQda{1}*a;
C{2} = Q + dQda{1}*a;
Any comments?
Thanks,
Tony
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[cid:image001.jpg@01CBCCEF.04F7F480]
Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles
Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,CA 90095
Tel: (310)206-1423|Email: [log in to unmask]
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<p class="MsoNormal">Can someone point me to some useful example/scripts/tutorial on how SPM take care of the serial correlations.
<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">It seems to me that SPM uses 0.2 as a default AR coefficient. (but in Statistical Parametric Maping book, p. 122. It says SPM uses a 1/e as coefficient).
<o:p></o:p></p>
<p class="MsoNormal">I am trying to figure out how to get the whitening matrix SPM uses without running SPM8 model specification. I tracked it down to
<o:p></o:p></p>
<p class="MsoNormal">spm_Ce.m and spm_Q.m but I got stuck on these codes<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal" style="text-autospace:none"><span style="font-size:10.0pt;
font-family:"Courier New";color:black">Q = spm_Q(a,v);</span><span style="font-size:12.0pt;font-family:"Courier New""><o:p></o:p></span></p>
<p class="MsoNormal" style="text-autospace:none"><span style="font-size:10.0pt;
font-family:"Courier New";color:black"> dQda = spm_diff(</span><span style="font-size:10.0pt;font-family:"Courier New";
color:#A020F0">'spm_Q'</span><span style="font-size:10.0pt;font-family:"Courier New";
color:black">,a,v,1);</span><span style="font-size:12.0pt;font-family:"Courier New""><o:p></o:p></span></p>
<p class="MsoNormal" style="text-autospace:none"><span style="font-size:10.0pt;
font-family:"Courier New";color:black"> C{1} = Q - dQda{1}*a;</span><span style="font-size:12.0pt;font-family:"Courier New""><o:p></o:p></span></p>
<p class="MsoNormal" style="text-autospace:none"><span style="font-size:10.0pt;
font-family:"Courier New";color:black"> C{2} = Q + dQda{1}*a;</span><span style="font-size:12.0pt;font-family:"Courier New""><o:p></o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Any comments?<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Thanks,<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">Tony<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal">------------------------------------------------------------------------------------------------------------------------------<o:p></o:p></p>
<p class="MsoNormal"><img width="448" height="48" id="Picture_x0020_1" src="cid:image001.jpg@01CBCCEF.04F7F480" alt="logo"><o:p></o:p></p>
<p class="MsoNormal">Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles<br>
Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,CA 90095<o:p></o:p></p>
<p class="MsoNormal">Tel: (310)206-1423|Email: [log in to unmask]<o:p></o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="MsoNormal"><o:p> </o:p></p>
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--_004_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9ECEEMGMB6admedct_--
========================================================================Date: Tue, 15 Feb 2011 17:29:28 +0000
Reply-To: Richard Binney <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Richard Binney <[log in to unmask]>
Subject: Re: New Segmentation Out of Memory Error
In-Reply-To: <[log in to unmask]>
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--20cf3054a163abc1b8049c5581a3
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Dear John (and other knowledgeable types),
I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise a
functional MRI timeseries. That worked fine - the anatomical images that
I additionally warped in this way look great - but I now cannot do anything
with the timeseries.
It is now almost 4Gb in size. I appreciate that using DARTEL to warp fMRI
data can result in massive files which are problematic (and thus a work
around is to apply the warp to contrast images), but I didn't expect the
segment deformation field to do the same.
Does this sound about right for a timeseries with 465 volumes (voxel dim 2.5
x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake
somewhere?
so I'm supposing my inability to use the warped timeseries is a memory
issue - I am running a 64-bit machine with 8Gb memory (I can see all this
being used in the task manager so doesn't seem to be a matlab version
problem) but maybe this file is just way too big for the SPM
platform.....???? I could crop the image, etc I suppose but maybe the
biggest problem is the shear number of volumes.
Thanks in advance for your thoughts
Richard
On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo <
[log in to unmask]> wrote:
> Thanks John.
>
> It turns out the problem was due to the version of Matlab I was running.
>
> Matlab2007 was only running 32bit, whilst the 2010 version is 64.
>
> Having set up with Matlab2010, the new segmentation with DARTEL import now
> runs without any problem, without me having to alter/crop any of the images.
>
> All the best
> William
>
--20cf3054a163abc1b8049c5581a3
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<div>Dear John (and other knowledgeable types),</div>
<div> </div>
<div>I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise a functional MRI timeseries. That worked fine - the anatomical images that I additionally warped in this way look great - but I now cannot do anything with the timeseries.</div>
<div> </div>
<div>It is now almost 4Gb in size. I appreciate that using DARTEL to warp fMRI data can result in massive files which are problematic (and thus a work around is to apply the warp to contrast images), but I didn't expect the segment deformation field to do the same.</div>
<div> </div>
<div>Does this sound about right for a timeseries with 465 volumes (voxel dim 2.5 x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake somewhere?</div>
<div> </div>
<div>so I'm supposing my inability to use the warped timeseries is a memory issue - I am running a 64-bit machine with 8Gb memory (I can see all this being used in the task manager so doesn't seem to be a matlab version problem) but maybe this file is just way too big for the SPM platform.....???? I could crop the image, etc I suppose but maybe the biggest problem is the shear number of volumes.</div>
<div> </div>
<div>Thanks in advance for your thoughts</div>
<div> </div>
<div>Richard<br><br></div>
<div class="gmail_quote">On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="PADDING-LEFT: 1ex; MARGIN: 0px 0px 0px 0.8ex; BORDER-LEFT: #ccc 1px solid">Thanks John.<br><br>It turns out the problem was due to the version of Matlab I was running.<br><br>Matlab2007 was only running 32bit, whilst the 2010 version is 64.<br>
<br>Having set up with Matlab2010, the new segmentation with DARTEL import now runs without any problem, without me having to alter/crop any of the images.<br><br>All the best<br><font color="#888888">William<br></font></blockquote>
</div><br>
--20cf3054a163abc1b8049c5581a3--
========================================================================Date: Tue, 15 Feb 2011 17:42:49 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: New Segmentation Out of Memory Error
Comments: To: Richard Binney <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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You may wish to change the voxel sizes and bounding box in order to
obtain slightly lower resolution versions (other than the default
1.5mm isotropic with a large bounding box). See the email I sent out
recently.
Best regards,
-John
On 15 February 2011 17:29, Richard Binney <[log in to unmask]> wrote:
> Dear John (and other knowledgeable types),
>
> I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise a
> functional MRI timeseries. That worked fine - the anatomical images that
> I additionally warped in this way look great - but I now cannot do anything
> with the timeseries.
>
> It is now almost 4Gb in size. I appreciate that using DARTEL to warp fMRI
> data can result in massive files which are problematic (and thus a work
> around is to apply the warp to contrast images), but I didn't expect the
> segment deformation field to do the same.
>
> Does this sound about right for a timeseries with 465 volumes (voxel dim 2.5
> x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake
> somewhere?
>
> so I'm supposing my inability to use the warped timeseries is a memory
> issue - I am running a 64-bit machine with 8Gb memory (I can see all this
> being used in the task manager so doesn't seem to be a matlab version
> problem) but maybe this file is just way too big for the SPM
> platform.....???? I could crop the image, etc I suppose but maybe the
> biggest problem is the shear number of volumes.
>
> Thanks in advance for your thoughts
>
> Richard
>
> On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo
> <[log in to unmask]> wrote:
>>
>> Thanks John.
>>
>> It turns out the problem was due to the version of Matlab I was running.
>>
>> Matlab2007 was only running 32bit, whilst the 2010 version is 64.
>>
>> Having set up with Matlab2010, the new segmentation with DARTEL import now
>> runs without any problem, without me having to alter/crop any of the images.
>>
>> All the best
>> William
>
>
========================================================================Date: Tue, 15 Feb 2011 17:36:09 -0500
Reply-To: Jonathan Peelle <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Jonathan Peelle <[log in to unmask]>
Subject: Re: Question regarding FIR and contrasts
Comments: To: "Claire (Jung Eun) Han" <[log in to unmask]>
In-Reply-To: <AANLkTimVLVP6zAXuj6b_hzNAYŠ[log in to unmask]>
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--0016363b959676d552049c59ca34
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Hi Claire
> There are two conditions A and B I would like to compare.
>
> In fMRI model specification, I chose to use the Finite Impulse Response
> such that my design has 9 three-second-bins for each condition.
>
> I would like to compare the two conditions in each time bin...is it
> possible to do this at once? Which of the t- or f-test should I be using and
> what should I put for contrasts?
>
Two ways to compare A and B in your design are:
1) Use an F test, where each row of the F test compares A vs. B at one time
point. For example:
1 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0
0 1 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0
0 0 1 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0
.
.
.
0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 -1
or alternatively, use some Matlab functions:
[eye(9) 0-eye(9)]
The trick is that if you get a significant difference, you don't know which
time bin caused it, or which direction the difference was in (A>B or B>A),
so you would need to investigate further to find out (e.g., plotting effects
for each region).
2) A simpler approach is to simply look at the total "area under the curve"
for each condition in a t-test. This effectively discards the timing
information:
[1 1 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1]
for example would give you the sum of condition A compared to the sum of
condition B (i.e., A > B). Alternatively you could write this [ones(1,9)
ones(1,9)*-1]. Because this is a t-test it can give you information about
directionality. However, you lose the temporal detail, which may be one of
the reasons to choose an FIR model in the first place.
Ultimately it will depend on what you are most interested in. There is some
discussion of FIR models in Chapter 30 of the SPM manual (Face Group fMRI
Data), which may also be of interest.
Hope this helps,
Jonathan
--
Dr. Jonathan Peelle
Department of Neurology
University of Pennsylvania
3 West Gates
3400 Spruce Street
Philadelphia, PA 19104
USA
http://jonathanpeelle.net/
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Hi Claire<div><div class="gmail_quote"><div> </div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">There are two conditions A and B I would like to compare.<br><br>In fMRI model specification, I chose to use the Finite Impulse Response such that my design has 9 three-second-bins for each condition.<br>
<br>
I would like to compare the two conditions in each time bin...is it possible to do this at once? Which of the t- or f-test should I be using and what should I put for contrasts? <br></blockquote><div><br></div><div>Two ways to compare A and B in your design are:</div>
<div><br></div><div>1) Use an F test, where each row of the F test compares A vs. B at one time point. For example:</div><div><br></div><div>1 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0 0</div><div>0 1 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0 0</div>
<div>0 0 1 0 0 0 0 0 0 0 0 -1 0 0 0 0 0 0</div><div>.</div><div>.</div><div>.</div><div>0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 -1</div><div><br></div><div>or alternatively, use some Matlab functions:</div><div><br></div><div>[eye(9) 0-eye(9)]</div>
<div><br></div><div>The trick is that if you get a significant difference, you don't know which time bin caused it, or which direction the difference was in (A>B or B>A), so you would need to investigate further to find out (e.g., plotting effects for each region).</div>
<div><br></div><div>2) A simpler approach is to simply look at the total "area under the curve" for each condition in a t-test. This effectively discards the timing information:</div><div><br></div><div>[1 1 1 1 1 1 1 1 1 -1 -1 -1 -1 -1 -1 -1 -1 -1]</div>
<div><br></div><div>for example would give you the sum of condition A compared to the sum of condition B (i.e., A > B). Alternatively you could write this [ones(1,9) ones(1,9)*-1]. Because this is a t-test it can give you information about directionality. However, you lose the temporal detail, which may be one of the reasons to choose an FIR model in the first place.</div>
<div><br></div><div>Ultimately it will depend on what you are most interested in. There is some discussion of FIR models in Chapter 30 of the SPM manual (Face Group fMRI Data), which may also be of interest.</div><div><br>
</div><div>Hope this helps,</div><div>Jonathan</div><div><br></div><div><br>-- <br>Dr. Jonathan Peelle<br>Department of Neurology<br>University of Pennsylvania<br>3 West Gates<br>3400 Spruce Street<br>Philadelphia, PA 19104<br>
USA<br><a href="http://jonathanpeelle.net/">http://jonathanpeelle.net/</a><br><br></div></div></div>
--0016363b959676d552049c59ca34--
========================================================================Date: Tue, 15 Feb 2011 18:58:35 -0800
Reply-To: Michael T Rubens <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Michael T Rubens <[log in to unmask]>
Subject: Fwd: Urge your Representative to Oppose Science Funding Cuts
Comments: To: [log in to unmask], Gazz Lab <[log in to unmask]>
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---------- Forwarded message ----------
From: Society for Neuroscience <[log in to unmask]>
Date: Tue, Feb 15, 2011 at 2:55 PM
Subject: Urge your Representative to Oppose Science Funding Cuts
To: Michael Rubens <[log in to unmask]>
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SfN urges members to contact their representatives *today* to support
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* Please urge five colleagues to add their voices* in opposition to cuts
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--
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University of California, San Francisco
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<br><br><div class="gmail_quote">---------- Forwarded message ----------<br>From: <b class="gmail_sendername">Society for Neuroscience</b> <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span><br>Date: Tue, Feb 15, 2011 at 2:55 PM<br>
Subject: Urge your Representative to Oppose Science Funding Cuts<br>To: Michael Rubens <<a href="mailto:[log in to unmask]">[log in to unmask]</a>><br><br><br>
<div><table align="center" border="0" cellpadding="0" cellspacing="0" width="600"><tbody><tr><td colspan="5"><a href="http://echo4.bluehornet.com/ct/10277316:12920715616:m:1:1359818956:516B9757A19B323372396B0BE5458AD8" target="_blank"><img src="" title="Society for Neuroscience banner graphic" alt="Society for Neuroscience banner graphic" border="0" height="132" width="600"></a></td>
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Urge Your Representative to Oppose $2.4
Billion in <br>Proposed Cuts for NIH and NSF</p><p>SfN urges members to contact their representatives <b>today</b> to support National
Institutes of Health (NIH) and National Science Foundation (NSF) funding. The House of Representatives is set to debate and vote <b>this
week</b> on legislation to reduce FY2011 funding for science, including a $1.6 billion cut for NIH and a $826 million cut for NSF. Such cuts,
proposed by the House Republican leadership, would reduce the NIH and NSF budgets to FY2008 levels.</p><p><a href="http://echo4.bluehornet.com/ct/10277317:12920715616:m:1:1359818956:516B9757A19B323372396B0BE5458AD8" target="_blank">Contact your representatives</a> <b>now</b> through the <a href="http://echo4.bluehornet.com/ct/10277317:12920715616:m:1:1359818956:516B9757A19B323372396B0BE5458AD8" target="_blank">SfN Legislative Action Center</a> and ask them to
<b>oppose cuts</b> that will delay treatments, eliminate jobs, and jeopardize America’s standing as a global leader in scientific
discovery.</p><p>House votes are expected Thursday – <a href="http://echo4.bluehornet.com/ct/10277317:12920715616:m:1:1359818956:516B9757A19B323372396B0BE5458AD8" target="_blank">contact
your member of Congress</a> today!<b>Â Please urge five colleagues to add their voices</b> in opposition to cuts that will hurt science
and hurt America.</p><p>Â </p></td><td colspan="2" bgcolor="#dcd9c7" width="24"><p>Â </p><p style="font-family: Arial,Helvetica,sans-serif; color: rgb(92, 41, 70); font-size: 10pt;">Â </p><p style="font-family: Arial,Helvetica,sans-serif; color: rgb(92, 41, 70); font-size: 10pt;">
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<div><p><font face="Verdana, Arial, Helvetica, sans-serif" size="1"><span style="font-size: 7.5pt;">Society for Neuroscience | 1121 14th St NW | Suite 1010 | Washington
| DC | 20005</span><span style="font-family: "Times New Roman","serif"; font-size: 12pt;"> </span></font></p><p><font color="#999999" face="Verdana, Arial, Helvetica, sans-serif" size="1">This message was sent to: <a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a> <span style="color: rgb(153, 153, 153); font-size: 7.5pt;">as part of an established
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--90e6ba6e8e7cc867ec049c5d7622--
========================================================================Date: Wed, 16 Feb 2011 14:57:13 +0000
Reply-To: Vladimir Litvak <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Litvak <[log in to unmask]>
Subject: Re: Warning in spm_eeg_convert2scalp (griddata) & spm_eeg_crop
Comments: To: Panagiotis Tsiatsis <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Dear Panagiotis,
Sorry for the late reply. I only now found time to look into it more carefully.
On Tue, Feb 8, 2011 at 2:46 PM, Panagiotis Tsiatsis
<[log in to unmask]> wrote:
> Hello dear Vladimir and thanks for your response
>
> First of all, apologies in advance for the long e-mail; I hope that it will
> be at least a bit important for other users of SPM, too
>
> It seems that the problem appeared after I used spm_eeg_crop to drop my
> continuous CTF meg data file straight after reading it in and before
> applying filtering / downsampling. Without spm_eeg_crop everything works
> fine.
>
> [A small recap :
> As explained in a previous e-mail of mine, the logic behind this is that the
> start and the end of a continuous MEG files might be contaminated by
> artifacts (i.e due to subject's movement at the end of the session if she
> for whatever reason realized that the session is over and started moving
> before you press 'stop recording' ) or / and zero values because of
> padding added by the CTF software. All this should affect any subsequent
> filtering function if they are not removed (even the padding because of the
> sudden jump from values of order 10^-12 to zero!!!!) so I decided to crop
> the edges of my continuous file before continuing. ]
>
> I remember that when you send me the spm_eeg_crop file you told me that it
> has not been tested for continuous data but it should in principle work.
>
> I call it by using the following code :
>
> Time_Offset_in_ms = 3000; % in ms
> S = [];
> S.D = D;
> S.timewin = [(Time_of_First_Trial_Onset_in_ms -
> Time_Offset_in_ms) (Time_of_Last_Trial_Onset_in_ms + Time_Offset_in_ms)]
> S.freqwin = [];
> S.channels = 'all'
>
> D = spm_eeg_crop(S);
> D.save;
>
>
> The problem seems to appear in spm_eeg_crop when applied on a continuous
> file to and more specifically somewhere in the creation of the new object:
>
> Dnew = coor2D(Dnew, [], coor2D(D, chanind));
>
> as some channels do indeed have the same coordinates :
>
>>> size(coor2D(D, chanind)')
>
> ans =
>
> 337 2
>
>>> size(unique(coor2D(D, chanind)','rows'))
>
> ans =
>
> 335 2
>
>
> The above number shows that spm_eeg_crop indeed puts in the coor2D matrix
> (or tries to put) the coordinates of all available channels. Up to now (and
> correct me if I am wrong) I knew that the coor2D matrix contains the
> coordinates of the MEGGRAD channels only. So I modified the code as follows
> :
>
> MEG_Channels = (D.meegchannels('MEG'));
> Dnew = coor2D(Dnew, [], coor2D(D, MEG_Channels));
>
>
> *And indeed the conversion to images of subsequent files was executed
> without warnings*. So, is this a bug or am I going the wrong direction here?
>
I tried this on my CTF dataset and I don't get identical 2D
coordinates. So this is something that happens for your data and I'm
not sure why. If you send me an example I can look. In general this
code should work as it is because 2D coordinates are defined for all
channels (for non-scalp channels these are just points on a square
grid).
> A couple more issues / clarifications :
>
> 1. The statistics at the end of the analysis (assuming all other equal) were
> slightly different depending on whether I cropped the start and the end of
> the continuous initial files that in many cases had large artifacts or zero
> values. I suppose this is because the subsequent filtering applied on the
> data was "smoother", in the sense that the filters did not have to
> compensate for the artifacts or the jumps because of the zero padding. Does
> this train of thought sound logical to you?
Yes that's a reasonable explanation, although how reasonable it is
depends on how far your trials were from the edges and how low was
your high-pass cutoff. It's not that the filters 'compensate' for
something it's just that due to filtering artefacts from the edges can
contaminate data slightly away from the edges.
>
> 2.The cropped file (where only a few non-trial seconds from the start and
> from the and are missing) contains exactly the same number of events as the
> original continuous file. Why??? It seems that some zombie events are
> retained after cropping!
>
>
That's indeed a bug, due to the fact that I wasn't planning this
function to be used on continuous data. I now fixed it and it'll be in
the next update.
> 3. Please note that there appears to be one more problem with spm_eeg_crop
> and continuous data :
>
> The line Dnew = badchannels(Dnew, [], badchannels(D, chanind)); always
> results in an error when there are no bad channels, i.e. when
> isempty(D.badchannels) == 1. so i have substituted the above line with
>
> if isempty(D.badchannels) == 0
> Dnew = badchannels(Dnew, [], badchannels(D, chanind));
> end
>
I can't reproduce this problem either. The code should work also for
no bad channels because badchannels(D, chanind) always returns a
vector of 0/1
Best,
Vladimir
>
> I suspect I should put an 'else' condition somewhere there, but of course
> you are the expert on this :)
>
> Thanks again and all the best,
> Panagiotis
>
> On 2/7/2011 8:10 PM, Vladimir Litvak wrote:
>>
>> Dear Panagiotis,
>>
>> Try doing:
>>
>> spm_eeg_load
>> D.coor2D(D.meegchannels('MEG'))
>>
>> and look which of the channels have the same 2D coordinates. In
>> principle this should not happen for CTF MEG. You can also look in
>> 'Prepare' tool at the 2D channel layout an modify it if necessary or
>> load a template layout from EEGtemplates.
>>
>> Best,
>>
>> Vladimir
>>
>> On Sun, Feb 6, 2011 at 5:23 PM, Panagiotis Tsiatsis
>> <[log in to unmask]> wrote:
>>>
>>> Dear All,
>>>
>>> When I am converting my CTF MEG epoched data to images, I am getting the
>>> following warning:
>>>
>>> Warning: Duplicate x-y data points detected: using average of the z
>>> values.
>>>>
>>>> In griddata at 107
>>>
>>> In spm_eeg_convert2scalp at 204
>>>
>>> My settings are:
>>>
>>> S = [];
>>> S.n = 32;
>>> S.Fname = Concatenated_File_Name_Time;
>>> S.interpolate_bad = 1;
>>> S.modality = 'MEG';
>>>
>>> I also have EOG channels whose chantype has been set to 'EEG' during the
>>> conversion (this of course should not interfere since I ve selected my
>>> modality to be 'MEG').
>>>
>>> By browsing the Mathwoks forum, I suspect that this happens when nearby
>>> values get very similar (as is the case with MEG data).
>>>
>>> Anybody seen that before? Any intuitions?
>>>
>>> Thanks and best,
>>> Panagiotis
>>>
>>>
>>> --
>>> Panagiotis S. Tsiatsis
>>> Max Planck Institute for Biological Cybernetics
>>> Cognitive NeuroImaging Group
>>> Tuebingen, Germany
>>>
>
>
> --
> Panagiotis S. Tsiatsis
> Max Planck Institute for Biological Cybernetics
> Cognitive NeuroImaging Group
> Tuebingen, Germany
>
>
========================================================================Date: Wed, 16 Feb 2011 15:39:18 +0000
Reply-To: Richard Binney <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Richard Binney <[log in to unmask]>
Subject: Re: New Segmentation Out of Memory Error
Comments: To: John Ashburner <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundary cf3054962583e57b049c681574
Message-ID: <[log in to unmask]>
--20cf3054962583e57b049c681574
Content-Type: text/plain; charset=ISO-8859-1
Ah, yes. I couldn't find the email you were refferring to, but I've done it
and it makes a huge difference. Pretty obvious really.
One more thing, John (or anybody else for that matter) - is there now a way
in which one can easily create an inverse of the DARTEL normalise-to-MNI
deformation composition (so from MNI --> study average --> individual
subject space)? Is it possible (i.e., is there script) to have this (forward
and/or backwards) written out for each subject or, alternatively, to have
the affine template-to-TPM transform outputted such that one can combine it
with a flow field, and subsequently inverse the composition using the
deformations tool?
All the best,
Richard
On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]>wrote:
> You may wish to change the voxel sizes and bounding box in order to
> obtain slightly lower resolution versions (other than the default
> 1.5mm isotropic with a large bounding box). See the email I sent out
> recently.
>
> Best regards,
> -John
>
> On 15 February 2011 17:29, Richard Binney <[log in to unmask]>
> wrote:
> > Dear John (and other knowledgeable types),
> >
> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise a
> > functional MRI timeseries. That worked fine - the anatomical images that
> > I additionally warped in this way look great - but I now cannot do
> anything
> > with the timeseries.
> >
> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp fMRI
> > data can result in massive files which are problematic (and thus a work
> > around is to apply the warp to contrast images), but I didn't expect the
> > segment deformation field to do the same.
> >
> > Does this sound about right for a timeseries with 465 volumes (voxel dim
> 2.5
> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake
> > somewhere?
> >
> > so I'm supposing my inability to use the warped timeseries is a memory
> > issue - I am running a 64-bit machine with 8Gb memory (I can see all this
> > being used in the task manager so doesn't seem to be a matlab version
> > problem) but maybe this file is just way too big for the SPM
> > platform.....???? I could crop the image, etc I suppose but maybe the
> > biggest problem is the shear number of volumes.
> >
> > Thanks in advance for your thoughts
> >
> > Richard
> >
> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo
> > <[log in to unmask]> wrote:
> >>
> >> Thanks John.
> >>
> >> It turns out the problem was due to the version of Matlab I was running.
> >>
> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64.
> >>
> >> Having set up with Matlab2010, the new segmentation with DARTEL import
> now
> >> runs without any problem, without me having to alter/crop any of the
> images.
> >>
> >> All the best
> >> William
> >
> >
>
--20cf3054962583e57b049c681574
Content-Type: text/html; charset=ISO-8859-1
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<div>Ah, yes. I couldn't find the email you were refferring to, but I've done it and it makes a huge difference. Pretty obvious really.</div>
<div> </div>
<div>One more thing, John (or anybody else for that matter) - is there now a way in which one can easily create an inverse of the DARTEL normalise-to-MNI deformation composition (so from MNI --> study average --> individual subject space)? Is it possible (i.e., is there script) to have this (forward and/or backwards) written out for each subject or, alternatively, to have the affine template-to-TPM transform outputted such that one can combine it with a flow field, and subsequently inverse the composition using the deformations tool? </div>
<div> </div>
<div>All the best,</div>
<div> </div>
<div>Richard <br><br></div>
<div class="gmail_quote">On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="PADDING-LEFT: 1ex; MARGIN: 0px 0px 0px 0.8ex; BORDER-LEFT: #ccc 1px solid">You may wish to change the voxel sizes and bounding box in order to<br>obtain slightly lower resolution versions (other than the default<br>
1.5mm isotropic with a large bounding box). See the email I sent out<br>recently.<br><br>Best regards,<br><font color="#888888">-John<br></font>
<div>
<div></div>
<div class="h5"><br>On 15 February 2011 17:29, Richard Binney <<a href="mailto:[log in to unmask]">[log in to unmask]</a>> wrote:<br>> Dear John (and other knowledgeable types),<br>><br>> I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise a<br>
> functional MRI timeseries. That worked fine - the anatomical images that<br>> I additionally warped in this way look great - but I now cannot do anything<br>> with the timeseries.<br>><br>> It is now almost 4Gb in size. I appreciate that using DARTEL to warp fMRI<br>
> data can result in massive files which are problematic (and thus a work<br>> around is to apply the warp to contrast images), but I didn't expect the<br>> segment deformation field to do the same.<br>><br>
> Does this sound about right for a timeseries with 465 volumes (voxel dim 2.5<br>> x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake<br>> somewhere?<br>><br>> so I'm supposing my inability to use the warped timeseries is a memory<br>
> issue - I am running a 64-bit machine with 8Gb memory (I can see all this<br>> being used in the task manager so doesn't seem to be a matlab version<br>> problem) but maybe this file is just way too big for the SPM<br>
> platform.....???? I could crop the image, etc I suppose but maybe the<br>> biggest problem is the shear number of volumes.<br>><br>> Thanks in advance for your thoughts<br>><br>> Richard<br>><br>> On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo<br>
> <<a href="mailto:[log in to unmask]">[log in to unmask]</a>> wrote:<br>>><br>>> Thanks John.<br>>><br>>> It turns out the problem was due to the version of Matlab I was running.<br>
>><br>>> Matlab2007 was only running 32bit, whilst the 2010 version is 64.<br>>><br>>> Having set up with Matlab2010, the new segmentation with DARTEL import now<br>>> runs without any problem, without me having to alter/crop any of the images.<br>
>><br>>> All the best<br>>> William<br>><br>><br></div></div></blockquote></div><br>
--20cf3054962583e57b049c681574--
========================================================================Date: Wed, 16 Feb 2011 15:44:35 +0000
Reply-To: Richard Binney <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Richard Binney <[log in to unmask]>
Subject: Re: New Segmentation Out of Memory Error
Comments: To: John Ashburner <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundary cf305641c971f168049c6828fc
Message-ID: <[log in to unmask]>
--20cf305641c971f168049c6828fc
Content-Type: text/plain; charset=ISO-8859-1
......I've just spotted that running normalise-to-mni now spits out a
'Template_6_2mni.mat' file. I don't recall seeing this before - would this
happen to be the affine transform I spoke of?
R
On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney <
[log in to unmask]> wrote:
> Ah, yes. I couldn't find the email you were refferring to, but I've done it
> and it makes a huge difference. Pretty obvious really.
>
> One more thing, John (or anybody else for that matter) - is there now a way
> in which one can easily create an inverse of the DARTEL normalise-to-MNI
> deformation composition (so from MNI --> study average --> individual
> subject space)? Is it possible (i.e., is there script) to have this (forward
> and/or backwards) written out for each subject or, alternatively, to have
> the affine template-to-TPM transform outputted such that one can combine it
> with a flow field, and subsequently inverse the composition using the
> deformations tool?
>
> All the best,
>
> Richard
>
> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]>wrote:
>
>> You may wish to change the voxel sizes and bounding box in order to
>> obtain slightly lower resolution versions (other than the default
>> 1.5mm isotropic with a large bounding box). See the email I sent out
>> recently.
>>
>> Best regards,
>> -John
>>
>> On 15 February 2011 17:29, Richard Binney <[log in to unmask]>
>> wrote:
>> > Dear John (and other knowledgeable types),
>> >
>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise a
>> > functional MRI timeseries. That worked fine - the anatomical images that
>> > I additionally warped in this way look great - but I now cannot do
>> anything
>> > with the timeseries.
>> >
>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp
>> fMRI
>> > data can result in massive files which are problematic (and thus a work
>> > around is to apply the warp to contrast images), but I didn't expect the
>> > segment deformation field to do the same.
>> >
>> > Does this sound about right for a timeseries with 465 volumes (voxel dim
>> 2.5
>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake
>> > somewhere?
>> >
>> > so I'm supposing my inability to use the warped timeseries is a memory
>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all
>> this
>> > being used in the task manager so doesn't seem to be a matlab version
>> > problem) but maybe this file is just way too big for the SPM
>> > platform.....???? I could crop the image, etc I suppose but maybe the
>> > biggest problem is the shear number of volumes.
>> >
>> > Thanks in advance for your thoughts
>> >
>> > Richard
>> >
>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo
>> > <[log in to unmask]> wrote:
>> >>
>> >> Thanks John.
>> >>
>> >> It turns out the problem was due to the version of Matlab I was
>> running.
>> >>
>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64.
>> >>
>> >> Having set up with Matlab2010, the new segmentation with DARTEL import
>> now
>> >> runs without any problem, without me having to alter/crop any of the
>> images.
>> >>
>> >> All the best
>> >> William
>> >
>> >
>>
>
>
--20cf305641c971f168049c6828fc
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
<div>......I've just spotted that running normalise-to-mni now spits out a 'Template_6_2mni.mat' file. I don't recall seeing this before - would this happen to be the affine transform I spoke of?</div>
<div> </div>
<div>R<br><br></div>
<div class="gmail_quote">On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="PADDING-LEFT: 1ex; MARGIN: 0px 0px 0px 0.8ex; BORDER-LEFT: #ccc 1px solid">
<div>Ah, yes. I couldn't find the email you were refferring to, but I've done it and it makes a huge difference. Pretty obvious really.</div>
<div> </div>
<div>One more thing, John (or anybody else for that matter) - is there now a way in which one can easily create an inverse of the DARTEL normalise-to-MNI deformation composition (so from MNI --> study average --> individual subject space)? Is it possible (i.e., is there script) to have this (forward and/or backwards) written out for each subject or, alternatively, to have the affine template-to-TPM transform outputted such that one can combine it with a flow field, and subsequently inverse the composition using the deformations tool? </div>
<div> </div>
<div>All the best,</div>
<div> </div><font color="#888888">
<div>Richard <br><br></div></font>
<div>
<div></div>
<div class="h5">
<div class="gmail_quote">On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="PADDING-LEFT: 1ex; MARGIN: 0px 0px 0px 0.8ex; BORDER-LEFT: #ccc 1px solid">You may wish to change the voxel sizes and bounding box in order to<br>obtain slightly lower resolution versions (other than the default<br>
1.5mm isotropic with a large bounding box). See the email I sent out<br>recently.<br><br>Best regards,<br><font color="#888888">-John<br></font>
<div>
<div></div>
<div><br>On 15 February 2011 17:29, Richard Binney <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>> wrote:<br>> Dear John (and other knowledgeable types),<br>><br>
> I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise a<br>> functional MRI timeseries. That worked fine - the anatomical images that<br>> I additionally warped in this way look great - but I now cannot do anything<br>
> with the timeseries.<br>><br>> It is now almost 4Gb in size. I appreciate that using DARTEL to warp fMRI<br>> data can result in massive files which are problematic (and thus a work<br>> around is to apply the warp to contrast images), but I didn't expect the<br>
> segment deformation field to do the same.<br>><br>> Does this sound about right for a timeseries with 465 volumes (voxel dim 2.5<br>> x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake<br>> somewhere?<br>
><br>> so I'm supposing my inability to use the warped timeseries is a memory<br>> issue - I am running a 64-bit machine with 8Gb memory (I can see all this<br>> being used in the task manager so doesn't seem to be a matlab version<br>
> problem) but maybe this file is just way too big for the SPM<br>> platform.....???? I could crop the image, etc I suppose but maybe the<br>> biggest problem is the shear number of volumes.<br>><br>> Thanks in advance for your thoughts<br>
><br>> Richard<br>><br>> On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo<br>> <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>> wrote:<br>
>><br>>> Thanks John.<br>>><br>>> It turns out the problem was due to the version of Matlab I was running.<br>>><br>>> Matlab2007 was only running 32bit, whilst the 2010 version is 64.<br>
>><br>>> Having set up with Matlab2010, the new segmentation with DARTEL import now<br>>> runs without any problem, without me having to alter/crop any of the images.<br>>><br>>> All the best<br>
>> William<br>><br>><br></div></div></blockquote></div><br></div></div></blockquote></div><br>
--20cf305641c971f168049c6828fc--
========================================================================Date: Wed, 16 Feb 2011 15:58:16 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: New Segmentation Out of Memory Error
Comments: To: Richard Binney <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
This file does indeed contain an affine transform, and is generated
when the affine registration between population average and MNI space
is estimated.
There's no script to do what you're after yet, but if the demand is
high enough I could introduce the option. Can I ask how you plan to
use the transforms?
Best regards,
-John
On 16 February 2011 15:44, Richard Binney <[log in to unmask]> wrote:
> ......I've just spotted that running normalise-to-mni now spits out a
> 'Template_6_2mni.mat' file. I don't recall seeing this before - would this
> happen to be the affine transform I spoke of?
>
> R
>
> On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney
> <[log in to unmask]> wrote:
>>
>> Ah, yes. I couldn't find the email you were refferring to, but I've done
>> it and it makes a huge difference. Pretty obvious really.
>>
>> One more thing, John (or anybody else for that matter) - is there now a
>> way in which one can easily create an inverse of the DARTEL normalise-to-MNI
>> deformation composition (so from MNI --> study average --> individual
>> subject space)? Is it possible (i.e., is there script) to have this (forward
>> and/or backwards) written out for each subject or, alternatively, to have
>> the affine template-to-TPM transform outputted such that one can combine it
>> with a flow field, and subsequently inverse the composition using the
>> deformations tool?
>>
>> All the best,
>>
>> Richard
>>
>> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]>
>> wrote:
>>>
>>> You may wish to change the voxel sizes and bounding box in order to
>>> obtain slightly lower resolution versions (other than the default
>>> 1.5mm isotropic with a large bounding box). See the email I sent out
>>> recently.
>>>
>>> Best regards,
>>> -John
>>>
>>> On 15 February 2011 17:29, Richard Binney <[log in to unmask]>
>>> wrote:
>>> > Dear John (and other knowledgeable types),
>>> >
>>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise
>>> > a
>>> > functional MRI timeseries. That worked fine - the anatomical
>>> > images that
>>> > I additionally warped in this way look great - but I now cannot do
>>> > anything
>>> > with the timeseries.
>>> >
>>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp
>>> > fMRI
>>> > data can result in massive files which are problematic (and thus a work
>>> > around is to apply the warp to contrast images), but I didn't expect
>>> > the
>>> > segment deformation field to do the same.
>>> >
>>> > Does this sound about right for a timeseries with 465 volumes (voxel
>>> > dim 2.5
>>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake
>>> > somewhere?
>>> >
>>> > so I'm supposing my inability to use the warped timeseries is a memory
>>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all
>>> > this
>>> > being used in the task manager so doesn't seem to be a matlab version
>>> > problem) but maybe this file is just way too big for the SPM
>>> > platform.....???? I could crop the image, etc I suppose but maybe the
>>> > biggest problem is the shear number of volumes.
>>> >
>>> > Thanks in advance for your thoughts
>>> >
>>> > Richard
>>> >
>>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo
>>> > <[log in to unmask]> wrote:
>>> >>
>>> >> Thanks John.
>>> >>
>>> >> It turns out the problem was due to the version of Matlab I was
>>> >> running.
>>> >>
>>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64.
>>> >>
>>> >> Having set up with Matlab2010, the new segmentation with DARTEL import
>>> >> now
>>> >> runs without any problem, without me having to alter/crop any of the
>>> >> images.
>>> >>
>>> >> All the best
>>> >> William
>>> >
>>> >
>>
>
>
========================================================================Date: Wed, 16 Feb 2011 17:48:33 +0100
Reply-To: michel grothe <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: michel grothe <[log in to unmask]>
Subject: Re: New Segmentation Out of Memory Error
Comments: To: [log in to unmask]
In-Reply-To: <[log in to unmask]>
Content-Type: multipart/alternative;
boundary="_fd2415e3-e3d7-48dc-a0ca-d5dba1603bcc_"
MIME-Version: 1.0
Message-ID: <[log in to unmask]>
--_fd2415e3-e3d7-48dc-a0ca-d5dba1603bcc_
Content-Type: text/plain; charset="iso-8859-1"
Content-Transfer-Encoding: quoted-printable
Hi,
I haven´t seen such a file in my version of SPM8 (the penultimate update, run on win32). Is it possible that this file is just generated during estimation of the affine transform and not stored permanently on disk? Or does it come with the latest update?
It would be very helpful to have such a file, mainly for the reasons Richard mentioned, i.e. use it to compose the complete transform 'native->DARTEL-mean->MNI'. The inverse of such a composition could be used to warp Atlas-labels in MNI space to native space.
Best regards,
Michel
> Date: Wed, 16 Feb 2011 15:58:16 +0000
> From: [log in to unmask]
> Subject: Re: [SPM] New Segmentation Out of Memory Error
> To: [log in to unmask]
>
> This file does indeed contain an affine transform, and is generated
> when the affine registration between population average and MNI space
> is estimated.
>
> There's no script to do what you're after yet, but if the demand is
> high enough I could introduce the option. Can I ask how you plan to
> use the transforms?
>
> Best regards,
> -John
>
>
> On 16 February 2011 15:44, Richard Binney <[log in to unmask]> wrote:
> > ......I've just spotted that running normalise-to-mni now spits out a
> > 'Template_6_2mni.mat' file. I don't recall seeing this before - would this
> > happen to be the affine transform I spoke of?
> >
> > R
> >
> > On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney
> > <[log in to unmask]> wrote:
> >>
> >> Ah, yes. I couldn't find the email you were refferring to, but I've done
> >> it and it makes a huge difference. Pretty obvious really.
> >>
> >> One more thing, John (or anybody else for that matter) - is there now a
> >> way in which one can easily create an inverse of the DARTEL normalise-to-MNI
> >> deformation composition (so from MNI --> study average --> individual
> >> subject space)? Is it possible (i.e., is there script) to have this (forward
> >> and/or backwards) written out for each subject or, alternatively, to have
> >> the affine template-to-TPM transform outputted such that one can combine it
> >> with a flow field, and subsequently inverse the composition using the
> >> deformations tool?
> >>
> >> All the best,
> >>
> >> Richard
> >>
> >> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]>
> >> wrote:
> >>>
> >>> You may wish to change the voxel sizes and bounding box in order to
> >>> obtain slightly lower resolution versions (other than the default
> >>> 1.5mm isotropic with a large bounding box). See the email I sent out
> >>> recently.
> >>>
> >>> Best regards,
> >>> -John
> >>>
> >>> On 15 February 2011 17:29, Richard Binney <[log in to unmask]>
> >>> wrote:
> >>> > Dear John (and other knowledgeable types),
> >>> >
> >>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise
> >>> > a
> >>> > functional MRI timeseries. That worked fine - the anatomical
> >>> > images that
> >>> > I additionally warped in this way look great - but I now cannot do
> >>> > anything
> >>> > with the timeseries.
> >>> >
> >>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp
> >>> > fMRI
> >>> > data can result in massive files which are problematic (and thus a work
> >>> > around is to apply the warp to contrast images), but I didn't expect
> >>> > the
> >>> > segment deformation field to do the same.
> >>> >
> >>> > Does this sound about right for a timeseries with 465 volumes (voxel
> >>> > dim 2.5
> >>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake
> >>> > somewhere?
> >>> >
> >>> > so I'm supposing my inability to use the warped timeseries is a memory
> >>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all
> >>> > this
> >>> > being used in the task manager so doesn't seem to be a matlab version
> >>> > problem) but maybe this file is just way too big for the SPM
> >>> > platform.....???? I could crop the image, etc I suppose but maybe the
> >>> > biggest problem is the shear number of volumes.
> >>> >
> >>> > Thanks in advance for your thoughts
> >>> >
> >>> > Richard
> >>> >
> >>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo
> >>> > <[log in to unmask]> wrote:
> >>> >>
> >>> >> Thanks John.
> >>> >>
> >>> >> It turns out the problem was due to the version of Matlab I was
> >>> >> running.
> >>> >>
> >>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64.
> >>> >>
> >>> >> Having set up with Matlab2010, the new segmentation with DARTEL import
> >>> >> now
> >>> >> runs without any problem, without me having to alter/crop any of the
> >>> >> images.
> >>> >>
> >>> >> All the best
> >>> >> William
> >>> >
> >>> >
> >>
> >
> >
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Hi,<br>I haven´t seen such a file in my version of SPM8 (the penultimate update, run on win32). Is it possible that this file is just generated during estimation of the affine transform and not stored permanently on disk? Or does it come with the latest update?<br>It would be very helpful to have such a file, mainly for the reasons Richard mentioned, i.e. use it to compose the complete transform 'native->DARTEL-mean->MNI'. The inverse of such a composition could be used to warp Atlas-labels in MNI space to native space.<br><br>Best regards,<br>Michel<br> <br><br>> Date: Wed, 16 Feb 2011 15:58:16 +0000<br>> From: [log in to unmask]<br>> Subject: Re: [SPM] New Segmentation Out of Memory Error<br>> To: [log in to unmask]<br>> <br>> This file does indeed contain an affine transform, and is generated<br>> when the affine registration between population average and MNI space<br>> is estimated.<br>> <br>> There's no script to do what you're after yet, but if the demand is<br>> high enough I could introduce the option. Can I ask how you plan to<br>> use the transforms?<br>> <br>> Best regards,<br>> -John<br>> <br>> <br>> On 16 February 2011 15:44, Richard Binney <[log in to unmask]> wrote:<br>> > ......I've just spotted that running normalise-to-mni now spits out a<br>> > 'Template_6_2mni.mat' file. I don't recall seeing this before - would this<br>> > happen to be the affine transform I spoke of?<br>> ><br>> > R<br>> ><br>> > On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney<br>> > <[log in to unmask]> wrote:<br>> >><br>> >> Ah, yes. I couldn't find the email you were refferring to, but I've done<br>> >> it and it makes a huge difference. Pretty obvious really.<br>> >><br>> >> One more thing, John (or anybody else for that matter) - is there now a<br>> >> way in which one can easily create an inverse of the DARTEL normalise-to-MNI<br>> >> deformation composition (so from MNI --> study average --> individual<br>> >> subject space)? Is it possible (i.e., is there script) to have this (forward<br>> >> and/or backwards) written out for each subject or, alternatively, to have<br>> >> the affine template-to-TPM transform outputted such that one can combine it<br>> >> with a flow field, and subsequently inverse the composition using the<br>> >> deformations tool?<br>> >><br>> >> All the best,<br>> >><br>> >> Richard<br>> >><br>> >> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]><br>> >> wrote:<br>> >>><br>> >>> You may wish to change the voxel sizes and bounding box in order to<br>> >>> obtain slightly lower resolution versions (other than the default<br>> >>> 1.5mm isotropic with a large bounding box). See the email I sent out<br>> >>> recently.<br>> >>><br>> >>> Best regards,<br>> >>> -John<br>> >>><br>> >>> On 15 February 2011 17:29, Richard Binney <[log in to unmask]><br>> >>> wrote:<br>> >>> > Dear John (and other knowledgeable types),<br>> >>> ><br>> >>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise<br>> >>> > a<br>> >>> > functional MRI timeseries. That worked fine - the anatomical<br>> >>> > images that<br>> >>> > I additionally warped in this way look great - but I now cannot do<br>> >>> > anything<br>> >>> > with the timeseries.<br>> >>> ><br>> >>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp<br>> >>> > fMRI<br>> >>> > data can result in massive files which are problematic (and thus a work<br>> >>> > around is to apply the warp to contrast images), but I didn't expect<br>> >>> > the<br>> >>> > segment deformation field to do the same.<br>> >>> ><br>> >>> > Does this sound about right for a timeseries with 465 volumes (voxel<br>> >>> > dim 2.5<br>> >>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake<br>> >>> > somewhere?<br>> >>> ><br>> >>> > so I'm supposing my inability to use the warped timeseries is a memory<br>> >>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all<br>> >>> > this<br>> >>> > being used in the task manager so doesn't seem to be a matlab version<br>> >>> > problem) but maybe this file is just way too big for the SPM<br>> >>> > platform.....???? I could crop the image, etc I suppose but maybe the<br>> >>> > biggest problem is the shear number of volumes.<br>> >>> ><br>> >>> > Thanks in advance for your thoughts<br>> >>> ><br>> >>> > Richard<br>> >>> ><br>> >>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo<br>> >>> > <[log in to unmask]> wrote:<br>> >>> >><br>> >>> >> Thanks John.<br>> >>> >><br>> >>> >> It turns out the problem was due to the version of Matlab I was<br>> >>> >> running.<br>> >>> >><br>> >>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64.<br>> >>> >><br>> >>> >> Having set up with Matlab2010, the new segmentation with DARTEL import<br>> >>> >> now<br>> >>> >> runs without any problem, without me having to alter/crop any of the<br>> >>> >> images.<br>> >>> >><br>> >>> >> All the best<br>> >>> >> William<br>> >>> ><br>> >>> ><br>> >><br>> ><br>> ><br> </body>
</html>
--_fd2415e3-e3d7-48dc-a0ca-d5dba1603bcc_--
========================================================================Date: Wed, 16 Feb 2011 17:10:35 +0000
Reply-To: Richard Binney <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Richard Binney <[log in to unmask]>
Subject: Fwd: [SPM] New Segmentation Out of Memory Error
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundarye6ba6e85e6fd0d09049c695bdd
Message-ID: <[log in to unmask]>
--90e6ba6e85e6fd0d09049c695bdd
Content-Type: text/plain; charset=ISO-8859-1
---------- Forwarded message ----------
From: Richard Binney <[log in to unmask]>
Date: Wed, Feb 16, 2011 at 5:10 PM
Subject: Re: [SPM] New Segmentation Out of Memory Error
To: John Ashburner <[log in to unmask]>
Hi John,
Thanks for the quick response.
So for example - I may have ROIs defined on the basis of coordinates taken
from functional data or probabilistic anatomical maps in MNI space. Say I
wanted to use these ROIs for DWI tractography which is of course performed
in the native diffusion space, and so I would require a transform from the
MNI space to the T1 - which would of course have been coreg'd to the
diffusion dataset prior to any transform estimation (alternatively I could
use the b0 image but the T1 or multispectral classification is better). I
can easily do this with the other normalisation methods (e.g., segment and
new segment), however, for interpreting the tractography maps (say in
averaging to get group maps) I require the best possible inter-subject
registration available to me. I believe that is where DARTEL comes in.
So I would need an MNI-to-native option in DARTEL or a file containing the
affine transform that I can combine with the DARTEL flowfield and
subsequently invert using the deformations utility (I can't use the current
.mat file in 'deformations', can I?).
I had previously thought I would need this option for performing functional
connectivity (etc) analyses seeded from ROIs defined on the basis of
standard group-level (in MNI) GLM analyses. This was because I had been
performing fixed effects (1st level) analyses in subject space
and normalising the contrast images and I ideally wanted to use the same
transform in the forward and backwards direction of course. I was doing this
because I found that normalising the whole time-series with DARTEL resulted
in enormous files that SPM couldn't deal with at the stage of the GLM. I've
now of course (quite embarrasingly) realised with your help, that changing
the voxel size and bounding box size circumvents this issue. This may remain
an issue for people who find themselves with datasets larger than mine.
Other people may know of other potential applications beyond DWI
tractography......
Any thoughts?
Kind regards,
Richard
On Wed, Feb 16, 2011 at 3:58 PM, John Ashburner <[log in to unmask]>wrote:
> This file does indeed contain an affine transform, and is generated
> when the affine registration between population average and MNI space
> is estimated.
>
> There's no script to do what you're after yet, but if the demand is
> high enough I could introduce the option. Can I ask how you plan to
> use the transforms?
>
> Best regards,
> -John
>
>
> On 16 February 2011 15:44, Richard Binney <[log in to unmask]>
> wrote:
> > ......I've just spotted that running normalise-to-mni now spits out a
> > 'Template_6_2mni.mat' file. I don't recall seeing this before - would
> this
> > happen to be the affine transform I spoke of?
> >
> > R
> >
> > On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney
> > <[log in to unmask]> wrote:
> >>
> >> Ah, yes. I couldn't find the email you were refferring to, but I've done
> >> it and it makes a huge difference. Pretty obvious really.
> >>
> >> One more thing, John (or anybody else for that matter) - is there now a
> >> way in which one can easily create an inverse of the DARTEL
> normalise-to-MNI
> >> deformation composition (so from MNI --> study average --> individual
> >> subject space)? Is it possible (i.e., is there script) to have this
> (forward
> >> and/or backwards) written out for each subject or, alternatively, to
> have
> >> the affine template-to-TPM transform outputted such that one can combine
> it
> >> with a flow field, and subsequently inverse the composition using the
> >> deformations tool?
> >>
> >> All the best,
> >>
> >> Richard
> >>
> >> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]>
> >> wrote:
> >>>
> >>> You may wish to change the voxel sizes and bounding box in order to
> >>> obtain slightly lower resolution versions (other than the default
> >>> 1.5mm isotropic with a large bounding box). See the email I sent out
> >>> recently.
> >>>
> >>> Best regards,
> >>> -John
> >>>
> >>> On 15 February 2011 17:29, Richard Binney <
> [log in to unmask]>
> >>> wrote:
> >>> > Dear John (and other knowledgeable types),
> >>> >
> >>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to
> normalise
> >>> > a
> >>> > functional MRI timeseries. That worked fine - the anatomical
> >>> > images that
> >>> > I additionally warped in this way look great - but I now cannot do
> >>> > anything
> >>> > with the timeseries.
> >>> >
> >>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp
> >>> > fMRI
> >>> > data can result in massive files which are problematic (and thus a
> work
> >>> > around is to apply the warp to contrast images), but I didn't expect
> >>> > the
> >>> > segment deformation field to do the same.
> >>> >
> >>> > Does this sound about right for a timeseries with 465 volumes (voxel
> >>> > dim 2.5
> >>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake
> >>> > somewhere?
> >>> >
> >>> > so I'm supposing my inability to use the warped timeseries is a
> memory
> >>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all
> >>> > this
> >>> > being used in the task manager so doesn't seem to be a matlab version
> >>> > problem) but maybe this file is just way too big for the SPM
> >>> > platform.....???? I could crop the image, etc I suppose but maybe the
> >>> > biggest problem is the shear number of volumes.
> >>> >
> >>> > Thanks in advance for your thoughts
> >>> >
> >>> > Richard
> >>> >
> >>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo
> >>> > <[log in to unmask]> wrote:
> >>> >>
> >>> >> Thanks John.
> >>> >>
> >>> >> It turns out the problem was due to the version of Matlab I was
> >>> >> running.
> >>> >>
> >>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64.
> >>> >>
> >>> >> Having set up with Matlab2010, the new segmentation with DARTEL
> import
> >>> >> now
> >>> >> runs without any problem, without me having to alter/crop any of the
> >>> >> images.
> >>> >>
> >>> >> All the best
> >>> >> William
> >>> >
> >>> >
> >>
> >
> >
>
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<br><br>
<div class="gmail_quote">---------- Forwarded message ----------<br>From: <b class="gmail_sendername">Richard Binney</b> <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span><br>
Date: Wed, Feb 16, 2011 at 5:10 PM<br>Subject: Re: [SPM] New Segmentation Out of Memory Error<br>To: John Ashburner <<a href="mailto:[log in to unmask]">[log in to unmask]</a>><br><br><br>
<div>Hi John,</div>
<div> </div>
<div>Thanks for the quick response.</div>
<div> </div>
<div>So for example - I may have ROIs defined on the basis of coordinates taken from functional data or probabilistic anatomical maps in MNI space. Say I wanted to use these ROIs for DWI tractography which is of course performed in the native diffusion space, and so I would require a transform from the MNI space to the T1 - which would of course have been coreg'd to the diffusion dataset prior to any transform estimation (alternatively I could use the b0 image but the T1 or multispectral classification is better). I can easily do this with the other normalisation methods (e.g., segment and new segment), however, for interpreting the tractography maps (say in averaging to get group maps) I require the best possible inter-subject registration available to me. I believe that is where DARTEL comes in.</div>
<div> </div>
<div>So I would need an MNI-to-native option in DARTEL or a file containing the affine transform that I can combine with the DARTEL flowfield and subsequently invert using the deformations utility (I can't use the current .mat file in 'deformations', can I?).</div>
<div> </div>
<div>I had previously thought I would need this option for performing functional connectivity (etc) analyses seeded from ROIs defined on the basis of standard group-level (in MNI) GLM analyses. This was because I had been performing fixed effects (1st level) analyses in subject space and normalising the contrast images and I ideally wanted to use the same transform in the forward and backwards direction of course. I was doing this because I found that normalising the whole time-series with DARTEL resulted in enormous files that SPM couldn't deal with at the stage of the GLM. I've now of course (quite embarrasingly) realised with your help, that changing the voxel size and bounding box size circumvents this issue. This may remain an issue for people who find themselves with datasets larger than mine.</div>
<div> </div>
<div>Other people may know of other potential applications beyond DWI tractography......</div>
<div> </div>
<div>Any thoughts?</div>
<div> </div>
<div>Kind regards,</div>
<div> </div><font color="#888888">
<div>Richard</div></font>
<div>
<div></div>
<div class="h5">
<div> </div>
<div class="gmail_quote">On Wed, Feb 16, 2011 at 3:58 PM, John Ashburner <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="PADDING-LEFT: 1ex; MARGIN: 0px 0px 0px 0.8ex; BORDER-LEFT: #ccc 1px solid">This file does indeed contain an affine transform, and is generated<br>when the affine registration between population average and MNI space<br>
is estimated.<br><br>There's no script to do what you're after yet, but if the demand is<br>high enough I could introduce the option. Can I ask how you plan to<br>use the transforms?<br><br>Best regards,<br><font color="#888888">-John<br>
</font>
<div>
<div></div>
<div><br><br>On 16 February 2011 15:44, Richard Binney <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>> wrote:<br>> ......I've just spotted that running normalise-to-mni now spits out a<br>
> 'Template_6_2mni.mat' file. I don't recall seeing this before - would this<br>> happen to be the affine transform I spoke of?<br>><br>> R<br>><br>> On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney<br>
> <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>> wrote:<br>>><br>>> Ah, yes. I couldn't find the email you were refferring to, but I've done<br>
>> it and it makes a huge difference. Pretty obvious really.<br>>><br>>> One more thing, John (or anybody else for that matter) - is there now a<br>>> way in which one can easily create an inverse of the DARTEL normalise-to-MNI<br>
>> deformation composition (so from MNI --> study average --> individual<br>>> subject space)? Is it possible (i.e., is there script) to have this (forward<br>>> and/or backwards) written out for each subject or, alternatively, to have<br>
>> the affine template-to-TPM transform outputted such that one can combine it<br>>> with a flow field, and subsequently inverse the composition using the<br>>> deformations tool?<br>>><br>>> All the best,<br>
>><br>>> Richard<br>>><br>>> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>><br>>> wrote:<br>>>><br>
>>> You may wish to change the voxel sizes and bounding box in order to<br>>>> obtain slightly lower resolution versions (other than the default<br>>>> 1.5mm isotropic with a large bounding box). See the email I sent out<br>
>>> recently.<br>>>><br>>>> Best regards,<br>>>> -John<br>>>><br>>>> On 15 February 2011 17:29, Richard Binney <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>><br>
>>> wrote:<br>>>> > Dear John (and other knowledgeable types),<br>>>> ><br>>>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise<br>>>> > a<br>
>>> > functional MRI timeseries. That worked fine - the anatomical<br>>>> > images that<br>>>> > I additionally warped in this way look great - but I now cannot do<br>>>> > anything<br>
>>> > with the timeseries.<br>>>> ><br>>>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp<br>>>> > fMRI<br>>>> > data can result in massive files which are problematic (and thus a work<br>
>>> > around is to apply the warp to contrast images), but I didn't expect<br>>>> > the<br>>>> > segment deformation field to do the same.<br>>>> ><br>>>> > Does this sound about right for a timeseries with 465 volumes (voxel<br>
>>> > dim 2.5<br>>>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake<br>>>> > somewhere?<br>>>> ><br>>>> > so I'm supposing my inability to use the warped timeseries is a memory<br>
>>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all<br>>>> > this<br>>>> > being used in the task manager so doesn't seem to be a matlab version<br>>>> > problem) but maybe this file is just way too big for the SPM<br>
>>> > platform.....???? I could crop the image, etc I suppose but maybe the<br>>>> > biggest problem is the shear number of volumes.<br>>>> ><br>>>> > Thanks in advance for your thoughts<br>
>>> ><br>>>> > Richard<br>>>> ><br>>>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo<br>>>> > <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>> wrote:<br>
>>> >><br>>>> >> Thanks John.<br>>>> >><br>>>> >> It turns out the problem was due to the version of Matlab I was<br>>>> >> running.<br>>>> >><br>
>>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64.<br>>>> >><br>>>> >> Having set up with Matlab2010, the new segmentation with DARTEL import<br>>>> >> now<br>
>>> >> runs without any problem, without me having to alter/crop any of the<br>>>> >> images.<br>>>> >><br>>>> >> All the best<br>>>> >> William<br>
>>> ><br>>>> ><br>>><br>><br>><br></div></div></blockquote></div><br></div></div></div><br>
--90e6ba6e85e6fd0d09049c695bdd--
========================================================================Date: Wed, 16 Feb 2011 17:15:28 +0000
Reply-To: Richard Binney <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Richard Binney <[log in to unmask]>
Subject: Re: New Segmentation Out of Memory Error
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundarye6ba6e8b88716639049c696db4
Message-ID: <[log in to unmask]>
--90e6ba6e8b88716639049c696db4
Content-Type: text/plain; charset=ISO-8859-1
Dear Michel,
I updated my SPM8 yesterday - today is the first time I have seen this file.
I ran DARTEL's normalise-to-MNI in the usual way and this file was stored to
disk permanently.
Richard
On Wed, Feb 16, 2011 at 5:10 PM, Richard Binney <
[log in to unmask]> wrote:
>
>
> ---------- Forwarded message ----------
> From: Richard Binney <[log in to unmask]>
> Date: Wed, Feb 16, 2011 at 5:10 PM
> Subject: Re: [SPM] New Segmentation Out of Memory Error
> To: John Ashburner <[log in to unmask]>
>
>
> Hi John,
>
> Thanks for the quick response.
>
> So for example - I may have ROIs defined on the basis of coordinates taken
> from functional data or probabilistic anatomical maps in MNI space. Say I
> wanted to use these ROIs for DWI tractography which is of course performed
> in the native diffusion space, and so I would require a transform from the
> MNI space to the T1 - which would of course have been coreg'd to the
> diffusion dataset prior to any transform estimation (alternatively I could
> use the b0 image but the T1 or multispectral classification is better). I
> can easily do this with the other normalisation methods (e.g., segment and
> new segment), however, for interpreting the tractography maps (say in
> averaging to get group maps) I require the best possible inter-subject
> registration available to me. I believe that is where DARTEL comes in.
>
> So I would need an MNI-to-native option in DARTEL or a file containing the
> affine transform that I can combine with the DARTEL flowfield and
> subsequently invert using the deformations utility (I can't use the current
> .mat file in 'deformations', can I?).
>
> I had previously thought I would need this option for performing functional
> connectivity (etc) analyses seeded from ROIs defined on the basis of
> standard group-level (in MNI) GLM analyses. This was because I had been
> performing fixed effects (1st level) analyses in subject space
> and normalising the contrast images and I ideally wanted to use the same
> transform in the forward and backwards direction of course. I was doing this
> because I found that normalising the whole time-series with DARTEL resulted
> in enormous files that SPM couldn't deal with at the stage of the GLM. I've
> now of course (quite embarrasingly) realised with your help, that changing
> the voxel size and bounding box size circumvents this issue. This may remain
> an issue for people who find themselves with datasets larger than mine.
>
> Other people may know of other potential applications beyond DWI
> tractography......
>
> Any thoughts?
>
> Kind regards,
>
> Richard
>
> On Wed, Feb 16, 2011 at 3:58 PM, John Ashburner <[log in to unmask]>wrote:
>
>> This file does indeed contain an affine transform, and is generated
>> when the affine registration between population average and MNI space
>> is estimated.
>>
>> There's no script to do what you're after yet, but if the demand is
>> high enough I could introduce the option. Can I ask how you plan to
>> use the transforms?
>>
>> Best regards,
>> -John
>>
>>
>> On 16 February 2011 15:44, Richard Binney <[log in to unmask]>
>> wrote:
>> > ......I've just spotted that running normalise-to-mni now spits out a
>> > 'Template_6_2mni.mat' file. I don't recall seeing this before - would
>> this
>> > happen to be the affine transform I spoke of?
>> >
>> > R
>> >
>> > On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney
>> > <[log in to unmask]> wrote:
>> >>
>> >> Ah, yes. I couldn't find the email you were refferring to, but I've
>> done
>> >> it and it makes a huge difference. Pretty obvious really.
>> >>
>> >> One more thing, John (or anybody else for that matter) - is there now a
>> >> way in which one can easily create an inverse of the DARTEL
>> normalise-to-MNI
>> >> deformation composition (so from MNI --> study average --> individual
>> >> subject space)? Is it possible (i.e., is there script) to have this
>> (forward
>> >> and/or backwards) written out for each subject or, alternatively, to
>> have
>> >> the affine template-to-TPM transform outputted such that one can
>> combine it
>> >> with a flow field, and subsequently inverse the composition using the
>> >> deformations tool?
>> >>
>> >> All the best,
>> >>
>> >> Richard
>> >>
>> >> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]>
>> >> wrote:
>> >>>
>> >>> You may wish to change the voxel sizes and bounding box in order to
>> >>> obtain slightly lower resolution versions (other than the default
>> >>> 1.5mm isotropic with a large bounding box). See the email I sent out
>> >>> recently.
>> >>>
>> >>> Best regards,
>> >>> -John
>> >>>
>> >>> On 15 February 2011 17:29, Richard Binney <
>> [log in to unmask]>
>> >>> wrote:
>> >>> > Dear John (and other knowledgeable types),
>> >>> >
>> >>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to
>> normalise
>> >>> > a
>> >>> > functional MRI timeseries. That worked fine - the anatomical
>> >>> > images that
>> >>> > I additionally warped in this way look great - but I now cannot do
>> >>> > anything
>> >>> > with the timeseries.
>> >>> >
>> >>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp
>> >>> > fMRI
>> >>> > data can result in massive files which are problematic (and thus a
>> work
>> >>> > around is to apply the warp to contrast images), but I didn't expect
>> >>> > the
>> >>> > segment deformation field to do the same.
>> >>> >
>> >>> > Does this sound about right for a timeseries with 465 volumes (voxel
>> >>> > dim 2.5
>> >>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake
>> >>> > somewhere?
>> >>> >
>> >>> > so I'm supposing my inability to use the warped timeseries is a
>> memory
>> >>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all
>> >>> > this
>> >>> > being used in the task manager so doesn't seem to be a matlab
>> version
>> >>> > problem) but maybe this file is just way too big for the SPM
>> >>> > platform.....???? I could crop the image, etc I suppose but maybe
>> the
>> >>> > biggest problem is the shear number of volumes.
>> >>> >
>> >>> > Thanks in advance for your thoughts
>> >>> >
>> >>> > Richard
>> >>> >
>> >>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo
>> >>> > <[log in to unmask]> wrote:
>> >>> >>
>> >>> >> Thanks John.
>> >>> >>
>> >>> >> It turns out the problem was due to the version of Matlab I was
>> >>> >> running.
>> >>> >>
>> >>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64.
>> >>> >>
>> >>> >> Having set up with Matlab2010, the new segmentation with DARTEL
>> import
>> >>> >> now
>> >>> >> runs without any problem, without me having to alter/crop any of
>> the
>> >>> >> images.
>> >>> >>
>> >>> >> All the best
>> >>> >> William
>> >>> >
>> >>> >
>> >>
>> >
>> >
>>
>
>
>
--90e6ba6e8b88716639049c696db4
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
<div>Dear Michel,</div>
<div> </div>
<div>I updated my SPM8 yesterday - today is the first time I have seen this file. I ran DARTEL's normalise-to-MNI in the usual way and this file was stored to disk permanently.</div>
<div> </div>
<div>Richard</div>
<div><br> </div>
<div class="gmail_quote">On Wed, Feb 16, 2011 at 5:10 PM, Richard Binney <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="PADDING-LEFT: 1ex; MARGIN: 0px 0px 0px 0.8ex; BORDER-LEFT: #ccc 1px solid"><br><br>
<div class="gmail_quote">
<div class="im">---------- Forwarded message ----------<br>From: <b class="gmail_sendername">Richard Binney</b> <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span><br>
Date: Wed, Feb 16, 2011 at 5:10 PM<br>Subject: Re: [SPM] New Segmentation Out of Memory Error<br></div>
<div>
<div></div>
<div class="h5">To: John Ashburner <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>><br><br><br>
<div>Hi John,</div>
<div> </div>
<div>Thanks for the quick response.</div>
<div> </div>
<div>So for example - I may have ROIs defined on the basis of coordinates taken from functional data or probabilistic anatomical maps in MNI space. Say I wanted to use these ROIs for DWI tractography which is of course performed in the native diffusion space, and so I would require a transform from the MNI space to the T1 - which would of course have been coreg'd to the diffusion dataset prior to any transform estimation (alternatively I could use the b0 image but the T1 or multispectral classification is better). I can easily do this with the other normalisation methods (e.g., segment and new segment), however, for interpreting the tractography maps (say in averaging to get group maps) I require the best possible inter-subject registration available to me. I believe that is where DARTEL comes in.</div>
<div> </div>
<div>So I would need an MNI-to-native option in DARTEL or a file containing the affine transform that I can combine with the DARTEL flowfield and subsequently invert using the deformations utility (I can't use the current .mat file in 'deformations', can I?).</div>
<div> </div>
<div>I had previously thought I would need this option for performing functional connectivity (etc) analyses seeded from ROIs defined on the basis of standard group-level (in MNI) GLM analyses. This was because I had been performing fixed effects (1st level) analyses in subject space and normalising the contrast images and I ideally wanted to use the same transform in the forward and backwards direction of course. I was doing this because I found that normalising the whole time-series with DARTEL resulted in enormous files that SPM couldn't deal with at the stage of the GLM. I've now of course (quite embarrasingly) realised with your help, that changing the voxel size and bounding box size circumvents this issue. This may remain an issue for people who find themselves with datasets larger than mine.</div>
<div> </div>
<div>Other people may know of other potential applications beyond DWI tractography......</div>
<div> </div>
<div>Any thoughts?</div>
<div> </div>
<div>Kind regards,</div>
<div> </div><font color="#888888">
<div>Richard</div></font>
<div>
<div></div>
<div>
<div> </div>
<div class="gmail_quote">On Wed, Feb 16, 2011 at 3:58 PM, John Ashburner <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="PADDING-LEFT: 1ex; MARGIN: 0px 0px 0px 0.8ex; BORDER-LEFT: #ccc 1px solid">This file does indeed contain an affine transform, and is generated<br>when the affine registration between population average and MNI space<br>
is estimated.<br><br>There's no script to do what you're after yet, but if the demand is<br>high enough I could introduce the option. Can I ask how you plan to<br>use the transforms?<br><br>Best regards,<br><font color="#888888">-John<br>
</font>
<div>
<div></div>
<div><br><br>On 16 February 2011 15:44, Richard Binney <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>> wrote:<br>> ......I've just spotted that running normalise-to-mni now spits out a<br>
> 'Template_6_2mni.mat' file. I don't recall seeing this before - would this<br>> happen to be the affine transform I spoke of?<br>><br>> R<br>><br>> On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney<br>
> <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>> wrote:<br>>><br>>> Ah, yes. I couldn't find the email you were refferring to, but I've done<br>
>> it and it makes a huge difference. Pretty obvious really.<br>>><br>>> One more thing, John (or anybody else for that matter) - is there now a<br>>> way in which one can easily create an inverse of the DARTEL normalise-to-MNI<br>
>> deformation composition (so from MNI --> study average --> individual<br>>> subject space)? Is it possible (i.e., is there script) to have this (forward<br>>> and/or backwards) written out for each subject or, alternatively, to have<br>
>> the affine template-to-TPM transform outputted such that one can combine it<br>>> with a flow field, and subsequently inverse the composition using the<br>>> deformations tool?<br>>><br>>> All the best,<br>
>><br>>> Richard<br>>><br>>> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>><br>>> wrote:<br>>>><br>
>>> You may wish to change the voxel sizes and bounding box in order to<br>>>> obtain slightly lower resolution versions (other than the default<br>>>> 1.5mm isotropic with a large bounding box). See the email I sent out<br>
>>> recently.<br>>>><br>>>> Best regards,<br>>>> -John<br>>>><br>>>> On 15 February 2011 17:29, Richard Binney <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>><br>
>>> wrote:<br>>>> > Dear John (and other knowledgeable types),<br>>>> ><br>>>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to normalise<br>>>> > a<br>
>>> > functional MRI timeseries. That worked fine - the anatomical<br>>>> > images that<br>>>> > I additionally warped in this way look great - but I now cannot do<br>>>> > anything<br>
>>> > with the timeseries.<br>>>> ><br>>>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp<br>>>> > fMRI<br>>>> > data can result in massive files which are problematic (and thus a work<br>
>>> > around is to apply the warp to contrast images), but I didn't expect<br>>>> > the<br>>>> > segment deformation field to do the same.<br>>>> ><br>>>> > Does this sound about right for a timeseries with 465 volumes (voxel<br>
>>> > dim 2.5<br>>>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake<br>>>> > somewhere?<br>>>> ><br>>>> > so I'm supposing my inability to use the warped timeseries is a memory<br>
>>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all<br>>>> > this<br>>>> > being used in the task manager so doesn't seem to be a matlab version<br>>>> > problem) but maybe this file is just way too big for the SPM<br>
>>> > platform.....???? I could crop the image, etc I suppose but maybe the<br>>>> > biggest problem is the shear number of volumes.<br>>>> ><br>>>> > Thanks in advance for your thoughts<br>
>>> ><br>>>> > Richard<br>>>> ><br>>>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo<br>>>> > <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>> wrote:<br>
>>> >><br>>>> >> Thanks John.<br>>>> >><br>>>> >> It turns out the problem was due to the version of Matlab I was<br>>>> >> running.<br>>>> >><br>
>>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64.<br>>>> >><br>>>> >> Having set up with Matlab2010, the new segmentation with DARTEL import<br>>>> >> now<br>
>>> >> runs without any problem, without me having to alter/crop any of the<br>>>> >> images.<br>>>> >><br>>>> >> All the best<br>>>> >> William<br>
>>> ><br>>>> ><br>>><br>><br>><br></div></div></blockquote></div><br></div></div></div></div></div><br></blockquote></div><br>
--90e6ba6e8b88716639049c696db4--
========================================================================Date: Wed, 16 Feb 2011 17:16:00 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: New Segmentation Out of Memory Error
Comments: To: michel grothe <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
> I haven´t seen such a file in my version of SPM8 (the penultimate update,
> run on win32). Is it possible that this file is just generated during
> estimation of the affine transform and not stored permanently on disk? Or
> does it come with the latest update?
This change occurred in SPM8 r3727, so should be in the current
updates, which are to r4010.
> It would be very helpful to have such a file, mainly for the reasons Richard
> mentioned, i.e. use it to compose the complete transform
> 'native->DARTEL-mean->MNI'. The inverse of such a composition could be used
> to warp Atlas-labels in MNI space to native space.
There's no easy way to use the file yet, but I'll try to code
something up for a future version of SPM8.
Best regards,
-John
>> Date: Wed, 16 Feb 2011 15:58:16 +0000
>> From: [log in to unmask]
>> Subject: Re: [SPM] New Segmentation Out of Memory Error
>> To: [log in to unmask]
>>
>> This file does indeed contain an affine transform, and is generated
>> when the affine registration between population average and MNI space
>> is estimated.
>>
>> There's no script to do what you're after yet, but if the demand is
>> high enough I could introduce the option. Can I ask how you plan to
>> use the transforms?
>>
>> Best regards,
>> -John
>>
>>
>> On 16 February 2011 15:44, Richard Binney <[log in to unmask]>
>> wrote:
>> > ......I've just spotted that running normalise-to-mni now spits out a
>> > 'Template_6_2mni.mat' file. I don't recall seeing this before - would
>> > this
>> > happen to be the affine transform I spoke of?
>> >
>> > R
>> >
>> > On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney
>> > <[log in to unmask]> wrote:
>> >>
>> >> Ah, yes. I couldn't find the email you were refferring to, but I've
>> >> done
>> >> it and it makes a huge difference. Pretty obvious really.
>> >>
>> >> One more thing, John (or anybody else for that matter) - is there now a
>> >> way in which one can easily create an inverse of the DARTEL
>> >> normalise-to-MNI
>> >> deformation composition (so from MNI --> study average --> individual
>> >> subject space)? Is it possible (i.e., is there script) to have this
>> >> (forward
>> >> and/or backwards) written out for each subject or, alternatively, to
>> >> have
>> >> the affine template-to-TPM transform outputted such that one can
>> >> combine it
>> >> with a flow field, and subsequently inverse the composition using the
>> >> deformations tool?
>> >>
>> >> All the best,
>> >>
>> >> Richard
>> >>
>> >> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]>
>> >> wrote:
>> >>>
>> >>> You may wish to change the voxel sizes and bounding box in order to
>> >>> obtain slightly lower resolution versions (other than the default
>> >>> 1.5mm isotropic with a large bounding box). See the email I sent out
>> >>> recently.
>> >>>
>> >>> Best regards,
>> >>> -John
>> >>>
>> >>> On 15 February 2011 17:29, Richard Binney
>> >>> <[log in to unmask]>
>> >>> wrote:
>> >>> > Dear John (and other knowledgeable types),
>> >>> >
>> >>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to
>> >>> > normalise
>> >>> > a
>> >>> > functional MRI timeseries. That worked fine - the anatomical
>> >>> > images that
>> >>> > I additionally warped in this way look great - but I now cannot do
>> >>> > anything
>> >>> > with the timeseries.
>> >>> >
>> >>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp
>> >>> > fMRI
>> >>> > data can result in massive files which are problematic (and thus a
>> >>> > work
>> >>> > around is to apply the warp to contrast images), but I didn't expect
>> >>> > the
>> >>> > segment deformation field to do the same.
>> >>> >
>> >>> > Does this sound about right for a timeseries with 465 volumes (voxel
>> >>> > dim 2.5
>> >>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake
>> >>> > somewhere?
>> >>> >
>> >>> > so I'm supposing my inability to use the warped timeseries is a
>> >>> > memory
>> >>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all
>> >>> > this
>> >>> > being used in the task manager so doesn't seem to be a matlab
>> >>> > version
>> >>> > problem) but maybe this file is just way too big for the SPM
>> >>> > platform.....???? I could crop the image, etc I suppose but maybe
>> >>> > the
>> >>> > biggest problem is the shear number of volumes.
>> >>> >
>> >>> > Thanks in advance for your thoughts
>> >>> >
>> >>> > Richard
>> >>> >
>> >>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo
>> >>> > <[log in to unmask]> wrote:
>> >>> >>
>> >>> >> Thanks John.
>> >>> >>
>> >>> >> It turns out the problem was due to the version of Matlab I was
>> >>> >> running.
>> >>> >>
>> >>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64.
>> >>> >>
>> >>> >> Having set up with Matlab2010, the new segmentation with DARTEL
>> >>> >> import
>> >>> >> now
>> >>> >> runs without any problem, without me having to alter/crop any of
>> >>> >> the
>> >>> >> images.
>> >>> >>
>> >>> >> All the best
>> >>> >> William
>> >>> >
>> >>> >
>> >>
>> >
>> >
>
========================================================================Date: Wed, 16 Feb 2011 11:24:12 -0600
Reply-To: Prabhakaran Vivek <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Prabhakaran Vivek <[log in to unmask]>
Subject: Postdoctoral position in neuroimaging applications in
translational and clinical research
MIME-Version: 1.0
Content-Type: text/plain; charset="iso-8859-1"
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
PhDs and/or MDs who are U.S. Citizens or Permanent Residents
interested in postdoctoral traineeships in the Computation and Informatics in Biology and Medicine (CIBM) Training Program, funded by the NLM Grant 5T15LM007359 are asked to apply.
The postdoctoral traineeship is for a one- to
two-year period. One is to begin by June, 2011, and the other to begin
Summer 2011 or later. (The last year is contingent on renewal of the NLM
Grant supporting the CIBM Training Program.)
These traineeships are designed for cross-disciplinary training - the candidate will have a secondary mentor in addition to the
primary mentor (one from computational / informatics areas, and the other
from biological areas).
If interested in a postdoctoral position in neuroimaging applications in translational and clinical research,
please email a CV to Vivek Prabhakaran M.D., Ph.D.,
Director of Functional Neuroimaging in Radiology.
Vivek Prabhakaran M.D., Ph.D.
Assistant Professor, Director of Functional Neuroimaging in Radiology
UWHealth UW Hospital and Clinics University of Wisconsin-Madison
Department of Radiology HealthEmotions Research Institute
600 Highland Avenue 6001 Research Park Blvd rm 1003
Madison, WI 53792 Madison, WI 53719
Office: 608-265-5269 Office: 608-232-3342
========================================================================Date: Wed, 16 Feb 2011 13:51:45 -0500
Reply-To: cliff <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: cliff <[log in to unmask]>
Subject: T1 & functional image reorigin
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--000e0cd6a8ccc443dd049c6ac5ce
Content-Type: text/plain; charset=ISO-8859-1
Now I always use SPM99's Head-edit funtion to do these stuff (set the origin
first and then apply to images).
In this way, the new origin can be read correctly both in SPM99 and SPM8.
However, it is quite a boring job.
Is there any independent funtion in SPM that I can use to reorigin images in
a modified batch way?
Or is there any assistant toolbox I can use?
Thanks!
--000e0cd6a8ccc443dd049c6ac5ce
Content-Type: text/html; charset=ISO-8859-1
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<div>Now I always use SPM99's Head-edit funtion to do these stuff (set the origin first and then apply to images).</div>
<div>In this way, the new origin can be read correctly both in SPM99 and SPM8.</div>
<div>However, it is quite a boring job.</div>
<div> </div>
<div>Is there any independent funtion in SPM that I can use to reorigin images in a modified batch way?</div>
<div>Or is there any assistant toolbox I can use?</div>
<div> </div>
<div>Thanks!</div>
--000e0cd6a8ccc443dd049c6ac5ce--
========================================================================Date: Wed, 16 Feb 2011 20:45:14 +0100
Reply-To: michel grothe <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: michel grothe <[log in to unmask]>
Subject: Re: New Segmentation Out of Memory Error
Comments: To: [log in to unmask]
In-Reply-To: <[log in to unmask]>
Content-Type: multipart/alternative;
boundary="_d114b127-7ddd-48ec-b028-6c3509a53daf_"
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--_d114b127-7ddd-48ec-b028-6c3509a53daf_
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Content-Transfer-Encoding: quoted-printable
Thanks, this would be great.
Meanwhile, I have some questions regarding a possible work-around:
Would it be OK to use an affine transform calculated with the
Normalise-Button to approximate the affine transform from
'DARTEL-Normalise to MNI' in order to be able to generate the mentioned
composition 'native->DARTEL->MNI'. Does the affine alignment step
in 'DARTEL-Normalise to MNI' register the DARTEL-GM template to the
GM-apriori map (or the GM map in TPM.nii,1?) or does it also use the
respective WM-maps?
How much does the affine alignment in 'DARTEL-Normalise to MNI' differ
from the one in 'Normalise', when registering DARTEL-GM to GM-apriori map (or TPM.nii,1) and setting non-linear iterations to 0?
Thanks again for your help.
Best regards,
Michel
> Date: Wed, 16 Feb 2011 17:16:00 +0000
> From: [log in to unmask]
> Subject: Re: [SPM] New Segmentation Out of Memory Error
> To: [log in to unmask]
>
> > I haven´t seen such a file in my version of SPM8 (the penultimate update,
> > run on win32). Is it possible that this file is just generated during
> > estimation of the affine transform and not stored permanently on disk? Or
> > does it come with the latest update?
>
> This change occurred in SPM8 r3727, so should be in the current
> updates, which are to r4010.
>
>
> > It would be very helpful to have such a file, mainly for the reasons Richard
> > mentioned, i.e. use it to compose the complete transform
> > 'native->DARTEL-mean->MNI'. The inverse of such a composition could be used
> > to warp Atlas-labels in MNI space to native space.
>
> There's no easy way to use the file yet, but I'll try to code
> something up for a future version of SPM8.
>
> Best regards,
> -John
>
>
> >> Date: Wed, 16 Feb 2011 15:58:16 +0000
> >> From: [log in to unmask]
> >> Subject: Re: [SPM] New Segmentation Out of Memory Error
> >> To: [log in to unmask]
> >>
> >> This file does indeed contain an affine transform, and is generated
> >> when the affine registration between population average and MNI space
> >> is estimated.
> >>
> >> There's no script to do what you're after yet, but if the demand is
> >> high enough I could introduce the option. Can I ask how you plan to
> >> use the transforms?
> >>
> >> Best regards,
> >> -John
> >>
> >>
> >> On 16 February 2011 15:44, Richard Binney <[log in to unmask]>
> >> wrote:
> >> > ......I've just spotted that running normalise-to-mni now spits out a
> >> > 'Template_6_2mni.mat' file. I don't recall seeing this before - would
> >> > this
> >> > happen to be the affine transform I spoke of?
> >> >
> >> > R
> >> >
> >> > On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney
> >> > <[log in to unmask]> wrote:
> >> >>
> >> >> Ah, yes. I couldn't find the email you were refferring to, but I've
> >> >> done
> >> >> it and it makes a huge difference. Pretty obvious really.
> >> >>
> >> >> One more thing, John (or anybody else for that matter) - is there now a
> >> >> way in which one can easily create an inverse of the DARTEL
> >> >> normalise-to-MNI
> >> >> deformation composition (so from MNI --> study average --> individual
> >> >> subject space)? Is it possible (i.e., is there script) to have this
> >> >> (forward
> >> >> and/or backwards) written out for each subject or, alternatively, to
> >> >> have
> >> >> the affine template-to-TPM transform outputted such that one can
> >> >> combine it
> >> >> with a flow field, and subsequently inverse the composition using the
> >> >> deformations tool?
> >> >>
> >> >> All the best,
> >> >>
> >> >> Richard
> >> >>
> >> >> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]>
> >> >> wrote:
> >> >>>
> >> >>> You may wish to change the voxel sizes and bounding box in order to
> >> >>> obtain slightly lower resolution versions (other than the default
> >> >>> 1.5mm isotropic with a large bounding box). See the email I sent out
> >> >>> recently.
> >> >>>
> >> >>> Best regards,
> >> >>> -John
> >> >>>
> >> >>> On 15 February 2011 17:29, Richard Binney
> >> >>> <[log in to unmask]>
> >> >>> wrote:
> >> >>> > Dear John (and other knowledgeable types),
> >> >>> >
> >> >>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to
> >> >>> > normalise
> >> >>> > a
> >> >>> > functional MRI timeseries. That worked fine - the anatomical
> >> >>> > images that
> >> >>> > I additionally warped in this way look great - but I now cannot do
> >> >>> > anything
> >> >>> > with the timeseries.
> >> >>> >
> >> >>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp
> >> >>> > fMRI
> >> >>> > data can result in massive files which are problematic (and thus a
> >> >>> > work
> >> >>> > around is to apply the warp to contrast images), but I didn't expect
> >> >>> > the
> >> >>> > segment deformation field to do the same.
> >> >>> >
> >> >>> > Does this sound about right for a timeseries with 465 volumes (voxel
> >> >>> > dim 2.5
> >> >>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake
> >> >>> > somewhere?
> >> >>> >
> >> >>> > so I'm supposing my inability to use the warped timeseries is a
> >> >>> > memory
> >> >>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all
> >> >>> > this
> >> >>> > being used in the task manager so doesn't seem to be a matlab
> >> >>> > version
> >> >>> > problem) but maybe this file is just way too big for the SPM
> >> >>> > platform.....???? I could crop the image, etc I suppose but maybe
> >> >>> > the
> >> >>> > biggest problem is the shear number of volumes.
> >> >>> >
> >> >>> > Thanks in advance for your thoughts
> >> >>> >
> >> >>> > Richard
> >> >>> >
> >> >>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo
> >> >>> > <[log in to unmask]> wrote:
> >> >>> >>
> >> >>> >> Thanks John.
> >> >>> >>
> >> >>> >> It turns out the problem was due to the version of Matlab I was
> >> >>> >> running.
> >> >>> >>
> >> >>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64.
> >> >>> >>
> >> >>> >> Having set up with Matlab2010, the new segmentation with DARTEL
> >> >>> >> import
> >> >>> >> now
> >> >>> >> runs without any problem, without me having to alter/crop any of
> >> >>> >> the
> >> >>> >> images.
> >> >>> >>
> >> >>> >> All the best
> >> >>> >> William
> >> >>> >
> >> >>> >
> >> >>
> >> >
> >> >
> >
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Thanks, this would be great.<br>
<br>
Meanwhile, I have some questions regarding a possible work-around:<br>
<br>
Would it be OK to use an affine transform calculated with the
Normalise-Button to approximate the affine transform from
'DARTEL-Normalise to MNI' in order to be able to generate the mentioned
composition 'native->DARTEL->MNI'. Does the affine alignment step
in 'DARTEL-Normalise to MNI' register the DARTEL-GM template to the
GM-apriori map (or the GM map in TPM.nii,1?) or does it also use the
respective WM-maps?<br>
How much does the affine alignment in 'DARTEL-Normalise to MNI' differ
from the one in 'Normalise', when registering DARTEL-GM to GM-apriori map (or TPM.nii,1) and setting non-linear iterations to 0?<br>
<br>
Thanks again for your help.<br>
Best regards,<br>
Michel<br><br>> Date: Wed, 16 Feb 2011 17:16:00 +0000<br>> From: [log in to unmask]<br>> Subject: Re: [SPM] New Segmentation Out of Memory Error<br>> To: [log in to unmask]<br>> <br>> > I haven´t seen such a file in my version of SPM8 (the penultimate update,<br>> > run on win32). Is it possible that this file is just generated during<br>> > estimation of the affine transform and not stored permanently on disk? Or<br>> > does it come with the latest update?<br>> <br>> This change occurred in SPM8 r3727, so should be in the current<br>> updates, which are to r4010.<br>> <br>> <br>> > It would be very helpful to have such a file, mainly for the reasons Richard<br>> > mentioned, i.e. use it to compose the complete transform<br>> > 'native->DARTEL-mean->MNI'. The inverse of such a composition could be used<br>> > to warp Atlas-labels in MNI space to native space.<br>> <br>> There's no easy way to use the file yet, but I'll try to code<br>> something up for a future version of SPM8.<br>> <br>> Best regards,<br>> -John<br>> <br>> <br>> >> Date: Wed, 16 Feb 2011 15:58:16 +0000<br>> >> From: [log in to unmask]<br>> >> Subject: Re: [SPM] New Segmentation Out of Memory Error<br>> >> To: [log in to unmask]<br>> >><br>> >> This file does indeed contain an affine transform, and is generated<br>> >> when the affine registration between population average and MNI space<br>> >> is estimated.<br>> >><br>> >> There's no script to do what you're after yet, but if the demand is<br>> >> high enough I could introduce the option. Can I ask how you plan to<br>> >> use the transforms?<br>> >><br>> >> Best regards,<br>> >> -John<br>> >><br>> >><br>> >> On 16 February 2011 15:44, Richard Binney <[log in to unmask]><br>> >> wrote:<br>> >> > ......I've just spotted that running normalise-to-mni now spits out a<br>> >> > 'Template_6_2mni.mat' file. I don't recall seeing this before - would<br>> >> > this<br>> >> > happen to be the affine transform I spoke of?<br>> >> ><br>> >> > R<br>> >> ><br>> >> > On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney<br>> >> > <[log in to unmask]> wrote:<br>> >> >><br>> >> >> Ah, yes. I couldn't find the email you were refferring to, but I've<br>> >> >> done<br>> >> >> it and it makes a huge difference. Pretty obvious really.<br>> >> >><br>> >> >> One more thing, John (or anybody else for that matter) - is there now a<br>> >> >> way in which one can easily create an inverse of the DARTEL<br>> >> >> normalise-to-MNI<br>> >> >> deformation composition (so from MNI --> study average --> individual<br>> >> >> subject space)? Is it possible (i.e., is there script) to have this<br>> >> >> (forward<br>> >> >> and/or backwards) written out for each subject or, alternatively, to<br>> >> >> have<br>> >> >> the affine template-to-TPM transform outputted such that one can<br>> >> >> combine it<br>> >> >> with a flow field, and subsequently inverse the composition using the<br>> >> >> deformations tool?<br>> >> >><br>> >> >> All the best,<br>> >> >><br>> >> >> Richard<br>> >> >><br>> >> >> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner <[log in to unmask]><br>> >> >> wrote:<br>> >> >>><br>> >> >>> You may wish to change the voxel sizes and bounding box in order to<br>> >> >>> obtain slightly lower resolution versions (other than the default<br>> >> >>> 1.5mm isotropic with a large bounding box). See the email I sent out<br>> >> >>> recently.<br>> >> >>><br>> >> >>> Best regards,<br>> >> >>> -John<br>> >> >>><br>> >> >>> On 15 February 2011 17:29, Richard Binney<br>> >> >>> <[log in to unmask]><br>> >> >>> wrote:<br>> >> >>> > Dear John (and other knowledgeable types),<br>> >> >>> ><br>> >> >>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to<br>> >> >>> > normalise<br>> >> >>> > a<br>> >> >>> > functional MRI timeseries. That worked fine - the anatomical<br>> >> >>> > images that<br>> >> >>> > I additionally warped in this way look great - but I now cannot do<br>> >> >>> > anything<br>> >> >>> > with the timeseries.<br>> >> >>> ><br>> >> >>> > It is now almost 4Gb in size. I appreciate that using DARTEL to warp<br>> >> >>> > fMRI<br>> >> >>> > data can result in massive files which are problematic (and thus a<br>> >> >>> > work<br>> >> >>> > around is to apply the warp to contrast images), but I didn't expect<br>> >> >>> > the<br>> >> >>> > segment deformation field to do the same.<br>> >> >>> ><br>> >> >>> > Does this sound about right for a timeseries with 465 volumes (voxel<br>> >> >>> > dim 2.5<br>> >> >>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a mistake<br>> >> >>> > somewhere?<br>> >> >>> ><br>> >> >>> > so I'm supposing my inability to use the warped timeseries is a<br>> >> >>> > memory<br>> >> >>> > issue - I am running a 64-bit machine with 8Gb memory (I can see all<br>> >> >>> > this<br>> >> >>> > being used in the task manager so doesn't seem to be a matlab<br>> >> >>> > version<br>> >> >>> > problem) but maybe this file is just way too big for the SPM<br>> >> >>> > platform.....???? I could crop the image, etc I suppose but maybe<br>> >> >>> > the<br>> >> >>> > biggest problem is the shear number of volumes.<br>> >> >>> ><br>> >> >>> > Thanks in advance for your thoughts<br>> >> >>> ><br>> >> >>> > Richard<br>> >> >>> ><br>> >> >>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo<br>> >> >>> > <[log in to unmask]> wrote:<br>> >> >>> >><br>> >> >>> >> Thanks John.<br>> >> >>> >><br>> >> >>> >> It turns out the problem was due to the version of Matlab I was<br>> >> >>> >> running.<br>> >> >>> >><br>> >> >>> >> Matlab2007 was only running 32bit, whilst the 2010 version is 64.<br>> >> >>> >><br>> >> >>> >> Having set up with Matlab2010, the new segmentation with DARTEL<br>> >> >>> >> import<br>> >> >>> >> now<br>> >> >>> >> runs without any problem, without me having to alter/crop any of<br>> >> >>> >> the<br>> >> >>> >> images.<br>> >> >>> >><br>> >> >>> >> All the best<br>> >> >>> >> William<br>> >> >>> ><br>> >> >>> ><br>> >> >><br>> >> ><br>> >> ><br>> ><br> </body>
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========================================================================Date: Wed, 16 Feb 2011 19:47:46 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: T1 & functional image reorigin
Comments: To: cliff <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Message-ID: <[log in to unmask]>
If all subjects have similar positioning in the scanner, then you can
use Display to adjust the positioning of one scan of one subject, and
then select all scans of all subjects when you actually do the
reorienting.
I had no idea that anyone was still seriously using SPM99. It's
really just a museum piece now. Although it may have some
archaeological interest, it probably isn't so great for doing science.
Best regards,
-John
On 16 February 2011 18:51, cliff <[log in to unmask]> wrote:
> Now I always use SPM99's Head-edit funtion to do these stuff (set the origin
> first and then apply to images).
> In this way, the new origin can be read correctly both in SPM99 and SPM8.
> However, it is quite a boring job.
>
> Is there any independent funtion in SPM that I can use to reorigin images in
> a modified batch way?
> Or is there any assistant toolbox I can use?
>
> Thanks!
========================================================================Date: Wed, 16 Feb 2011 22:59:07 +0100
Reply-To: SPM-Workshop Hannover <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: SPM-Workshop Hannover <[log in to unmask]>
Subject: SPM-Workshop Hannover (Germany)
MIME-Version: 1.0
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SPM-Workshop (German)
Hannover, Germany
May 11-13, 2011
Hands-on workshop for new users at Hannover Medical School in Germany
focussing on the practical use of SPM8. This three-day course is designed
for researchers with little or no experience in the analysis of functional
imaging data. The course will be held in German.
The aim of this workshop is to explain the basic principles of fMRI analysis
with SPM and Matlab, and to apply this knowledge right away. All steps of
data analysis in SPM8 will be covered, from importing the raw data, logfile
extraction, preprocessing, 1st and 2nd level analysis to results inspection,
extraction and visualization.
For more information please visit www.spmworkshop-hannover.de or mail to
[log in to unmask]
--
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*SPM-Workshop Hannover*
...learn how to do it!
Email: [log in to unmask]
Web: www.spmworkshop-hannover.de
°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°
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SPM-Workshop (German)<br>Hannover, Germany<br>May 11-13, 2011<br><br>Hands-on workshop for new users at Hannover Medical School in Germany focussing on the practical use of SPM8. This three-day course is designed for researchers with little or no experience in the analysis of functional imaging data. The course will be held in German.<br>
<br>The aim of this workshop is to explain the basic principles of fMRI analysis with SPM and Matlab, and to apply this knowledge right away. All steps of data analysis in SPM8 will be covered, from importing the raw data, logfile extraction, preprocessing, 1st and 2nd level analysis to results inspection, extraction and visualization.<br>
<br>For more information please visit <a href="http://www.spmworkshop-hannover.de">www.spmworkshop-hannover.de</a> or mail to <a href="mailto:[log in to unmask]">[log in to unmask]</a><br><br clear="all"><br>-- <br>
°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°<br><b>SPM-Workshop Hannover</b><br>...learn how to do it!<br><br>Email: <a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a><br>
Web: <a href="http://www.spmworkshop-hannover.de" target="_blank">www.spmworkshop-hannover.de</a><br>°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°°<br><br>
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========================================================================Date: Wed, 16 Feb 2011 14:34:00 -0800
Reply-To: kambiz rakhshan <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: kambiz rakhshan <[log in to unmask]>
Subject: DARTEL template in MNI space
MIME-Version: 1.0
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Dear all,
I am wondering how to use spm_get_space to affine normalize the DARTEL template in MNI space? Shall one use *Template_6 as P and *_2mni.mat as M?
Isn't it possible to use normalize_write function instead?
My second question concerns the use of suitable mask after applying normalize to MNI function for fMRI data to get ride of potential aliasing effects.Is it better to use normalized template of dartel as an explicit mask (while doing second level analysis) or to apply the deformation to the first level subject-specific mask and then multiply with the smoothed normalized images created by normalize to MNI function?
Thanks in advance
/Kami
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<table cellspacing="0" cellpadding="0" border="0" ><tr><td valign="top" style="font: inherit;"><DIV>Dear all,</DIV>
<DIV> </DIV>
<DIV>I am wondering how to use spm_get_space to affine normalize the DARTEL template in MNI space? Shall one use *Template_6 as P and *_2mni.mat as M?</DIV>
<DIV>Isn't it possible to use normalize_write function instead?</DIV>
<DIV> </DIV>
<DIV>My second question concerns the use of suitable mask after applying normalize to MNI function for fMRI data to get ride of potential aliasing effects.Is it better to use normalized template of dartel as an explicit mask (while doing second level analysis) or to apply the deformation to the first level subject-specific mask and then multiply with the smoothed normalized images created by normalize to MNI function? </DIV>
<DIV> </DIV>
<DIV>Thanks in advance</DIV>
<DIV>/<SPAN>Kami</SPAN></DIV>
<DIV> </DIV></td></tr></table><br>
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========================================================================Date: Wed, 16 Feb 2011 20:02:00 -0500
Reply-To: James Bursley <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: James Bursley <[log in to unmask]>
Subject: VOI Batch Module
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Hi SPMers,
I'm having some trouble using the Volume of Interest batch module. I'm trying to extract a sphere from a single-subject .mat file, and I want to adjust the extraction to an F-contrast I've run for that subject. When I enter a '1' as the parameter for "Adjust data," though, to select that contrast, I get this error:
Error with file beta_0001.img
Failed 'Volume of Interest'
Error using --> spm_sample_vol
Cant open image file.
In file "/usr/local/analysistools/fmritools/matlab/toolbox/spm8/spm_get_data.m" (v3950), function "spm_get_data" at line 44.
In file "/usr/local/analysistools/fmritools/matlab/toolbox/spm8/spm_regions.m" (v3812), function "spm_regions" at line 147.
In file "/usr/local/analysistools/fmritools/matlab/toolbox/spm8/config/spm_run_voi.m" (v3780), function "spm_run_voi" at line 68.
Any idea what's going on? Thanks very much!
James========================================================================Date: Thu, 17 Feb 2011 02:21:17 +0000
Reply-To: James Bursley <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: James Bursley <[log in to unmask]>
Subject: Re: VOI Batch Module
Mime-Version: 1.0
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Message-ID: <[log in to unmask]>
Two other pieces of information regarding this problem:
1. No errors come up when I extract a VOI using the GUI; I only encounter this error when using the batch script.
2. The error only appears when "Adjust data" is set to something other than '0', i.e., when data IS being adjusted. Otherwise, the batch runs fine.
========================================================================Date: Thu, 17 Feb 2011 02:41:30 +0000
Reply-To: Toni Pitcher <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Toni Pitcher <[log in to unmask]>
Subject: multiple conditions/regressors in SPM8 batch
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Hi All
I have just converted to SPM8 and have constructed a batch template for processing fMRI model specification, estimation, contrast manager and results report for chosen study, whereby each subject has 4 sessions.
After assigning dependencies etc and using the 'save and script' option, the following was produced.
% List of open inputs
% fMRI model specification: Directory - cfg_files
% fMRI model specification: Scans - cfg_files
% fMRI model specification: Scans - cfg_files
% fMRI model specification: Scans - cfg_files
% fMRI model specification: Scans - cfg_files
nrun = X; % enter the number of runs here
jobfile = {'/Users/stats_job.m'};
jobs = repmat(jobfile, 1, nrun);
inputs = cell(5, nrun);
for crun = 1:nrun
inputs{1, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Directory - cfg_files
inputs{2, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Scans - cfg_files
inputs{3, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Scans - cfg_files
inputs{4, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Scans - cfg_files
inputs{5, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Scans - cfg_files
end
spm('defaults', 'FMRI');
spm_jobman('serial', jobs, '', inputs{:});
Required inputs being directory and scans for each of the 4 sessions.
What I would also like to do is specify multiple conditions and multiple regressors files for each session. I would like to do this in a script so that the batch template can be applied across subjects. These are not listed as required inputs (<-X) in the batch, so am unsure how to specify.
Thanks
Toni
========================================================================Date: Thu, 17 Feb 2011 09:32:54 +0000
Reply-To: Rik Henson <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Rik Henson <[log in to unmask]>
Subject: Research Assistant Position in Cambridge
Content-Type: multipart/alternative;
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Dear colleagues -
Please see Research Assistant position available below:
MRC Cognition and Brain Sciences Unit - Cambridge
Research Assistant
Ref: IRC14207
The MRC Cognition and Brain Sciences Unit (CBSU) is an internationally renowned research institute with state-of-the-art cognitive neuroscience facilities, including on-site fMRI, MEG, and EEG laboratories, and neuropsychological patient panel and large panel of healthy volunteers. Applications are invited for a Research Assistant to provide scientific support for Dr Richard Henson's MRC programme on the systems neuroscience of human memory and perception, particularly for acquiring behavioural, EEG/MEG and/or fMRI data during cognitive experiments on volunteers.
You will be highly motivated and proactive, with a degree in psychology or a related discipline. Experience in scientific research, particularly with human volunteers, is important, as is familiarity with computer programming and statistics. Experience with EEG, MEG or MRI acquisition and/or analysis would be an advantage. Good communication skills plus the ability to work as part of a large, multi-disciplinary team are essential.
The starting salary will be in the range of £20,074 - £27,271 per annum, depending upon qualifications and experience. We offer a flexible pay and reward policy, 30 days annual leave entitlement, and an optional MRC final salary Pension Scheme. On site car and bicycle parking is available.
Applications are handled by the RCUK Shared Services Centre; to apply please visit our job board at https://ext.ssc.rcuk.ac.uk<https://ext.ssc.rcuk.ac.uk/> and complete an online application form. If you are unable to apply online please contact us on 01793 867003 quoting reference IRC14207.
Closing date: 21st March 2011
This position is subject to pre-employment screening
The Medical Research Council is an Equal Opportunities Employer
-------------------------------------------------------
Dr Richard Henson
Assistant Director, Neuroimaging
MRC Cognition & Brain Sciences Unit
15 Chaucer Road
Cambridge, CB2 7EF, UK
Office: +44 (0)1223 355 294 x522
Mob: +44 (0)794 1377 345
Fax: +44 (0)1223 359 062
http://www.mrc-cbu.cam.ac.uk/people/rik.henson
-------------------------------------------------------
--_000_3822A22C3FB6974099045A8DB62D39F93919E5D1WSR21mrccbsuloc_
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<font face="Calibri" size="2"><span style="font-size:11pt;">
<div> </div>
<div>Dear colleagues –</div>
<div> </div>
<div>Please see Research Assistant position available below:</div>
<div> </div>
<div> </div>
<div><b>MRC Cognition and Brain Sciences Unit – Cambridge</b></div>
<div><b>Research Assistant</b></div>
<div> </div>
<div><b>Ref: IRC14207</b></div>
<div> </div>
<div>The MRC Cognition and Brain Sciences Unit (CBSU) is an internationally renowned research institute with state-of-the-art cognitive neuroscience facilities, including on-site fMRI, MEG, and EEG laboratories, and neuropsychological patient panel and large
panel of healthy volunteers. Applications are invited for a Research Assistant to provide scientific support for Dr Richard Henson’s MRC programme on the systems neuroscience of human memory and perception, particularly for acquiring behavioural, EEG/MEG and/or
fMRI data during cognitive experiments on volunteers.</div>
<div>You will be highly motivated and proactive, with a degree in psychology or a related discipline. Experience in scientific research, particularly with human volunteers, is important, as is familiarity with computer programming and statistics. Experience
with EEG, MEG or MRI acquisition and/or analysis would be an advantage. Good communication skills plus the ability to work as part of a large, multi-disciplinary team are essential. </div>
<div> </div>
<div>The starting salary will be in the range of £20,074 - £27,271 per annum, depending upon qualifications and experience. We offer a flexible pay and reward policy, 30 days annual leave entitlement, and an optional MRC final salary Pension Scheme. On site
car and bicycle parking is available.</div>
<div> </div>
<div>Applications are handled by the RCUK Shared Services Centre; to apply please visit our job board at <a href="https://ext.ssc.rcuk.ac.uk/"><font color="blue"><u>https://ext.ssc.rcuk.ac.uk</u></font></a> and complete an online application form. If you are
unable to apply online please contact us on 01793 867003 quoting reference IRC14207.</div>
<div> </div>
<div><b>Closing date: 21</b><font size="1"><span style="font-size:7.3pt;"><b><sup>st</sup></b></span></font><b> March 2011</b></div>
<div> </div>
<div><i>This position is subject to pre-employment screening</i></div>
<div><i>The Medical Research Council is an Equal Opportunities Employer</i></div>
<div> </div>
<div> </div>
<div>-------------------------------------------------------</div>
<div><font face="Courier New"> Dr Richard Henson</font></div>
<div><font face="Courier New"> Assistant Director, Neuroimaging</font></div>
<div><font face="Courier New"> MRC Cognition & Brain Sciences Unit</font></div>
<div><font face="Courier New"> 15 Chaucer Road</font></div>
<div><font face="Courier New"> Cambridge, CB2 7EF, UK</font></div>
<div><font face="Courier New"> </font></div>
<div><font face="Courier New"> Office: +44 (0)1223 355 294 x522</font></div>
<div><font face="Courier New"> Mob: +44 (0)794 1377 345</font></div>
<div><font face="Courier New"> Fax: +44 (0)1223 359 062</font></div>
<div><font face="Courier New"> </font></div>
<div><font face="Courier New"> <a href="http://www.mrc-cbu.cam.ac.uk/people/rik.henson">
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--_000_3822A22C3FB6974099045A8DB62D39F93919E5D1WSR21mrccbsuloc_--
========================================================================Date: Thu, 17 Feb 2011 11:49:09 +0100
Reply-To: Michael Erb <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Michael Erb <[log in to unmask]>
Subject: Re: multiple conditions/regressors in SPM8 batch
Comments: To: Toni Pitcher <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Hi Toni,
if you right click on the 'multiple conditions' or 'multiple regressors'
in the batch editor, you can apply 'Clear Value' to get the <-X.
Michael.
Am 17.02.2011 03:41, schrieb Toni Pitcher:
> Hi All
>
> I have just converted to SPM8 and have constructed a batch template for processing fMRI model specification, estimation, contrast manager and results report for chosen study, whereby each subject has 4 sessions.
>
> After assigning dependencies etc and using the 'save and script' option, the following was produced.
>
> % List of open inputs
> % fMRI model specification: Directory - cfg_files
> % fMRI model specification: Scans - cfg_files
> % fMRI model specification: Scans - cfg_files
> % fMRI model specification: Scans - cfg_files
> % fMRI model specification: Scans - cfg_files
> nrun = X; % enter the number of runs here
> jobfile = {'/Users/stats_job.m'};
> jobs = repmat(jobfile, 1, nrun);
> inputs = cell(5, nrun);
> for crun = 1:nrun
> inputs{1, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Directory - cfg_files
> inputs{2, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Scans - cfg_files
> inputs{3, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Scans - cfg_files
> inputs{4, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Scans - cfg_files
> inputs{5, crun} = MATLAB_CODE_TO_FILL_INPUT; % fMRI model specification: Scans - cfg_files
> end
> spm('defaults', 'FMRI');
> spm_jobman('serial', jobs, '', inputs{:});
>
>
> Required inputs being directory and scans for each of the 4 sessions.
>
> What I would also like to do is specify multiple conditions and multiple regressors files for each session. I would like to do this in a script so that the batch template can be applied across subjects. These are not listed as required inputs (<-X) in the batch, so am unsure how to specify.
>
>
>
> Thanks
>
> Toni
>
--
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
Dr. Michael Erb
Sektion f. experimentelle Kernspinresonanz des ZNS
Abteilung Neuroradiologie, Universitaetsklinikum
Hoppe-Seyler_Str. 3, D-72076 Tuebingen
Tel.: +49(0)7071/2987753 priv. +49(0)7071/61559
Fax.: +49(0)7071/294371
e-mail: <[log in to unmask]>
www: http://www.medizin.uni-tuebingen.de/nrad/sektion/
<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
========================================================================Date: Thu, 17 Feb 2011 13:11:05 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: DARTEL template in MNI space
Comments: To: kambiz rakhshan <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--00163630f77d500ad5049c7a214f
Content-Type: text/plain; charset=ISO-8859-1
> I am wondering how to use spm_get_space to affine normalize the DARTEL
> template in MNI space? Shall one use *Template_6 as P and *_2mni.mat as M?
> Isn't it possible to use normalize_write function instead?
>
You could write the matrix in *_2mni.mat into the header of the
Template_6.nii image. The spm_get_space function would do that.
> My second question concerns the use of suitable mask after applying
> normalize to MNI function for fMRI data to get ride of potential aliasing
> effects.Is it better to use normalized template of dartel as an
> explicit mask (while doing second level analysis) or to apply the
> deformation to the first level subject-specific mask and then multiply with
> the smoothed normalized images created by normalize to MNI function?
>
These are not really aliasing effects. It's the effect of missing data in
the images, where SPM tries to guess about what values should be there.
Usually, these regions contain zeros, which cause even greater artifacts
after smoothing, as some subjects' scans will have data, whereas others have
zeros.
Because this seems to be a concern for so many, I'll have a go at including
a fix for the data so that those voxels with very little signal are set to
zero.
Best regards,
-John
--00163630f77d500ad5049c7a214f
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
<br><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;"><table cellspacing="0" cellpadding="0" border="0"><tbody><tr><td valign="top" style="font:inherit">
<div>I am wondering how to use spm_get_space to affine normalize the DARTEL template in MNI space? Shall one use *Template_6 as P and *_2mni.mat as M?</div>
<div>Isn't it possible to use normalize_write function instead?</div></td></tr></tbody></table></blockquote><div><br></div><div>You could write the matrix in *_2mni.mat into the header of the Template_6.nii image. The spm_get_space function would do that.</div>
<div><br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;"><table cellspacing="0" cellpadding="0" border="0"><tbody><tr><td valign="top" style="font:inherit">
<div> </div>
<div>My second question concerns the use of suitable mask after applying normalize to MNI function for fMRI data to get ride of potential aliasing effects.Is it better to use normalized template of dartel as an explicit mask (while doing second level analysis) or to apply the deformation to the first level subject-specific mask and then multiply with the smoothed normalized images created by normalize to MNI function? </div>
</td></tr></tbody></table></blockquote><div><br></div><div>These are not really aliasing effects. It's the effect of missing data in the images, where SPM tries to guess about what values should be there. Usually, these regions contain zeros, which cause even greater artifacts after smoothing, as some subjects' scans will have data, whereas others have zeros.</div>
<div><br></div><div>Because this seems to be a concern for so many, I'll have a go at including a fix for the data so that those voxels with very little signal are set to zero.</div><div><br></div><div>Best regards,</div>
<div>-John</div></div>
--00163630f77d500ad5049c7a214f--
========================================================================Date: Thu, 17 Feb 2011 13:54:19 +0000
Reply-To: Vladimir Litvak <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Litvak <[log in to unmask]>
Subject: Re: methodological question
Comments: To: Pierre Larigneux <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Message-ID: <[log in to unmask]>
Dear Gaetan,
You should contrast similar things so if you have 20 ms activation
time window you should compare it to a 20 ms baseline time window. You
can indeed generate multiple windows from the baseline and put them
all in the same design matrix as in your option (3). You could also
compare your activation windows to each other across trials/subjects,
then you could talk about sources being more strongly activated at
time A vs. time B. Note that for these comparisons you need to specify
all the time windows together (separated by ';' ) when you export to
images.
Best,
Vladimir
On Thu, Feb 17, 2011 at 11:42 AM, Pierre Larigneux <[log in to unmask]> wrote:
> Dear Vladimir,
>
> I changed my methodology. I do not work anymore on the amplitude.
> I come back to you to have some advise about the way I should look onto my
> data.
>
> Now, to bring to light the region activated by my task I do a paired t-test
> between 20 ms time windows post stimulus against baseline.
> I reconstruct the sources on the global time window -200 to 600 ms and I use
> gaussian windows as contrast.
>
> I am wondering about the most proper way to do it, I have got three main
> ideas:
>
> 1. contrasting my 20 ms time windows against the global mean on the baseline
> -200 to 0.
> 2. constrating my 20 ms time windows against the mean energy of ten (or a
> bit less) 20 ms time windows on the baseline.
> 3. constrating my 20 ms time windows against each 20 ms time windows in a
> big design matrix.
>
> I do not know what is the best way to do it. I tried the different things
> and it gave me really close results in the localisation of the activity.
> it change mainly the T of the statistics from significant to very
> significant.
>
> Thanks for your attention,
>
> bests regards,
>
> Gaetan Yvert
========================================================================Date: Thu, 17 Feb 2011 15:02:45 +0000
Reply-To: Nick Davis <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Nick Davis <[log in to unmask]>
Organization: Bangor University
Subject: Creating an "average" structural image
MIME-Version: 1.0
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Message-ID: <[log in to unmask]>
Hello SPM nerds.
I would like to project my activation blobs onto some kind of average
image of the brains of the subjects in my experiment. I can't see a way
to create this average image in SPM8, so is there another method I could
use?
Thanks!
Nick
--
[log in to unmask]
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unwaith a dilëwch y neges. Os na fwriadwyd anfon y neges atoch chi,
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gynhwysir ynddi. Mae unrhyw farn neu safbwynt yn eiddo i'r sawl a'i
hanfonodd yn unig ac nid yw o anghenraid yn cynrychioli barn
Prifysgol Bangor. Nid yw Prifysgol Bangor yn gwarantu
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Cyllid Prifysgol Bangor. www.bangor.ac.uk
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========================================================================Date: Thu, 17 Feb 2011 15:19:16 +0000
Reply-To: Michael O'Sullivan <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Michael O'Sullivan <[log in to unmask]>
Subject: Re: Creating an "average" structural image
Comments: To: Nick Davis <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Dear Nick
Assuming they are all in the same space.... you should be able to just add
them all up with ImCalc. You will probably need to smooth the final
sum/average to make it look nicer. If the results are in MNI space an
alternative is to use one of the canonical images.
Mike
From: Nick Davis <[log in to unmask]>
To: [log in to unmask]
Date: 17/02/2011 15:13
Subject: [SPM] Creating an "average" structural image
Sent by: "SPM (Statistical Parametric Mapping)"
<[log in to unmask]>
Hello SPM nerds.
I would like to project my activation blobs onto some kind of average
image of the brains of the subjects in my experiment. I can't see a way
to create this average image in SPM8, so is there another method I could
use?
Thanks!
Nick
--
[log in to unmask]
--
Gall y neges e-bost hon, ac unrhyw atodiadau a anfonwyd gyda hi,
gynnwys deunydd cyfrinachol ac wedi eu bwriadu i'w defnyddio'n unig
gan y sawl y cawsant eu cyfeirio ato (atynt). Os ydych wedi derbyn y
neges e-bost hon trwy gamgymeriad, rhowch wybod i'r anfonwr ar
unwaith a dilëwch y neges. Os na fwriadwyd anfon y neges atoch chi,
rhaid i chi beidio â defnyddio, cadw neu ddatgelu unrhyw wybodaeth a
gynhwysir ynddi. Mae unrhyw farn neu safbwynt yn eiddo i'r sawl a'i
hanfonodd yn unig ac nid yw o anghenraid yn cynrychioli barn
Prifysgol Bangor. Nid yw Prifysgol Bangor yn gwarantu
bod y neges e-bost hon neu unrhyw atodiadau yn rhydd rhag firysau neu
100% yn ddiogel. Oni bai fod hyn wedi ei ddatgan yn uniongyrchol yn
nhestun yr e-bost, nid bwriad y neges e-bost hon yw ffurfio contract
rhwymol - mae rhestr o lofnodwyr awdurdodedig ar gael o Swyddfa
Cyllid Prifysgol Bangor. www.bangor.ac.uk
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<font size=2 face="sans-serif">Dear Nick</font>
<br><font size=2 face="sans-serif">Assuming they are all in the same space....
you should be able to just add them all up with ImCalc. You will probably
need to smooth the final sum/average to make it look nicer. If the results
are in MNI space an alternative is to use one of the canonical images.</font>
<br>
<br><font size=2 face="sans-serif">Mike</font>
<br>
<br>
<br>
<br>
<br>
<br><font size=1 color=#5f5f5f face="sans-serif">From:
</font><font size=1 face="sans-serif">Nick Davis <[log in to unmask]></font>
<br><font size=1 color=#5f5f5f face="sans-serif">To:
</font><font size=1 face="sans-serif">[log in to unmask]</font>
<br><font size=1 color=#5f5f5f face="sans-serif">Date:
</font><font size=1 face="sans-serif">17/02/2011 15:13</font>
<br><font size=1 color=#5f5f5f face="sans-serif">Subject:
</font><font size=1 face="sans-serif">[SPM] Creating
an "average" structural image</font>
<br><font size=1 color=#5f5f5f face="sans-serif">Sent by:
</font><font size=1 face="sans-serif">"SPM (Statistical
Parametric Mapping)" <[log in to unmask]></font>
<br>
<hr noshade>
<br>
<br>
<br><tt><font size=2>Hello SPM nerds.<br>
<br>
I would like to project my activation blobs onto some kind of average <br>
image of the brains of the subjects in my experiment. I can't see a way
<br>
to create this average image in SPM8, so is there another method I could
<br>
use?<br>
<br>
Thanks!<br>
Nick<br>
<br>
<br>
<br>
-- <br>
[log in to unmask]<br>
<br>
-- <br>
Gall y neges e-bost hon, ac unrhyw atodiadau a anfonwyd gyda hi,<br>
gynnwys deunydd cyfrinachol ac wedi eu bwriadu i'w defnyddio'n unig<br>
gan y sawl y cawsant eu cyfeirio ato (atynt). Os ydych wedi derbyn y<br>
neges e-bost hon trwy gamgymeriad, rhowch wybod i'r anfonwr ar<br>
unwaith a dilëwch y neges. Os na fwriadwyd anfon y neges atoch chi,<br>
rhaid i chi beidio â defnyddio, cadw neu ddatgelu unrhyw wybodaeth a<br>
gynhwysir ynddi. Mae unrhyw farn neu safbwynt yn eiddo i'r sawl a'i<br>
hanfonodd yn unig ac nid yw o anghenraid yn cynrychioli barn<br>
Prifysgol Bangor. Nid yw Prifysgol Bangor yn gwarantu<br>
bod y neges e-bost hon neu unrhyw atodiadau yn rhydd rhag firysau neu<br>
100% yn ddiogel. Oni bai fod hyn wedi ei ddatgan yn uniongyrchol yn<br>
nhestun yr e-bost, nid bwriad y neges e-bost hon yw ffurfio contract<br>
rhwymol - mae rhestr o lofnodwyr awdurdodedig ar gael o Swyddfa<br>
Cyllid Prifysgol Bangor. </font></tt><a href=www.bangor.ac.uk><tt><font size=2>www.bangor.ac.uk</font></tt></a><tt><font size=2><br>
<br>
This email and any attachments may contain confidential material and<br>
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you<br>
must not use, retain or disclose any information contained in this<br>
email. Any views or opinions are solely those of the sender and do<br>
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Bangor University does not guarantee that this email or<br>
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</font></tt>
<br>
--=_alternative 0054298F8025783A_=--
========================================================================Date: Thu, 17 Feb 2011 15:43:36 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: Creating an "average" structural image
Comments: To: Nick Davis <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
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Message-ID: <AANLkTiý[log in to unmask]>
Creating an average is just the same as fitting a GLM through the
data, where the design matrix is simply a column of ones. Therefore,
you could just use the stats options.
Best regards,
John
On 17 February 2011 15:02, Nick Davis <[log in to unmask]> wrote:
> Hello SPM nerds.
>
> I would like to project my activation blobs onto some kind of average image
> of the brains of the subjects in my experiment. I can't see a way to create
> this average image in SPM8, so is there another method I could use?
>
> Thanks!
> Nick
>
>
>
> --
> [log in to unmask]
>
> --
> Gall y neges e-bost hon, ac unrhyw atodiadau a anfonwyd gyda hi,
> gynnwys deunydd cyfrinachol ac wedi eu bwriadu i'w defnyddio'n unig
> gan y sawl y cawsant eu cyfeirio ato (atynt). Os ydych wedi derbyn y
> neges e-bost hon trwy gamgymeriad, rhowch wybod i'r anfonwr ar
> unwaith a dilëwch y neges. Os na fwriadwyd anfon y neges atoch chi,
> rhaid i chi beidio â defnyddio, cadw neu ddatgelu unrhyw wybodaeth a
> gynhwysir ynddi. Mae unrhyw farn neu safbwynt yn eiddo i'r sawl a'i
> hanfonodd yn unig ac nid yw o anghenraid yn cynrychioli barn
> Prifysgol Bangor. Nid yw Prifysgol Bangor yn gwarantu
> bod y neges e-bost hon neu unrhyw atodiadau yn rhydd rhag firysau neu
> 100% yn ddiogel. Oni bai fod hyn wedi ei ddatgan yn uniongyrchol yn
> nhestun yr e-bost, nid bwriad y neges e-bost hon yw ffurfio contract
> rhwymol - mae rhestr o lofnodwyr awdurdodedig ar gael o Swyddfa
> Cyllid Prifysgol Bangor. www.bangor.ac.uk
>
> This email and any attachments may contain confidential material and
> is solely for the use of the intended recipient(s). If you have
> received this email in error, please notify the sender immediately
> and delete this email. If you are not the intended recipient(s), you
> must not use, retain or disclose any information contained in this
> email. Any views or opinions are solely those of the sender and do
> not necessarily represent those of the Bangor University.
> Bangor University does not guarantee that this email or
> any attachments are free from viruses or 100% secure. Unless
> expressly stated in the body of the text of the email, this email is
> not intended to form a binding contract - a list of authorised
> signatories is available from the Bangor University Finance
> Office. www.bangor.ac.uk
>
========================================================================Date: Thu, 17 Feb 2011 16:56:40 +0100
Reply-To: claus lamm <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: claus lamm <[log in to unmask]>
Subject: Re: Creating an "average" structural image
Comments: To: Nick Davis <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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On 2/17/2011 4:02 PM, Nick Davis wrote:
> Hello SPM nerds.
>
> I would like to project my activation blobs onto some kind of average
> image of the brains of the subjects in my experiment. I can't see a
> way to create this average image in SPM8, so is there another method I
> could use?
>
> Thanks!
> Nick
>
>
>
you could also use imcalc - mean(X) after loading all the individual
normalized images as input
========================================================================Date: Thu, 17 Feb 2011 16:33:07 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: New Segmentation Out of Memory Error
Comments: To: michel grothe <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
The best possible workaround would be the following. In principle, if
you have scans from other subjects that have additional information
(eg structure label images etc), then you can align these scans to the
population average images, using Dartel. Once you have done this, the
information can be warped so that it overlays on your individuals'
scans via a procedure described towards the end of the "Using Dartel"
chapter of the SPM8 manual.
Alternatively, you could create a copy of the MNI-space image of
labels, and tweak the matrix in its header, so that it is in alignment
with the population average. This is untested, but I think you'd set
the matrix to M3*inv(M2)*M1, where:
M1 = voxel-to-world matrix of your MNI-space labels
M2 = voxel-to-world matrix of the population average space Template_6
M3 = matrix in the Template_6_2mni.mat
If this works, you should be able to use one of the options in the
Dartel toolbox (or the Deformations Utility) to warp the data on to
your original scans.
Best regards,
-John
On 16 February 2011 19:45, michel grothe <[log in to unmask]> wrote:
> Thanks, this would be great.
>
> Meanwhile, I have some questions regarding a possible work-around:
>
> Would it be OK to use an affine transform calculated with the
> Normalise-Button to approximate the affine transform from 'DARTEL-Normalise
> to MNI' in order to be able to generate the mentioned composition
> 'native->DARTEL->MNI'. Does the affine alignment step in 'DARTEL-Normalise
> to MNI' register the DARTEL-GM template to the GM-apriori map (or the GM map
> in TPM.nii,1?) or does it also use the respective WM-maps?
> How much does the affine alignment in 'DARTEL-Normalise to MNI' differ from
> the one in 'Normalise', when registering DARTEL-GM to GM-apriori map (or
> TPM.nii,1) and setting non-linear iterations to 0?
>
> Thanks again for your help.
> Best regards,
> Michel
>
>> Date: Wed, 16 Feb 2011 17:16:00 +0000
>> From: [log in to unmask]
>> Subject: Re: [SPM] New Segmentation Out of Memory Error
>> To: [log in to unmask]
>>
>> > I haven´t seen such a file in my version of SPM8 (the penultimate
>> > update,
>> > run on win32). Is it possible that this file is just generated during
>> > estimation of the affine transform and not stored permanently on disk?
>> > Or
>> > does it come with the latest update?
>>
>> This change occurred in SPM8 r3727, so should be in the current
>> updates, which are to r4010.
>>
>>
>> > It would be very helpful to have such a file, mainly for the reasons
>> > Richard
>> > mentioned, i.e. use it to compose the complete transform
>> > 'native->DARTEL-mean->MNI'. The inverse of such a composition could be
>> > used
>> > to warp Atlas-labels in MNI space to native space.
>>
>> There's no easy way to use the file yet, but I'll try to code
>> something up for a future version of SPM8.
>>
>> Best regards,
>> -John
>>
>>
>> >> Date: Wed, 16 Feb 2011 15:58:16 +0000
>> >> From: [log in to unmask]
>> >> Subject: Re: [SPM] New Segmentation Out of Memory Error
>> >> To: [log in to unmask]
>> >>
>> >> This file does indeed contain an affine transform, and is generated
>> >> when the affine registration between population average and MNI space
>> >> is estimated.
>> >>
>> >> There's no script to do what you're after yet, but if the demand is
>> >> high enough I could introduce the option. Can I ask how you plan to
>> >> use the transforms?
>> >>
>> >> Best regards,
>> >> -John
>> >>
>> >>
>> >> On 16 February 2011 15:44, Richard Binney
>> >> <[log in to unmask]>
>> >> wrote:
>> >> > ......I've just spotted that running normalise-to-mni now spits out a
>> >> > 'Template_6_2mni.mat' file. I don't recall seeing this before - would
>> >> > this
>> >> > happen to be the affine transform I spoke of?
>> >> >
>> >> > R
>> >> >
>> >> > On Wed, Feb 16, 2011 at 3:39 PM, Richard Binney
>> >> > <[log in to unmask]> wrote:
>> >> >>
>> >> >> Ah, yes. I couldn't find the email you were refferring to, but I've
>> >> >> done
>> >> >> it and it makes a huge difference. Pretty obvious really.
>> >> >>
>> >> >> One more thing, John (or anybody else for that matter) - is there
>> >> >> now a
>> >> >> way in which one can easily create an inverse of the DARTEL
>> >> >> normalise-to-MNI
>> >> >> deformation composition (so from MNI --> study average -->
>> >> >> individual
>> >> >> subject space)? Is it possible (i.e., is there script) to have this
>> >> >> (forward
>> >> >> and/or backwards) written out for each subject or, alternatively, to
>> >> >> have
>> >> >> the affine template-to-TPM transform outputted such that one can
>> >> >> combine it
>> >> >> with a flow field, and subsequently inverse the composition using
>> >> >> the
>> >> >> deformations tool?
>> >> >>
>> >> >> All the best,
>> >> >>
>> >> >> Richard
>> >> >>
>> >> >> On Tue, Feb 15, 2011 at 5:42 PM, John Ashburner
>> >> >> <[log in to unmask]>
>> >> >> wrote:
>> >> >>>
>> >> >>> You may wish to change the voxel sizes and bounding box in order to
>> >> >>> obtain slightly lower resolution versions (other than the default
>> >> >>> 1.5mm isotropic with a large bounding box). See the email I sent
>> >> >>> out
>> >> >>> recently.
>> >> >>>
>> >> >>> Best regards,
>> >> >>> -John
>> >> >>>
>> >> >>> On 15 February 2011 17:29, Richard Binney
>> >> >>> <[log in to unmask]>
>> >> >>> wrote:
>> >> >>> > Dear John (and other knowledgeable types),
>> >> >>> >
>> >> >>> > I used the deformation file (e.g., Y_p1_T1.nii) from Seg8 to
>> >> >>> > normalise
>> >> >>> > a
>> >> >>> > functional MRI timeseries. That worked fine - the anatomical
>> >> >>> > images that
>> >> >>> > I additionally warped in this way look great - but I now cannot
>> >> >>> > do
>> >> >>> > anything
>> >> >>> > with the timeseries.
>> >> >>> >
>> >> >>> > It is now almost 4Gb in size. I appreciate that using DARTEL to
>> >> >>> > warp
>> >> >>> > fMRI
>> >> >>> > data can result in massive files which are problematic (and thus
>> >> >>> > a
>> >> >>> > work
>> >> >>> > around is to apply the warp to contrast images), but I didn't
>> >> >>> > expect
>> >> >>> > the
>> >> >>> > segment deformation field to do the same.
>> >> >>> >
>> >> >>> > Does this sound about right for a timeseries with 465 volumes
>> >> >>> > (voxel
>> >> >>> > dim 2.5
>> >> >>> > x 2.5 x 3; matrix 96x96; numslices = 42)? Or have I made a
>> >> >>> > mistake
>> >> >>> > somewhere?
>> >> >>> >
>> >> >>> > so I'm supposing my inability to use the warped timeseries is a
>> >> >>> > memory
>> >> >>> > issue - I am running a 64-bit machine with 8Gb memory (I can see
>> >> >>> > all
>> >> >>> > this
>> >> >>> > being used in the task manager so doesn't seem to be a matlab
>> >> >>> > version
>> >> >>> > problem) but maybe this file is just way too big for the SPM
>> >> >>> > platform.....???? I could crop the image, etc I suppose but maybe
>> >> >>> > the
>> >> >>> > biggest problem is the shear number of volumes.
>> >> >>> >
>> >> >>> > Thanks in advance for your thoughts
>> >> >>> >
>> >> >>> > Richard
>> >> >>> >
>> >> >>> > On Thu, Feb 10, 2011 at 4:05 PM, William Pettersson-Yeo
>> >> >>> > <[log in to unmask]> wrote:
>> >> >>> >>
>> >> >>> >> Thanks John.
>> >> >>> >>
>> >> >>> >> It turns out the problem was due to the version of Matlab I was
>> >> >>> >> running.
>> >> >>> >>
>> >> >>> >> Matlab2007 was only running 32bit, whilst the 2010 version is
>> >> >>> >> 64.
>> >> >>> >>
>> >> >>> >> Having set up with Matlab2010, the new segmentation with DARTEL
>> >> >>> >> import
>> >> >>> >> now
>> >> >>> >> runs without any problem, without me having to alter/crop any of
>> >> >>> >> the
>> >> >>> >> images.
>> >> >>> >>
>> >> >>> >> All the best
>> >> >>> >> William
>> >> >>> >
>> >> >>> >
>> >> >>
>> >> >
>> >> >
>> >
>
========================================================================Date: Thu, 17 Feb 2011 17:55:36 +0100
Reply-To: Christian Doeller <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Christian Doeller <[log in to unmask]>
Subject: PhD Student and Postdoctoral Researcher Positions in
Neuroimaging, Donders Institute, Nijmegen
MIME-Version: 1.0
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PhD Student and Postdoctoral Researcher Positions in Neuroimaging (1,0 fte)
*Donders Institute, Centre for Cognitive Neuroimaging*
*Vacancy number: 30.03.11*
*Closing date: 15 March 2011*
*Job description*
The newly established PI group of Dr Christian Doeller at the Donders
Institute for Brain, Cognition and Behaviour seeks highly talented and
dedicated postdoctoral researchers and PhD students to work on an exciting
project funded by a recently awarded ERC Starting Grant.
The goal of this project is to gain understanding of how the human brain
maps space and forms episodic memories. Our specific aim is to infer the
fine-scale properties of neural systems in humans by building on models from
single-cell electrophysiology (see Doeller et al., Evidence for Grid Cells
in a Human Memory Network, Nature 2010, 463, 657-661).
The techniques involve combinations of functional magnetic resonance imaging
(fMRI, including 7T high-field scanning), virtual reality technologies,
psychophysics and advanced tools for neuroimaging data analysis.
*Requirements*
As a candidate you should have a Master’s degree or equivalent (for the PhD
student positions) or a PhD degree (for the postdoctoral positions) in a
field related to cognitive neuroscience, e.g. experimental psychology,
cognitive science, biology, neuroscience or related disciplines. Excellent
candidates with a background in quantitative disciplines such as
mathematics, physics or engineering who are interested in neuroscience are
also encouraged to apply.
Selection criteria for the postdoctoral positions will consider the record
of published research, familiarity with neuroimaging techniques and
programming skills (Matlab). For the PhD student positions, familiarity with
neuroimaging techniques and good programming skills (Matlab) are desirable.
Proficiency in oral and written English is required. You are expected to
work in an interdisciplinary environment, sharing technical know-how and
ideas.
*Organization*
The Donders Institute for Brain, Cognition and Behaviour consists of the
Centre for Cognition, the Centre for Cognitive Neuroimaging, and the Centre
for Neuroscience.
The mission of the Centre for Cognitive Neuroimaging is to conduct
cutting-edge fundamental research in cognitive neuroscience. Much of the
rapid progress in this field is being driven by the development of complex
neuroimaging techniques for measuring activity in the human working brain -
an area in which the Centre plays a leading role. The research themes cover
central cognitive functions, such as perception, action, control, decision
making, attention, memory, language, learning and plasticity. The Centre
also aims to establish how the different brain areas coordinate their
activity with very high temporal precision to enable human and animal
cognition. The internationally renowned centre currently hosts more than 100
PhD students and postdoctoral researchers from more than 20 nationalities,
offering a stimulating and multidisciplinary research environment. The
centre is equipped with three MRI scanners (7T, 3T, 1.5T), a 275-channel MEG
system, an EEG-TMS laboratory, several (MR-compatible) EEG systems, and
high-performance computational facilities. English is the lingua franca at
the centre.
Website: http://www.ru.nl/donders
*Conditions of employment*
Employment: 1,0 fte
PhD student: The starting salary is €2,042 per month and will increase to
€2,612 per month in the fourth year.
Postdoctoral researcher: Depending on experience, the gross salary will be
between €3,195 and €4,374.
*Additional conditions of employment*
Duration of the PhD-student contracts: 4 years.
Duration of the postdoctoral contracts: 3 years (extension possible).
In addition to the gross monthly salary, you will receive two yearly 8%
bonuses (holiday and end-of-year).
*Other Information*
You should submit your application in a single PDF file, including an
application letter, a statement of research interests, your CV, and the
names of two persons who can provide references.
*Additional Information*
Dr Christian Doeller, Principal Investigator
Telephone: +31 24 3610983
E-mail: [log in to unmask]
*Application*
You can apply for the job (mention the vacancy number 30.03.11) *before 15
March 2011* by sending your application -preferably by email- to:
Radboud University Nijmegen, P&O department
PO Box 7005, 6503 GM NIJMEGEN, NL
Telephone: +31 24 3611173
E-mail: [log in to unmask]
--001485f92356386899049c7d4470
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<span style="color:rgb(102, 102, 102);font-family:arial, helvetica, sans-serif;font-size:12px"><h1 style="margin-top:0px;margin-right:0px;margin-bottom:0.5em;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;font-size:1.5em;font-weight:bold;color:rgb(0, 153, 204);font-family:Arial, Helvetica, sans-serif;line-height:1em;clear:left">
PhD Student and Postdoctoral Researcher Positions in Neuroimaging (1,0 fte)</h1><br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
<b style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Donders Institute, Centre for Cognitive Neuroimaging</b> <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
<b style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Vacancy number: 30.03.11</b> <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
<b style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Closing date: 15 March 2011</b> <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
<p style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:1em;padding-left:0px"><strong style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Job description</strong><br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
The newly established PI group of Dr Christian Doeller at the Donders Institute for Brain, Cognition and Behaviour seeks highly talented and dedicated postdoctoral researchers and PhD students to work on an exciting project funded by a recently awarded ERC Starting Grant. <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
<br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">The goal of this project is to gain understanding of how the human brain maps space and forms episodic memories. Our specific aim is to infer the fine-scale properties of neural systems in humans by building on models from single-cell electrophysiology (see Doeller et al., Evidence for Grid Cells in a Human Memory Network, Nature 2010, 463, 657-661). <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
<br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">The techniques involve combinations of functional magnetic resonance imaging (fMRI, including 7T high-field scanning), virtual reality technologies, psychophysics and advanced tools for neuroimaging data analysis.</p>
<p style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:1em;padding-left:0px"><strong style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Requirements</strong><br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
As a candidate you should have a Master’s degree or equivalent (for the PhD student positions) or a PhD degree (for the postdoctoral positions) in a field related to cognitive neuroscience, e.g. experimental psychology, cognitive science, biology, neuroscience or related disciplines. Excellent candidates with a background in quantitative disciplines such as mathematics, physics or engineering who are interested in neuroscience are also encouraged to apply. <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
<br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Selection criteria for the postdoctoral positions will consider the record of published research, familiarity with neuroimaging techniques and programming skills (Matlab). For the PhD student positions, familiarity with neuroimaging techniques and good programming skills (Matlab) are desirable. <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
<br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Proficiency in oral and written English is required. You are expected to work in an interdisciplinary environment, sharing technical know-how and ideas.</p>
<p style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:1em;padding-left:0px"><strong style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Organization</strong><br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
The Donders Institute for Brain, Cognition and Behaviour consists of the Centre for Cognition, the Centre for Cognitive Neuroimaging, and the Centre for Neuroscience. <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
<br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">The mission of the Centre for Cognitive Neuroimaging is to conduct cutting-edge fundamental research in cognitive neuroscience. Much of the rapid progress in this field is being driven by the development of complex neuroimaging techniques for measuring activity in the human working brain - an area in which the Centre plays a leading role. The research themes cover central cognitive functions, such as perception, action, control, decision making, attention, memory, language, learning and plasticity. The Centre also aims to establish how the different brain areas coordinate their activity with very high temporal precision to enable human and animal cognition. The internationally renowned centre currently hosts more than 100 PhD students and postdoctoral researchers from more than 20 nationalities, offering a stimulating and multidisciplinary research environment. The centre is equipped with three MRI scanners (7T, 3T, 1.5T), a 275-channel MEG system, an EEG-TMS laboratory, several (MR-compatible) EEG systems, and high-performance computational facilities. English is the lingua franca at the centre. <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
Website: <a href="http://www.ru.nl/donders" style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;color:rgb(190, 49, 26);text-decoration:underline" target="_blank">http://www.ru.nl/donders</a> <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
<br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px"><b style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Conditions of employment</b> <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
Employment: 1,0 fte <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">PhD student: The starting salary is €2,042 per month and will increase to €2,612 per month in the fourth year. <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
Postdoctoral researcher: Depending on experience, the gross salary will be between €3,195 and €4,374.</p><p style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:1em;padding-left:0px">
<strong style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Additional conditions of employment</strong><br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
Duration of the PhD-student contracts: 4 years. <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Duration of the postdoctoral contracts: 3 years (extension possible). <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
<br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">In addition to the gross monthly salary, you will receive two yearly 8% bonuses (holiday and end-of-year).</p>
<p style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:1em;padding-left:0px"><strong style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Other Information</strong><br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
You should submit your application in a single PDF file, including an application letter, a statement of research interests, your CV, and the names of two persons who can provide references. <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
<br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px"><b style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Additional Information</b> <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
Dr Christian Doeller, Principal Investigator <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Telephone: +31 24 3610983 <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
E-mail: <a href="mailto:[log in to unmask]" style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px;color:rgb(190, 49, 26);text-decoration:underline" target="_blank">[log in to unmask]</a></p>
<p style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:1em;padding-left:0px"><strong style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Application</strong><br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
You can apply for the job (mention the vacancy number 30.03.11) <b style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">before 15 March 2011</b> by sending your application -preferably by email- to: <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
<br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Radboud University Nijmegen, P&O department <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
PO Box 7005, 6503 GM NIJMEGEN, NL <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">Telephone: +31 24 3611173 <br style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px;padding-top:0px;padding-right:0px;padding-bottom:0px;padding-left:0px">
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--001485f92356386899049c7d4470--
========================================================================Date: Thu, 17 Feb 2011 16:49:21 +0000
Reply-To: Vy Dinh <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vy Dinh <[log in to unmask]>
Subject: Re: VOI Batch Module
Comments: To: James Bursley <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
Content-Type: text/plain; charset="us-ascii"
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Hi James,
I think that this issue may have been discussed on the forums before. When you specify the adjust step, you set this field to the index for your F-contrast, which means that the F-contrast needed to be specified at the first level model or that your index was wrong. Since you mentioned that there were not any errors when you did this via the GUI, the latter may be the issue. Hope that helps.
Vy T.U. Dinh
Research Assistant, Neurological Sciences
Rush University Medical Center
Phone: (312) 563-3853
Fax: (312) 563-4660
Email: [log in to unmask]
________________________________________
From: SPM (Statistical Parametric Mapping) [[log in to unmask]] on behalf of James Bursley [[log in to unmask]]
Sent: Wednesday, February 16, 2011 8:21 PM
To: [log in to unmask]
Subject: Re: [SPM] VOI Batch Module
Two other pieces of information regarding this problem:
1. No errors come up when I extract a VOI using the GUI; I only encounter this error when using the batch script.
2. The error only appears when "Adjust data" is set to something other than '0', i.e., when data IS being adjusted. Otherwise, the batch runs fine.
========================================================================Date: Thu, 17 Feb 2011 11:28:46 -0800
Reply-To: Siddharth Srivastava <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Siddharth Srivastava <[log in to unmask]>
Subject: help with spm_snbasis code
MIME-Version: 1.0
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Hi everyone,
I have been reading source for spm_snbasis and spm_brainwarp to get a better
understanding of the
underlying math, and I came across these lines in spm_snbasis (spm99 source,
line no 82-85):
s1 = 3*prod(k);
s2 = s1 + prod(size(VG))*4;
T = zeros(s2,1);
T(s1+(1:4:prod(size(VG))*4)) = 1;
I wanted to know why T is initialized the way it is by the last line of the
source snippet? Also, what does
the multiplication by 4 imply?
thanks,
sid.
--bcaec53f944ffb757a049c7f67ac
Content-Type: text/html; charset=ISO-8859-1
Hi everyone,<br><br>I have been reading source for spm_snbasis and spm_brainwarp to get a better understanding of the <br>underlying math, and I came across these lines in spm_snbasis (spm99 source, line no 82-85):<br><br>
s1 = 3*prod(k);<br>s2 = s1 + prod(size(VG))*4;<br>T = zeros(s2,1);<br>T(s1+(1:4:prod(size(VG))*4)) = 1;<br><br>I wanted to know why T is initialized the way it is by the last line of the source snippet? Also, what does<br>
the multiplication by 4 imply? <br><br>thanks,<br>sid.<br><br><br>
--bcaec53f944ffb757a049c7f67ac--
========================================================================Date: Thu, 17 Feb 2011 20:08:30 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: help with spm_snbasis code
Comments: To: Siddharth Srivastava <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Message-ID: <[log in to unmask]>
There are additional parameters that model linear intensity gradients
in the x, y and z directions. The parameters would essentially be
coefficients for a linear combination if
template1 template1.*x template1.*y template1.*z template2
template2.*x template2.*y template2.*z ...
Best regards,
-John
On 17 February 2011 19:28, Siddharth Srivastava <[log in to unmask]> wrote:
> Hi everyone,
>
> I have been reading source for spm_snbasis and spm_brainwarp to get a better
> understanding of the
> underlying math, and I came across these lines in spm_snbasis (spm99 source,
> line no 82-85):
>
> s1 = 3*prod(k);
> s2 = s1 + prod(size(VG))*4;
> T = zeros(s2,1);
> T(s1+(1:4:prod(size(VG))*4)) = 1;
>
> I wanted to know why T is initialized the way it is by the last line of the
> source snippet? Also, what does
> the multiplication by 4 imply?
>
> thanks,
> sid.
>
>
>
========================================================================Date: Thu, 17 Feb 2011 15:38:34 -0500
Reply-To: [log in to unmask]
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Jake Thompson <[log in to unmask]>
Subject: How to display mutltiple spm results for one subject with
multiple t-contrasts with batch editor?
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative;
boundary="--------MB_8CD9CF9C7F2EC49_D14_23BBC_webmail-d011.sysops.aol.com"
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This is a multi-part message in MIME format.
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How to display mutltiple spm results for one subject with multiple t-contrasts with batch editor? I am only able to display the results from the last T-contrasts, but I want to display the results for all the t-contrasts I pu tinto the batch editor.
----------MB_8CD9CF9C7F2EC49_D14_23BBC_webmail-d011.sysops.aol.com
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<div id=AOLMsgPart_1_8e9dde64-b803-4eb6-978d-333d507b2c48><FONT color=black size=2 face=arial><FONT color=black size=2 face=arial>
<div><FONT face="Arial, Helvetica, sans-serif">How to display mutltiple spm results for one subject with multiple t-contrasts with batch editor?</FONT> I am only able to display the results from the last T-contrasts, but I want to display the results for all the t-contrasts I pu tinto the batch editor.<br>
</div>
<div style="CLEAR: both"></div>
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========================================================================Date: Fri, 18 Feb 2011 00:10:35 +0100
Reply-To: Saemann Philipp <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Saemann Philipp <[log in to unmask]>
Subject: sensitivity of DARTEL
Mime-Version: 1.0
Content-Type: text/plain; charset="us-ascii"
Message-ID: <[log in to unmask]>
Dear SPMers,
in a recent paper by Pereira et al. on Resgistration accuracy for VBM
studies, NeuroImage 2010;49:2205-15 DARTEL as implemented
in SPM5 is presented as very insensitive to focal GM changes - in their
work, they used a small Alzheimer Disease sample to
to (amongmany other analyses) check the sensitivity to detect hippocampal
atrophy.
However, sensitivity and regional specificity were completely regained when
three preprocessing steps were performed:
1. skull stripping, 2. bias correction, and 3., "fine tuned BET".
> Has anybody made similar observations on insensitivity of DARTEL?
> Is the SPM Bias correction (default parameters) really so non-optimal?
> Is it the order of steps (first skull stripping, then bias correction)
the crucial difference?
> Is DARTEL in SPM8 in combination with the "New Segment" function more
sensitive?
I think for VBM where DARTEL has (my perception) repeatedly been
recommended as excellent/best registration tool,
it would be very useful if also the preprocessing/optimizations can be
performed within SPM. - It is somewhat
strange that extra preprocessing steps seem necessary outside SPM.
I am very interested in comments/opinions here,
thanks,
Philipp
Max Planck Institute of Psychiatry
NMR Research Group
Kraepelinstr. 2-10
80804 Munich
Mail: [log in to unmask]
Phone: 0049-89-30622-413
========================================================================Date: Thu, 17 Feb 2011 18:53:01 -0500
Reply-To: Lisa Pan <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Lisa Pan <[log in to unmask]>
Subject: SVC correction
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--001485f1dbbc0e0ec4049c831997
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Does anyone know what exactly the SVC option in SPM5 is doing? You can
select and area to SVC correct and then you can also choose FWE or FDR, but
you do not have to. If you just do SVC, what is it calculating?
Thank you for any information.
Lisa
--
Become a Friend!
http://FriendsofPineRidgeReservation.org
--001485f1dbbc0e0ec4049c831997
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<div>Does anyone know what exactly the SVC option in SPM5 is doing? You can select and area to SVC correct and then you can also choose FWE or FDR, but you do not have to. If you just do SVC, what is it calculating? </div>
<div> </div>
<div>Thank you for any information.</div>
<div><br>Lisa<br clear="all"><br>-- <br>Become a Friend!<br><a href="http://FriendsofPineRidgeReservation.org">http://FriendsofPineRidgeReservation.org</a><br><br></div>
--001485f1dbbc0e0ec4049c831997--
========================================================================Date: Fri, 18 Feb 2011 00:09:00 +0000
Reply-To: Anup Bidesi <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Anup Bidesi <[log in to unmask]>
Subject: VBM results question
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Hello SPM'ers,
I have question about VBM data results (from 10, Normals and 19, Schizophrenic subjects) upon segmenting them using VBM8 w/Dartel normalization and looking at the stats using non-parametric SnPM toolbox.
For the GM/WM Segmentaion: I've used VBM8's Estimate & Write option and set all other parameters to defaults in extended options (with modulated normalized "non-linear only" selected) and later smoothed the data using a 12 mm kernel using SPM8, to obtain the the processed data as sm0wrp1/p2/p3 associated with GM/WM/CSF segmentation, To compare both groups: design/set-up, estimation/compute and Results were done using SnPM. I've downloaded SnPM for SPM8 (see Readme.txt file attached from snpm folder), however while loading up SnPM in SPM8 it is stated as version SnPM5.
In the SnPM Set-up following parameters were used: 2 Groups: Two Sample T test; 1scan/subject, Subject index specified as: AAA...(for Normals)...BBB...(for SZ subjs.) ;
Selected Yes, for # Perms. Use approx. test? and Specified no. of Permutions as 1000 to begin with to 10,000;
Variance smoothing of 10 mm, specified Yes, for Collect supra-Threshold stats?; with "No" for Define the thresh now? ,
No global normalisation and grand mean scaling; specified Absolute Threshold masking of 0.15.
For Analysis mask specified:Smooth modulated sample subject scan (sm0wrp1....img) with 1000 & 10,000 permutations and also tried canonical "avg_152T1_GM.hdr/.img" with 5000 permutations as Gray matter masks.
Upon looking up for GM differences between 2 groups with these SnPM settings, estimate and looking up results specifying "+ve" for effects (since looking for NC>SZ)
Selecting to "Write filtered statistic img"; specifying P for "Results for which img? T/P" and then specifying FDR-corrected p value threshold of 0.05 and saved the outputs attached.
There GM differences between both groups did not survive corrected threshold at 0.05 threshold, but showed meaningful differences using 10,000 permutations with smoothed modulated subject GM segmented scan as analysis mask and for 5000 permutations with avg_152T1_GM .img as GM mask.
Upon looking the filtered_statistic image from both the tests using xjview, the clusters seem to contain considerable WM voxels (upto 40-50%) while looking for GM differences between groups ? why do they appear and how can these WM voxels be cleaned out from the results ?
Thanks for all your help!
Anup
Research Associate
Dept. of Psychiatry,
UT Southwestern Medical Center
________________________________
UT Southwestern Medical Center
The future of medicine, today.
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<font style="font-family: Tahoma;" size="2">Hello SPM'ers,<br>
<br>
I have question about VBM data results (from 10, Normals and 19, Schizophrenic subjects) upon segmenting them using VBM8 w/Dartel normalization and looking at the stats using non-parametric SnPM toolbox.<br>
<br>
For the GM/WM Segmentaion: I've used VBM8's Estimate & Write option </font><font style="font-family: Tahoma;" color="black" size="2"><span style="font-size: 13px;" dir="ltr">and set all other parameters to defaults in extended options (with modulated normalized
"non-linear only" selected) and later smoothed the data using a 12 mm kernel using SPM8, to obtain the the processed data as sm0wrp1/p2/p3 associated with GM/WM/CSF segmentation, To compare both groups: design/set-up, estimation/compute and Results were done
using SnPM</span></font><font style="font-family: Tahoma;" size="2">. I've downloaded SnPM for SPM8 (see Readme.txt file attached from snpm folder), however while loading up SnPM in SPM8 it is stated as version SnPM5.<br>
<br>
In the SnPM Set-up following parameters were used: 2 Groups: Two Sample T test; 1scan/subject, Subject index specified as: AAA...(for Normals)...BBB...(for SZ subjs.) ;</font><font style="font-family: Tahoma;" size="2"><span style="font-size: 120%;"><font style="font-family: Tahoma;"><br>
<span style="font-family: Times New Roman;">Selected Yes, for # Perms. Use approx. test? and Specified no. of Permutions as 1000 to begin with to 10,000;
</span><br style="font-family: Times New Roman;">
<span style="font-family: Times New Roman;">Variance smoothing of 10 mm, specified Yes, for Collect supra-Threshold stats?; with "No" for Define the thresh now? ,
</span><br style="font-family: Times New Roman;">
<span style="font-family: Times New Roman;">No global normalisation and grand mean scaling; specified Absolute Threshold masking of 0.15.</span><br style="font-family: Times New Roman;">
<span style="font-family: Times New Roman;">For Analysis mask</span></font><span style="font-family: Times New Roman;">
</span></span></font><font style="font-family: Times New Roman;" size="2"><span style="font-size: 120%;">specified</span></font><font style="font-family: Times New Roman;" size="2"><span style="font-size: 120%;">:Smooth modulated sample subject scan (sm0wrp1....img)
with 1000 & 10,000 permutations and also tried canonical "avg_152T1_GM.hdr/.img" with 5000 permutations as Gray matter masks.<br>
<br>
Upon looking up for GM differences between 2 groups with these SnPM settings, estimate and looking up results</span></font><font style="font-family: Tahoma;" size="2"><span style="font-size: 10pt; color: black;"><span style="font-family: Times New Roman;">
</span>specifying "<span style="font-weight: bold;">+ve</span>" for effects (since looking for NC>SZ)<br>
<br>
Selecting to "Write filtered statistic img"; specifying <span style="font-weight: bold;">
P</span> for "Results for which img? T/P"</span></font><span style="font-size: 10pt; color: black; font-family: Tahoma;"><font size="3"><font size="2"> and then specifying
<span style="font-weight: bold;">FDR-corrected </span>p value threshold of <span style="font-weight: bold;">
0.05 </span>and saved the outputs attached. <br>
There GM differences between both groups did not survive corrected threshold at 0.05 threshold, but showed meaningful differences using 10,000 permutations with smoothed modulated subject GM segmented scan as analysis mask and for 5000 permutations with avg_152T1_GM
.img as GM mask.<br>
<br>
Upon looking the filtered_statistic image from both the tests using xjview, the clusters seem to contain considerable WM voxels (upto 40-50%) while looking for GM differences between groups ? why do they appear and how can these WM voxels be cleaned out from
the results ?<br>
<br>
Thanks for all your help!<br>
<br>
Anup<br>
<br>
Research Associate<br>
Dept. of Psychiatry,<br>
UT Southwestern Medical Center <br>
</font></font></span><span style="font-size: 10pt; color: black;"><br>
</span></div>
<br>
<hr>
<font face="Arial" color="Blue" size="2"><br>
UT Southwestern Medical Center<br>
The future of medicine, today.<br>
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--_008_006C97B7E36B0340BBD2B31E976738F306CF72B7swmsmail2swmedo_--
========================================================================Date: Fri, 18 Feb 2011 00:10:48 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: sensitivity of DARTEL
Comments: To: Saemann Philipp <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Message-ID: <[log in to unmask]>
> in a recent paper by Pereira et al. on Resgistration accuracy for VBM
> studies, NeuroImage 2010;49:2205-15 DARTEL as implemented
> in SPM5 is presented as very insensitive to focal GM changes - in their
> work, they used a small Alzheimer Disease sample to
> to (amongmany other analyses) check the sensitivity to detect hippocampal
> atrophy.
>
> However, sensitivity and regional specificity were completely regained when
> three preprocessing steps were performed:
> 1. skull stripping, 2. bias correction, and 3., "fine tuned BET".
The limited set of tissue probability maps mean that SPM5 segmentation
doesn't handle separating bone from CSF very well for some data, so
skull stripping could be of some benefit. This is usually of less
importance for SPM8 though, although there are still occasional scans
that cause problems if there are strange artifacts in the background
(from strange preprocessing done to the data) or if the subject is
very heavily built (with lots of tissue outside the skull).
The optimal amount of bias correction will depend on the data used.
If the scans contain a lot of artifact, then lots of bias correction
is needed. If there is no bias artifact, then the best results should
result when no bias correction is done. If the data are heavily
corrupted, the default amount of correction may not be great enough.
In these cases, it is relatively easy to decrease the amount of
regularisation used within the segmentation algorithm, rather than
bias correcting the data first.
>
>> Has anybody made similar observations on insensitivity of DARTEL?
>> Is the SPM Bias correction (default parameters) really so non-optimal?
>> Is it the order of steps (first skull stripping, then bias correction)
> the crucial difference?
>> Is DARTEL in SPM8 in combination with the "New Segment" function more
> sensitive?
I'd be slightly curious about feedback too, although I no longer care
that much about the accuracy of results from SPM5, as it is now six
years out of date.
>
> I think for VBM where DARTEL has (my perception) repeatedly been
> recommended as excellent/best registration tool,
> it would be very useful if also the preprocessing/optimizations can be
> performed within SPM. - It is somewhat
> strange that extra preprocessing steps seem necessary outside SPM.
All I can say is that the evaluations done for the paper by Klein et
al show that Dartel and a number of other approaches achieved better
alignment (as judged via manual segmentations) than the other spatial
normalisation procedures available within SPM. More accurate
alignment should translate into more accurate interpetation of results
in terms of localising volumetric differences. This may not
necessarily translate into smaller p values, as systematic
mis-registration could potentially cause significant differences to
occur.
Best regards,
-John
========================================================================Date: Thu, 17 Feb 2011 17:05:35 -0800
Reply-To: Siddharth Srivastava <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Siddharth Srivastava <[log in to unmask]>
Subject: Re: help with spm_snbasis code
Comments: To: John Ashburner <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Hi John,
Thanks for the reply. regarding my other question, what does the
multiplication by 4 do? I am guessing that a constant term will also
be modeled (3 + 1 paramater sets for this, constant term = 1), can you
please verify if I am thinking correctly about this initialization strategy?
thanks again,
sid.
On Thu, Feb 17, 2011 at 12:08 PM, John Ashburner <[log in to unmask]>wrote:
> There are additional parameters that model linear intensity gradients
> in the x, y and z directions. The parameters would essentially be
> coefficients for a linear combination if
>
> template1 template1.*x template1.*y template1.*z template2
> template2.*x template2.*y template2.*z ...
>
> Best regards,
> -John
>
> On 17 February 2011 19:28, Siddharth Srivastava <[log in to unmask]> wrote:
> > Hi everyone,
> >
> > I have been reading source for spm_snbasis and spm_brainwarp to get a
> better
> > understanding of the
> > underlying math, and I came across these lines in spm_snbasis (spm99
> source,
> > line no 82-85):
> >
> > s1 = 3*prod(k);
> > s2 = s1 + prod(size(VG))*4;
> > T = zeros(s2,1);
> > T(s1+(1:4:prod(size(VG))*4)) = 1;
> >
> > I wanted to know why T is initialized the way it is by the last line of
> the
> > source snippet? Also, what does
> > the multiplication by 4 imply?
> >
> > thanks,
> > sid.
> >
> >
> >
>
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Hi John,<br> Thanks for the reply. regarding my other question, what does the <br>multiplication by 4 do? I am guessing that a constant term will also<br>be modeled (3 + 1 paramater sets for this, constant term = 1), can you <br>
please verify if I am thinking correctly about this initialization strategy? <br>thanks again,<br>sid.<br><br><div class="gmail_quote">On Thu, Feb 17, 2011 at 12:08 PM, John Ashburner <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">There are additional parameters that model linear intensity gradients<br>
in the x, y and z directions. The parameters would essentially be<br>
coefficients for a linear combination if<br>
<br>
template1 template1.*x template1.*y template1.*z template2<br>
template2.*x template2.*y template2.*z ...<br>
<br>
Best regards,<br>
<font color="#888888">-John<br>
</font><div><div></div><div class="h5"><br>
On 17 February 2011 19:28, Siddharth Srivastava <<a href="mailto:[log in to unmask]">[log in to unmask]</a>> wrote:<br>
> Hi everyone,<br>
><br>
> I have been reading source for spm_snbasis and spm_brainwarp to get a better<br>
> understanding of the<br>
> underlying math, and I came across these lines in spm_snbasis (spm99 source,<br>
> line no 82-85):<br>
><br>
> s1 = 3*prod(k);<br>
> s2 = s1 + prod(size(VG))*4;<br>
> T = zeros(s2,1);<br>
> T(s1+(1:4:prod(size(VG))*4)) = 1;<br>
><br>
> I wanted to know why T is initialized the way it is by the last line of the<br>
> source snippet? Also, what does<br>
> the multiplication by 4 imply?<br>
><br>
> thanks,<br>
> sid.<br>
><br>
><br>
><br>
</div></div></blockquote></div><br>
--bcaec53f944f8ec7fa049c841ce9--
========================================================================Date: Fri, 18 Feb 2011 02:45:09 +0100
Reply-To: michel grothe <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: michel grothe <[log in to unmask]>
Subject: Re: sensitivity of DARTEL
Comments: To: [log in to unmask]
In-Reply-To: <[log in to unmask]>
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Dear Philipp and others,
with my own data, or more exactly with the OASIS database (not skull-stripped), I´ve made similar observations, i.e. SPM8-new-segmentation (or "normal" SPM5-unified-segmentation)+DARTEL VBM failed to detect significant hippocampus atrophy for most parts of the hippocampus in AD and MCI. While the effects seemed to be consistent with the literature for most parts of the cortex (atrophy in PCC, ACC, temporoparietal areas and relative sparing of somoatosensory areas and the cerebellum in AD), effects in the MTL were restricted to the amygdala and anterior parts of the hippocampus.
However, VBM procedures rely on a mixture of accurate segmentation AND normalisation to a common space and I think the error here may rather lie in inaccurate segmentaion than in the DARTEL normalisation. Repeating the analysis with "Gaser"-segmented images (VBM8) and exactly the same DARTEL-settings revealed "perfect" (i.e. expected or more consistent with the literature) results for the MTL and the rest of the cortex.
A possible explanation for this observation is that registration accuracy (using tissue-priors based on a normal population) and hence segmentation accuracy of the unified approaches in spm5/8 decreases with increasing atrophy. Although the detailed tissue information in "New-segment" normally leads to convincingly improved segmentation results and the high-dimensional warping in DARTEL is capable to model large deformations, it might still be necessary to adjust the tissue priors when dealing with groups that differ strongly from the anatomy of the normal population. The tissue prior-free approach of the VBM8-toolbox produced much more "realistic" results when comparing a large cohort of healthy elderly and AD patients (the OASIS database).
Would be interesting to hear about the experiences of the rest of the SPM/VBM-community.
Best regards,
Michel
> Date: Fri, 18 Feb 2011 00:10:35 +0100
> From: [log in to unmask]
> Subject: [SPM] sensitivity of DARTEL
> To: [log in to unmask]
>
> Dear SPMers,
>
> in a recent paper by Pereira et al. on Resgistration accuracy for VBM
> studies, NeuroImage 2010;49:2205-15 DARTEL as implemented
> in SPM5 is presented as very insensitive to focal GM changes - in their
> work, they used a small Alzheimer Disease sample to
> to (amongmany other analyses) check the sensitivity to detect hippocampal
> atrophy.
>
> However, sensitivity and regional specificity were completely regained when
> three preprocessing steps were performed:
> 1. skull stripping, 2. bias correction, and 3., "fine tuned BET".
>
> > Has anybody made similar observations on insensitivity of DARTEL?
> > Is the SPM Bias correction (default parameters) really so non-optimal?
> > Is it the order of steps (first skull stripping, then bias correction)
> the crucial difference?
> > Is DARTEL in SPM8 in combination with the "New Segment" function more
> sensitive?
>
> I think for VBM where DARTEL has (my perception) repeatedly been
> recommended as excellent/best registration tool,
> it would be very useful if also the preprocessing/optimizations can be
> performed within SPM. - It is somewhat
> strange that extra preprocessing steps seem necessary outside SPM.
>
> I am very interested in comments/opinions here,
> thanks,
> Philipp
> Max Planck Institute of Psychiatry
> NMR Research Group
> Kraepelinstr. 2-10
> 80804 Munich
> Mail: [log in to unmask]
> Phone: 0049-89-30622-413
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Dear Philipp and others,<br><br>with my own data, or more exactly with the OASIS database (not skull-stripped), I´ve made similar observations, i.e. SPM8-new-segmentation (or "normal" SPM5-unified-segmentation)+DARTEL VBM failed to detect significant hippocampus atrophy for most parts of the hippocampus in AD and MCI. While the effects seemed to be consistent with the literature for most parts of the cortex (atrophy in PCC, ACC, temporoparietal areas and relative sparing of somoatosensory areas and the cerebellum in AD), effects in the MTL were restricted to the amygdala and anterior parts of the hippocampus. <br>However, VBM procedures rely on a mixture of accurate segmentation AND normalisation to a common space and I think the error here may rather lie in inaccurate segmentaion than in the DARTEL normalisation. Repeating the analysis with "Gaser"-segmented images (VBM8) and exactly the same DARTEL-settings revealed "perfect" (i.e. expected or more consistent with the literature) results for the MTL and the rest of the cortex.<br>A possible explanation for this observation is that registration accuracy (using tissue-priors based on a normal population) and hence segmentation accuracy of the unified approaches in spm5/8 decreases with increasing atrophy. Although the detailed tissue information in "New-segment" normally leads to convincingly improved segmentation results and the high-dimensional warping in DARTEL is capable to model large deformations, it might still be necessary to adjust the tissue priors when dealing with groups that differ strongly from the anatomy of the normal population. The tissue prior-free approach of the VBM8-toolbox produced much more "realistic" results when comparing a large cohort of healthy elderly and AD patients (the OASIS database).<br><br>Would be interesting to hear about the experiences of the rest of the SPM/VBM-community.<br><br>Best regards,<br>Michel<br><br>> Date: Fri, 18 Feb 2011 00:10:35 +0100<br>> From: [log in to unmask]<br>> Subject: [SPM] sensitivity of DARTEL<br>> To: [log in to unmask]<br>> <br>> Dear SPMers,<br>> <br>> in a recent paper by Pereira et al. on Resgistration accuracy for VBM<br>> studies, NeuroImage 2010;49:2205-15 DARTEL as implemented<br>> in SPM5 is presented as very insensitive to focal GM changes - in their<br>> work, they used a small Alzheimer Disease sample to<br>> to (amongmany other analyses) check the sensitivity to detect hippocampal<br>> atrophy.<br>> <br>> However, sensitivity and regional specificity were completely regained when<br>> three preprocessing steps were performed:<br>> 1. skull stripping, 2. bias correction, and 3., "fine tuned BET".<br>> <br>> > Has anybody made similar observations on insensitivity of DARTEL?<br>> > Is the SPM Bias correction (default parameters) really so non-optimal?<br>> > Is it the order of steps (first skull stripping, then bias correction)<br>> the crucial difference?<br>> > Is DARTEL in SPM8 in combination with the "New Segment" function more<br>> sensitive?<br>> <br>> I think for VBM where DARTEL has (my perception) repeatedly been<br>> recommended as excellent/best registration tool,<br>> it would be very useful if also the preprocessing/optimizations can be<br>> performed within SPM. - It is somewhat<br>> strange that extra preprocessing steps seem necessary outside SPM.<br>> <br>> I am very interested in comments/opinions here,<br>> thanks,<br>> Philipp<br>> Max Planck Institute of Psychiatry<br>> NMR Research Group<br>> Kraepelinstr. 2-10<br>> 80804 Munich<br>> Mail: [log in to unmask]<br>> Phone: 0049-89-30622-413<br> </body>
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========================================================================Date: Fri, 18 Feb 2011 10:59:22 +0900
Reply-To: HwangArum <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: HwangArum <[log in to unmask]>
Subject: Asking "interaction analysis" in spm !! :D
Comments: To: [log in to unmask], [log in to unmask], [log in to unmask]
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We conducted 2x2 within-subject design in fMRI study. Each subjects performed two different kinds of tasks A, B in two different contexts X, Y (e.g., X[A-B]-Y[A-B]).
Is it allowed to make an ¡°interaction analysis¡± (e.g., [XA-XB]-[YA-YB]) with two-sample t-test in within-subject design (using spm)?
For example, put [XA-XB] con image into one group and put [YA-YB] con image into another group. If yes, indepence option should be set as yes(default) or should be changed into no?
Best regards, Arum Hwang
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<P style="MARGIN: 0cm 0cm 0pt" class=MsoNormal><SPAN lang=EN-US><FONT face="¸¼Àº °íµñ">We conducted 2x2 within-subject design in fMRI study. Each subjects performed two different kinds of tasks A, B in two different contexts X, Y (e.g., X[A-B]-Y[A-B]).</FONT></SPAN></P>
<P style="MARGIN: 0cm 0cm 0pt" class=MsoNormal><SPAN lang=EN-US><FONT face="¸¼Àº °íµñ"></FONT></SPAN> </P>
<P style="MARGIN: 0cm 0cm 0pt" class=MsoNormal><SPAN lang=EN-US><FONT face="¸¼Àº °íµñ">Is it allowed to make an ¡°interaction analysis¡± (e.g., [XA-XB]-[YA-YB]) with two-sample t-test in within-subject design (using spm)?</FONT></SPAN></P>
<P style="MARGIN: 0cm 0cm 0pt" class=MsoNormal><SPAN lang=EN-US><FONT face="¸¼Àº °íµñ"></FONT></SPAN> </P>
<P style="MARGIN: 0cm 0cm 0pt" class=MsoNormal><SPAN lang=EN-US><FONT face="¸¼Àº °íµñ">For example, put [XA-XB] con image into one group and put [YA-YB] con image into another group. If yes, indepence option should be set as yes(default) or should be changed into no?</FONT></SPAN></P>
<P style="MARGIN: 0cm 0cm 0pt" class=MsoNormal><SPAN lang=EN-US><FONT face="¸¼Àº °íµñ"></FONT></SPAN> </P>
<P style="MARGIN: 0cm 0cm 0pt" class=MsoNormal><SPAN lang=EN-US><FONT face="¸¼Àº °íµñ"></FONT></SPAN> </P>
<P style="MARGIN: 0cm 0cm 0pt" class=MsoNormal><SPAN lang=EN-US><FONT face="¸¼Àº °íµñ">Best regards, Arum Hwang</FONT></SPAN></P> </body>
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--_d7c4f5ac-ffad-4f6f-88e1-ca978ece01e9_--
========================================================================Date: Thu, 17 Feb 2011 21:25:03 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: Asking "interaction analysis" in spm !! :D
Comments: To: HwangArum <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=windows-1252
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You need to use a paired t-test.
On Thursday, February 17, 2011, HwangArum <a[log in to unmask]> wrote:
>
>
>
>
>
> We conducted 2x2 within-subject design in fMRI study. Each subjects performed two different kinds of tasks A, B in two different contexts X, Y (e.g., X[A-B]-Y[A-B]).
>
> Is it allowed to make an “interaction analysis” (e.g., [XA-XB]-[YA-YB]) with two-sample t-test in within-subject design (using spm)?
>
> For example, put [XA-XB] con image into one group and put [YA-YB] con image into another group. If yes, indepence option should be set as yes(default) or should be changed into no?
>
>
> Best regards, Arum Hwang
>
--
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED
HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is
intended only for the use of the individual or entity named above. If the
reader of the e-mail is not the intended recipient or the employee or agent
responsible for delivering it to the intended recipient, you are hereby
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action in reliance on the contents of this information is strictly
prohibited and may be unlawful. If you have received this e-mail
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406-2464 or email.
========================================================================Date: Fri, 18 Feb 2011 02:18:46 +0000
Reply-To: chihiro <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: chihiro <[log in to unmask]>
Subject: HELP!with VBM (estimation error)
Mime-Version: 1.0
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During my data processing, I encountered some problems.
I used VBM to analyze.
First, I used the DARTEL tool for VBM following the DARTEL manual and got the smwc images.
In the statistical analysis, I tried to find the differences
between a patient and 20 age matched healthy controls with two-sample t-test (one group was a patient, and
the other group was the healthy individuals).
I could do the design specification but when I did model estimation, the following error was occured.
Running job #1
------------------------------------------------------------------------
Running 'Factorial design specification'
Mapping files : ...done
Design configuration : ...done
Saving SPM configuration : ...SPM.mat saved
Design reporting : ...done
Done
Done 'Factorial design specification'
Done
------------------------------------------------------------------------
Running job #2
------------------------------------------------------------------------
Running 'Model estimation'
SPM8: spm_spm (v3468) 10:39:13 - 18/02/2011
========================================================================
Initialising parameters : ...done
Plane 121/121, block 1/1 : ...done
Temporal non-sphericity (over voxels) : ...ReML estimation
ReML Iteration 1 : ...0.000000e+00 [+4.25]
SPM8: spm_spm (v3468) 10:39:28 - 18/02/2011
========================================================================
Initialising parameters : ...computingFailed 'Model estimation'
Index exceeds matrix dimensions.
In file "/Applications/MATLAB_R2009b.app/toolbox/matlab/sparfun/spdiags.m" (???), function "spdiags" at line 114.
In file "/Applications/spm8/spm_spm.m" (v3468), function "spm_spm" at line 427.
In file "/Applications/spm8/spm_spm.m" (v3468), function "spm_spm" at line 878.
In file "/Applications/spm8/config/spm_run_fmri_est.m" (v3327), function "spm_run_fmri_est" at line 53.
The following modules did not run:
Failed: Model estimation
I uses the MAC OS X 10.6.6,
the MATLAB 7.9.0.529 (R2009b),
and the SPM 8.
I appreciate if you can help me!
========================================================================Date: Thu, 17 Feb 2011 21:53:22 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: HELP!with VBM (estimation error)
Comments: To: chihiro <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
Set the variance to equal.
On Thursday, February 17, 2011, chihiro <[log in to unmask]> wrote:
> During my data processing, I encountered some problems.
>
> I used VBM to analyze.
> First, I used the DARTEL tool for VBM following the DARTEL manual and got the smwc images.
>
> In the statistical analysis, I tried to find the differences
> between a patient and 20 age matched healthy controls with two-sample t-test (one group was a patient, and
> the other group was the healthy individuals).
>
> I could do the design specification but when I did model estimation, the following error was occured.
>
> Running job #1
> ------------------------------------------------------------------------
> Running 'Factorial design specification'
> Mapping files : ...done
> Design configuration : ...done
> Saving SPM configuration : ...SPM.mat saved
> Design reporting : ...done
> Done
> Done 'Factorial design specification'
> Done
>
>
>
> ------------------------------------------------------------------------
> Running job #2
> ------------------------------------------------------------------------
> Running 'Model estimation'
>
> SPM8: spm_spm (v3468) 10:39:13 - 18/02/2011
> ========================================================================
> Initialising parameters : ...done
> Plane 121/121, block 1/1 : ...done
> Temporal non-sphericity (over voxels) : ...ReML estimation
> ReML Iteration 1 : ...0.000000e+00 [+4.25]
>
> SPM8: spm_spm (v3468) 10:39:28 - 18/02/2011
> ========================================================================
> Initialising parameters : ...computingFailed 'Model estimation'
> Index exceeds matrix dimensions.
> In file "/Applications/MATLAB_R2009b.app/toolbox/matlab/sparfun/spdiags.m" (???), function "spdiags" at line 114.
> In file "/Applications/spm8/spm_spm.m" (v3468), function "spm_spm" at line 427.
> In file "/Applications/spm8/spm_spm.m" (v3468), function "spm_spm" at line 878.
> In file "/Applications/spm8/config/spm_run_fmri_est.m" (v3327), function "spm_run_fmri_est" at line 53.
>
> The following modules did not run:
> Failed: Model estimation
>
>
>
> I uses the MAC OS X 10.6.6,
> the MATLAB 7.9.0.529 (R2009b),
> and the SPM 8.
>
> I appreciate if you can help me!
>
--
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED
HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is
intended only for the use of the individual or entity named above. If the
reader of the e-mail is not the intended recipient or the employee or agent
responsible for delivering it to the intended recipient, you are hereby
notified that you are in possession of confidential and privileged
information. Any unauthorized use, disclosure, copying or the taking of any
action in reliance on the contents of this information is strictly
prohibited and may be unlawful. If you have received this e-mail
unintentionally, please immediately notify the sender via telephone at (773)
406-2464 or email.
========================================================================Date: Thu, 17 Feb 2011 22:10:06 -0500
Reply-To: John Fredy <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Fredy <[log in to unmask]>
Subject: vbm8 for single subject
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Hello all,
Is possible use the vbm8 toolbox for compare a single subject against the
template
Thanks in advance
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Hello all,<br><br>Is possible use the vbm8 toolbox for compare a single subject against the template<br><br>Thanks in advance<br>
--0016e6d7843fdfb662049c85d9f5--
========================================================================Date: Fri, 18 Feb 2011 03:32:16 +0000
Reply-To: Nero Evero <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Nero Evero <[log in to unmask]>
Subject: MATLAB Out of Memory error
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Hello All,
We are trying to run a fixed effects analysis using SPM8 on a Windows 7 32-bit OS with 4GM of RAM. We have 120 subjects/sessions with a total of 6600 scans (55/session). The analysis stops during the parameter estimation and gives the following erroe message:
Initialising parameters : ...done
Output images : ...initialised
Plane 1/46 , block 1/3 : ...estimation??? Out of memory. Type HELP MEMORY for your options.
Error in ==> spm_spm at 715
CY = CY + Y*Y';
Error in ==> spm_getSPM at 233
SPM = spm_spm(SPM);
Error in ==> spm_results_ui at 277
[SPM,xSPM] = spm_getSPM;
??? Error while evaluating uicontrol Callback
I tried adding more memory to SPM (i.e. by changing defaults.stats.maxmem to about 2GB), but I still received the error. Looking through the archives it seems like the common solution is to change to a 64-bit OS, but I wanted to know if there was any other possible solutions? Thanks in adavance.
Nero
========================================================================Date: Thu, 17 Feb 2011 23:46:29 -0500
Reply-To: Jonathan Peelle <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Jonathan Peelle <[log in to unmask]>
Subject: Re: vbm8 for single subject
Comments: To: John Fredy <[log in to unmask]>
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Hi John
> Is possible use the vbm8 toolbox for compare a single subject against the template
You can't get any sort of statistics out of comparing a single subject against a template because you have no variance. The best you could do is a simple subtraction but this woudn't be particularly informative I don't think. To look at a single participant you really need a sample with which to compare them—e.g., 1 patient vs. 20 age- and sex-matched controls.
Jonathan
--
Dr. Jonathan Peelle
Department of Neurology
University of Pennsylvania
3 West Gates
3400 Spruce Street
Philadelphia, PA 19104
USA
http://jonathanpeelle.net/
========================================================================Date: Fri, 18 Feb 2011 08:44:01 +0100
Reply-To: Lucas Eggert <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Lucas Eggert <[log in to unmask]>
Subject: TIV as nuisance factor for unmodulated data in a VBM-analysis
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Dear Dr. Gaser,
Dear List,
at the moment I am unsure about whether correcting for total brain
volume (TIV) in
a VBM analysis of unmodulated gray matter segments (density) does make
sense or not.
On Dr. Gaser's homepage --- I have the feeling --- there are two
slightly opposite statements:
statement (a) (please refer below) clearly states that TIV should not be
used as a nuisacne factor with unmodulated data.
On the other hand, I read statement (b) (please see below) as correcting
unmodulated data for TIV gives me relative
density after correcting for TIV, which seems to make more sense to me.
Which procedure is the more valid one, or for which kind of question
would I use which procedure respectively?
Thanks for any help in advance.
Best regards,
Lucas Eggert
******************
STATEMENT (a):
http://dbm.neuro.uni-jena.de/vbm/vbm2-for-spm2/calculate-raw-volumes/
"The use of raw volumes as nuisance or globals is only recommended for
modulated data.
These data are corrected for size changes due to spatial normalization
and are thought
to be in raw (un-normalized) space. In contrast, un-modulated data are
yet corrected
for differences in size due to spatial normalization to a reference
brain and there
is no need to correct for these differences again."
STATEMENT (b):
http://dbm.neuro.uni-jena.de/vbm/segmentation/modulation/
Interpretation
If we follow the commonly used terms "volume" for modulated data and
"density" (or concentration)
for unmodulated data and concentrate on GM there are many possible ways
to correct or not correct for different brain size:
*No modulation:*
*Correction * *Interpretation*
nothing relative density
globals "localised" relative density after correcting for total GM or
TIV (multiplicative effects)
AnCova "localised" relative density that can not be explained by total
GM or TIV (additive effects)
--
Lucas Eggert, M.Sc.
Institute of Cognitive Science
University of Osnabrueck
Albrechtstrasse 28
D-49076 Osnabrueck
Germany
Phone: +49-541-969-44-28
Website: http://www.cogsci.uni-osnabrueck.de/~leggert/
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<meta http-equiv="content-type" content="text/html; charset=ISO-8859-15">
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Dear Dr. Gaser,<br>
Dear List,<br>
<br>
at the moment I am unsure about whether correcting for total brain
volume (TIV) in<br>
a VBM analysis of unmodulated gray matter segments (density) does
make sense or not.<br>
<br>
On Dr. Gaser's homepage --- I have the feeling --- there are two
slightly opposite statements:<br>
statement (a) (please refer below) clearly states that TIV should
not be used as a nuisacne factor with unmodulated data.<br>
<br>
On the other hand, I read statement (b) (please see below) as
correcting unmodulated data for TIV gives me relative<br>
density after correcting for TIV, which seems to make more sense to
me.<br>
<br>
Which procedure is the more valid one, or for which kind of question
would I use which procedure respectively?<br>
<br>
Thanks for any help in advance.<br>
<br>
Best regards,<br>
Lucas Eggert<br>
<br>
******************<br>
<br>
STATEMENT (a):<br>
<a class="moz-txt-link-freetext" href="http://dbm.neuro.uni-jena.de/vbm/vbm2-for-spm2/calculate-raw-volumes/">http://dbm.neuro.uni-jena.de/vbm/vbm2-for-spm2/calculate-raw-volumes/</a><br>
<br>
"The use of raw volumes as nuisance or globals is only recommended
for modulated data. <br>
These data are corrected for size changes due to spatial
normalization and are thought <br>
to be in raw (un-normalized) space. In contrast, un-modulated data
are yet corrected <br>
for differences in size due to spatial normalization to a reference
brain and there <br>
is no need to correct for these differences again."<br>
<br>
STATEMENT (b):<br>
<a class="moz-txt-link-freetext" href="http://dbm.neuro.uni-jena.de/vbm/segmentation/modulation/">http://dbm.neuro.uni-jena.de/vbm/segmentation/modulation/</a><br>
<br>
<h4>Interpretation</h4>
<p>If we follow the commonly used terms “volume” for modulated data
and “density” (or concentration) <br>
for unmodulated data and concentrate on GM there are many possible
ways to correct or not correct for different brain size:</p>
<p><strong>No modulation:</strong></p>
<table border="0" cellpadding="0" cellspacing="2">
<tbody>
<tr>
<td><strong>Correction </strong></td>
<td><strong>Interpretation</strong></td>
</tr>
<tr>
<td>nothing</td>
<td>relative density</td>
</tr>
<tr>
<td>globals</td>
<td>“localised” relative density after correcting for total GM
or TIV (multiplicative effects)</td>
</tr>
<tr>
<td>AnCova</td>
<td>“localised” relative density that can not be explained by
total GM or TIV (additive effects)</td>
</tr>
</tbody>
</table>
<br>
<br>
<pre class="moz-signature" cols="72">--
Lucas Eggert, M.Sc.
Institute of Cognitive Science
University of Osnabrueck
Albrechtstrasse 28
D-49076 Osnabrueck
Germany
Phone: +49-541-969-44-28
Website: <a class="moz-txt-link-freetext" href="http://www.cogsci.uni-osnabrueck.de/~leggert/">http://www.cogsci.uni-osnabrueck.de/~leggert/</a>
</pre>
</body>
</html>
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--------------ms040708000206040902000902--
========================================================================Date: Fri, 18 Feb 2011 12:04:06 +0100
Reply-To: Cesar Caballero <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Cesar Caballero <[log in to unmask]>
Subject: is possible to register to another image than first one in
realignment
In-Reply-To: <[log in to unmask]>
MIME-version: 1.0
Content-transfer-encoding: 7BIT
Content-type: text/plain; CHARSET=US-ASCII
Message-ID: <[log in to unmask]>
Hello all,
In realignment, there are two options: register to mean or register to first.
Is there any way to do realignment to a different image that is not the first one in SPM 8?
Thanks very much,
Cesar
========================================================================Date: Fri, 18 Feb 2011 11:21:44 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: is possible to register to another image than first one in
realignment
Comments: To: Cesar Caballero <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Message-ID: <[log in to unmask]>
You could select the images in a different order. There is no
temporal information used by the realignment.
Best regards,
-John
On 18 February 2011 11:04, Cesar Caballero
<[log in to unmask]> wrote:
> Hello all,
>
> In realignment, there are two options: register to mean or register to first.
> Is there any way to do realignment to a different image that is not the first one in SPM 8?
>
> Thanks very much,
> Cesar
>
========================================================================Date: Fri, 18 Feb 2011 12:48:50 +0100
Reply-To: Cesar Caballero <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Cesar Caballero <[log in to unmask]>
Subject: Re: is possible to register to another image than first one in
realignment
Comments: To: John Ashburner <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-version: 1.0
Content-transfer-encoding: 7BIT
Content-type: text/plain; CHARSET=US-ASCII
Message-ID: <[log in to unmask]>
Hello John,
Thanks very much for your answer,
I was wondering about the temporal information of the realignment parameters for the analysis.
What would it be the effect if I select the scans on different order?
Best wishes,
Cesar
On Feb 18, 2011, at 12:21 PM, John Ashburner wrote:
> You could select the images in a different order. There is no
> temporal information used by the realignment.
>
> Best regards,
> -John
>
> On 18 February 2011 11:04, Cesar Caballero
> <[log in to unmask]> wrote:
>> Hello all,
>>
>> In realignment, there are two options: register to mean or register to first.
>> Is there any way to do realignment to a different image that is not the first one in SPM 8?
>>
>> Thanks very much,
>> Cesar
>>
========================================================================Date: Fri, 18 Feb 2011 11:51:34 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: is possible to register to another image than first one in
realignment
Comments: To: Cesar Caballero <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
All scans would be aligned to the first image you select.
Best regards,
-John
On 18 February 2011 11:48, Cesar Caballero
<[log in to unmask]> wrote:
> Hello John,
>
> Thanks very much for your answer,
> I was wondering about the temporal information of the realignment parameters for the analysis.
> What would it be the effect if I select the scans on different order?
>
> Best wishes,
> Cesar
>
> On Feb 18, 2011, at 12:21 PM, John Ashburner wrote:
>
>> You could select the images in a different order. There is no
>> temporal information used by the realignment.
>>
>> Best regards,
>> -John
>>
>> On 18 February 2011 11:04, Cesar Caballero
>> <[log in to unmask]> wrote:
>>> Hello all,
>>>
>>> In realignment, there are two options: register to mean or register to first.
>>> Is there any way to do realignment to a different image that is not the first one in SPM 8?
>>>
>>> Thanks very much,
>>> Cesar
>>>
>
>
========================================================================Date: Fri, 18 Feb 2011 13:00:17 +0100
Reply-To: jmrabanal <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: jmrabanal <[log in to unmask]>
Subject: CURSO DE NEUROIMAGEN AVANZADA
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: quoted-printable
Message-ID: <8244675c248d4ae58ca01f7da700c7ee@localhost>
V CURSO DE NEUROIMAGEN
AVANZADA EN NEUROCIENCIA
COGNITIVA Y PSIQUIATRÃA.
INSCRIPCIÓN NO PRESENCIAL:
Teléfono: 93 248 38 71
E-mail: •[log in to unmask]
INSCRIPCIÓN PRESENCIAL:
Escola de Postgrau • Servei atenció a l’usuari • Edifici U Planta 1
Campus UAB • Bellaterra (Cerdanyola del Vallès)
IMPORTE MATRICULA:
PRECIO: 375 Euros
LUGAR DE REALIZACIÓN DEL CURSO:
Parc de Recerca Biomèdica de Barcelona (PRBB) • Sala Xipre 1ª planta
C/ Doctor Aiguader, 88 • 08003 BARCELONA •
PLAZO DE INSCRIPCIÓN:
Del 10 de Diciembre 2010 al 20 de Marzo 2011
El curso se impartirá en castellano.
Este curso de postgrado es una introducción a las técnicas y los diseños
experimentales de neuroimagen que se utilizan en la investigación en
neurociencia cognitiva y psiquiatrÃa, orientado fundamentalmente hacia la
resonancia magnética estructural y funcional. En este curso se abordan los
fundamentos fÃsicos y biológicos de dichas técnicas, se adentra en los
procedimientos de pre y postprocesado y en la descripción de los paradigmas
empleados en la investigación básica en neurociencia cognitiva y en la
investigación clÃnica en psiquiatrÃa.
¿a quién va dirigido?
A clÃnicos e investigadores que provienen de disciplinas como la
psiquiatrÃa, la neurologÃa, la psicologÃa, la neurorradiologÃa y la
medicina nuclear.
El curso se estructura en cuatro módulos:
Módulo 1: “Fundamentos de neuroimagenâ€, está dedicado a los conceptos
fundamentales de las principales técnicas avanzadas de neuroimagen, con
énfasis en la resonancia magnética estructural y funcional. Nuevas
aplicaciones de la resonancia magnética como la espectroscopia protónica y
la imaginerÃa por tensores de difusión reciben también especial atención.
Se exponen asimismo las bases biológicas de la RM funcional.
Módulo 2: “Paradigmas experimentales en RM funcionalâ€, aborda los
fundamentos de los diseños experimentales en RM funcional. Se estudian los
principales paradigmas experimentales empleados en neurociencia cognitiva
(atención, memoria, percepción, emoción y mixtos), tras una exposición de
los correspondientes circuitos anatómico-funcionales implicados.
Módulo 3: “Post-procesado en neuroimagenâ€, está dedicado a los
procedimientos empleados en el pre-procesamiento (realineamiento espacial,
normalización estereotáxica, corregistro y suavizado espacial) y
post-procesamiento funcional (especÃficamente Voxel-based morphometry
“VBMâ€). Se exponen los fundamentos de la estadÃstica inferencial y las
principales técnicas estadÃsticas empleados en post-procesado de
neuroimagen (la t de Student, el análisis correlacional, el análisis de
Fourier y el modelo lineal general). Se estudian asimismo análisis de
regiones de interés.
Módulo 4: “Neuroimagen en los trastornos psiquiátricosâ€, está dedicado a
la exposición de las contribuciones que las técnicas de neuroimagen han
aportado al conocimiento de la neurobiologÃa de los trastornos mentales,
especÃficamente, en los trastornos de ansiedad, la esquizofrenia, el
trastorno obsesivo compulsivo y el trastorno por déficit de atención e
hiperactividad.
========================================================================Date: Fri, 18 Feb 2011 07:15:33 -0500
Reply-To: John Fredy <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Fredy <[log in to unmask]>
Subject: Re: vbm8 for single subject
Comments: To: Jonathan Peelle <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--0016e6d58dac861487049c8d78d3
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Thanks!
On Thu, Feb 17, 2011 at 11:46 PM, Jonathan Peelle <[log in to unmask]> wrote:
> Hi John
>
> > Is possible use the vbm8 toolbox for compare a single subject against the
> template
>
> You can't get any sort of statistics out of comparing a single subject
> against a template because you have no variance. The best you could do is a
> simple subtraction but this woudn't be particularly informative I don't
> think. To look at a single participant you really need a sample with which
> to compare them—e.g., 1 patient vs. 20 age- and sex-matched controls.
>
> Jonathan
>
>
>
> --
> Dr. Jonathan Peelle
> Department of Neurology
> University of Pennsylvania
> 3 West Gates
> 3400 Spruce Street
> Philadelphia, PA 19104
> USA
> http://jonathanpeelle.net/
>
>
>
>
--0016e6d58dac861487049c8d78d3
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Thanks!<div><br><div class="gmail_quote">On Thu, Feb 17, 2011 at 11:46 PM, Jonathan Peelle <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
Hi John<br>
<div class="im"><br>
> Is possible use the vbm8 toolbox for compare a single subject against the template<br>
<br>
</div>You can't get any sort of statistics out of comparing a single subject against a template because you have no variance. The best you could do is a simple subtraction but this woudn't be particularly informative I don't think. To look at a single participant you really need a sample with which to compare them—e.g., 1 patient vs. 20 age- and sex-matched controls.<br>
<br>
Jonathan<br>
<br>
<br>
<br>
--<br>
Dr. Jonathan Peelle<br>
Department of Neurology<br>
University of Pennsylvania<br>
3 West Gates<br>
3400 Spruce Street<br>
Philadelphia, PA 19104<br>
USA<br>
<a href="http://jonathanpeelle.net/" target="_blank">http://jonathanpeelle.net/</a><br>
<br>
<br>
<br>
</blockquote></div><br></div>
--0016e6d58dac861487049c8d78d3--
========================================================================Date: Fri, 18 Feb 2011 12:15:01 +0000
Reply-To: "<Holly> <Rossiter>" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "<Holly> <Rossiter>" <[log in to unmask]>
Subject: SPM error
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain; charset="UTF-8"
Message-ID: <[log in to unmask]>
Hi all,
I repeatedly get this error when trying to convert/filter/downsample my MEG data, although not every time...
??? Error using ==> mat2file
Cant open file.
Error in ==> file_array.subsasgn>subfun at 132
mat2file(sobj,dat,va{:});
Error in ==> file_array.subsasgn at 70
subfun(sobj,double(dat),args{:});
Error in ==> meeg.subsasgn at 29
this.data.y = subsasgn(this.data.y, subs, dat);
Error in ==> spm_eeg_downsample at 124
Dnew(blkchan,:,1)=Dtemp(:,1:nsamples_new,1);
Error in ==> spm at 961
evalin('base',CBs{v-1})
??? Error while evaluating uicontrol Callback
I've been told it's generally to do with permissions but I have got full permission/access etc to the data and the folders in which the data is in and there is plenty of room on my hard drive.
The other issue is that the error is inconsistent in that sometimes when I repeat the same step having got the error, it will then work, sometimes not.
Does anyone know of any other reason that I might be getting this error?
Kind regards,
Holly
========================================================================Date: Fri, 18 Feb 2011 12:30:02 +0000
Reply-To: Vladimir Litvak <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Litvak <[log in to unmask]>
Subject: Re: SPM error
Comments: To: "<Holly> <Rossiter>" <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
Hi Holly,
This has to do with the backup process that runs on your PC. Disable
it (from the toolbar tray) before doing SPM analysis.
Vladimir
On Fri, Feb 18, 2011 at 12:15 PM, <Holly> <Rossiter>
<[log in to unmask]> wrote:
> Hi all,
>
> I repeatedly get this error when trying to convert/filter/downsample my MEG data, although not every time...
>
> ??? Error using ==> mat2file
> Cant open file.
>
> Error in ==> file_array.subsasgn>subfun at 132
> mat2file(sobj,dat,va{:});
>
> Error in ==> file_array.subsasgn at 70
> subfun(sobj,double(dat),args{:});
>
> Error in ==> meeg.subsasgn at 29
> this.data.y = subsasgn(this.data.y, subs, dat);
>
> Error in ==> spm_eeg_downsample at 124
> Dnew(blkchan,:,1)=Dtemp(:,1:nsamples_new,1);
>
> Error in ==> spm at 961
> evalin('base',CBs{v-1})
>
> ??? Error while evaluating uicontrol Callback
>
> I've been told it's generally to do with permissions but I have got full permission/access etc to the data and the folders in which the data is in and there is plenty of room on my hard drive.
>
> The other issue is that the error is inconsistent in that sometimes when I repeat the same step having got the error, it will then work, sometimes not.
>
> Does anyone know of any other reason that I might be getting this error?
>
> Kind regards,
>
> Holly
>
========================================================================Date: Fri, 18 Feb 2011 13:51:47 +0000
Reply-To: Stephen Smith <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Stephen Smith <[log in to unmask]>
Subject: Announcement: The 2011 FSL & FreeSurfer Course
Content-Type: multipart/alternative; boundary=Apple-Mail-198--245155489
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charset=us-ascii
With kind permission from Karl and John:
----------------------------
We are pleased to announce the 2011 FSL & FreeSurfer Course, to be held in Montreal, Canada. It will run immediately before the Human Brain Mapping conference, which will be in Quebec City, a short plane/train/road journey from Montreal.
The intensive course covers both the theory and practice of functional and structural brain image analysis. Background concepts and the practicalities of analyses are taught in detailed lectures; these are interleaved with hands-on practical sessions where attendees learn how to carry out analysis for themselves on real data, with one computer provided for every two attendees. After completing the course, attendees should be able to analyse their own FMRI and MRI data sets.
The course lasts 5 days, from June 20-24.
Numbers of attendees are strictly limited and are available on a first-come-first-served basis.
For full information and to register for the course, please go to:
http://www.fmrib.ox.ac.uk/fslcourse/montreal2011.html
Regards, course organisers.
---------------------------------------------------------------------------
Stephen M. Smith, Professor of Biomedical Engineering
Associate Director, Oxford University FMRIB Centre
FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK
+44 (0) 1865 222726 (fax 222717)
[log in to unmask] http://www.fmrib.ox.ac.uk/~steve
---------------------------------------------------------------------------
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charset=us-ascii
<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div>With kind permission from Karl and John:</div><div>----------------------------</div><div><br><div>We are pleased to announce the 2011 FSL & FreeSurfer Course, to be held in Montreal, Canada. It will run immediately before the Human Brain Mapping conference, which will be in Quebec City, a short plane/train/road journey from Montreal.<br><br>The intensive course covers both the theory and practice of functional and structural brain image analysis. Background concepts and the practicalities of analyses are taught in detailed lectures; these are interleaved with hands-on practical sessions where attendees learn how to carry out analysis for themselves on real data, with one computer provided for every two attendees. After completing the course, attendees should be able to analyse their own FMRI and MRI data sets.<br><br>The course lasts 5 days, from June 20-24.<br><br>Numbers of attendees are strictly limited and are available on a first-come-first-served basis.<br><br>For full information and to register for the course, please go to:<br><br><a href="http://www.fmrib.ox.ac.uk/fslcourse/montreal2011.html">http://www.fmrib.ox.ac.uk/fslcourse/montreal2011.html</a><br><br>Regards, course organisers.<br><br></div></div><div><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-size: medium; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; "><div><br class="Apple-interchange-newline">---------------------------------------------------------------------------<br>Stephen M. Smith, Professor of Biomedical Engineering<br>Associate Director, Oxford University FMRIB Centre<br><br>FMRIB, JR Hospital, Headington, Oxford OX3 9DU, UK<br>+44 (0) 1865 222726 (fax 222717)<br><a href="mailto:[log in to unmask]">[log in to unmask]</a> <a href="http://www.fmrib.ox.ac.uk/~steve">http://www.fmrib.ox.ac.uk/~steve</a><br>---------------------------------------------------------------------------<br></div><div><br></div></span><br class="Apple-interchange-newline">
</div>
<br></body></html>
--Apple-Mail-198--245155489--
========================================================================Date: Fri, 18 Feb 2011 09:03:32 -0500
Reply-To: Fred Sanders <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Fred Sanders <[log in to unmask]>
Subject: DCM and global signal
MIME-Version: 1.0
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--20cf30433ef2b1ed73049c8efa9c
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I recently used DCM (in SPM8) to model the connectivity between 2 brain
regions. A reviewer was concerned that DCM contains a regressor for global
signal and that including this regressor could
introduce artificial anti-correlations. I would greatly appreciate it if
someone could help me understand this. I did not think DCM used a regressor
for global signal - was this done in prior versions of DCM? Or is this
referring to the constant term from the first level analysis? Also, how
could that introduce anti-correlations?
Any thoughts?
Thanks!!
--20cf30433ef2b1ed73049c8efa9c
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
I recently used DCM (in SPM8) to model the connectivity between 2 brain regions. A reviewer was concerned that DCM contains a regressor for global signal and that including this regressor could introduce artificial anti-correlations. I would greatly appreciate it if someone could help me understand this. I did not think DCM used a regressor for global signal - was this done in prior versions of DCM? Or is this referring to the constant term from the first level analysis? Also, how could that introduce anti-correlations? <div>
<br></div><div>Any thoughts?</div><div><br></div><div><br></div><div>Thanks!!</div>
--20cf30433ef2b1ed73049c8efa9c--
========================================================================Date: Fri, 18 Feb 2011 14:56:05 +0000
Reply-To: Ghazal Mohades <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Ghazal Mohades <[log in to unmask]>
Subject: Interpret pediatric fMRI
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
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Message-ID: <[log in to unmask]>
Dear colleagues
fro the mailing list I understood that there had been a few of you having the same question as mine, however I couldn't get an exact answer to it.
I am using TOM template to create a group specific template based on the data from NIH study (that I suppose works the best for a group of 42 8-11 year old children). and I used that template in normalization step during preprocessing. it goes without saying that there is no meaningful dependence or relation between this template and the known template for adults (e.g. MNI template) and it makes the interpretation of the activation spots quite tricky!! since I cannot use the coordinates given by SPM and look them up in available brain atlases to find the anatomical point for activation. (as these atlases are all based on adults brains)
sop how can I report about the anatomical point of activation. i am not very good at anatomy too look at the activity and guess the name of that point. even if I was that wouldn't be scientifically accepted I suppose
Thanks in advance for your help
regrds
Ghazal Mohades
PhD student
VUB Brussels
========================================================================Date: Fri, 18 Feb 2011 10:01:07 -0500
Reply-To: Deepika Nandiraju <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Deepika Nandiraju <[log in to unmask]>
Subject: how to save realignment output
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--0016e646a53aabad9a049c8fc8fa
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Hi everybody,
I have a quick question regarding realignment of fMRI data.
" Is there an easy way to save the realignment output plot when batching
other than copying the r* text file into excel an doing it ourselves?"
Thank you very much.
Best Regards,
Deepika.
--0016e646a53aabad9a049c8fc8fa
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<div>Hi everybody,</div>
<div> </div>
<div>I have a quick question regarding realignment of fMRI data.</div>
<div>" Is there an easy way to save the realignment output plot when batching other than copying the r* text file into excel an doing it ourselves?"</div>
<div> </div>
<div>Thank you very much.</div>
<div> </div>
<div>Best Regards,</div>
<div>Deepika.</div>
--0016e646a53aabad9a049c8fc8fa--
========================================================================Date: Fri, 18 Feb 2011 16:14:49 +0100
Reply-To: Marko Wilke <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Marko Wilke <[log in to unmask]>
Subject: Re: how to save realignment output
Comments: To: Deepika Nandiraju <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Message-ID: <[log in to unmask]>
Hi Deepika,
> I have a quick question regarding realignment of fMRI data.
> " Is there an easy way to save the realignment output plot when batching
> other than copying the r* text file into excel an doing it ourselves?"
I dimly remember I already saw a code snippet on the list for this
purpose, but if you cannot find it, this may help:
% === Start ===
filt = ['^rp_*','.*\.txt$'];
b = spm_select([Inf],'any','Select realignment parameters',[],pwd,filt);
scaleme = [-3 3];
mydata = pwd;
for i = 1:size(b,1)
[p nm e v] = spm_fileparts(b(i,:));
printfig = figure;
set(printfig, 'Name', ['Motion parameters: subject ' num2str(i) ],
'Visible', 'on');
loadmot = load(deblank(b(i,:)));
subplot(2,1,1);
plot(loadmot(:,1:3));
grid on;
% ylim(scaleme); % enable to always scale between fixed values
title(['Motion parameters: shifts (top, in mm) and rotations (bottom,
in dg)'], 'interpreter', 'none');
subplot(2,1,2);
plot(loadmot(:,4:6)*180/pi);
grid on;
% ylim(scaleme);
title(['Data from ' p], 'interpreter', 'none');
mydate = date;
motname = [mydata filesep 'motion_sub_' sprintf('%02.0f', i) '_' mydate
'.png'];
print(printfig, '-dpng', '-noui', '-r100', motname);
close(printfig)
end;
% === End ===
Watch out for linebreaks before pasting it into the Matlab window.
Cheers,
Marko
--
____________________________________________________
PD Dr. med. Marko Wilke
Facharzt für Kinder- und Jugendmedizin
Leiter, Experimentelle Pädiatrische Neurobildgebung
Universitäts-Kinderklinik
Abt. III (Neuropädiatrie)
Marko Wilke, MD, PhD
Pediatrician
Head, Experimental Pediatric Neuroimaging
University Children's Hospital
Dept. III (Pediatric Neurology)
Hoppe-Seyler-Str. 1
D - 72076 Tübingen, Germany
Tel. +49 7071 29-83416
Fax +49 7071 29-5473
[log in to unmask]
http://www.medizin.uni-tuebingen.de/kinder/epn
____________________________________________________
========================================================================Date: Fri, 18 Feb 2011 10:24:56 -0500
Reply-To: Xin Di <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Xin Di <[log in to unmask]>
Subject: mis-registration of subject's anatomical and functional images
MIME-Version: 1.0
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Dear Experts,
I tried to coregister subject's functional images to it's own T1 image, but
they did not registered very well (please see attached). I also tried to run
skull strip to the anatomical image and reorient the functional images, but
the results were still not good. Can any one kindly give me some suggestions
on how to do next? Thank you very much!
Sincerely,
--
Xin Di, PhD
Postdoctoral Researcher
Department of Radiology
University of Medicine and Dentistry of New Jersey
30 Bergen Street, ADMC 582
Newark, NJ 07101
--20cf3054a15f008c5d049c901f19
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Dear Experts,<br><br>I tried to coregister subject's functional images to it's own T1 image, but they did not registered very well (please see attached). I also tried to run skull strip to the anatomical image and reorient the functional images, but the results were still not good. Can any one kindly give me some suggestions on how to do next? Thank you very much!<br>
<br>Sincerely,<br clear="all">
<br>-- <br>Xin Di, PhD<br><br>Postdoctoral Researcher<br>Department of Radiology<br>University of Medicine and Dentistry of New Jersey<br>30 Bergen Street, ADMC 582<br>Newark, NJ 07101<br><br>
--20cf3054a15f008c5d049c901f19--
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--20cf3054a15f008c72049c901f1b--
========================================================================Date: Fri, 18 Feb 2011 16:39:19 +0100
Reply-To: Michel Thiebaut de Schotten <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Michel Thiebaut de Schotten <[log in to unmask]>
Subject: International School of Clinical Neuroanatomy on the Frontal
Lobes.
Comments: To: [log in to unmask]
Content-Type: text/plain; charset=iso-8859-1
Content-Transfer-Encoding: quoted-printable
Mime-Version: 1.0 (Apple Message framework v1082)
Message-ID: <[log in to unmask]>
We're pleased to invite you to attend to the first meeting of the INTERNATIONAL SCHOOL OF CLINICAL NEUROANATOMY to be held May 25-27, 2011 in Palermo, Sicily.
The meeting is dedicated to the clinical neuroanatomy of the Frontal Lobes.
Keynote speakers include Donald Stuss, Michael Petrides, Geraint Rees, Michael Kopelman, Mark Richardson and Marco Catani
Topics cover comparative anatomy, cytoarchitectonic maps, connectional anatomy, executive functions, intra-operative mapping, consciousness, fronto-temporal dementia, motor neuron disease, traumatic brain injury, memory and confabulation, neuropsychological testing, virtual tractography dissections and epilepsy.
A hands-on workshop on diffusion tensor imaging tractography will be run in parallel.
Deadline for registration and abstract submission is 15 April 2011.
For further information please visit our website http://www.isocn.eu
We look forward to seeing you in Sicily!
On behalf of the program committee.
Michel
Michel Thiebaut de Schotten, PhD
NATBRAINLAB
ANR-CAFORPFC/ANR-HMTC
CRICM-INSERM UMRS 975
Pavillon de l'Enfance et l'Adolescence
47 Bd de l'Hôpital
75651 Paris Cedex 13, France
www.natbrainlab.com
skype: michel_thiebaut_de_schotten
+33 613579133
========================================================================Date: Fri, 18 Feb 2011 16:57:24 +0100
Reply-To: Marko Wilke <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Marko Wilke <[log in to unmask]>
Subject: Re: Interpret pediatric fMRI
Comments: To: Ghazal Mohades <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8; format=flowed
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
Hi Ghazal,
> fro the mailing list I understood that there had been a few of you having the same question as mine, however I couldn't get an exact answer to it.
That may be because there is no exact answer :)
> I am using TOM template to create a group specific template based on the data from NIH study (that I suppose works the best for a group of 42 8-11 year old children). and I used that template in normalization step during preprocessing. it goes without saying that there is no meaningful dependence or relation between this template and the known template for adults (e.g. MNI template) and it makes the interpretation of the activation spots quite tricky!! since I cannot use the coordinates given by SPM and look them up in available brain atlases to find the anatomical point for activation. (as these atlases are all based on adults brains)
I agree: the whole point of using a custom template is to be have more
appropriate reference data for your group under study. When using such a
template, you bring the images into (in SPM's case) MNI space, but they
are not necessarily in alignment with the templates that were used to
generate automatic lookup-helper-applications (although they may not be
very far off, either). Note that this issue is by no means one of the
TOM approach but will apply to each and every study using customized
templates. I see that the famous Good et al paper (NI 2001) has been
cited about 1500 times, and if all of them used the there-described
approach to creating a custom template... you get my point.
> sop how can I report about the anatomical point of activation. i am not very good at anatomy too look at the activity and guess the name of that point. even if I was that wouldn't be scientifically accepted I suppose
I agree that guessing is not a good idea but I do not see why it should
not be scientifically acceptable to use your eyes. Actually, I think
that it may be less scientific to feed a long row of numbers into an
algorithm and take the results at face value without checking at least
some of them, using common sense and an anatomy textbook. The good thing
is that the images may speak for themselves, so including representative
slice that allow the reader to assess the localization goes a long way
(you should still be able to name the structures involved, though :). I
have written long paragraphs in a number of revisions, not all of them
successful, trying to convey may unwillingness to include a table of
activation foci, because, yes, this makes meta-analyses harder. But it
is a compromise, and somehting's got to give.
Cheers,
Marko
--
____________________________________________________
PD Dr. med. Marko Wilke
Facharzt für Kinder- und Jugendmedizin
Leiter, Experimentelle Pädiatrische Neurobildgebung
Universitäts-Kinderklinik
Abt. III (Neuropädiatrie)
Marko Wilke, MD, PhD
Pediatrician
Head, Experimental Pediatric Neuroimaging
University Children's Hospital
Dept. III (Pediatric Neurology)
Hoppe-Seyler-Str. 1
D - 72076 Tübingen, Germany
Tel. +49 7071 29-83416
Fax +49 7071 29-5473
[log in to unmask]
http://www.medizin.uni-tuebingen.de/kinder/epn
____________________________________________________
========================================================================Date: Fri, 18 Feb 2011 16:00:12 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: mis-registration of subject's anatomical and functional images
Comments: To: Xin Di <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Message-ID: <[log in to unmask]>
Are you sure these two scans are from the same subject? One brain
looks much bigger than the other. Maybe the voxel sizes in the
headers are wrong.
Best regards,
-John
On 18 February 2011 15:24, Xin Di <[log in to unmask]> wrote:
> Dear Experts,
>
> I tried to coregister subject's functional images to it's own T1 image, but
> they did not registered very well (please see attached). I also tried to run
> skull strip to the anatomical image and reorient the functional images, but
> the results were still not good. Can any one kindly give me some suggestions
> on how to do next? Thank you very much!
>
> Sincerely,
>
> --
> Xin Di, PhD
>
> Postdoctoral Researcher
> Department of Radiology
> University of Medicine and Dentistry of New Jersey
> 30 Bergen Street, ADMC 582
> Newark, NJ 07101
>
>
========================================================================Date: Fri, 18 Feb 2011 11:25:23 -0500
Reply-To: Xin Di <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Xin Di <[log in to unmask]>
Subject: Re: mis-registration of subject's anatomical and functional images
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--00221532ccd02f4158049c90f736
Content-Type: text/plain; charset=ISO-8859-1
Dear John,
Thank you for your reply! I am pretty sure that they are from the same
subject. The functional images might be transformed into 4D nii image by
other software (AFNI or FSL). Does it cause the problem? Thank you!
Sincerely,
On Fri, Feb 18, 2011 at 11:00 AM, John Ashburner <[log in to unmask]>wrote:
> Are you sure these two scans are from the same subject? One brain
> looks much bigger than the other. Maybe the voxel sizes in the
> headers are wrong.
>
> Best regards,
> -John
>
> On 18 February 2011 15:24, Xin Di <[log in to unmask]> wrote:
> > Dear Experts,
> >
> > I tried to coregister subject's functional images to it's own T1 image,
> but
> > they did not registered very well (please see attached). I also tried to
> run
> > skull strip to the anatomical image and reorient the functional images,
> but
> > the results were still not good. Can any one kindly give me some
> suggestions
> > on how to do next? Thank you very much!
> >
> > Sincerely,
> >
> > --
> > Xin Di, PhD
> >
> > Postdoctoral Researcher
> > Department of Radiology
> > University of Medicine and Dentistry of New Jersey
> > 30 Bergen Street, ADMC 582
> > Newark, NJ 07101
> >
> >
>
--
Xin Di, PhD
Postdoctoral Researcher
Department of Radiology
University of Medicine and Dentistry of New Jersey
30 Bergen Street, ADMC 582
Newark, NJ 07101
--00221532ccd02f4158049c90f736
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Dear John,<br><br>Thank you for your reply! I am pretty sure that they are from the same subject. The functional images might be transformed into 4D nii image by other software (AFNI or FSL). Does it cause the problem? Thank you!<br>
<br>Sincerely,<br><br><br><div class="gmail_quote">On Fri, Feb 18, 2011 at 11:00 AM, John Ashburner <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
Are you sure these two scans are from the same subject? One brain<br>
looks much bigger than the other. Maybe the voxel sizes in the<br>
headers are wrong.<br>
<br>
Best regards,<br>
<font color="#888888">-John<br>
</font><div><div></div><div class="h5"><br>
On 18 February 2011 15:24, Xin Di <<a href="mailto:[log in to unmask]">[log in to unmask]</a>> wrote:<br>
> Dear Experts,<br>
><br>
> I tried to coregister subject's functional images to it's own T1 image, but<br>
> they did not registered very well (please see attached). I also tried to run<br>
> skull strip to the anatomical image and reorient the functional images, but<br>
> the results were still not good. Can any one kindly give me some suggestions<br>
> on how to do next? Thank you very much!<br>
><br>
> Sincerely,<br>
><br>
> --<br>
> Xin Di, PhD<br>
><br>
> Postdoctoral Researcher<br>
> Department of Radiology<br>
> University of Medicine and Dentistry of New Jersey<br>
> 30 Bergen Street, ADMC 582<br>
> Newark, NJ 07101<br>
><br>
><br>
</div></div></blockquote></div><br><br clear="all"><br>-- <br>Xin Di, PhD<br><br>Postdoctoral Researcher<br>Department of Radiology<br>University of Medicine and Dentistry of New Jersey<br>30 Bergen Street, ADMC 582<br>Newark, NJ 07101<br>
<br>
--00221532ccd02f4158049c90f736--
========================================================================Date: Fri, 18 Feb 2011 18:26:09 +0100
Reply-To: Javier Navas <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Javier Navas <[log in to unmask]>
Subject: Parametric or categorical design
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--00235440bd494e9ac7049c91cf71
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Hi SPMers!
I have some doubts in the analyses of fMRI data:
What is the difference between the Parametric or categorical design?? and,
regarding the contrast specification?
And, in case you use temporal derivatives of the canonical HRF how should
I´ll be the contrast for a single task?
thank you all!!
--
/**********************************************************\
Francisco Javier Navas Sánchez
Laboratorio de Imagen Médica U.M.C.E.
Hospital General Universitario Gregorio Marañón
Dr Esquerdo 46, 28007 Madrid, Spain
Phone : (+34) 91 426 50 67 Fax: (+34) 91 426 5108
e-mail:[log in to unmask] http://image.hggm.es
--00235440bd494e9ac7049c91cf71
Content-Type: text/html; charset=ISO-8859-1
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Hi SPMers!<br>I have some doubts in the analyses of fMRI data:<br><br>What is the difference between the Parametric or categorical design?? and, regarding the contrast specification?<br><br>And, in case you use temporal derivatives of the canonical HRF how should I´ll be the contrast for a single task?<br>
<br>thank you all!!<br clear="all"><br>-- <br>/**********************************************************\ <br>Francisco Javier Navas Sánchez<br> Laboratorio de Imagen Médica U.M.C.E.<br> Hospital General Universitario Gregorio Marañón<br>
Dr Esquerdo 46, 28007 Madrid, Spain<br> Phone : (+34) 91 426 50 67 Fax: (+34) 91 426 5108 <br> <a href="mailto:[log in to unmask]">e-mail:[log in to unmask]</a> <a href="http://image.hggm.es">http://image.hggm.es</a> <br>
--00235440bd494e9ac7049c91cf71--
========================================================================Date: Fri, 18 Feb 2011 12:51:27 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: Parametric or categorical design
Comments: To: Javier Navas <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
It could be fundamentally different or it could just be in the
analysis. Categorical data has no assumption about the relationship
between values or conditions; whereas parametric designs assume a
relationship. However, parametric designs can also be modelled like
categorical data, but not vice versa. When I've done a parametric
design, I've treated it like categorical data so that I don't have to
assume any apriori relationship and can define the relationship from
the data. If you model something parametrically, there is one column
for it.
Most people just look at the canonical HRF term. If you want to
incorporate the derivative terms, I'd read the earlier posts from this
week or last week by Jason Steffener.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
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On Fri, Feb 18, 2011 at 12:26 PM, Javier Navas <[log in to unmask]> wrote:
> Hi SPMers!
> I have some doubts in the analyses of fMRI data:
>
> What is the difference between the Parametric or categorical design?? and,
> regarding the contrast specification?
>
> And, in case you use temporal derivatives of the canonical HRF how should
> I´ll be the contrast for a single task?
>
> thank you all!!
>
> --
> /**********************************************************\
> Francisco Javier Navas Sánchez
> Laboratorio de Imagen Médica U.M.C.E.
> Hospital General Universitario Gregorio Marañón
> Dr Esquerdo 46, 28007 Madrid, Spain
> Phone : (+34) 91 426 50 67 Fax: (+34) 91 426 5108
> e-mail:[log in to unmask] http://image.hggm.es
>
========================================================================Date: Fri, 18 Feb 2011 17:58:01 +0000
Reply-To: Chris Filo Gorgolewski <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Chris Filo Gorgolewski <[log in to unmask]>
Subject: Neuroimaging Data Processing Workshop 23-24 March 2011,
Edinburgh, UK
Comments: To: nipy-devel <[log in to unmask]>,
[log in to unmask], [log in to unmask],
[log in to unmask], freesurfer
<[log in to unmask]>,
FSL - FMRIB's Software Library <[log in to unmask]>
Comments: cc: dtc-edinburgh <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Message-ID: <[log in to unmask]>
Dear all,
I am delighted to invite you to a Neuroimaging Data Processing
Workshop which will be held on 23-24th of March 2011 at University of
Edinburgh, UK. During the workshop we will cover neuroimaging data
analysis and management with reusable, scalable and reproducible
processing workflows that combine different software packages (such as
SPM, FSL, and FreeSurfer) using NiPyPE framework. The workshop will
also feature talks and demos covering data management, encapsulation
and visualization using PyXNAT and the Connectome Library and Viewer.
More details can be found at the workshop website:
http://nipype.blogspot.com
Best regards,
Chris Gorgolewski
========================================================================Date: Fri, 18 Feb 2011 18:29:02 +0000
Reply-To: Todd Hare <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Todd Hare <[log in to unmask]>
Subject: Job Ad: Postdoctoral and PhD positions in decision-making SNS
lab, University of Zurich
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain
Message-ID: <[log in to unmask]>
Applications are invited for both PhD and postdoctoral positions at the Laboratory for Social and Neural Systems Research at the University of Zurich under the direction of Prof. Todd Hare. The projects will focus on the neurobiology and causal mechanisms of value computation and decision-making in different contexts using functional magnetic resonance imaging (fMRI) and transcranial magnetic stimulation (TMS). We are looking for candidates familiar with fMRI, TMS, neuroeconomics, and/or computational modeling approaches to decision-making.
The successful candidate will join a friendly and multi-disciplinary research environment with a research-dedicated 3T scanner, TMS, TCDS, and EEG facilities and a large laboratory for conducting behavioral experiments. For details, please see www.sns.uzh.ch.
The working language in the laboratory is English, but knowledge of German would be beneficial. Employment can begin immediately but is negotiable. For postdoctoral positions, initial appointment will be for 1 year with the possibility of renewal for at least two additional years. Salaries will follow Swiss National Science Foundation guidelines.
Applications, including CV, names and email addresses or letter of reference from two referees, a brief statement of research experience related to the mentioned requirements and research papers should be submitted electronically to Prof. Todd Hare: [log in to unmask] The application deadline is April 1, 2011.
The University of Zurich is an equal opportunity employer, and as such welcomes applications from women and minority candidates.
========================================================================Date: Fri, 18 Feb 2011 11:44:21 -0800
Reply-To: Dennis Thompson <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Dennis Thompson <[log in to unmask]>
Subject: Re: MATLAB Out of Memory error
Comments: To: Nero Evero <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Window 32 bit limits you to 2 GB of memory by default . There are some
tricks to boost that to 3GB as outlined by Matlab in this link
http://www.mathworks.com/help/techdoc/matlab_prog/brh72ex-49.html#brh72ex-67
Here at the IRC we do all our processing on 64 bit linux computers. Have
not had a "Out of memory error" in years.
On Thu, Feb 17, 2011 at 7:32 PM, Nero Evero <[log in to unmask]> wrote:
> Hello All,
>
> We are trying to run a fixed effects analysis using SPM8 on a Windows 7
> 32-bit OS with 4GM of RAM. We have 120 subjects/sessions with a total of
> 6600 scans (55/session). The analysis stops during the parameter estimation
> and gives the following erroe message:
>
> Initialising parameters : ...done
> Output images : ...initialised
> Plane 1/46 , block 1/3 : ...estimation???
> Out of memory. Type HELP MEMORY for your options.
>
> Error in ==> spm_spm at 715
> CY = CY + Y*Y';
>
> Error in ==> spm_getSPM at 233
> SPM = spm_spm(SPM);
>
> Error in ==> spm_results_ui at 277
> [SPM,xSPM] = spm_getSPM;
>
> ??? Error while evaluating uicontrol Callback
>
> I tried adding more memory to SPM (i.e. by changing defaults.stats.maxmem
> to about 2GB), but I still received the error. Looking through the archives
> it seems like the common solution is to change to a 64-bit OS, but I wanted
> to know if there was any other possible solutions? Thanks in adavance.
>
> Nero
>
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<div>Window 32 bit limits you to 2 GB of memory by default . There are some tricks to boost that to 3GB as outlined by Matlab in this link</div><div><br></div><a href="http://www.mathworks.com/help/techdoc/matlab_prog/brh72ex-49.html#brh72ex-67">http://www.mathworks.com/help/techdoc/matlab_prog/brh72ex-49.html#brh72ex-67</a><div>
<br></div><div><br></div><div>Here at the IRC we do all our processing on 64 bit linux computers. Have not had a "Out of memory error" in years.</div><div><br></div><div><br><br><div class="gmail_quote">On Thu, Feb 17, 2011 at 7:32 PM, Nero Evero <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">Hello All,<br>
<br>
We are trying to run a fixed effects analysis using SPM8 on a Windows 7 32-bit OS with 4GM of RAM. We have 120 subjects/sessions with a total of 6600 scans (55/session). The analysis stops during the parameter estimation and gives the following erroe message:<br>
<br>
Initialising parameters : ...done<br>
Output images : ...initialised<br>
Plane 1/46 , block 1/3 : ...estimation??? Out of memory. Type HELP MEMORY for your options.<br>
<br>
Error in ==> spm_spm at 715<br>
CY = CY + Y*Y';<br>
<br>
Error in ==> spm_getSPM at 233<br>
SPM = spm_spm(SPM);<br>
<br>
Error in ==> spm_results_ui at 277<br>
[SPM,xSPM] = spm_getSPM;<br>
<br>
??? Error while evaluating uicontrol Callback<br>
<br>
I tried adding more memory to SPM (i.e. by changing defaults.stats.maxmem to about 2GB), but I still received the error. Looking through the archives it seems like the common solution is to change to a 64-bit OS, but I wanted to know if there was any other possible solutions? Thanks in adavance.<br>
<font color="#888888"><br>
Nero<br>
</font></blockquote></div><br></div>
--0023547c902391b159049c93bd39--
========================================================================Date: Sat, 19 Feb 2011 13:38:33 +0000
Reply-To: Richard Binney <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Richard Binney <[log in to unmask]>
Subject: acceptable high pass filtering
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Dear all,
Somrthing has recently been said to me that has made me think, and I want to
get my head around this a bit more.....
At what point does high pass filtering becomes ineffective? It has not been
a concern for me in the past, but recently it has been suggested to me
that a rule of thumb is to set the high pass filter at a minimum of 1.5 x
your longest SOA. That seems reasonable if this is equal to or less than
128secs. I say that because I beleive that this is the optimum filter
setting to remove low-freq noise associated with cardiac/respiratory noise.
But what if you use a filter at a lower frequency (e.g., 260secs)? It has
been said to me that this would be OK. But aren't you running a risk of
alliasing signal of interest at frequencies lower than 0.01Hz with
cardiac/respiratory noise etc? IF this is an acceptable risk with a filter
of say 260secs, at what point does it becomes unacceptable and the filter
becomes ineffective? Eg., Say you had an enomrous longest SOA of 800 secs,
you might want to use a filter set at 1000secs (0.001Hz) just to be sure.
would this filter be effectively redundant?
What other factors might speak to this? Jittered SOAs for example? Using a
range of SOAs would of course spread the signal of interest across
frequencies increasing sensitivity. Does this have an effect of reducing the
risk of alliasing with low-freq noise or are you still losing some of your
signal in the <0.1Hz frequencies (either due to noise or a 128 sec filter)?
In what manner should this inform your high-pass filter? Should you be
concerned with the longest SOA or the mean SOA (fundamental frequency) of
your signal?
Thanks in advance for your help,
Richard
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<div>Dear all,</div>
<div> </div>
<div>Somrthing has recently been said to me that has made me think, and I want to get my head around this a bit more.....</div>
<div> </div>
<div>At what point does high pass filtering becomes ineffective? It has not been a concern for me in the past, but recently it has been suggested to me that a rule of thumb is to set the high pass filter at a minimum of 1.5 x your longest SOA. That seems reasonable if this is equal to or less than 128secs. I say that because I beleive that this is the optimum filter setting to remove low-freq noise associated with cardiac/respiratory noise. But what if you use a filter at a lower frequency (e.g., 260secs)? It has been said to me that this would be OK. But aren't you running a risk of alliasing signal of interest at frequencies lower than 0.01Hz with cardiac/respiratory noise etc? IF this is an acceptable risk with a filter of say 260secs, at what point does it becomes unacceptable and the filter becomes ineffective? Eg., Say you had an enomrous longest SOA of 800 secs, you might want to use a filter set at 1000secs (0.001Hz) just to be sure. would this filter be effectively redundant?</div>
<div> </div>
<div>What other factors might speak to this? Jittered SOAs for example? Using a range of SOAs would of course spread the signal of interest across frequencies increasing sensitivity. Does this have an effect of reducing the risk of alliasing with low-freq noise or are you still losing some of your signal in the <0.1Hz frequencies (either due to noise or a 128 sec filter)? In what manner should this inform your high-pass filter? Should you be concerned with the longest SOA or the mean SOA (fundamental frequency) of your signal? </div>
<div> </div>
<div>Thanks in advance for your help,</div>
<div> </div>
<div>Richard</div>
--20cf3043478e322880049ca2bff3--
========================================================================Date: Sat, 19 Feb 2011 17:43:29 +0000
Reply-To: Klaas Enno Stephan <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Klaas Enno Stephan <[log in to unmask]>
Subject: Re: DCM and global signal
Comments: To: Fred Sanders <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Dear Fred,
This reviewer seems to be referring to the problem of artificial
anti-correlations in the context of global signal regression for resting state
fMRI data (i.e., resting state data are sometimes orthogonalized to the global
mean brain signal using the global signal as a confound regressor in a GLM,
prior to the actual functional connectivity analyses). For details, see this
paper by Murphy et al.:
http://www.ncbi.nlm.nih.gov/pubmed/18976716
This is not an issue for DCM. DCM for fMRI is only informed about local signals
(i.e., those from your regions of interest) and does not consider anything
related to global brain signal (unless you manually construct an input that
reflects global brain signal).
Best wishes
Klaas
________________________________
Von: Fred Sanders <[log in to unmask]>
An: [log in to unmask]
Gesendet: Freitag, den 18. Februar 2011, 15:03:32 Uhr
Betreff: [SPM] DCM and global signal
I recently used DCM (in SPM8) to model the connectivity between 2 brain regions.
A reviewer was concerned that DCM contains a regressor for global signal and
that including this regressor could introduce artificial anti-correlations. I
would greatly appreciate it if someone could help me understand this. I did not
think DCM used a regressor for global signal - was this done in prior versions
of DCM? Or is this referring to the constant term from the first level
analysis? Also, how could that introduce anti-correlations?
Any thoughts?
Thanks!!
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<html><head><style type="text/css"><!-- DIV {margin:0px;} --></style></head><body><div style="font-family:times new roman,new york,times,serif;font-size:12pt"><div>Dear Fred,<br><br>This reviewer seems to be referring to the problem of artificial anti-correlations in the context of global signal regression for resting state fMRI data (i.e., resting state data are sometimes orthogonalized to the global mean brain signal using the global signal as a confound regressor in a GLM, prior to the actual functional connectivity analyses). For details, see this paper by Murphy et al.:<br><span><a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed/18976716">http://www.ncbi.nlm.nih.gov/pubmed/18976716</a></span><br><br>This is not an issue for DCM. DCM for fMRI is only informed about local signals (i.e., those from your regions of interest) and does not consider anything related to global brain signal (unless you manually construct an input that
reflects global brain signal).<br><br>Best wishes<br>Klaas<br><br><br><br></div><div style="font-family: times new roman,new york,times,serif; font-size: 12pt;"><br><div style="font-family: times new roman,new york,times,serif; font-size: 12pt;"><font face="Tahoma" size="2"><hr size="1"><b><span style="font-weight: bold;">Von:</span></b> Fred Sanders <[log in to unmask]><br><b><span style="font-weight: bold;">An:</span></b> [log in to unmask]<br><b><span style="font-weight: bold;">Gesendet:</span></b> Freitag, den 18. Februar 2011, 15:03:32 Uhr<br><b><span style="font-weight: bold;">Betreff:</span></b> [SPM] DCM and global signal<br></font><br><meta http-equiv="x-dns-prefetch-control" content="off">I recently used DCM (in SPM8) to model the connectivity between 2 brain regions. A reviewer was concerned that DCM contains a regressor for global signal and that including this regressor could introduce artificial anti-correlations. I
would greatly appreciate it if someone could help me understand this. I did not think DCM used a regressor for global signal - was this done in prior versions of DCM? Or is this referring to the constant term from the first level analysis? Also, how could that introduce anti-correlations? <div>
<br></div><div>Any thoughts?</div><div><br></div><div><br></div><div>Thanks!!</div>
<meta http-equiv="x-dns-prefetch-control" content="on"></div></div>
</div><br></body></html>
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========================================================================Date: Sun, 20 Feb 2011 02:10:46 +0000
Reply-To: Jerome Courtemanche <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Jerome Courtemanche <[log in to unmask]>
Subject: SPM to Fieldtrip for ICA
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Hi Gurus,
Here is the commands and error command. Any help greatly appreciated.
D=spm_eeg_load
raw=D.ftraw;
D.ftraw(0);
cfg=[];cfg.channel='MEG';
ic_data = ft_componentanalysis(cfg,raw);
cfg.viewmode='component';
cfg.continuous='yes';
cfg.blocksize=30;
cfg.channels=[1:10];
ft_databrowser(cfg,ic_data)
??? Error using ==> fetch_header at 38
trial definition is not internally consistent
Error in ==> ft_databrowser at 135
hdr = fetch_header(data);
========================================================================Date: Sat, 19 Feb 2011 22:50:38 -0500
Reply-To: MP <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: MP <[log in to unmask]>
Subject: SPM EEG Pre processing: Not sure if everything looks OK
MIME-Version: 1.0
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Hi all,
I am doing the TF analysis on EEG data and I see some worrisome "spots" in
my rescaled TF, which translate as sharp "dips" in the waveform of averaged
TF over 12-20Hz when I convert TF into image. I know that these spots could
be from the noise (since it is a single subject data), but I am worried that
they will influence the statistics.
I have attached the ERP waveform (-200 to 2000ms), its TF representation
(4-20Hz, -198-2000ms), rescaled TF (using Log rescaling, baseline=-148 to
0), and the waveform when TF is averaged over 12-20Hz range.
Is it possible that since these dips are random they will not influence the
results in the scalp-space considerably? Is there anything I can do to
minimize these dips, like lowpass filter the ERP before doing TF?
thanks for your help in advance
- Muhammad
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Hi all,<div><br></div><div>I am doing the TF analysis on EEG data and I see some worrisome "spots" in my rescaled TF, which translate as sharp "dips" in the waveform of averaged TF over 12-20Hz when I convert TF into image. I know that these spots could be from the noise (since it is a single subject data), but I am worried that they will influence the statistics.</div>
<div><br></div><div>I have attached the ERP waveform (-200 to 2000ms), its TF representation (4-20Hz, -198-2000ms), rescaled TF (using Log rescaling, baseline=-148 to 0), and the waveform when TF is averaged over 12-20Hz range.</div>
<div><br></div><div>Is it possible that since these dips are random they will not influence the results in the scalp-space considerably? Is there anything I can do to minimize these dips, like lowpass filter the ERP before doing TF?</div>
<div><br></div><div>thanks for your help in advance</div><div><br></div><div>- Muhammad</div>
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gAooooA//9k--20cf305496b5866b64049caea6ca--
========================================================================Date: Sun, 20 Feb 2011 08:49:56 -0500
Reply-To: Steven Liu <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Steven Liu <[log in to unmask]>
Subject: Conjunction analysis bug in SPM8 ?
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Message-ID: <[log in to unmask]>
Dear Experts:
In SPM software, using the minimum of two T maps as their
conjunction without doing this for posive and negative activations
separately! If within the same regions, the voxels are activated from
map X, but deactivated from map Y, they are NOT conjunctive, right ?
But using SPM, these voxels may be considered as conjunctive if the
minimum (negative values in this case because they are always smaller
than positives no matter how big or how small their absolute values )
are significant! Why we do not do positive and negative voxels
separately so that we just compare voxels in the same direction
(positive or negative) ??
Thank you!
Steven
========================================================================Date: Sun, 20 Feb 2011 14:26:49 -0500
Reply-To: John Fredy <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Fredy <[log in to unmask]>
Subject: medial temporal lobe roi mask
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--0016e6d7e01e8b7aa3049cbbba86
Content-Type: text/plain; charset=ISO-8859-1
Hello all,
I am using a memory encoding paradigm and I use the wfu pickatlas for roi
definition but the mask seems that only covers gray matter (I use the
broadmman areas 34, 35, 36 and hippocampus), and I lose too many voxels that
appears active when no use roi. Exists any mask that cover all the medial
temporal lobe region including white matter?
Thanks in advance
John Ochoa
Universidad de Antioquia
--0016e6d7e01e8b7aa3049cbbba86
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Hello all,<br><br>I am using a memory encoding paradigm and I use the wfu pickatlas for roi definition but the mask seems that only covers gray matter (I use the broadmman areas 34, 35, 36 and hippocampus), and I lose too many voxels that appears active when no use roi. Exists any mask that cover all the medial temporal lobe region including white matter?<br>
<br>Thanks in advance<br><br>John Ochoa<br>Universidad de Antioquia<br>
--0016e6d7e01e8b7aa3049cbbba86--
========================================================================Date: Sun, 20 Feb 2011 14:20:37 -0800
Reply-To: Vladimir Bogdanov <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Bogdanov <[log in to unmask]>
Subject: Check Reg Figures SPM8 - size and arrangement
MIME-Version: 1.0
Content-Type: multipart/mixed; boundary="0-1068215501-1298240437=:67343"
Message-ID: <[log in to unmask]>
--0-1068215501-1298240437=:67343
Content-Type: text/plain; charset=iso-8859-1
Content-Transfer-Encoding: quoted-printable
Dear SPM experts,
By default the size of the images in Check Reg figure does not allow to see well anatomical details (see screen1 in attachment). This can be changed manually (screen2), but those changes take time.
Is there a simple way do it fast? Is it possible to change SPM settings somewhere to make the figure in the same way (different from default) every time we call Check Reg?
Thank you in advance for your help,
Vladimir
Volodymyr Bogdanov, PhD
Headache Research Unit
GIGA - Neurosciences
University of Liege
CHU T4 +1, Sart Tilman
Av de l'Hôpital 1
4000 Liege, Belgium
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--0-1068215501-1298240437=:67343--
========================================================================Date: Sun, 20 Feb 2011 22:27:16 +0000
Reply-To: Vladimir Litvak <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Litvak <[log in to unmask]>
Subject: Re: SPM EEG Pre processing: Not sure if everything looks OK
Comments: To: MP <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
Mime-Version: 1.0 (iPad Mail 8C148)
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <-101706040748529818@unknownmsgid>
Dear Muhammad,
The way one would usually do TF analysis is to apply TF to an epoched
file, then average the result and then rescale. If you apply TF to an
ERP which is quite noisy you'll get basically single trial TF which is
noisy as well.
Filtering will not help since you will either remove your frequency
range of interest or not affect it.
Best,
Vladimir
On 20 Feb 2011, at 03:51, MP <[log in to unmask]> wrote:
> Hi all,
>
> I am doing the TF analysis on EEG data and I see some worrisome "spots" in my rescaled TF, which translate as sharp "dips" in the waveform of averaged TF over 12-20Hz when I convert TF into image. I know that these spots could be from the noise (since it is a single subject data), but I am worried that they will influence the statistics.
>
> I have attached the ERP waveform (-200 to 2000ms), its TF representation (4-20Hz, -198-2000ms), rescaled TF (using Log rescaling, baseline=-148 to 0), and the waveform when TF is averaged over 12-20Hz range.
>
> Is it possible that since these dips are random they will not influence the results in the scalp-space considerably? Is there anything I can do to minimize these dips, like lowpass filter the ERP before doing TF?
>
> thanks for your help in advance
>
> - Muhammad
> <Average_ERP_Cz.jpg>
> <TF_Wavelet6_Cz.jpg>
> <RescaleTF_Log_Cz.jpg>
> <Average_12-20Hz_Cz.jpg>
========================================================================Date: Sun, 20 Feb 2011 15:53:22 -0800
Reply-To: Michael T Rubens <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Michael T Rubens <[log in to unmask]>
Subject: Re: medial temporal lobe roi mask
Comments: To: John Fredy <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundary cf30549f63930a26049cbf7571
Message-ID: <[log in to unmask]>
--20cf30549f63930a26049cbf7571
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try AAL, they have Hipp and Parahipp. Talarach deamon may have them too.or
you can take your BA mask and dilate it.
Cheers,
Michael
On Sun, Feb 20, 2011 at 11:26 AM, John Fredy <[log in to unmask]> wrote:
> Hello all,
>
> I am using a memory encoding paradigm and I use the wfu pickatlas for roi
> definition but the mask seems that only covers gray matter (I use the
> broadmman areas 34, 35, 36 and hippocampus), and I lose too many voxels that
> appears active when no use roi. Exists any mask that cover all the medial
> temporal lobe region including white matter?
>
> Thanks in advance
>
> John Ochoa
> Universidad de Antioquia
>
--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco
--20cf30549f63930a26049cbf7571
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try AAL, they have Hipp and Parahipp. Talarach deamon may have them too.or you can take your BA mask and dilate it.<div><br></div><div>Cheers,</div><div>Michael<br><br><div class="gmail_quote">On Sun, Feb 20, 2011 at 11:26 AM, John Fredy <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">Hello all,<br><br>I am using a memory encoding paradigm and I use the wfu pickatlas for roi definition but the mask seems that only covers gray matter (I use the broadmman areas 34, 35, 36 and hippocampus), and I lose too many voxels that appears active when no use roi. Exists any mask that cover all the medial temporal lobe region including white matter?<br>
<br>Thanks in advance<br><br>John Ochoa<br>Universidad de Antioquia<br>
</blockquote></div><br><br clear="all"><br>-- <br>Research Associate<br>Gazzaley Lab<br>Department of Neurology<br>University of California, San Francisco<br>
</div>
--20cf30549f63930a26049cbf7571--
========================================================================Date: Mon, 21 Feb 2011 00:22:19 +0000
Reply-To: Anna McCarrey <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Anna McCarrey <[log in to unmask]>
Subject: Reporting stats
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain; charset="UTF-8"
Message-ID: <[log in to unmask]>
Hi all,
I have a 2x2 flexible factorial design in fMRI and wish to report main effects and the interaction.
When I run the F contrast for Main effect of Group and click whole brain analysis, many voxel clusters appear. So I must follow up with t-tests to understand in which the direction the groups differ. Do I write this as: Group1 had significantly more voxel activation in the (e.g.) amygdala region than Group2 (t(64) = etc. and then report the t and p values?
If there are many voxel clusters, what's the best way to set a minimum threshold?
Lastly, when I run the Group x Condition interaction, no suprathreshold clusters appear - with no F statistic, how do I formally report this?
Many thanks in advance.
Anna
========================================================================Date: Mon, 21 Feb 2011 03:35:30 +0000
Reply-To: G Elliott Wimmer <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: G Elliott Wimmer <[log in to unmask]>
Subject: Re: medial temporal lobe roi mask
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain
Message-ID: <[log in to unmask]>
Hi,
Regarding the AAL template (which is most likely what you have already seen in the WFU pickatlas), I too have noticed that the AAL hippocampus definition tries to only cover grey matter. This exclusion leads to gaps and holes in the mask that I think you are referring to.
Given the anatomical variability between subjects in the hippocampus, it appears to me that the AAL mask attempts to be far too precise; moreover, it is not left-right symmetric. In the past, I have used AFNI's 'draw dataset' function to correct these problems with the AAL mask and used the resulting modified mask to define ROIs.
Alternatively, I've seen probabilistic anatomical masks that I think were used in FSL, and that may be worth checking out.
Best Regards,
G Elliott Wimmer
Dept. of Psychology
Columbia University
========================================================================Date: Mon, 21 Feb 2011 11:02:28 +0000
Reply-To: Vladimir Litvak <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Litvak <[log in to unmask]>
Subject: Re: SPM to Fieldtrip for ICA
Comments: To: Jerome Courtemanche <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
Dear Jerome,
Since you are getting the error not in SPM but in Fieldtrip on data
that has already been processed by a Fieldtrip function I suggest that
you ask this question on the Fieldtrip mailing list. Just note that
you don't need the D.ftraw(0) line. You could try raw=D.ftraw(0) , but
that probably wouldn't help in this case.
Vladimir
On Sun, Feb 20, 2011 at 2:10 AM, Jerome Courtemanche
<[log in to unmask]> wrote:
> Hi Gurus,
>
> Here is the commands and error command. Any help greatly appreciated.
>
> D=spm_eeg_load
> raw=D.ftraw;
> D.ftraw(0);
> cfg=[];cfg.channel='MEG';
> ic_data = ft_componentanalysis(cfg,raw);
> cfg.viewmode='component';
> cfg.continuous='yes';
> cfg.blocksize=30;
> cfg.channels=[1:10];
> ft_databrowser(cfg,ic_data)
>
> ??? Error using ==> fetch_header at 38
> trial definition is not internally consistent
>
> Error in ==> ft_databrowser at 135
> hdr = fetch_header(data);
>
========================================================================Date: Mon, 21 Feb 2011 11:29:52 +0000
Reply-To: Poornima Kumar <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Poornima Kumar <[log in to unmask]>
Subject: Realignment parameters
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain
Message-ID: <[log in to unmask]>
Hi
I have searched through the archives for info on acceptable threshold for movement in our analysis, but with no luck.
Can any one suggest if we use the realignment parameters as covariates in 1st level, what is the maximum movement (translation, rotation....) that is removed by these steps?
For example, if a participant has moved say 8mm and 12deg in one block, can we include the participant in the analysis, assuming that using motion regressors in 1st level has taken care of this?
Any advice on this will be very helpful!
Thanks
Poornima
========================================================================Date: Mon, 21 Feb 2011 07:50:09 -0500
Reply-To: John Fredy <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Fredy <[log in to unmask]>
Subject: Re: medial temporal lobe roi mask
Comments: To: G Elliott Wimmer <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--001636c598d9d2363d049cca4dfb
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Thanks Alexander, Michael and Elliot for the reply, I will see the AAL atlas
and the maxprobmap more deeply. I am in the beggining in fmri in the
hippocampus for
epilepsy patients, so, I will have time to explore more and more in working
with roi analysis.
Best wishes from Antioquia
On Sun, Feb 20, 2011 at 10:35 PM, G Elliott Wimmer <[log in to unmask]>wrote:
> Hi,
>
> Regarding the AAL template (which is most likely what you have already seen
> in the WFU pickatlas), I too have noticed that the AAL hippocampus
> definition tries to only cover grey matter. This exclusion leads to gaps and
> holes in the mask that I think you are referring to.
>
> Given the anatomical variability between subjects in the hippocampus, it
> appears to me that the AAL mask attempts to be far too precise; moreover, it
> is not left-right symmetric. In the past, I have used AFNI's 'draw dataset'
> function to correct these problems with the AAL mask and used the resulting
> modified mask to define ROIs.
>
> Alternatively, I've seen probabilistic anatomical masks that I think were
> used in FSL, and that may be worth checking out.
>
> Best Regards,
>
> G Elliott Wimmer
>
> Dept. of Psychology
> Columbia University
>
--001636c598d9d2363d049cca4dfb
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<div><font class="Apple-style-span" face="arial, sans-serif"><span class="Apple-style-span" style="border-collapse: collapse;"><div>Thanks Alexander, Michael and Elliot for the reply, I will see the AAL atlas and the maxprobmap more deeply. I am in the beggining in fmri in the hippocampus for </div>
<div>epilepsy patients, so, I will have time to explore more and more in working with roi analysis.</div><div><br></div><div>Best wishes from Antioquia</div></span></font><br><div class="gmail_quote">On Sun, Feb 20, 2011 at 10:35 PM, G Elliott Wimmer <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">Hi,<br>
<br>
Regarding the AAL template (which is most likely what you have already seen in the WFU pickatlas), I too have noticed that the AAL hippocampus definition tries to only cover grey matter. This exclusion leads to gaps and holes in the mask that I think you are referring to.<br>
<br>
Given the anatomical variability between subjects in the hippocampus, it appears to me that the AAL mask attempts to be far too precise; moreover, it is not left-right symmetric. In the past, I have used AFNI's 'draw dataset' function to correct these problems with the AAL mask and used the resulting modified mask to define ROIs.<br>
<br>
Alternatively, I've seen probabilistic anatomical masks that I think were used in FSL, and that may be worth checking out.<br>
<br>
Best Regards,<br>
<br>
G Elliott Wimmer<br>
<br>
Dept. of Psychology<br>
Columbia University<br>
</blockquote></div><br></div>
--001636c598d9d2363d049cca4dfb--
========================================================================Date: Mon, 21 Feb 2011 13:16:01 +0000
Reply-To: Joseph Whittaker <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Joseph Whittaker <[log in to unmask]>
Subject: SPM functions for BMS
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--0016e65a09c0517a27049ccaaab5
Content-Type: text/plain; charset=ISO-8859-1
Dear DCMers
I am doing BMS with families of models, which is easy using the DCM GUI in
SPM8, but I like to write a script to speed up the process. This is because
I will be doing it multiple times and would like to save the results into
one file.
Is there anyone who has done anything similar who can tell me what the
relevant functions are and how they relate to one another? Thank you.
Regards
Joe
--
Joseph Whittaker, M.Sc
Ph.D Student
G.803 Neuroscience and Psychiatry Unit
Stopford Building
University of Manchester
Oxford Road
Manchester, UK
--0016e65a09c0517a27049ccaaab5
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<br clear="all">Dear DCMers<br><br>I am doing BMS with families of models, which is easy using the DCM GUI in SPM8, but I like to write a script to speed up the process. This is because I will be doing it multiple times and would like to save the results into one file. <br>
<br>Is there anyone who has done anything similar who can tell me what the relevant functions are and how they relate to one another? Thank you.<br><br>Regards<br><br>Joe<br><br>-- <br>Joseph Whittaker, M.Sc<br>Ph.D Student<br>
G.803 Neuroscience and Psychiatry Unit<br>Stopford Building<br>University of Manchester<br>Oxford Road<br>Manchester, UK<br>
--0016e65a09c0517a27049ccaaab5--
========================================================================Date: Mon, 21 Feb 2011 14:16:56 +0100
Reply-To: Marko Wilke <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Marko Wilke <[log in to unmask]>
Subject: Re: Realignment parameters
Comments: To: Poornima Kumar <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1; format=flowed
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
Hi Poornima,
> I have searched through the archives for info on acceptable threshold for movement in our analysis, but with no luck.
This is because no-one really knows for sure. There is a rule of thumb
that "motion exceeding one voxel size" may be bad, but then again,
cannot see that from the realignment parameters as you will have to
combine translations and rotations. See the toolbox by Paul Mazaika for
an approach that does that for you. There also likely is no fixed number
as I guess it would depend on your data, paradigm, sequence...
> Can any one suggest if we use the realignment parameters as covariates in 1st level, what is the maximum movement (translation, rotation....) that is removed by these steps?
This question I believe cannot be answered, as you do not remove motion,
but the additional variance associated with it. See Friston et al.,
1996, or Johnstone et al., 2006, for some hints.
> For example, if a participant has moved say 8mm and 12deg in one block, can we include the participant in the analysis, assuming that using motion regressors in 1st level has taken care of this?
I am used to working with children, but 8 mm and 12 degrees is massive
motion even in my book. I would not expect the realignment parameters to
"take care" of this.
Cheers,
Marko
--
____________________________________________________
PD Dr. med. Marko Wilke
Facharzt für Kinder- und Jugendmedizin
Leiter, Experimentelle Pädiatrische Neurobildgebung
Universitäts-Kinderklinik
Abt. III (Neuropädiatrie)
Marko Wilke, MD, PhD
Pediatrician
Head, Experimental Pediatric Neuroimaging
University Children's Hospital
Dept. III (Pediatric Neurology)
Hoppe-Seyler-Str. 1
D - 72076 Tübingen, Germany
Tel. +49 7071 29-83416
Fax +49 7071 29-5473
[log in to unmask]
http://www.medizin.uni-tuebingen.de/kinder/epn
____________________________________________________
========================================================================Date: Mon, 21 Feb 2011 16:07:40 +0000
Reply-To: "Veena A. Nair" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "Veena A. Nair" <[log in to unmask]>
Subject: PostDoctoral Job Announcement - University of Wisconsin-Madison
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain; charset="UTF-8"
Message-ID: <[log in to unmask]>
Hello All, posting this on behalf of the neuroimaging lab at UW-Madison:
"postdoctoral traineeships in the Computation and Informatics in Biology and Medicine (CIBM) Training Program, funded by the NLM Grant 5T15LM007359"
see complete announcement below:-
------------------------------------------------------------------------------------------------------------------
PostDoctoral Job Announcement University of Wisconsin-Madison
University of Wisconsin-Madison HERI & WIMR Radiology are currently recruiting a post-doctoral fellow or research associate interested in
characterizing brain abnormalities affecting cognitive, affective & sensory processes, in psychiatric, neurological (Stroke,
Epilepsy) and neurosurgical populations(Tumor and Vascular lesions).
The individual will have access to a state-of the-art 3T MRI, 19 HD-EEG machines as part of World's largest Sleep Center, TMS, neurophysiology
bioinstrumentation at HERI as well as state-of the-art MRI and PET systems in Radiology, WIMR, and at the Waismann Center Keck Lab.
The fellow will work as part of a multidisciplinary team of Psychologists, Neuroscientists, Neurologists, Psychiatrists, Neuroradiologists, Engineers &
Medical Physicists integrating findings from a broad spectrum of approaches including:
- resting state functional MRI
- task-based functional MRI
- DTI, MRS, Perfusion imaging, HYPR-VIPR MRA
- Cortical Thinning, VBM
- Brain-Computer Interface
- Multimodal approach: Awake EEG/ERP and Sleep EEG, PET, TMS
Minimum qualifications for a successful candidate include
- Must be a US Citizen or Permanent Resident to apply.
- completed MD and/or PhD in Cognitive
neuroscience/psychology, Statistics, Computer Science, Engineering, or
Medical Physics or other equivalent background
- significant prior neuroimaging experience with functional MRI/EEG/TMS,
strong skills in usage of one or more common functional neuroimaging (FSL,
SPM, AFNI), programming experience in Matlab, C/C++ or similar platform are
all plusses, but not required.
postdoctoral traineeships in the Computation and Informatics in Biology and Medicine (CIBM) Training Program, funded by the NLM Grant 5T15LM007359.
The postdoctoral traineeship is for a one- to -two-year period. One is to begin by June, 2011, and the other to begin
Summer 2011 or later. (The last year is contingent on renewal of the NLM Grant supporting the CIBM Training Program.)
These traineeships are designed for cross-disciplinary training - the candidate will have a secondary mentor in addition to the
primary mentor (one from computational / informatics areas, and the other
from biological areas).
How to apply: Please email CV and 3 references to
[log in to unmask]
Vivek Prabhakaran M.D., Ph.D. (Stanford Neurosciences, 2001)
Assistant Professor, Director of Functional Neuroimaging in Radiology
Board-Certified Neurologist, Radiologist, & Neuroradiologist (Johns Hopkins,2008)
University of Wisconsin Health Hospital and Clinics
Department of Radiology
600 Highland Avenue
Madison, WI 53792
Office: 608-265-5269 Office: 608-232-3343
Feel free to contact with any questions prior to applying for the position.
For more information about HERI & WIMR Radiology, please visit us at
http://healthemotions.org// or
http://ntp.neuroscience.wisc.edu/faculty/prabhakaran.html
http://www.radiology.wisc.edu/research/neuroimagingLab/index.php
========================================================================Date: Mon, 21 Feb 2011 16:27:39 +0000
Reply-To: Molly Crockett <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Molly Crockett <[log in to unmask]>
Subject: ArtRepair and Realign-Unwarp
Mime-Version: 1.0
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Message-ID: <[log in to unmask]>
Hello,
I'm looking into using the ArtRepair toolbox to deal with motion-related artifacts in a couple of subjects. My study is a repeated-measures pharmacological design, and I don't want to have to exclude subjects that only moved in one of the sessions.
I see from the ArtRepair documentation that detecting and repairing bad volumes should take place after realignment. On my non-moving subjects, I've used the Realign & Unwarp procedure, and I'd like to keep things consistent across subjects. Is it reasonable to use ArtRepair on images that have been realigned & unwarped? Or is ArtRepair only valid for use on images that have been realigned without unwarping?
Thanks,
Molly
========================================================================Date: Mon, 21 Feb 2011 11:39:14 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
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From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: Realignment parameters
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Additionally,
You could exclude TR-to-TR motion that is excessive, by adding a
column of a single 1 in the design matrix for each bad point, but
again there is no standard for a bad point. We've used .75mm
translation and 1 degree rotation. In summary, if you have 5 bad
points, then you will have 5 columns added to your design matrix.
As a side note to the above procedure, the question arises as to how
many bad points are allowable in a single session. We have excluded
runs that have greater than 10-15% bad points.
Finally, neither motion correction nor motion parameters can correct
for within TR motion, so data inspection is also necessary to make
sure there is no motion artifacts. We usually check the ResMS, spmT,
and beta images. One can also inspect the raw EPI data for artifacts
as well.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
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On Mon, Feb 21, 2011 at 8:16 AM, Marko Wilke
<[log in to unmask]> wrote:
> Hi Poornima,
>
>> I have searched through the archives for info on acceptable threshold for
>> movement in our analysis, but with no luck.
>
> This is because no-one really knows for sure. There is a rule of thumb that
> "motion exceeding one voxel size" may be bad, but then again, cannot see
> that from the realignment parameters as you will have to combine
> translations and rotations. See the toolbox by Paul Mazaika for an approach
> that does that for you. There also likely is no fixed number as I guess it
> would depend on your data, paradigm, sequence...
>
>> Can any one suggest if we use the realignment parameters as covariates in
>> 1st level, what is the maximum movement (translation, rotation....) that is
>> removed by these steps?
>
> This question I believe cannot be answered, as you do not remove motion, but
> the additional variance associated with it. See Friston et al., 1996, or
> Johnstone et al., 2006, for some hints.
>
>> For example, if a participant has moved say 8mm and 12deg in one block,
>> can we include the participant in the analysis, assuming that using motion
>> regressors in 1st level has taken care of this?
>
> I am used to working with children, but 8 mm and 12 degrees is massive
> motion even in my book. I would not expect the realignment parameters to
> "take care" of this.
>
> Cheers,
> Marko
> --
> ____________________________________________________
> PD Dr. med. Marko Wilke
> Facharzt für Kinder- und Jugendmedizin
> Leiter, Experimentelle Pädiatrische Neurobildgebung
> Universitäts-Kinderklinik
> Abt. III (Neuropädiatrie)
>
>
> Marko Wilke, MD, PhD
> Pediatrician
> Head, Experimental Pediatric Neuroimaging
> University Children's Hospital
> Dept. III (Pediatric Neurology)
>
>
> Hoppe-Seyler-Str. 1
> D - 72076 Tübingen, Germany
> Tel. +49 7071 29-83416
> Fax +49 7071 29-5473
> [log in to unmask]
>
> http://www.medizin.uni-tuebingen.de/kinder/epn
> ____________________________________________________
>
========================================================================Date: Mon, 21 Feb 2011 11:47:22 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: Reporting stats
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(1) The main effect of group is inflated in your model. To test the
main effect of group properly, you need to average your conditions and
then compute a model using the averages. If you only have two groups,
I'd skip the F-test and just do the T-test.
(2) If you have more than 2 groups, you need to do post-hoc t-tests. I
would say that the had greater activation rather than more voxel as
more voxel seems to imply extent, which isn't being tested.
(3) For the interaction, make sure you have subject in your model and
in the design matrix. If you still get no significant results, you can
say that the interaction was not statistically significant or no
voxels passed the significance threshold.
(4) There are a number of ways to determine thresholds. Personally, I
like using AlphaSim in AFNI if possible. For exploratory analysis, you
can lower the voxel extent criteria a bit. In general, most studies
use p<0.001.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain
PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
and which is intended only for the use of the individual or entity
named above. If the reader of the e-mail is not the intended recipient
or the employee or agent responsible for delivering it to the intended
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On Sun, Feb 20, 2011 at 7:22 PM, Anna McCarrey
<[log in to unmask]> wrote:
> Hi all,
>
> I have a 2x2 flexible factorial design in fMRI and wish to report main effects and the interaction.
>
> When I run the F contrast for Main effect of Group and click whole brain analysis, many voxel clusters appear. So I must follow up with t-tests to understand in which the direction the groups differ. Do I write this as: Group1 had significantly more voxel activation in the (e.g.) amygdala region than Group2 (t(64) = etc. and then report the t and p values?
> If there are many voxel clusters, what's the best way to set a minimum threshold?
>
> Lastly, when I run the Group x Condition interaction, no suprathreshold clusters appear - with no F statistic, how do I formally report this?
>
> Many thanks in advance.
>
> Anna
>
========================================================================Date: Mon, 21 Feb 2011 18:22:01 +0100
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From: Alexander Hammers <[log in to unmask]>
Subject: Re: Anatomical Regions
Comments: To: Carlton CHU <[log in to unmask]>
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Dear Carlton,
Thanks and sorry about the delay in getting back to you.
I don't think there is a right or wrong in the choice of probabilistic or maximum probability resources - as long of course as you avoid population inferences based on single brains or hemispheres ;-).
Re: numbers: For healthy controls, there is probably not that much to be gained after averaging of say 15-20 or so datasets in MNI space. The boundaries of the Jülich maps at n=10 can be a bit rough; visually the maxprobmaps didn't change that much after about n=15 (Figure 1 in Hammers, Allom et al. 2003). This will depend a bit on the region, too - compare the more or less invariant basal ganglia (Ahsan et al. 2007) with a cortical region, e.g. the inferior frontal gyrus (Hammers, Chen et al. 2007; Amunts K et al. 1999). After that many datasets, there is also increasingly less (but still some) gain from multi-atlas propagation and label fusion in individual space (Fig. 5 in Heckemann RA et al. 2006; see also Aljabar P et al. 2009). However, large atlas sets may be more important with very large age ranges and/or pathology; well-done intermediate propagations steps (Wolz R et al., LEAP, NI 2010) are another option for anatomically segmenting individual MRIs. I need to stop expanding to Simon Warfield's STAPLE, Louis Collins' work, etc., because you wanted a pragmatic answer and not a review on atlasing!
Pragmatically I would check out the regions you are interested in (are they available?), and compare the protocols and distributions for those. One major difference between the Loni and our maps is the number of people drawing and checking the regions; one would expect systematic differences but I haven't checked. Depending on your research question cytoarchitectonic fields may be more important (in which case the Jülich maps are for you), or macroscopic landmarks (in which case LONI's or our maps may be more appropriate).
Bob Turner sent me a mail off-list after the recent discussion and correctly pointed out that in vivo cytoarchitectonic mapping is possible in principle, too (e.g. Geyer S et al. Frontiers in Human Neuroscience, in press) - the examples for the moment are mainly V1 / Brodmann 17 and some other primary areas at 7T, but SNR keeps increasing.
Then there's of course also folding, connectivity, etc. etc. (Fischl, Behrens,...).
Happy mapping, and I hope the brief comments above are useful.
WBW,
Alexander
-----------------------------
Alexander Hammers, MD PhD
Chair in Functional Neuroimaging
Neurodis Foundation
http://www.fondation-neurodis.org/
Postal Address:
CERMEP – Imagerie du Vivant
Hôpital Neurologique Pierre Wertheimer
59 Boulevard Pinel, 69003 Lyon, France
Telephone +33-(0)4-72 68 86 34
Fax +33-(0)4-72 68 86 10
Email [log in to unmask];[log in to unmask]
---------------------------------
Other affiliations:
Visiting Reader; Honorary Consultant Neurologist
Division of Neuroscience and Mental Health, Faculty of Medicine
Imperial College London, UK
---------------------------------
Honorary Reader in Neurology; Honorary Consultant Neurologist
Department of Clinical and Experimental Epilepsy
National Hospital for Neurology and Neurosurgery/ Institute of Neurology, University College London, UK
On 6 Feb 2011, at 04:51, Carlton CHU wrote:
> Dear Alexander:
>
> In your opinion, how does LPBA40 (http://www.loni.ucla.edu/~shattuck/resources/lpba40/) compare with the n30r83? LPBA40 only has 56 structures, but it was constructed from 10 more subjects. It seems both n30r83 and LPBA40 have maximum probability maps.
>
> I do not want to get into the details of comparing those two templates. As a user, I just want to choose one template to mask regions in my fMRI data. Any suggestions?
>
> Best,
> Carlton
>
> On Sat, Feb 5, 2011 at 2:56 PM, Alexander Hammers <[log in to unmask]> wrote:
> Dear Steffie,
>
>
> Brodmann areas are histological entities; you can therefore only really get to them via histology. There is a long-term initiative under way in Jülich, Germany to provide such maps via histological workup of ten brains and spatial transfer to MNI space - see http://www.fz-juelich.de/inm/inm-1/index.php?index=396 and papers by Zilles K, Amunts K, Eickhoff E et al.
>
> They are great but don't exist for all areas.
>
> The next best thing is to define your regions based on multiple brains. The good news is that for major sulci, there is, on average, actually a reasonable correspondence between histology and macroscopic landmarks (which you can see on MRI), see e.g. Figure 6 in Hammers A et al. Hum Brain Mapp 2007.
>
> One such atlas is our maximum probability map in MNI space (Hammers A, Allom R et al. Hum Brain Mapp 2003; since then expanded to include 83 regions and now based on 30 brains (n30r83; see Gousias IS et al. Neuroimage 2008 for the additional region definitions)), which you can get by agreeing to a one-page free academic licence (attached).
>
> There are many more atlases based on _single_ brains, including AAL (Tzourio-Mazoyer et al. 2002); our earlier segmentation of the same brain (Hammers et al. 2002); and digitalized versions based on the single Talairach hemisphere (minus cerebellum) (e.g. WFU Pickatlas). However, single brain / hemisphere atlases are by definition not representative and are therefore not likely to well represent your subject under study. This can be, and has been, quantified for AAL and PickAtlas versus the maximum probability map (Rodionov R et al.Magn Reson Imaging 2009).
>
> The most exhaustive quantification of the errors involved in single subject atlasing is in Heckemann RA et al. Neuroimage 2006. In terms of spatial overlap, for single atlases, you loose five Dice index points compared to the maxprobmap. You'd need to train a student for several months to improve that much, and by using a maximum probabilty map you get the same improvement for free :-).
>
> Obviously there are other more involved and more accurate forms of atlasing (see e.g. Heckemann RA et al. Neuroimage 2010 for one example and a review), but for working in MNI space with low-res techniques (fMRI, PET, SPECT, MEG, etc.) I think maxprobmaps are an excellent compromise.
>
> Hope this helps,
>
> ATB, A
> PS: If you want the n30r83 maxprobmap, just email me off-list if the licence is acceptable to you (it essentially asks to cite our work and not use it commercially).
>
>
> On 4 Feb 2011, at 19:38, Michael T Rubens wrote:
>
>> WFU pickatlas
>>
>> http://fmri.wfubmc.edu/software/PickAtlas
>>
>> cheers,
>> Michael
>>
>> On Fri, Feb 4, 2011 at 10:30 AM, Steffie Tomson <[log in to unmask]> wrote:
>> Hi Everyone –
>>
>>
>> I’d like to pull BOLD data from a set of anatomical regions. For example, I’d like to look at traces in the anterior cingulate or the left ITG. Does anyone know how to find a database of MNI-normalized anatomical regions like this? Ideally, somewhere to download individual masks for anatomical regions, or even Brodmann’s areas?
>>
>>
>> Thanks so much,
>>
>> Steffie
>>
>>
>>
>>
>>
>> --
>> Research Associate
>> Gazzaley Lab
>> Department of Neurology
>> University of California, San Francisco
>
>
>
>
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<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Dear Carlton,<div><br></div><div><br></div><div>Thanks and sorry about the delay in getting back to you.</div><div><br></div><div>I don't think there is a right or wrong in the choice of probabilistic or maximum probability resources - as long of course as you avoid population inferences based on single brains or hemispheres ;-).</div><div><br></div><div>Re: numbers: For healthy controls, there is probably not that much to be gained after averaging of say 15-20 or so datasets in MNI space. The boundaries of the Jülich maps at n=10 can be a bit rough; visually the maxprobmaps didn't change that much after about n=15 (Figure 1 in Hammers, Allom et al. 2003). This will depend a bit on the region, too - compare the more or less invariant basal ganglia (Ahsan et al. 2007) with a cortical region, e.g. the inferior frontal gyrus (Hammers, Chen et al. 2007; Amunts K et al. 1999). After that many datasets, there is also increasingly less (but still some) gain from multi-atlas propagation and label fusion in individual space (Fig. 5 in Heckemann RA et al. 2006; see also Aljabar P et al. 2009). However, large atlas sets may be more important with very large age ranges and/or pathology; well-done intermediate propagations steps (Wolz R et al., LEAP, NI 2010) are another option for anatomically segmenting individual MRIs. I need to stop expanding to Simon Warfield's STAPLE, Louis Collins' work, etc., because you wanted a pragmatic answer and not a review on atlasing!</div><div><br></div><div>Pragmatically I would check out the regions you are interested in (are they available?), and compare the protocols and distributions for those. One major difference between the Loni and our maps is the number of people drawing and checking the regions; one would expect systematic differences but I haven't checked. Depending on your research question cytoarchitectonic fields may be more important (in which case the Jülich maps are for you), or macroscopic landmarks (in which case LONI's or our maps may be more appropriate).</div><div><br></div><div>Bob Turner sent me a mail off-list after the recent discussion and correctly pointed out that <i>in vivo</i> cytoarchitectonic mapping is possible in principle, too (e.g. Geyer S et al. Frontiers in Human Neuroscience, in press) - the examples for the moment are mainly V1 / Brodmann 17 and some other primary areas at 7T, but SNR keeps increasing.</div><div><br></div><div>Then there's of course also folding, connectivity, etc. etc. (Fischl, Behrens,...).</div><div><br></div><div>Happy mapping, and I hope the brief comments above are useful.</div><div><br></div><div>WBW,</div><div><br></div><div>Alexander</div><div><br></div><div><br></div><div>
<br><div>
<span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; font-size: medium; "><div><div>-----------------------------</div><div>Alexander Hammers, MD PhD</div><div><br></div></div><br><span></span><span><img height="58" width="176" id="b33659ab-bff1-4f9b-8aa5-dfe6a3d7eb3d" apple-width="yes" apple-height="yes" src="cid:3335947574_1343002"></span><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; font-size: medium; "><div><br class="Apple-interchange-newline"><br><br>Chair in Functional Neuroimaging<br>Neurodis Foundation<br><a href="http://www.fondation-neurodis.org/">http://www.fondation-neurodis.org/</a><br>Postal Address:<br>CERMEP – Imagerie du Vivant<br>Hôpital Neurologique Pierre Wertheimer<br>59 Boulevard Pinel, 69003 Lyon, France<br><br>Telephone<span class="Apple-tab-span" style="white-space: pre; "> </span>+33-(0)4-72 68 86 34<br>Fax<span class="Apple-tab-span" style="white-space: pre; "> </span>+33-(0)4-72 68 86 10<br>Email<span class="Apple-tab-span" style="white-space: pre; "> </span>[log in to unmask];[log in to unmask]<br>---------------------------------<br>Other affiliations:<br><br>Visiting Reader; Honorary Consultant Neurologist<br>Division of Neuroscience and Mental Health, Faculty of Medicine<br>Imperial College London, UK<br>---------------------------------<br>Honorary Reader in Neurology; Honorary Consultant Neurologist<br>Department of Clinical and Experimental Epilepsy<br>National Hospital for Neurology and Neurosurgery/ Institute of Neurology, University College London, UK<div><br></div></div><br><br></span>
</span></div>
<br><div><div>On 6 Feb 2011, at 04:51, Carlton CHU wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite">Dear Alexander:<div><br></div><div>In your opinion, how does LPBA40 (<a href="http://www.loni.ucla.edu/~shattuck/resources/lpba40/">http://www.loni.ucla.edu/~shattuck/resources/lpba40/</a>) compare with the n30r83? LPBA40 only has 56 structures, but it was constructed from 10 more subjects. It seems both n30r83 and LPBA40 have maximum probability maps. </div>
<div><br></div><div>I do not want to get into the details of comparing those two templates. As a user, I just want to choose one template to mask regions in my fMRI data. Any suggestions?</div><div><br></div><div>Best,</div>
<div>Carlton</div><div><br><div class="gmail_quote">On Sat, Feb 5, 2011 at 2:56 PM, Alexander Hammers <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;"><div style="word-wrap:break-word">Dear Steffie,<div><br></div><div><br></div><div>Brodmann areas are histological entities; you can therefore only really get to them via histology. There is a long-term initiative under way in Jülich, Germany to provide such maps via histological workup of ten brains and spatial transfer to MNI space - see <a href="http://www.fz-juelich.de/inm/inm-1/index.php?index=396" target="_blank">http://www.fz-juelich.de/inm/inm-1/index.php?index=396</a> and papers by Zilles K, Amunts K, Eickhoff E et al.</div>
<div><br></div><div>They are great but don't exist for all areas.</div><div><br></div><div>The next best thing is to define your regions based on multiple brains. The good news is that for major sulci, there is, on average, actually a reasonable correspondence between histology and macroscopic landmarks (which you can see on MRI), see e.g. Figure 6 in Hammers A et al. Hum Brain Mapp 2007.</div>
<div><br></div><div>One such atlas is our maximum probability map in MNI space (Hammers A, Allom R et al. Hum Brain Mapp 2003; since then expanded to include 83 regions and now based on 30 brains (n30r83; see Gousias IS et al. Neuroimage 2008 for the additional region definitions)), which you can get by agreeing to a one-page free academic licence (attached).</div>
<div><br></div><div>There are many more atlases based on _single_ brains, including AAL (Tzourio-Mazoyer et al. 2002); our earlier segmentation of the same brain (Hammers et al. 2002); and digitalized versions based on the single Talairach hemisphere (minus cerebellum) (e.g. WFU Pickatlas). However, single brain / hemisphere atlases are by definition not representative and are therefore not likely to well represent your subject under study. This can be, and has been, quantified for AAL and PickAtlas versus the maximum probability map (Rodionov R et al.Magn Reson Imaging 2009).</div>
<div><br></div><div>The most exhaustive quantification of the errors involved in single subject atlasing is in Heckemann RA et al. Neuroimage 2006. In terms of spatial overlap, for single atlases, you loose five Dice index points compared to the maxprobmap. You'd need to train a student for several months to improve that much, and by using a maximum probabilty map you get the same improvement for free :-).</div>
<div><br></div><div>Obviously there are other more involved and more accurate forms of atlasing (see e.g. Heckemann RA et al. Neuroimage 2010 for one example and a review), but for working in MNI space with low-res techniques (fMRI, PET, SPECT, MEG, etc.) I think maxprobmaps are an excellent compromise.</div>
<div><br></div><div>Hope this helps,</div><div><br></div><div>ATB, A</div><div>PS: If you want the n30r83 maxprobmap, just email me off-list if the licence is acceptable to you (it essentially asks to cite our work and not use it commercially).</div>
</div><br><div style="word-wrap:break-word"><div></div><div><br><div><div>On 4 Feb 2011, at 19:38, Michael T Rubens wrote:</div><br><blockquote type="cite">WFU pickatlas<div><br></div><div><a href="http://fmri.wfubmc.edu/software/PickAtlas" target="_blank">http://fmri.wfubmc.edu/software/PickAtlas</a></div>
<div><br></div><div>cheers,</div><div>Michael<br><br><div class="gmail_quote">On Fri, Feb 4, 2011 at 10:30 AM, Steffie Tomson <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0.8ex;border-left-width:1px;border-left-color:rgb(204, 204, 204);border-left-style:solid;padding-left:1ex"><div lang="EN-US" link="blue" vlink="purple">
<div><p class="MsoNormal"><span style="font-size:12pt;font-family:Cambria, serif">Hi Everyone –</span></p><div><span style="font-size:12pt;font-family:Cambria, serif"> </span><br></div><p class="MsoNormal"><span style="font-size:12pt;font-family:Cambria, serif">I’d like to pull BOLD data from a set of anatomical regions. For example, I’d like to look at traces in the anterior cingulate or the left ITG. Does anyone know how to find a database of MNI-normalized anatomical regions like this? Ideally, somewhere to download individual masks for anatomical regions, or even Brodmann’s areas?</span></p>
<div><span style="font-size:12pt;font-family:Cambria, serif"> </span><br></div><p class="MsoNormal"><span style="font-size:12pt;font-family:Cambria, serif">Thanks so much,</span></p><p class="MsoNormal"><span style="font-size:12pt;font-family:Cambria, serif">Steffie</span><span style="font-family:Cambria, serif"></span></p>
<div> <br></div></div></div></blockquote></div><br><br clear="all"><br>-- <br>Research Associate<br>Gazzaley Lab<br>Department of Neurology<br>University of California, San Francisco<br></div></blockquote></div></div><div>
<div><br></div></div></div><br></blockquote></div><br></div>
</blockquote></div><br></div></body></html>
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========================================================================Date: Mon, 21 Feb 2011 18:26:07 +0100
Reply-To: Alexander Hammers <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Alexander Hammers <[log in to unmask]>
Subject: Re: medial temporal lobe roi mask
Comments: To: John Fredy <[log in to unmask]>,
G Elliott Wimmer <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Dear John, Elliott,
Thanks. Just a comment on two of the points made -
1. I don't think the AAL mask is trying to be "too precise", rather the contrary: I've just measured the AAL hippocampi and they weigh in at a hefty ~7,500mm^3. Ok, they're in Colin Holmes / MNI space which is about 35% (Ashburner et al. 1997) to 46% (Hammers, Allom et al. 2003) bigger than average, but still they're big - as partly explained by the protocol (Tzourio-Mazoyer et al. 2002). For comparison, our protocol (Niemann K et al. 2000) yields volumes of about 2100mm^3 in native space and 3300mm^3 in MNI space (Hammers, Allom et al. 2003); they are the volumes that went into the maxprobmap in MNI space recently discussed here. There are reviews/overviews of protocols and differences (e.g. Konrad C et al. 2009).
If a "GM only" mask bothers you, you can always get rid of the "holes" with simple morphological operations - e.g. by smoothing say with a 4mm kernel and then thresholding back. Obviously if you loose many voxels, your activation may not be in the hippocampus at all - which might also be interesting.
2. "moreover, it's not left-right symmetric": Well, why would it be? The physiological R>L asymmetry is well known (e.g. Pedraza O et al. 2004), certainly depends a bit on protocols, but should probably be around 5-12%; personally I use its detection as a rough indicator of the quality/plausability of a delineation protocol. In the case of AAL, it's present but a bit small at 1.5% (remember this is an atlas based on a _single_subject_). Ignoring the physiological asymmetry is in my view dangerous, especially when clinical decisions are at stake, as for example in epilepsy surgery (see Hammers et al. NI 2007 for a discussion with example).
The only instance when hippocampi would be expected to be symmetrical is when only _one_hemisphere_ is available and then mirrored, as in the Talairach atlas (and derivatives). But of course that carries no information about physiological asymmetry.
Hope this helps,
Best wishes,
Alexander
-----------------------------
Alexander Hammers, MD PhD
Chair in Functional Neuroimaging
Neurodis Foundation
http://www.fondation-neurodis.org/
Postal Address:
CERMEP – Imagerie du Vivant
Hôpital Neurologique Pierre Wertheimer
59 Boulevard Pinel, 69003 Lyon, France
Telephone +33-(0)4-72 68 86 34
Fax +33-(0)4-72 68 86 10
Email [log in to unmask];[log in to unmask]
---------------------------------
Other affiliations:
Visiting Reader; Honorary Consultant Neurologist
Division of Neuroscience and Mental Health, Faculty of Medicine
Imperial College London, UK
---------------------------------
Honorary Reader in Neurology; Honorary Consultant Neurologist
Department of Clinical and Experimental Epilepsy
National Hospital for Neurology and Neurosurgery/ Institute of Neurology, University College London, UK
On 21 Feb 2011, at 13:50, John Fredy wrote:
> Thanks Alexander, Michael and Elliot for the reply, I will see the AAL atlas and the maxprobmap more deeply. I am in the beggining in fmri in the hippocampus for
> epilepsy patients, so, I will have time to explore more and more in working with roi analysis.
>
> Best wishes from Antioquia
>
> On Sun, Feb 20, 2011 at 10:35 PM, G Elliott Wimmer <[log in to unmask]> wrote:
> Hi,
>
> Regarding the AAL template (which is most likely what you have already seen in the WFU pickatlas), I too have noticed that the AAL hippocampus definition tries to only cover grey matter. This exclusion leads to gaps and holes in the mask that I think you are referring to.
>
> Given the anatomical variability between subjects in the hippocampus, it appears to me that the AAL mask attempts to be far too precise; moreover, it is not left-right symmetric. In the past, I have used AFNI's 'draw dataset' function to correct these problems with the AAL mask and used the resulting modified mask to define ROIs.
>
> Alternatively, I've seen probabilistic anatomical masks that I think were used in FSL, and that may be worth checking out.
>
> Best Regards,
>
> G Elliott Wimmer
>
> Dept. of Psychology
> Columbia University
>
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<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Dear John, Elliott,<div><br></div><div><br></div><div>Thanks. Just a comment on two of the points made - </div><div><br></div><div>1. I don't think the AAL mask is trying to be "too precise", rather the contrary: I've just measured the AAL hippocampi and they weigh in at a hefty ~7,500mm^3. Ok, they're in Colin Holmes / MNI space which is about 35% (Ashburner et al. 1997) to 46% (Hammers, Allom et al. 2003) bigger than average, but still they're big - as partly explained by the protocol (Tzourio-Mazoyer et al. 2002). For comparison, our protocol (Niemann K et al. 2000) yields volumes of about 2100mm^3 in native space and 3300mm^3 in MNI space (Hammers, Allom et al. 2003); they are the volumes that went into the maxprobmap in MNI space recently discussed here. There are reviews/overviews of protocols and differences (e.g. Konrad C et al. 2009).</div><div><br></div><div>If a "GM only" mask bothers you, you can always get rid of the "holes" with simple morphological operations - e.g. by smoothing say with a 4mm kernel and then thresholding back. Obviously if you loose many voxels, your activation may not be in the hippocampus at all - which might also be interesting.</div><div><br></div><div><br></div><div>2. "moreover, it's not left-right symmetric": Well, why would it be? The physiological R>L asymmetry is well known (e.g. Pedraza O et al. 2004), certainly depends a bit on protocols, but should probably be around 5-12%; personally I use its detection as a rough indicator of the quality/plausability of a delineation protocol. In the case of AAL, it's present but a bit small at 1.5% (remember this is an atlas based on a _single_subject_). Ignoring the physiological asymmetry is in my view dangerous, especially when clinical decisions are at stake, as for example in epilepsy surgery (see Hammers et al. NI 2007 for a discussion with example).</div><div>The only instance when hippocampi would be expected to be symmetrical is when only _one_hemisphere_ is available and then mirrored, as in the Talairach atlas (and derivatives). But of course that carries no information about physiological asymmetry.</div><div><br></div><div>Hope this helps,</div><div><br></div><div>Best wishes,</div><div><br></div><div>Alexander</div><div><br></div><div><br></div><div><br></div><div><br>
<br><div>
<span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; font-size: medium; "><div><div>-----------------------------</div><div>Alexander Hammers, MD PhD</div><div><br></div></div><br><span></span><span><img height="58" width="176" id="87b4357c-2227-45f0-ac27-27c3ea3fe4a0" apple-width="yes" apple-height="yes" src="cid:3335947574_1343002"></span><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-align: auto; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; font-size: medium; "><div><br class="Apple-interchange-newline"><br><br>Chair in Functional Neuroimaging<br>Neurodis Foundation<br><a href="http://www.fondation-neurodis.org/">http://www.fondation-neurodis.org/</a><br>Postal Address:<br>CERMEP – Imagerie du Vivant<br>Hôpital Neurologique Pierre Wertheimer<br>59 Boulevard Pinel, 69003 Lyon, France<br><br>Telephone<span class="Apple-tab-span" style="white-space: pre; "> </span>+33-(0)4-72 68 86 34<br>Fax<span class="Apple-tab-span" style="white-space: pre; "> </span>+33-(0)4-72 68 86 10<br>Email<span class="Apple-tab-span" style="white-space: pre; "> </span>[log in to unmask];[log in to unmask]<br>---------------------------------<br>Other affiliations:<br><br>Visiting Reader; Honorary Consultant Neurologist<br>Division of Neuroscience and Mental Health, Faculty of Medicine<br>Imperial College London, UK<br>---------------------------------<br>Honorary Reader in Neurology; Honorary Consultant Neurologist<br>Department of Clinical and Experimental Epilepsy<br>National Hospital for Neurology and Neurosurgery/ Institute of Neurology, University College London, UK<div><br></div></div><br><br></span>
</span></div>
<br><div><div>On 21 Feb 2011, at 13:50, John Fredy wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite"><div><font class="Apple-style-span" face="arial, sans-serif"><span class="Apple-style-span" style="border-collapse: collapse;"><div>Thanks Alexander, Michael and Elliot for the reply, I will see the AAL atlas and the maxprobmap more deeply. I am in the beggining in fmri in the hippocampus for </div>
<div>epilepsy patients, so, I will have time to explore more and more in working with roi analysis.</div><div><br></div><div>Best wishes from Antioquia</div></span></font><br><div class="gmail_quote">On Sun, Feb 20, 2011 at 10:35 PM, G Elliott Wimmer <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">Hi,<br>
<br>
Regarding the AAL template (which is most likely what you have already seen in the WFU pickatlas), I too have noticed that the AAL hippocampus definition tries to only cover grey matter. This exclusion leads to gaps and holes in the mask that I think you are referring to.<br>
<br>
Given the anatomical variability between subjects in the hippocampus, it appears to me that the AAL mask attempts to be far too precise; moreover, it is not left-right symmetric. In the past, I have used AFNI's 'draw dataset' function to correct these problems with the AAL mask and used the resulting modified mask to define ROIs.<br>
<br>
Alternatively, I've seen probabilistic anatomical masks that I think were used in FSL, and that may be worth checking out.<br>
<br>
Best Regards,<br>
<br>
G Elliott Wimmer<br>
<br>
Dept. of Psychology<br>
Columbia University<br>
</blockquote></div><br></div>
</blockquote></div><br></div></body></html>
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--Apple-Mail-11-26904457--
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========================================================================Date: Mon, 21 Feb 2011 19:28:23 +0000
Reply-To: G Elliott Wimmer <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: G Elliott Wimmer <[log in to unmask]>
Subject: Re: medial temporal lobe roi mask
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Hi,
That is very useful information! Regarding hippocampal lateralization, I think an asymmetry may not be known broadly enough in the imaging community (after checking, I now see it in a recent dataset, at approx. 2.5%). This may particularly be an issue to consider when researchers are using atlas-based ROIs, as sometimes they want (and assume) equal numbers of voxels in left/right ROIs.
(Also, I should have qualified my answer by noting that these aspects of the AAL may be concerns if you are considering an fMRI study of a population.)
However, I would quibble about the AAL hippocampus definition, as it does seem, to me at least, to be more *precisely* defined than the rest of the atlas, with respect to excluding white matter. (e.g., compared to frontal regions - unless the atlas brain has a huge amount of grey matter!) If this is the case - and it may not be - there is probably a good reason for it, but nevertheless it may be something to think about when using such this ROI.
========================================================================Date: Mon, 21 Feb 2011 21:29:36 +0100
Reply-To: Andrea Cherubini <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Andrea Cherubini <[log in to unmask]>
Subject: Research Assistant and Postdoctoral Fellowship Positions
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Postdoctoral/Research Assistant positions in medical neuroimaging with a particular focus on magnetic resonance imaging (MRI) are available in the Neuroimaging Laboratory, Magna Grecia University/CNR, Catanzaro, Italy. One position for a period of five years, four positions for a period of one year, with possible extension to a second year.
Research in the host laboratory focuses on the development and application of quantitative MRI methods, including advanced image processing and mathematical modeling. Current projects in the laboratory include the development of methods for acquisition, processing and analysis of images from multiple modalities with applications toward the elucidation of structure, function and physiology at the tissue level, with particular focus on the brain. The laboratory is staffed by physicians, psychologists, physicists and others focused on translational use of MRI. Anticipated activities entail the further development, implementation, validation and maintenance of imaging techniques as well as large-scale image data processing and analysis. Candidates are expected to actively participate in translational studies applying the above methodology to patients affected by neurodegenerative disorders.
Candidates are expected to have an advanced degree in any field related to neuroscience (physics, mathematics, informatics, engineering, neurology, radiology, or psychology). Candidates with documented experience in medical research are preferred. Experience in scientific programming related to medical imaging is a plus. Fluency in English language is essential.
Applicants are requested to submit their curriculum vitae along with two letters of recommendation as attachments to email.
Andrea Cherubini
Neuroimaging Laboratory
National Research Council
University Magna Grecia
Germaneto (Catanzaro)
Italy
Tel: +39 0984 9801768
Fax: +39 0984 9801791
For enquiries please e-mail: [log in to unmask]
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<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Postdoctoral/Research Assistant positions in medical neuroimaging with a particular focus on magnetic resonance imaging (MRI) are available in the Neuroimaging Laboratory, Magna Grecia University/CNR, Catanzaro, Italy. One position for a period of five years, four positions for a period of one year, with possible extension to a second year.<br><br>Research in the host laboratory focuses on the development and application of quantitative MRI methods, including advanced image processing and mathematical modeling. Current projects in the laboratory include the development of methods for acquisition, processing and analysis of images from multiple modalities with applications toward the elucidation of structure, function and physiology at the tissue level, with particular focus on the brain. The laboratory is staffed by physicians, psychologists, physicists and others focused on translational use of MRI. Anticipated activities entail the further development, implementation, validation and maintenance of imaging techniques as well as large-scale image data processing and analysis. Candidates are expected to actively participate in translational studies applying the above methodology to patients affected by neurodegenerative disorders.<br><br>Candidates are expected to have an advanced degree in any field related to neuroscience (physics, mathematics, informatics, engineering, neurology, radiology, or psychology). Candidates with documented experience in medical research are preferred. Experience in scientific programming related to medical imaging is a plus. Fluency in English language is essential.<br><br>Applicants are requested to submit their curriculum vitae along with two letters of recommendation as attachments to email.<br><br>Andrea Cherubini<br>Neuroimaging Laboratory<br>National Research Council<br>University Magna Grecia<br>Germaneto (Catanzaro)<br>Italy<br><br>Tel: +39 0984 9801768<br>Fax: +39 0984 9801791<br><br>For enquiries please e-mail: <a href="mailto:[log in to unmask]">[log in to unmask]</a></body></html>
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========================================================================Date: Mon, 21 Feb 2011 16:40:59 -0500
Reply-To: Jonathan Peelle <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Jonathan Peelle <[log in to unmask]>
Subject: Re: TIV as nuisance factor for unmodulated data in a VBM-analysis
Comments: To: Lucas Eggert <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Hi Lucas
> at the moment I am unsure about whether correcting for total brain volume (TIV) in
> a VBM analysis of unmodulated gray matter segments (density) does make sense or not.
>
> On Dr. Gaser's homepage --- I have the feeling --- there are two slightly opposite statements:
> statement (a) (please refer below) clearly states that TIV should not be used as a nuisacne factor with unmodulated data.
>
> On the other hand, I read statement (b) (please see below) as correcting unmodulated data for TIV gives me relative
> density after correcting for TIV, which seems to make more sense to me.
>
> Which procedure is the more valid one, or for which kind of question would I use which procedure respectively?
One important issue that you bring up is the use of modulated gray matter (GM) images and what an analysis using these images would tell you. I think the prevailing view on this is as follows: if normalization were perfect, and there was no modulation, then all GM images would be perfectly aligned. Because we haven't modulated to account for changes in size, the images will be identical. Thus, any differences we see in an unmodulated analysis are likely due to errors in registration. So the first issue, i think, is to make sure that unmodulated images are answering the question you want to answer.
I think that the most common view of TIV and unmodulated analyses is that unmodulated GM images represent a property of the tissue, which (unlike its volume) should not depend on head size. Thus, the advice you came across not to include TIV in an unmodulated analysis.
[One thing I wonder about the above interpretation of unmodulated analyses, though, is the application to patients with various types of neurodegenerative disease. If there are small pockets of damage (e.g. spongiform encephalopathies), then I would think that an unmodulated analysis might pick this up. Which is to say, I think there may be things other than registration differences that may lead to differences in unmodulated analyses. However, I would be very interested to hear from someone who has had some experience with this.]
So, as to your specific question, if you don't have a reason to think that what you are looking at (e.g. tissue density) would be impacted by head size, then I wouldn't think there is a good reason to include TIV. (Although you may want to do some exploratory analyses, preferably in another group of subjects, and include it—then you could see empirically what variance in density, if any, was accounted for by TIV, which might be informative for you, and others.) :)
Best regards,
Jonathan
--
Dr. Jonathan Peelle
Department of Neurology
University of Pennsylvania
3 West Gates
3400 Spruce Street
Philadelphia, PA 19104
USA
http://jonathanpeelle.net/
========================================================================Date: Mon, 21 Feb 2011 18:06:31 -0600
Reply-To: Erik Kastman <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Erik Kastman <[log in to unmask]>
Subject: Research Specialist Position - University of Wisconsin, Madison
Comments: To: [log in to unmask]
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The Wisconsin Alzheimer's Disease Research Center NeuroImaging group is seeking a talented Masters or Bachelors level research specialist to join our functional and structural brain imaging team. We focus on neuroimaging research studies in early Alzheimer's disease and other dementias, with a focus on memory and other co-occuring cognitive changes.
We are looking for someone to assist with image processing, multi-modal data analysis, documentation, scripting and management of a wide range of modalities, including fMRI, DTI, ASL, FDG PET and PiB PET data. Familiarity and skill with image analysis and imaging software is a plus.
Further details and contact information can be found here: http://www.ohr.wisc.edu/pvl/pv_066683.html
If interested, please send resume and cover letter referring to Position Vacancy Listing #66683 to Amy Hawley at [log in to unmask]
Erik Kastman
brainmap.wisc.edu
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<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div><div><div><div style="margin-top: 4px; margin-right: 4px; margin-bottom: 1px; margin-left: 4px; font: normal normal normal 10pt/normal Tahoma; "><div style="margin-top: 0px; margin-bottom: 0px; "><font class="Apple-style-span" face="Verdana, Arial, Helvetica, sans-serif" size="3"><span class="Apple-style-span" style="font-size: 12px;"><font class="Apple-style-span" face="'Lucida Grande'"><span class="Apple-style-span" style="font-size: medium;"><div style="margin-top: 0px; margin-bottom: 0px; ">The Wisconsin Alzheimer's Disease Research Center NeuroImaging group is seeking a talented Masters or Bachelors level research specialist to join our functional and structural brain imaging team. We focus on neuroimaging research studies in early Alzheimer's disease and other dementias, with a focus on memory and other co-occuring cognitive changes. </div><div style="margin-top: 0px; margin-bottom: 0px; "><br></div><div style="margin-top: 0px; margin-bottom: 0px; ">We are looking for someone to assist with image processing, multi-modal data analysis, documentation, scripting and management of a wide range of modalities, including fMRI, DTI, ASL, FDG PET and PiB PET data. Familiarity and skill with image analysis and imaging software is a plus.</div><div style="margin-top: 0px; margin-bottom: 0px; "><br></div><div style="margin-top: 0px; margin-bottom: 0px; ">Further details and contact information can be found here: <a href="http://www.ohr.wisc.edu/pvl/pv_066683.html">http://www.ohr.wisc.edu/pvl/pv_066683.html</a> </div><div style="margin-top: 0px; margin-bottom: 0px; "><br></div><div style="margin-top: 0px; margin-bottom: 0px; ">If interested, please send resume and cover letter referring to Position Vacancy Listing #66683 to Amy Hawley at <a href="mailto:[log in to unmask]">[log in to unmask]</a></div><div style="margin-top: 0px; margin-bottom: 0px; "><br></div></span></font></span></font></div><div style="margin-top: 0px; margin-bottom: 0px; "><span class="Apple-style-span" style="font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 12px; ">Erik Kastman</span></div><div style="margin-top: 0px; margin-bottom: 0px; "><span class="Apple-style-span" style="font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 12px; "><a href="http://brainmap.wisc.edu">brainmap.wisc.edu</a></span></div><div style="margin-top: 0px; margin-bottom: 0px; "><span class="Apple-style-span" style="font-family: Verdana, Arial, Helvetica, sans-serif; font-size: 12px; "><br></span></div></div></div></div></div></div></body></html>
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========================================================================Date: Tue, 22 Feb 2011 11:46:58 +0000
Reply-To: tony han <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: tony han <[log in to unmask]>
Subject: bandpass filter in REST toolbox
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Dear REST developers,
Sorry for the simple question but when I try to bandpass my dataset, the error occurs as follows:
>> rest_bandpass('C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1', 3, 0.08, 0.009, 'No', 'C:\Program Files\MATLAB71\work\Masks\mask001.img');
Ideal rectangular filter: "C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1"
Read 3D EPI functional images: "C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1".??? Error using ==> rest_ReadNiftiImage
Meet error while reading data. Please restart MATLAB, this problem may be solved.
Error in ==> rest_readfile at 61
[Outdata,Header]= rest_ReadNiftiImage(imageIN,volumeIndex);
Error in ==> rest_to4d at 41
[theOneTimePoint, VoxelSize, Header] = rest_readfile(theFilename);
Error in ==> rest_bandpass at 39
[AllVolume,vsize,theImgFileList, Header] =rest_to4d(ADataDir);
What's the problem? And how can I solve it? Thanks.
Tony
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Dear REST developers,<br><br> Sorry for the simple question but when I try to bandpass my dataset, the error occurs as follows:<br><br>>> rest_bandpass('C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1', 3, 0.08, 0.009, 'No', 'C:\Program Files\MATLAB71\work\Masks\mask001.img');<br><br>Ideal rectangular filter: "C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1"<br> Read 3D EPI functional images: "C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1".??? Error using ==> rest_ReadNiftiImage<br>Meet error while reading data. Please restart MATLAB, this problem may be solved.<br><br>Error in ==> rest_readfile at 61<br> [Outdata,Header]= rest_ReadNiftiImage(imageIN,volumeIndex);<br><br>Error in ==> rest_to4d at 41<br> [theOneTimePoint, VoxelSize, Header] = rest_readfile(theFilename);<br><br>Error in ==> rest_bandpass at 39<br> [AllVolume,vsize,theImgFileList, Header] =rest_to4d(ADataDir);<br><br><br>What's the problem? And how can I solve it? Thanks.<br><br>Tony<br> </body>
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========================================================================Date: Tue, 22 Feb 2011 11:48:38 +0000
Reply-To: Richard Binney <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Richard Binney <[log in to unmask]>
Subject: acceptable high pass filtering
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Dear all,
Something has recently been said to me that has made me think, and I want to
get my head around this a bit more.....
At what point does high pass filtering becomes ineffective? It has not been
a concern for me in the past, but recently it has been suggested to me that
a rule of thumb is to set the high pass filter at a minimum of 1.5 x your
longest SOA. That seems reasonable if this is equal to or less than 128secs.
I say that because I beleive that this is the optimum filter setting to
remove low-freq noise associated with cardiac/respiratory noise, etc. But
what if you use a filter at a lower frequency (e.g., 260secs)? It has been
said to me that this would be OK. But aren't you running a risk of alliasing
signal of interest at frequencies lower than 0.01Hz with cardiac/respiratory
noise etc? IF this is an acceptable risk with a filter of say 260secs, at
what point does it becomes unacceptable and the filter becomes ineffective?
Eg., Say you had an enomrous longest SOA of 800 secs, you might want to use
a filter set at 1000secs (0.001Hz) just to be sure. would this filter be
effectively redundant?
What other factors might speak to this? Jittered SOAs for example? Using a
range of SOAs would of course spread the signal of interest across
frequencies increasing sensitivity. Does this have an effect of reducing the
risk of alliasing with low-freq noise or are you still losing a significant
proprotion of your signal in the <0.1Hz frequencies (either due to noise or
a 128 sec filter)? In what manner should this inform your high-pass filter?
Should you be concerned with the longest SOA or the mean SOA (fundamental
frequency) of your signal?
Thanks in advance for your help,
Richard
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Dear all,<br> <br>Something has recently been said to me that has made me think, and I want to get my head around this a bit more.....<br> <br>At what point does high pass filtering becomes ineffective? It has not been a concern for me in the past, but recently it has been suggested to me that a rule of thumb is to set the high pass filter at a minimum of 1.5 x your longest SOA. That seems reasonable if this is equal to or less than 128secs. I say that because I beleive that this is the optimum filter setting to remove low-freq noise associated with cardiac/respiratory noise, etc. But what if you use a filter at a lower frequency (e.g., 260secs)? It has been said to me that this would be OK. But aren't you running a risk of alliasing signal of interest at frequencies lower than 0.01Hz with cardiac/respiratory noise etc? IF this is an acceptable risk with a filter of say 260secs, at what point does it becomes unacceptable and the filter becomes ineffective? Eg., Say you had an enomrous longest SOA of 800 secs, you might want to use a filter set at 1000secs (0.001Hz) just to be sure. would this filter be effectively redundant?<br>
<br>What other factors might speak to this? Jittered SOAs for example? Using a range of SOAs would of course spread the signal of interest across frequencies increasing sensitivity. Does this have an effect of reducing the risk of alliasing with low-freq noise or are you still losing a significant proprotion of your signal in the <0.1Hz frequencies (either due to noise or a 128 sec filter)? In what manner should this inform your high-pass filter? Should you be concerned with the longest SOA or the mean SOA (fundamental frequency) of your signal? <br>
<br>Thanks in advance for your help,<br> <br>Richard
--0016e6d447daaea4f7049cdd8f85--
========================================================================Date: Tue, 22 Feb 2011 19:54:22 +0800
Reply-To: YAN Chao-Gan <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: YAN Chao-Gan <[log in to unmask]>
Subject: Re: bandpass filter in REST toolbox
Comments: To: tony han <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Dear Tony,
What's you operation system?
Usually, run "rest_Fix_Read_Write_Error" right after you started
MATLAB, then this problem would be fixed.
Further questions about REST are also welcomed to the REST forum:
www.restfmri.net
Best wishes!
YAN Chao-Gan
On Tue, Feb 22, 2011 at 7:46 PM, tony han <[log in to unmask]> wrote:
> Dear REST developers,
>
> Sorry for the simple question but when I try to bandpass my dataset,
> the error occurs as follows:
>
> >> rest_bandpass('C:\Program
> Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1', 3, 0.08, 0.009, 'No',
> 'C:\Program Files\MATLAB71\work\Masks\mask001.img');
>
> Ideal rectangular filter: "C:\Program
> Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1"
> Read 3D EPI functional images: "C:\Program
> Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1".??? Error using ==>
> rest_ReadNiftiImage
> Meet error while reading data. Please restart MATLAB, this problem may be
> solved.
>
> Error in ==> rest_readfile at 61
> [Outdata,Header]= rest_ReadNiftiImage(imageIN,volumeIndex);
>
> Error in ==> rest_to4d at 41
> [theOneTimePoint, VoxelSize, Header] = rest_readfile(theFilename);
>
> Error in ==> rest_bandpass at 39
> [AllVolume,vsize,theImgFileList, Header] =rest_to4d(ADataDir);
>
>
> What's the problem? And how can I solve it? Thanks.
>
> Tony
>
--
YAN Chao-Gan, Ph.D. Candidate
State Key Laboratory of Cognitive Neuroscience and Learning
Beijing Normal University
Beijing, China, 100875
Phone: +86-10-58801023
[log in to unmask]
http://www.restfmri.net/forum/yanchaogan
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Dear Tony,<br> What's you operation system?<br> Usually, run "rest_Fix_Read_Write_Error" right after you started MATLAB, then this problem would be fixed.<br> Further questions about REST are also welcomed to the REST forum: <a href="http://www.restfmri.net">www.restfmri.net</a><br>
Best wishes!<br> YAN Chao-Gan<br><br><div class="gmail_quote">On Tue, Feb 22, 2011 at 7:46 PM, tony han <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
<div>
Dear REST developers,<br><br> Sorry for the simple question but when I try to bandpass my dataset, the error occurs as follows:<br><br>>> rest_bandpass('C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1', 3, 0.08, 0.009, 'No', 'C:\Program Files\MATLAB71\work\Masks\mask001.img');<br>
<br>Ideal rectangular filter: "C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1"<br> Read 3D EPI functional images: "C:\Program Files\MATLAB71\work\FunImgNormalizedSmoothed\sub1".??? Error using ==> rest_ReadNiftiImage<br>
Meet error while reading data. Please restart MATLAB, this problem may be solved.<br><br>Error in ==> rest_readfile at 61<br> [Outdata,Header]= rest_ReadNiftiImage(imageIN,volumeIndex);<br><br>Error in ==> rest_to4d at 41<br>
[theOneTimePoint, VoxelSize, Header] = rest_readfile(theFilename);<br><br>Error in ==> rest_bandpass at 39<br> [AllVolume,vsize,theImgFileList, Header] =rest_to4d(ADataDir);<br><br><br>What's the problem? And how can I solve it? Thanks.<br>
<br>Tony<br> </div>
</blockquote></div><br><br clear="all"><br>-- <br>YAN Chao-Gan, Ph.D. Candidate<br>State Key Laboratory of Cognitive Neuroscience and Learning<br>Beijing Normal University<br>Beijing, China, 100875 <br>Phone: +86-10-58801023<br>
<a href="mailto:[log in to unmask]">[log in to unmask]</a><br><a href="http://www.restfmri.net/forum/yanchaogan">http://www.restfmri.net/forum/yanchaogan</a><br>
--20cf30549fcd20a463049cdda4f6--
========================================================================Date: Tue, 22 Feb 2011 14:39:31 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: Check Reg Figures SPM8 - size and arrangement
Comments: To: Vladimir Bogdanov <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
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There are no default settings that could be changed to make SPM do
what you would like it to do. However, the beauty of having SPM in
MATLAB is that it is relatively easy to tweak the code to make it do
exactly what you want.
Best regards,
-John
On 20 February 2011 22:20, Vladimir Bogdanov <[log in to unmask]> wrote:
> Dear SPM experts,
>
> By default the size of the images in Check Reg figure does not allow to see well anatomical details (see screen1 in attachment). This can be changed manually (screen2), but those changes take time.
>
> Is there a simple way do it fast? Is it possible to change SPM settings somewhere to make the figure in the same way (different from default) every time we call Check Reg?
>
> Thank you in advance for your help,
> Vladimir
>
> Volodymyr Bogdanov, PhD
>
> Headache Research Unit
> GIGA - Neurosciences
> University of Liege
> CHU T4 +1, Sart Tilman
> Av de l'Hôpital 1
> 4000 Liege, Belgium
>
>
>
========================================================================Date: Tue, 22 Feb 2011 06:58:14 -0800
Reply-To: Vladimir Bogdanov <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Bogdanov <[log in to unmask]>
Subject: Re: Check Reg Figures SPM8 - size and arrangement
Comments: To: John Ashburner <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=iso-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
Dear John,
Thank you for your response! Where is the part of the code responsible for this located? The SPM script is huge and I am only a beginner. :)
Sincerely yours,
Vladimir
--- On Tue, 2/22/11, John Ashburner <[log in to unmask]> wrote:
> From: John Ashburner <[log in to unmask]>
> Subject: Re: [SPM] Check Reg Figures SPM8 - size and arrangement
> To: [log in to unmask]
> Date: Tuesday, February 22, 2011, 3:39 PM
> There are no default settings that
> could be changed to make SPM do
> what you would like it to do. However, the beauty of
> having SPM in
> MATLAB is that it is relatively easy to tweak the code to
> make it do
> exactly what you want.
>
> Best regards,
> -John
>
> On 20 February 2011 22:20, Vladimir Bogdanov <[log in to unmask]>
> wrote:
> > Dear SPM experts,
> >
> > By default the size of the images in Check Reg figure
> does not allow to see well anatomical details (see screen1
> in attachment). This can be changed manually (screen2), but
> those changes take time.
> >
> > Is there a simple way do it fast? Is it possible to
> change SPM settings somewhere to make the figure in the same
> way (different from default) every time we call Check Reg?
> >
> > Thank you in advance for your help,
> > Vladimir
> >
> > Volodymyr Bogdanov, PhD
> >
> > Headache Research Unit
> > GIGA - Neurosciences
> > University of Liege
> > CHU T4 +1, Sart Tilman
> > Av de l'Hôpital 1
> > 4000 Liege, Belgium
> >
> >
> >
>
========================================================================Date: Tue, 22 Feb 2011 15:08:49 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: Check Reg Figures SPM8 - size and arrangement
Comments: To: Vladimir Bogdanov <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
When you run Check Reg, Display or any of the other routines calling
spm_orthviews.m, there is a data structure saved as a global variable.
So if you call spm_check_registration and type the following, you
should get access to various pieces of information that could be
useful....
global st
st.vols{1}
Some of the fields you see refer to the positions of the axes on the
screen. You'd need to modify these.
Best regards,
-John
On 22 February 2011 14:58, Vladimir Bogdanov <[log in to unmask]> wrote:
> Dear John,
>
> Thank you for your response! Where is the part of the code responsible for this located? The SPM script is huge and I am only a beginner. :)
>
> Sincerely yours,
> Vladimir
>
>
> --- On Tue, 2/22/11, John Ashburner <[log in to unmask]> wrote:
>
>> From: John Ashburner <[log in to unmask]>
>> Subject: Re: [SPM] Check Reg Figures SPM8 - size and arrangement
>> To: [log in to unmask]
>> Date: Tuesday, February 22, 2011, 3:39 PM
>> There are no default settings that
>> could be changed to make SPM do
>> what you would like it to do. However, the beauty of
>> having SPM in
>> MATLAB is that it is relatively easy to tweak the code to
>> make it do
>> exactly what you want.
>>
>> Best regards,
>> -John
>>
>> On 20 February 2011 22:20, Vladimir Bogdanov <[log in to unmask]>
>> wrote:
>> > Dear SPM experts,
>> >
>> > By default the size of the images in Check Reg figure
>> does not allow to see well anatomical details (see screen1
>> in attachment). This can be changed manually (screen2), but
>> those changes take time.
>> >
>> > Is there a simple way do it fast? Is it possible to
>> change SPM settings somewhere to make the figure in the same
>> way (different from default) every time we call Check Reg?
>> >
>> > Thank you in advance for your help,
>> > Vladimir
>> >
>> > Volodymyr Bogdanov, PhD
>> >
>> > Headache Research Unit
>> > GIGA - Neurosciences
>> > University of Liege
>> > CHU T4 +1, Sart Tilman
>> > Av de l'Hôpital 1
>> > 4000 Liege, Belgium
>> >
>> >
>> >
>>
>
>
>
>
========================================================================Date: Tue, 22 Feb 2011 16:16:14 +0000
Reply-To: Jonas Persson <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Jonas Persson <[log in to unmask]>
Subject: Regarding scanner sequences
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Hello,
I have a couple of questions regarding settings of scanner sequences and how it affects subsequent data processing in SPM.
1. If we scan, say, coronally or saggitally during our T1-sequence and acquire our functional data with an axial EPI-sequence, will this create a problem later in SPM for example with the normalisation process? I.e. is it recommended to always acquire all the data in the same direction?
2. A follow-up question: If i scan during the same paradigm with the same EPI-sequence, but collecting the slices in different directions (i.e. coronally vs. axially) should I then expect any differences in the data acquired? (other than the slice gaps obviously appearing at different places)
3. We use an EPI-sequence at our lab with a TR=3, which collects all slices as fast as possible and then rests during the reminder of the TR. I guess this should be good for minimizing the time differences between slices, but could this create a problem for an event related paradigm? So far we have only used traditional block designs.
Thank you for any reply!
Best regards, Jonas
========================================================================Date: Tue, 22 Feb 2011 16:39:32 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: Regarding scanner sequences
Comments: To: Jonas Persson <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
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Message-ID: <AANLkTiË[log in to unmask]>
> 1. If we scan, say, coronally or saggitally during our T1-sequence and acquire our functional data with an axial EPI-sequence, will this create a problem later in SPM for example with the normalisation process? I.e. is it recommended to always acquire all the data in the same direction?
This should be fine, providing the orientation information in the
image headers is correct. If you used the DICOM conversion routines
in SPM, then this should be the case.
>
> 2. A follow-up question: If i scan during the same paradigm with the same EPI-sequence, but collecting the slices in different directions (i.e. coronally vs. axially) should I then expect any differences in the data acquired? (other than the slice gaps obviously appearing at different places)
The distortion and dropout in the data is likely to differ, depending
on whether the phase-encoding direction is the same or not. There may
be other effects, but the easiest way to discover them would be to try
it.
>
> 3. We use an EPI-sequence at our lab with a TR=3, which collects all slices as fast as possible and then rests during the reminder of the TR. I guess this should be good for minimizing the time differences between slices, but could this create a problem for an event related paradigm? So far we have only used traditional block designs.
I can't really comment on this as it's not my area.
Best regards,
-John
========================================================================Date: Tue, 22 Feb 2011 09:24:50 -0800
Reply-To: Manish Dalwani <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Manish Dalwani <[log in to unmask]>
Subject: SPM8 batchscripts?
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Hello SPM'ers
Is there a model preprocessing SPM8 batchscript? ...that you can modify as per your preprocessing plans! If so, where can I find it?
Regards,Manish DalwaniSr. PRADept. of PsychiatryUniversity of Colorado
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<table cellspacing="0" cellpadding="0" border="0" ><tr><td valign="top" style="font: inherit;"><div>Hello SPM'ers</div><div><br></div>Is there a model preprocessing SPM8 batchscript? ...that you can modify as per your preprocessing plans! If so, where can I find it?<div><br></div><div>Regards,</div><div>Manish Dalwani</div><div>Sr. PRA</div><div>Dept. of Psychiatry</div><div>University of Colorado</div></td></tr></table><br>
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========================================================================Date: Tue, 22 Feb 2011 13:56:23 -0600
Reply-To: Pamela LaMontagne <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Pamela LaMontagne <[log in to unmask]>
Subject: modelling assumed & unassumed hrf together
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I have a mixed-design study with blocks representing a task-set (2 tasks)
and events representing different trial-types (2 trial types/task) within
the block. Based on Visscher 2003 I am interested in modeling the assumed
hrf function with the blocks and an unassumed hrf function for the trials. I
know that the unassumed response can be modeled using FIR. However, how do I
model these in the same model specification? The current in-house program I
am using allows for conditions to be specified with an assumed or unassumed
response within the same design matrix but I would prefer to switch over to
SPM8.
How is this implemented in SPM? Is there a toolbox that can do this?
Thanks,
Pamela LaMontagne
Post Doctoral Fellow
Washington University St. Louis
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<br>I have a mixed-design study with blocks representing a task-set (2 tasks) and events representing different trial-types (2 trial types/task) within the block. Based on Visscher 2003 I am interested in modeling the assumed hrf function with the blocks and an unassumed hrf function for the trials. I know that the unassumed response can be modeled using FIR. However, how do I model these in the same model specification? The current in-house program I am using allows for conditions to be specified with an assumed or unassumed response within the same design matrix but I would prefer to switch over to SPM8.<br>
<br>How is this implemented in SPM? Is there a toolbox that can do this?<br><br>Thanks,<br><br>Pamela LaMontagne<br>Post Doctoral Fellow<br>Washington University St. Louis<br>
--0016e6dab4dfff0f36049ce45f78--
========================================================================Date: Wed, 23 Feb 2011 04:16:00 +0800
Reply-To: [log in to unmask]
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Kuo Bo-Cheng <[log in to unmask]>
Subject: Re: SPM EEG Pre processing: Not sure if everything looks OK
Comments: To: Vladimir Litvak <[log in to unmask]>
In-Reply-To: <-101706040748529818@unknownmsgid>
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Dear Vladmir,
Can we rescale single trial TF before average?
Many thanks!
Bo-Cheng
引述 Vladimir Litvak <[log in to unmask]>:
> Dear Muhammad,
>
> The way one would usually do TF analysis is to apply TF to an epoched
> file, then average the result and then rescale. If you apply TF to an
> ERP which is quite noisy you'll get basically single trial TF which is
> noisy as well.
>
> Filtering will not help since you will either remove your frequency
> range of interest or not affect it.
>
> Best,
>
> Vladimir
>
>
> On 20 Feb 2011, at 03:51, MP <[log in to unmask]> wrote:
>
>> Hi all,
>>
>> I am doing the TF analysis on EEG data and I see some worrisome
>> "spots" in my rescaled TF, which translate as sharp "dips" in the
>> waveform of averaged TF over 12-20Hz when I convert TF into image.
>> I know that these spots could be from the noise (since it is a
>> single subject data), but I am worried that they will influence the
>> statistics.
>>
>> I have attached the ERP waveform (-200 to 2000ms), its TF
>> representation (4-20Hz, -198-2000ms), rescaled TF (using Log
>> rescaling, baseline=-148 to 0), and the waveform when TF is
>> averaged over 12-20Hz range.
>>
>> Is it possible that since these dips are random they will not
>> influence the results in the scalp-space considerably? Is there
>> anything I can do to minimize these dips, like lowpass filter the
>> ERP before doing TF?
>>
>> thanks for your help in advance
>>
>> - Muhammad
>> <Average_ERP_Cz.jpg>
>> <TF_Wavelet6_Cz.jpg>
>> <RescaleTF_Log_Cz.jpg>
>> <Average_12-20Hz_Cz.jpg>
>
========================================================================Date: Tue, 22 Feb 2011 21:05:13 +0000
Reply-To: Vladimir Litvak <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Litvak <[log in to unmask]>
Subject: Re: SPM EEG Pre processing: Not sure if everything looks OK
Comments: To: "[log in to unmask]" <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
Mime-Version: 1.0 (iPad Mail 8C148)
Content-Type: text/plain; charset=UTF-8
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Message-ID: <-3545688754431432339@unknownmsgid>
Dear Bo-Cheng,
Technically, yes. The question is whether this is something that makes
sense. Average of rescaled data and rescaled average are usually not
the same thing.
Vladimir
On 22 Feb 2011, at 20:16, "[log in to unmask]" <[log in to unmask]> wrote:
> Dear Vladmir,
>
> Can we rescale single trial TF before average?
> Many thanks!
>
> Bo-Cheng
>
>
> 引述 Vladimir Litvak <[log in to unmask]>:
>
>> Dear Muhammad,
>>
>> The way one would usually do TF analysis is to apply TF to an epoched
>> file, then average the result and then rescale. If you apply TF to an
>> ERP which is quite noisy you'll get basically single trial TF which is
>> noisy as well.
>>
>> Filtering will not help since you will either remove your frequency
>> range of interest or not affect it.
>>
>> Best,
>>
>> Vladimir
>>
>>
>> On 20 Feb 2011, at 03:51, MP <[log in to unmask]> wrote:
>>
>>> Hi all,
>>>
>>> I am doing the TF analysis on EEG data and I see some worrisome "spots" in my rescaled TF, which translate as sharp "dips" in the waveform of averaged TF over 12-20Hz when I convert TF into image. I know that these spots could be from the noise (since it is a single subject data), but I am worried that they will influence the statistics.
>>>
>>> I have attached the ERP waveform (-200 to 2000ms), its TF representation (4-20Hz, -198-2000ms), rescaled TF (using Log rescaling, baseline=-148 to 0), and the waveform when TF is averaged over 12-20Hz range.
>>>
>>> Is it possible that since these dips are random they will not influence the results in the scalp-space considerably? Is there anything I can do to minimize these dips, like lowpass filter the ERP before doing TF?
>>>
>>> thanks for your help in advance
>>>
>>> - Muhammad
>>> <Average_ERP_Cz.jpg>
>>> <TF_Wavelet6_Cz.jpg>
>>> <RescaleTF_Log_Cz.jpg>
>>> <Average_12-20Hz_Cz.jpg>
>>
>
>
========================================================================Date: Tue, 22 Feb 2011 15:21:45 -0800
Reply-To: J S Lee <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: J S Lee <[log in to unmask]>
Subject: Can't find driving effects for PPI analysis
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Dear list,
I conducted a PPI analysis in an experiment with 6 conditions. To
replicate a previous study's PPI analysis, I was interested in
connectivity differences between 5 of the conditions compared to the
control (6th) condition, so extracted my VOI (using an all effects of
interest contrast), then created a PPI model with a [1 1 1 1 1 -1]
weighting for the psychological context regressor. I get a reasonable
replication of the same PPI effects from the previous study, so the
results are sensible.
However, in that previous study, there were not enough trials of each of
the 5 conditions to realistically analyze them separately, which is why
I collapsed across them. In this study, there are many more trials, so I
was hoping to look at which of the 5 conditions were driving the
original PPI results. I was given hope when the initial PPI replicated
in this new study. However, when I create separate PPI models for each
condition versus control (e.g., context regressors using [1 0 0 0 0 -1]
for model 1, [0 1 0 0 0 -1] for model 2, etc.), NONE of these analyses
show the same pattern as the 1 1 1 1 1 -1 model does. Mostly there are
no significant (or anywhere near significant) results, and those random
speckles that do show up at low threshold are not in the same places.
Is it theoretically possible that 5 conditions vs 1 other can produce a
PPI, but that none of those conditions singly vs the 1 other can do
that? Or must there be an error? I have checked the microtime onset
files to make the context is specified correctly, and made sure
everything matches up in terms of specifying the conditions. Everything
about the models looks fine to me. I know the 5 conditions vs 1 is a bit
unbalanced, but it replicates the previous study (in which the 5 vs 1
were equal in terms of number of trials), and I understand that when
creating the context variable one does NOT sum the vector to zero the
way one would in defining a contrast for a regional activation analysis.
Many thanks in advance for any thoughts,
Jamie Lee
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Dear list,<br>
<br>
I conducted a PPI analysis in an experiment with 6 conditions. To replicate a previous study's PPI analysis, I was interested in connectivity differences between 5 of the conditions compared to the control (6th) condition, so extracted my VOI (using an all effects of interest contrast), then created a PPI model with a [1 1 1 1 1 -1] weighting for the psychological context regressor. I get a reasonable replication of the same PPI effects from the previous study, so the results are sensible.<br>
<br>
However, in that previous study, there were not enough trials of each of the 5 conditions to realistically analyze them separately, which is why I collapsed across them. In this study, there are many more trials, so I was hoping to look at which of the 5 conditions were driving the original PPI results. I was given hope when the initial PPI replicated in this new study. However, when I create separate PPI models for each condition versus control (e.g., context regressors using [1 0 0 0 0 -1] for model 1, [0 1 0 0 0 -1] for model 2, etc.), NONE of these analyses show the same pattern as the 1 1 1 1 1 -1 model does. Mostly there are no significant (or anywhere near significant) results, and those random speckles that do show up at low threshold are not in the same places.<br>
<br>
Is it theoretically possible that 5 conditions vs 1 other can produce a PPI, but that none of those conditions singly vs the 1 other can do that? Or must there be an error? I have checked the microtime onset files to make the context is specified correctly, and made sure everything matches up in terms of specifying the conditions. Everything about the models looks fine to me. I know the 5 conditions vs 1 is a bit unbalanced, but it replicates the previous study (in which the 5 vs 1 were equal in terms of number of trials), and I understand that when creating the context variable one does NOT sum the vector to zero the way one would in defining a contrast for a regional activation analysis.<br>
<br>
Many thanks in advance for any thoughts,<br>
Jamie Lee
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========================================================================Date: Tue, 22 Feb 2011 18:52:25 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: Can't find driving effects for PPI analysis
Comments: To: J S Lee <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
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Message-ID: <AANLkTin8E2F=UxZWOMJNQxha6 [log in to unmask]>
Are you using SPM8? The issue of summing was fixed in one of the later
releases of SPM5, so if you have an older version, that could explain
some of the issue. You could check to make sure that negative aspects
of the SPM.xX.X for the PPI term are the same for all subjects. You
could plot them.
Are you using the same adjustment for all models?
Are you using exactly the same voxels for all models?
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
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On Tue, Feb 22, 2011 at 6:21 PM, J S Lee <[log in to unmask]> wrote:
> Dear list,
>
> I conducted a PPI analysis in an experiment with 6 conditions. To replicate
> a previous study's PPI analysis, I was interested in connectivity
> differences between 5 of the conditions compared to the control (6th)
> condition, so extracted my VOI (using an all effects of interest contrast),
> then created a PPI model with a [1 1 1 1 1 -1] weighting for the
> psychological context regressor. I get a reasonable replication of the same
> PPI effects from the previous study, so the results are sensible.
>
> However, in that previous study, there were not enough trials of each of the
> 5 conditions to realistically analyze them separately, which is why I
> collapsed across them. In this study, there are many more trials, so I was
> hoping to look at which of the 5 conditions were driving the original PPI
> results. I was given hope when the initial PPI replicated in this new study.
> However, when I create separate PPI models for each condition versus control
> (e.g., context regressors using [1 0 0 0 0 -1] for model 1, [0 1 0 0 0 -1]
> for model 2, etc.), NONE of these analyses show the same pattern as the 1 1
> 1 1 1 -1 model does. Mostly there are no significant (or anywhere near
> significant) results, and those random speckles that do show up at low
> threshold are not in the same places.
>
> Is it theoretically possible that 5 conditions vs 1 other can produce a PPI,
> but that none of those conditions singly vs the 1 other can do that? Or must
> there be an error? I have checked the microtime onset files to make the
> context is specified correctly, and made sure everything matches up in terms
> of specifying the conditions. Everything about the models looks fine to me.
> I know the 5 conditions vs 1 is a bit unbalanced, but it replicates the
> previous study (in which the 5 vs 1 were equal in terms of number of
> trials), and I understand that when creating the context variable one does
> NOT sum the vector to zero the way one would in defining a contrast for a
> regional activation analysis.
>
> Many thanks in advance for any thoughts,
> Jamie Lee
>
> _______________________________________________________________
> Get the Free email that has everyone talking at http://www.mail2world.com
> Unlimited Email Storage – POP3 – Calendar – SMS – Translator – Much More!
========================================================================Date: Wed, 23 Feb 2011 01:29:50 +0000
Reply-To: Dashjamts Jargal <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Dashjamts Jargal <[log in to unmask]>
Subject: DARTEL modulation versus no modulation: accuracy?
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Dear all,
I am trying to examine the diagnositc performance in DARTEL-based VBM. My unmodulated data show larger relevant difference areas and have higher diagnostic accuracy than the modulated. What could explain such result? Why the unmodulated demonstrated much more affected areas than the modulated? Is the following correct according to the definition of how modulation works: "perfectly registered unmodulated data should show no difference between the groups"?Does it mean that unmodulated data have some compound of registration failures? What areas in the brain are more prone to DARTEL-registration failures?
Thank you in advance for your consideration.
========================================================================Date: Wed, 23 Feb 2011 08:15:40 +0100
Reply-To: Lucas Eggert <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Lucas Eggert <[log in to unmask]>
Subject: Re: TIV as nuisance factor for unmodulated data in a VBM-analysis
Comments: To: Jonathan Peelle <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Hi Jonathan,
thanks very much for your response.
I guess for now, I will rather go for unmodulated images without
correcting for globals; and I will definitely do some tests to
see what difference the correction for TIV would make.
All the best,
Lucas
> Hi Lucas
>
>> at the moment I am unsure about whether correcting for total brain volume (TIV) in
>> a VBM analysis of unmodulated gray matter segments (density) does make sense or not.
>>
>> On Dr. Gaser's homepage --- I have the feeling --- there are two slightly opposite statements:
>> statement (a) (please refer below) clearly states that TIV should not be used as a nuisacne factor with unmodulated data.
>>
>> On the other hand, I read statement (b) (please see below) as correcting unmodulated data for TIV gives me relative
>> density after correcting for TIV, which seems to make more sense to me.
>>
>> Which procedure is the more valid one, or for which kind of question would I use which procedure respectively?
>
> One important issue that you bring up is the use of modulated gray matter (GM) images and what an analysis using these images would tell you. I think the prevailing view on this is as follows: if normalization were perfect, and there was no modulation, then all GM images would be perfectly aligned. Because we haven't modulated to account for changes in size, the images will be identical. Thus, any differences we see in an unmodulated analysis are likely due to errors in registration. So the first issue, i think, is to make sure that unmodulated images are answering the question you want to answer.
>
> I think that the most common view of TIV and unmodulated analyses is that unmodulated GM images represent a property of the tissue, which (unlike its volume) should not depend on head size. Thus, the advice you came across not to include TIV in an unmodulated analysis.
>
> [One thing I wonder about the above interpretation of unmodulated analyses, though, is the application to patients with various types of neurodegenerative disease. If there are small pockets of damage (e.g. spongiform encephalopathies), then I would think that an unmodulated analysis might pick this up. Which is to say, I think there may be things other than registration differences that may lead to differences in unmodulated analyses. However, I would be very interested to hear from someone who has had some experience with this.]
>
> So, as to your specific question, if you don't have a reason to think that what you are looking at (e.g. tissue density) would be impacted by head size, then I wouldn't think there is a good reason to include TIV. (Although you may want to do some exploratory analyses, preferably in another group of subjects, and include it—then you could see empirically what variance in density, if any, was accounted for by TIV, which might be informative for you, and others.) :)
>
> Best regards,
> Jonathan
>
>
>
>
--
Lucas Eggert, M.Sc.
Institute of Cognitive Science
University of Osnabrueck
Albrechtstrasse 28
D-49076 Osnabrueck
Germany
Phone: +49-541-969-44-28
Website: http://www.cogsci.uni-osnabrueck.de/~leggert/
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--------------ms040108020106000704050504--
========================================================================Date: Wed, 23 Feb 2011 10:10:09 +0100
Reply-To: Marine Lazouret <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Marine Lazouret <[log in to unmask]>
Subject: Batch issue
MIME-Version: 1.0
Content-Type: multipart/alternative; boundarye6ba3fccf3b65375049cef7647
Message-ID: <[log in to unmask]>
--90e6ba3fccf3b65375049cef7647
Content-Type: text/plain; charset=ISO-8859-1
Hello,
I made a batch for spatial pre processing and it stops every time during
segmentation with this message:
Running 'Segment'
Failed 'Segment'
Error using ==> spm_vol>subfunc at 111
File "/Applications/MATLAB_R2008a/spm8/tpm/grey.nii" does not exist.
In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc" at line
111.
In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc1" at line
84.
In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc2" at line
70.
In file "/Applications/spm8/spm_vol.m" (v2237), function "spm_vol" at line
54.
In file "/Applications/spm8/spm_preproc.m" (v1143), function "spm_preproc"
at line 66.
In file "/Applications/spm8/config/spm_run_preproc.m" (v2312), function
"spm_run_preproc" at line 19.
No executable modules, but still unresolved dependencies or incomplete
module inputs.
The following modules did not run:
Failed: Segment
Skipped: Normalise: Write
Skipped: Smooth
If i treat my images step by step, nothing goes wrong and I can get to the
end of it. Does any one have an idea where this problem could be coming
from?
--
*Marine*
--90e6ba3fccf3b65375049cef7647
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Hello,<div>I made a batch for spatial pre processing and it stops every time during segmentation with this message: </div><div><div>Running 'Segment'</div><div>Failed 'Segment'</div><div>Error using ==> spm_vol>subfunc at 111</div>
<div>File "/Applications/MATLAB_R2008a/spm8/tpm/grey.nii" does not exist.</div><div>In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc" at line 111.</div><div>In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc1" at line 84.</div>
<div>In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc2" at line 70.</div><div>In file "/Applications/spm8/spm_vol.m" (v2237), function "spm_vol" at line 54.</div><div>
In file "/Applications/spm8/spm_preproc.m" (v1143), function "spm_preproc" at line 66.</div><div>In file "/Applications/spm8/config/spm_run_preproc.m" (v2312), function "spm_run_preproc" at line 19.</div>
<div><br></div><div>No executable modules, but still unresolved dependencies or incomplete module inputs.</div><div>The following modules did not run:</div><div>Failed: Segment</div><div>Skipped: Normalise: Write</div><div>
Skipped: Smooth</div><div><br></div><div>If i treat my images step by step, nothing goes wrong and I can get to the end of it. Does any one have an idea where this problem could be coming from?</div><br>-- <br><b><i><font face="georgia, serif">Marine</font></i></b><br>
</div>
--90e6ba3fccf3b65375049cef7647--
========================================================================Date: Wed, 23 Feb 2011 03:03:28 -0800
Reply-To: Vladimir Bogdanov <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Bogdanov <[log in to unmask]>
Subject: Re: Check Reg Figures SPM8 - size and arrangement
Comments: To: Guillaume Flandin <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/mixed; boundary="0-1398234352-1298459008=:63850"
Message-ID: <[log in to unmask]>
--0-1398234352-1298459008=:63850
Content-Type: text/plain; charset=iso-8859-1
Content-Transfer-Encoding: quoted-printable
Thank you, Guillaume!
This was very helpful. But I faced other difficulty. The position parameters are in relative units and if we change the proportions of the figure, the images are stretched. I suppose, I need to get the real size of the figure and to incorporate this in the script to keep correct proportions of the images, but I do not know, how to do it.
Thank you in advance for your help and sorry for my primitive questions.
Sincerely yours,
Vladimir
--- On Tue, 2/22/11, Guillaume Flandin <[log in to unmask]> wrote:
> From: Guillaume Flandin <[log in to unmask]>
> Subject: Re: [SPM] Check Reg Figures SPM8 - size and arrangement
> To: "Vladimir Bogdanov" <[log in to unmask]>
> Date: Tuesday, February 22, 2011, 4:49 PM
> Dear Vladimir,
>
> the following code should get you started (to be executed
> when images
> are displayed with checkreg):
>
> global st
> h = 1;
> for i=1:nnz(~cellfun('isempty',st.vols))
> p1 = get(st.vols{i}.ax{1}.ax,'Position');
> p2 = get(st.vols{i}.ax{2}.ax,'Position');
> p3 = get(st.vols{i}.ax{3}.ax,'Position');
> l = p1(3) + p2(3) + p3(3); sc =
> (1-0.01)/l;
> p1 = p1*sc; p2 = p2 * sc; p3 = p3 * sc;
> set(st.vols{i}.ax{1}.ax,'Position',...
> [0 h-p1(4) p1(3) p1(4)]);
> set(st.vols{i}.ax{2}.ax,'Position',...
> [p1(3)+0.005 h-p2(4) p2(3)
> p2(4)]);
> set(st.vols{i}.ax{3}.ax,'Position',....
> [p1(3)+p2(3)+0.01 h-p3(4) p3(3)
> p3(4)]);
> h = h - p1(4)-0.005;
> end
>
> Best,
> Guillaume.
>
> On 22/02/11 14:58, Vladimir Bogdanov wrote:
> > Dear John,
> >
> > Thank you for your response! Where is the part of the
> code responsible for this located? The SPM script is huge
> and I am only a beginner. :)
> >
> > Sincerely yours,
> > Vladimir
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--0-1398234352-1298459008=:63850--
========================================================================Date: Wed, 23 Feb 2011 11:56:05 -0000
Reply-To: Lorelei Howard <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Lorelei Howard <[log in to unmask]>
Subject: contrasting/masking parametric variables
MIME-Version: 1.0
Content-Type: text/plain;charset=iso-8859-1
Content-Transfer-Encoding: 8bit
Message-ID: <[log in to unmask]>
Hi everyone,
I am running a study which has two types of blocks (1 experimental and 1
control). For each of these blocks I have second-by-second measurements of
the same parametric variable. I was wondering whether it would be valid to
run a standard t-contrast [1 -1] to identify regions associated with the
parametric variable that are more active in the experimental blocks than
in the control blocks.
Is it OK to do this with parametric regressors?
Or, alternatively, could I use some sort of masking procedure to remove
brain regions associated with the parametric variable in the control
blocks when I examine the effects of the same parametric variable in the
experimental blocks?
Many Thanks,
Lorelei
--
Lorelei Howard
Ph.D. Student
Institute of Behavioural Neuroscience
Cognitive, Perceptual and Brain Sciences Department
University College London
26 Bedford Way
WC1H 0AP
+44 (0)20 7679 8553
========================================================================Date: Wed, 23 Feb 2011 03:57:39 -0800
Reply-To: Vladimir Bogdanov <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Bogdanov <[log in to unmask]>
Subject: Re: Check Reg Figures SPM8 - size and arrangement: solved!
Comments: To: Guillaume Flandin <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/mixed; boundary="0-2071618827-1298462259=:64948"
Message-ID: <[log in to unmask]>
--0-2071618827-1298462259=:64948
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Dear Guillaume and SPM community,
The problem is solved pretty easily. I had to set new figure position parameters and calculate the value of ratio new/old figure proportions (1.7 in my case) which can included in the script, what gives the necessary result (attached) .
set(1, 'Position', [600 40 1000 800])
global st
h = 1;
for i=1:nnz(~cellfun('isempty',st.vols))
p1 = get(st.vols{i}.ax{1}.ax,'Position');
p2 = get(st.vols{i}.ax{2}.ax,'Position');
p3 = get(st.vols{i}.ax{3}.ax,'Position');
l = p1(3) + p2(3) + p3(3); sc = (1-0.01)/l;
p1 = p1*sc; p2 = p2 * sc; p3 = p3 * sc;
p1(4)=p1(4)*1.7; p1(2)= p1(2)-p1(4);
p2(4)=p2(4)*1.7; p2(2)= p2(2)-p1(4);
p3(4)=p3(4)*1.7; p3(2)= p3(2)-p1(4);
set(st.vols{i}.ax{1}.ax,'Position',...
[0 h-p1(4) p1(3) p1(4)]);
set(st.vols{i}.ax{2}.ax,'Position',...
[p1(3)+0.005 h-p2(4) p2(3) p2(4)]);
set(st.vols{i}.ax{3}.ax,'Position',....
[p1(3)+p2(3)+0.01 h-p3(4) p3(3) p3(4)]);
h = h - p1(4)-0.005;
end
Thanks a lot for your advices!
Vladimir
--- On Tue, 2/22/11, Guillaume Flandin <[log in to unmask]> wrote:
> From: Guillaume Flandin <[log in to unmask]>
> Subject: Re: [SPM] Check Reg Figures SPM8 - size and arrangement
> To: "Vladimir Bogdanov" <[log in to unmask]>
> Date: Tuesday, February 22, 2011, 4:49 PM
> Dear Vladimir,
>
> the following code should get you started (to be executed
> when images
> are displayed with checkreg):
>
> global st
> h = 1;
> for i=1:nnz(~cellfun('isempty',st.vols))
> p1 = get(st.vols{i}.ax{1}.ax,'Position');
> p2 = get(st.vols{i}.ax{2}.ax,'Position');
> p3 = get(st.vols{i}.ax{3}.ax,'Position');
> l = p1(3) + p2(3) + p3(3); sc =
> (1-0.01)/l;
> p1 = p1*sc; p2 = p2 * sc; p3 = p3 * sc;
> set(st.vols{i}.ax{1}.ax,'Position',...
> [0 h-p1(4) p1(3) p1(4)]);
> set(st.vols{i}.ax{2}.ax,'Position',...
> [p1(3)+0.005 h-p2(4) p2(3)
> p2(4)]);
> set(st.vols{i}.ax{3}.ax,'Position',....
> [p1(3)+p2(3)+0.01 h-p3(4) p3(3)
> p3(4)]);
> h = h - p1(4)-0.005;
> end
>
> Best,
> Guillaume.
>
> On 22/02/11 14:58, Vladimir Bogdanov wrote:
> > Dear John,
> >
> > Thank you for your response! Where is the part of the
> code responsible for this located? The SPM script is huge
> and I am only a beginner. :)
> >
> > Sincerely yours,
> > Vladimir
> >
> >
> > --- On Tue, 2/22/11, John Ashburner <[log in to unmask]>
> wrote:
> >
> >> From: John Ashburner <[log in to unmask]>
> >> Subject: Re: [SPM] Check Reg Figures SPM8 - size
> and arrangement
> >> To: [log in to unmask]
> >> Date: Tuesday, February 22, 2011, 3:39 PM
> >> There are no default settings that
> >> could be changed to make SPM do
> >> what you would like it to do. However, the
> beauty of
> >> having SPM in
> >> MATLAB is that it is relatively easy to tweak the
> code to
> >> make it do
> >> exactly what you want.
> >>
> >> Best regards,
> >> -John
> >>
> >> On 20 February 2011 22:20, Vladimir Bogdanov
> <[log in to unmask]>
> >> wrote:
> >>> Dear SPM experts,
> >>>
> >>> By default the size of the images in Check Reg
> figure
> >> does not allow to see well anatomical details (see
> screen1
> >> in attachment). This can be changed manually
> (screen2), but
> >> those changes take time.
> >>>
> >>> Is there a simple way do it fast? Is it
> possible to
> >> change SPM settings somewhere to make the figure
> in the same
> >> way (different from default) every time we call
> Check Reg?
> >>>
> >>> Thank you in advance for your help,
> >>> Vladimir
> >>>
> >>> Volodymyr Bogdanov, PhD
> >>>
> >>> Headache Research Unit
> >>> GIGA - Neurosciences
> >>> University of Liege
> >>> CHU T4 +1, Sart Tilman
> >>> Av de l'Hôpital 1
> >>> 4000 Liege, Belgium
> >>>
> >>>
> >>>
> >>
> >
> >
> >
> >
> >
>
>
> --
> Guillaume Flandin, PhD
> Wellcome Trust Centre for Neuroimaging
> University College London
> 12 Queen Square
> London WC1N 3BG
>
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--0-2071618827-1298462259=:64948--
========================================================================Date: Wed, 23 Feb 2011 12:54:14 +0000
Reply-To: Ciara Greene <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Ciara Greene <[log in to unmask]>
Subject: Multivariate Bayes
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
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Message-ID: <[log in to unmask]>
Hi all,
I'm trying to use the multivariate bayes analysis implemented in SPM8 to compare neural coding models in my data. I have two questions I hope you can help me with:
1. I'm a bit confused by the terminology for the model priors. In the Friston et al. NeuroImage paper where this method is described, the various priors were listed as spatial, smooth, singular and support, while in the SPM GUI the options are compact, sparse, smooth and support. I can't find a description of these new labels anywhere, so I'm not sure if the priors have simply been renamed or if these represent new models that weren't in the old version. What, for example, does a 'compact' model reflect?
2. Is it possible to compare models across subjects using random effects? I know that the Bayesian model selection facility in DCM allows for this, but I don't know how to do it in Multivariate Bayes analysis. The BMS button in the GUI allows comparison of different models, but presumably that uses fixed effects, and isn't suitable for group analysis. I haven't been able to find any options to change the inference method.
Thanks for your help,
Ciara
========================================================================Date: Wed, 23 Feb 2011 14:25:03 +0100
Reply-To: Torben Ellegaard Lund <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Torben Ellegaard Lund <[log in to unmask]>
Subject: Re: SPM Digest - 21 Feb 2011 to 22 Feb 2011 (#2011-56)
Comments: To: [log in to unmask]
In-Reply-To: <[log in to unmask]>
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--Boundary_(ID_NJMB/RhTT/0+yoWGjAWH2A)
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Dear Richard
If you scan a gel phantom you will be convinced that there is low frequency drift coming from the scanner it self. This is due to a lot of things but probably mainly due to heating induced by mechanical vibrations. I would pick the cut-off frequency based on your particular scanner, (most recent scanners that I have seen have little non-white noise faster than the default 128s period used in SPM). Regarding aliasing of cardiac and respiratory noise it is in no way guaranteed to end up at low frequencies. In fact for typical heart and respiration rates and TR values around 2s the variation i e.g. heart rate is so large that there will be cardiac noise at all frequencies, not just the low ones.
you can read more here:
Foerster BU, Tomasi D, Caparelli EC. Magnetic field shift due to mechanical
vibration in functional magnetic resonance imaging. Magn Reson Med. 2005
Nov;54(5):1261-7. PubMed PMID: 16215962; PubMed Central PMCID: PMC2408718.
Lund TE, Madsen KH, Sidaros K, Luo WL, Nichols TE. Non-white noise in fMRI:
does modelling have an impact? Neuroimage. 2006 Jan 1;29(1):54-66. Epub 2005 Aug
11. PubMed PMID: 16099175.
Smith AM, Lewis BK, Ruttimann UE, Ye FQ, Sinnwell TM, Yang Y, Duyn JH, Frank
JA. Investigation of low frequency drift in fMRI signal. Neuroimage. 1999
May;9(5):526-33. PubMed PMID: 10329292.
https://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=SPM;651c37f7.00
Best
Torben
Torben Ellegaard Lund
Associate Professor, PhD
The Danish National Research Foundation's Center of Functionally Integrative Neuroscience (CFIN)
Aarhus University
Aarhus University Hospital
Building 10G, 5th floor, room 31
Noerrebrogade 44
8000 Aarhus C
Denmark
Phone: +4589494380
Fax: +4589494400
http://www.cfin.au.dk
[log in to unmask]
Den Uge:8 23/02/2011 kl. 01.00 skrev SPM automatic digest system:
> Fra: Richard Binney <[log in to unmask]>
> Dato: Uge:8 22. feb 2011 12.48.38 CET
> Emne: acceptable high pass filtering
>
>
> Dear all,
>
> Something has recently been said to me that has made me think, and I want to get my head around this a bit more.....
>
> At what point does high pass filtering becomes ineffective? It has not been a concern for me in the past, but recently it has been suggested to me that a rule of thumb is to set the high pass filter at a minimum of 1.5 x your longest SOA. That seems reasonable if this is equal to or less than 128secs. I say that because I beleive that this is the optimum filter setting to remove low-freq noise associated with cardiac/respiratory noise, etc. But what if you use a filter at a lower frequency (e.g., 260secs)? It has been said to me that this would be OK. But aren't you running a risk of alliasing signal of interest at frequencies lower than 0.01Hz with cardiac/respiratory noise etc? IF this is an acceptable risk with a filter of say 260secs, at what point does it becomes unacceptable and the filter becomes ineffective? Eg., Say you had an enomrous longest SOA of 800 secs, you might want to use a filter set at 1000secs (0.001Hz) just to be sure. would this filter be effectively redundant?
>
> What other factors might speak to this? Jittered SOAs for example? Using a range of SOAs would of course spread the signal of interest across frequencies increasing sensitivity. Does this have an effect of reducing the risk of alliasing with low-freq noise or are you still losing a significant proprotion of your signal in the <0.1Hz frequencies (either due to noise or a 128 sec filter)? In what manner should this inform your high-pass filter? Should you be concerned with the longest SOA or the mean SOA (fundamental frequency) of your signal?
>
> Thanks in advance for your help,
>
> Richard
--Boundary_(ID_NJMB/RhTT/0+yoWGjAWH2A)
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<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Dear Richard<div><br></div><div>If you scan a gel phantom you will be convinced that there is low frequency drift coming from the scanner it self. This is due to a lot of things but probably mainly due to heating induced by mechanical vibrations. I would pick the cut-off frequency based on your particular scanner, (most recent scanners that I have seen have little non-white noise faster than the default 128s period used in SPM). Regarding aliasing of cardiac and respiratory noise it is in no way guaranteed to end up at low frequencies. In fact for typical heart and respiration rates and TR values around 2s the variation i e.g. heart rate is so large that there will be cardiac noise at all frequencies, not just the low ones.</div><div><br></div><div>you can read more here:</div><div><div><br></div><div>Foerster BU, Tomasi D, Caparelli EC. Magnetic field shift due to mechanical</div></div><div>vibration in functional magnetic resonance imaging. Magn Reson Med. 2005<br>Nov;54(5):1261-7. PubMed PMID: 16215962; PubMed Central PMCID: PMC2408718.<br><br></div><div>Lund TE, Madsen KH, Sidaros K, Luo WL, Nichols TE. Non-white noise in fMRI:</div><div><div>does modelling have an impact? Neuroimage. 2006 Jan 1;29(1):54-66. Epub 2005 Aug </div><div>11. PubMed PMID: 16099175.</div></div><div><font class="Apple-style-span" face="sans-serif" size="3"><div style="font-size: 13px; line-height: 19px; "><br></div><div style="font-size: 13px; line-height: 19px; "><font class="Apple-style-span" face="Helvetica"><span class="Apple-style-span" style="line-height: normal; font-size: medium;"><font class="Apple-style-span" face="sans-serif" size="3"><span class="Apple-style-span" style="font-size: 13px; line-height: 19px;">Smith AM, Lewis BK, Ruttimann UE, Ye FQ, Sinnwell TM, Yang Y, Duyn JH, Frank<br>JA. Investigation of low frequency drift in fMRI signal. Neuroimage. 1999<br>May;9(5):526-33. PubMed PMID: 10329292.<br><br><a href="https://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=SPM;651c37f7.00">https://www.jiscmail.ac.uk/cgi-bin/wa.exe?A2=SPM;651c37f7.00</a></span></font></span></font></div><div style="font-size: 13px; line-height: 19px; "><font class="Apple-style-span" face="Helvetica"><span class="Apple-style-span" style="line-height: normal; font-size: medium;"><font class="Apple-style-span" face="sans-serif" size="3"><span class="Apple-style-span" style="font-size: 13px; line-height: 19px;"><br></span></font></span></font></div><div style="font-size: 13px; line-height: 19px; "><font class="Apple-style-span" face="Helvetica"><span class="Apple-style-span" style="line-height: normal; font-size: medium;"><font class="Apple-style-span" face="sans-serif" size="3"><span class="Apple-style-span" style="font-size: 13px; line-height: 19px;"><br></span></font></span></font></div><div style="font-size: 13px; line-height: 19px; "><font class="Apple-style-span" face="Helvetica"><span class="Apple-style-span" style="line-height: normal; font-size: medium;"><font class="Apple-style-span" face="sans-serif" size="3"><span class="Apple-style-span" style="font-size: 13px; line-height: 19px;"><br></span></font></span></font></div><div style="font-size: 13px; line-height: 19px; "><font class="Apple-style-span" face="Helvetica"><span class="Apple-style-span" style="line-height: normal; font-size: medium;"><font class="Apple-style-span" face="sans-serif" size="3"><span class="Apple-style-span" style="font-size: 13px; line-height: 19px;">Best</span></font></span></font></div><div style="font-size: 13px; line-height: 19px; "><font class="Apple-style-span" face="Helvetica"><span class="Apple-style-span" style="line-height: normal; font-size: medium;"><font class="Apple-style-span" face="sans-serif" size="3"><span class="Apple-style-span" style="font-size: 13px; line-height: 19px;">Torben</span></font></span></font></div><div style="font-size: 13px; line-height: 19px; "><font class="Apple-style-span" face="Helvetica"><span class="Apple-style-span" style="line-height: normal; font-size: medium;"><font class="Apple-style-span" face="sans-serif" size="3"><span class="Apple-style-span" style="font-size: 13px; line-height: 19px;"><br></span></font></span></font></div><div style="font-size: 13px; line-height: 19px; "><font class="Apple-style-span" face="Helvetica"><span class="Apple-style-span" style="line-height: normal; font-size: medium;"><font class="Apple-style-span" face="sans-serif" size="3"><span class="Apple-style-span" style="font-size: 13px; line-height: 19px;"><br></span></font></span></font></div><div style="font-size: 13px; line-height: 19px; "><font class="Apple-style-span" face="Helvetica"><span class="Apple-style-span" style="line-height: normal; font-size: medium;"><font class="Apple-style-span" face="sans-serif" size="3"><span class="Apple-style-span" style="font-size: 13px; line-height: 19px;"><div style="font-family: Helvetica; line-height: normal; font-size: medium; "><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; font-size: medium; "><span class="Apple-style-span" style="border-collapse: separate; color: rgb(0, 0, 0); font-family: Helvetica; font-size: medium; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; orphans: 2; text-indent: 0px; text-transform: none; white-space: normal; widows: 2; word-spacing: 0px; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; -webkit-text-decorations-in-effect: none; -webkit-text-size-adjust: auto; -webkit-text-stroke-width: 0px; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><div>Torben Ellegaard Lund<br>Associate Professor, PhD<br>The Danish National Research Foundation's Center of Functionally Integrative Neuroscience (CFIN)<br>Aarhus University<br>Aarhus University Hospital<br>Building 10G, 5th floor, room 31<br>Noerrebrogade 44<br>8000 Aarhus C<br>Denmark<br>Phone: +4589494380<br>Fax: +4589494400<br><a href="http://www.cfin.au.dk">http://www.cfin.au.dk</a><br>[log in to unmask]</div><div><br></div></div></span><br class="Apple-interchange-newline"></span><br class="Apple-interchange-newline"></div></span></font></span></font></div><div style="font-size: 13px; line-height: 19px; "><font class="Apple-style-span" face="Helvetica"><span class="Apple-style-span" style="line-height: normal; font-size: medium;"><font class="Apple-style-span" face="sans-serif" size="3"><span class="Apple-style-span" style="font-size: 13px; line-height: 19px;"><br></span></font></span></font></div></font></div><div><br></div><div><br></div><div><br></div><div><div><div>Den Uge:8 23/02/2011 kl. 01.00 skrev SPM automatic digest system:</div><br class="Apple-interchange-newline"><blockquote type="cite"><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font-family: Tahoma; font-size: 13px; "><span style="font-family: Helvetica; font-size: medium; color: rgb(127, 127, 127); "><b>Fra:<span class="Apple-converted-space"> </span></b></span><span style="font-family: Helvetica; font-size: medium; ">Richard Binney <<a href="mailto:[log in to unmask]">[log in to unmask]</a>><br></span></div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font-family: Tahoma; font-size: 13px; "><span style="font-family: Helvetica; font-size: medium; color: rgb(127, 127, 127); "><b>Dato:<span class="Apple-converted-space"> </span></b></span><span style="font-family: Helvetica; font-size: medium; ">Uge:8 22. feb 2011 12.48.38 CET<br></span></div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; font-family: Tahoma; font-size: 13px; "><span style="font-family: Helvetica; font-size: medium; color: rgb(127, 127, 127); "><b>Emne:<span class="Apple-converted-space"> </span></b></span><span style="font-family: Helvetica; font-size: medium; "><b>acceptable high pass filtering</b><br></span></div><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "><br></span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "><br></span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; ">Dear all,</span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "><br></span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "> </span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "><br></span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; ">Something has recently been said to me that has made me think, and I want to get my head around this a bit more.....</span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "><br></span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "> </span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "><br></span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; ">At what point does high pass filtering becomes ineffective? It has not been a concern for me in the past, but recently it has been suggested to me that a rule of thumb is to set the high pass filter at a minimum of 1.5 x your longest SOA. That seems reasonable if this is equal to or less than 128secs. I say that because I beleive that this is the optimum filter setting to remove low-freq noise associated with cardiac/respiratory noise, etc. But what if you use a filter at a lower frequency (e.g., 260secs)? It has been said to me that this would be OK. But aren't you running a risk of alliasing signal of interest at frequencies lower than 0.01Hz with cardiac/respiratory noise etc? IF this is an acceptable risk with a filter of say 260secs, at what point does it becomes unacceptable and the filter becomes ineffective? Eg., Say you had an enomrous longest SOA of 800 secs, you might want to use a filter set at 1000secs (0.001Hz) just to be sure. would this filter be effectively redundant?</span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "><br></span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "> </span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "><br></span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; ">What other factors might speak to this? Jittered SOAs for example? Using a range of SOAs would of course spread the signal of interest across frequencies increasing sensitivity. Does this have an effect of reducing the risk of alliasing with low-freq noise or are you still losing a significant proprotion of your signal in the <0.1Hz frequencies (either due to noise or a 128 sec filter)? In what manner should this inform your high-pass filter? Should you be concerned with the longest SOA or the mean SOA (fundamental frequency) of your signal?</span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "><span class="Apple-converted-space"> </span></span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "><br></span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "> </span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "><br></span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; ">Thanks in advance for your help,</span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "><br></span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "> </span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "><br></span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; ">Richard</span><span class="Apple-style-span" style="font-family: Tahoma; font-size: 13px; "><span class="Apple-converted-space"> </span></span></blockquote></div><br></div><br><br></body></html>
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========================================================================Date: Wed, 23 Feb 2011 13:25:54 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: Batch issue
Comments: To: Marine Lazouret <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
I'd guess that you have a batch that was created with one version of
SPM8 (which is in /Applications/MATLAB_R2008a/spm8
), and you are trying to run it on a different computer where
/Applications/MATLAB_R2008a/spm8/tpm/grey.nii does not exist.
Best regards,
-John
On 23 February 2011 09:10, Marine Lazouret <[log in to unmask]> wrote:
> Hello,
> I made a batch for spatial pre processing and it stops every time during
> segmentation with this message:
> Running 'Segment'
> Failed 'Segment'
> Error using ==> spm_vol>subfunc at 111
> File "/Applications/MATLAB_R2008a/spm8/tpm/grey.nii" does not exist.
> In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc" at line
> 111.
> In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc1" at line
> 84.
> In file "/Applications/spm8/spm_vol.m" (v2237), function "subfunc2" at line
> 70.
> In file "/Applications/spm8/spm_vol.m" (v2237), function "spm_vol" at line
> 54.
> In file "/Applications/spm8/spm_preproc.m" (v1143), function "spm_preproc"
> at line 66.
> In file "/Applications/spm8/config/spm_run_preproc.m" (v2312), function
> "spm_run_preproc" at line 19.
> No executable modules, but still unresolved dependencies or incomplete
> module inputs.
> The following modules did not run:
> Failed: Segment
> Skipped: Normalise: Write
> Skipped: Smooth
> If i treat my images step by step, nothing goes wrong and I can get to the
> end of it. Does any one have an idea where this problem could be coming
> from?
> --
> Marine
>
========================================================================Date: Wed, 23 Feb 2011 09:55:14 -0500
Reply-To: justine dupont <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: justine dupont <[log in to unmask]>
Subject: Segmentation anomalies
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Dear experts
I recently saw something very puzzling with a VBM analysis. I have 40 T1 scans. They all have the same dimensions and voxel size and the raw images all look consistent and normal. Segmentation (with normalisation and modulation) works fine for 38 but for one the GM result is an empty brain (top row of attached image) and for the second the result is a kind of 'half brain' (bottom row of attached image).
What causes this and can it be fixed?
Many thanks
Justine
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Dear experts<div><br></div><div>I recently saw something very puzzling with a VBM analysis. I have 40 T1 scans. They all have the same dimensions and voxel size and the raw images all look consistent and normal. Segmentation (with normalisation and modulation) works fine for 38 but for one the GM result is an empty brain (top row of attached image) and for the second the result is a kind of 'half brain' (bottom row of attached image). </div><div><br></div><div>What causes this and can it be fixed?</div><div><br></div><div>Many thanks</div><div><br></div><div>Justine</div><div><br></div> </body>
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========================================================================Date: Wed, 23 Feb 2011 14:59:57 +0000
Reply-To: Nero Evero <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Nero Evero <[log in to unmask]>
Subject: paired t-test issues
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Hello All,
After specifying my second level analysis using a paired t-test design, I noticed in the parameter estimability my “betas are not uniquely specifiedâ€. I am not sure what this means and wanted to know if I have made a mistake. At the first level analysis from individual subject models, I created two contrasts images for each subject. Each subject underwent two sessions (after different treatments for each scan, at different times points, with identical paradigms) and I wanted to test whether condition A > condition B between the two sessions. I am using spm8 and entered each pair of subject contrasts (17 total using fMRI data) into the second level and used all the defaults for a paired t-test. I have also attached the design matrix. Also, I am not sure what the contrast weights should look like to for the paired t-test design. Any help would be appreciated, also please let me know if you need more information. Thanks in advance.
Nero
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--part-gwZJ1dAgGbAA3cEDw04oc1CAA3yAQAnX3Uu27qORZYmBd--
========================================================================Date: Wed, 23 Feb 2011 16:17:06 +0100
Reply-To: Marko Wilke <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Marko Wilke <[log in to unmask]>
Subject: Re: Segmentation anomalies
Comments: To: justine dupont <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1; format=flowed
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
Hello Justine,
this looks like the initial affine registration did not work, perhaps
due to image inhomogeneity. Aligning the images nicely, for example by
coregistering to a template prior to segmentation, may help, as may
fiddling with the inhomogeneity parameters (bias FHWM etc.).
Good luck,
Marko
justine dupont wrote:
> Dear experts
>
> I recently saw something very puzzling with a VBM analysis. I have 40 T1
> scans. They all have the same dimensions and voxel size and the raw
> images all look consistent and normal. Segmentation (with normalisation
> and modulation) works fine for 38 but for one the GM result is an empty
> brain (top row of attached image) and for the second the result is a
> kind of 'half brain' (bottom row of attached image).
>
> What causes this and can it be fixed?
>
> Many thanks
>
> Justine
>
--
____________________________________________________
PD Dr. med. Marko Wilke
Facharzt für Kinder- und Jugendmedizin
Leiter, Experimentelle Pädiatrische Neurobildgebung
Universitäts-Kinderklinik
Abt. III (Neuropädiatrie)
Marko Wilke, MD, PhD
Pediatrician
Head, Experimental Pediatric Neuroimaging
University Children's Hospital
Dept. III (Pediatric Neurology)
Hoppe-Seyler-Str. 1
D - 72076 Tübingen, Germany
Tel. +49 7071 29-83416
Fax +49 7071 29-5473
[log in to unmask]
http://www.medizin.uni-tuebingen.de/kinder/epn
____________________________________________________
========================================================================Date: Wed, 23 Feb 2011 16:29:39 +0100
Reply-To: Martin Jensen Dietz <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Martin Jensen Dietz <[log in to unmask]>
Subject: Re: paired t-test issues
Comments: To: Nero Evero <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
Content-Type: text/plain; charset="Windows-1252"
Content-Transfer-Encoding: quoted-printable
MIME-Version: 1.0
Message-ID: <[log in to unmask]>
Hi Nero,
The betas are not unique because subject columns introduce non-orthogonality in the design matrix. This is completely normal however and the default for a paired t-test design matrix.
Your contrast weights are
zeros(1,17) 1 -1
zeros(1,17) -1 1
Best wishes,
Martin
On Feb 23, 2011, at 3:59 PM, Nero Evero wrote:
> Hello All,
>
> After specifying my second level analysis using a paired t-test design, I noticed in the parameter estimability my “betas are not uniquely specified”. I am not sure what this means and wanted to know if I have made a mistake. At the first level analysis from individual subject models, I created two contrasts images for each subject. Each subject underwent two sessions (after different treatments for each scan, at different times points, with identical paradigms) and I wanted to test whether condition A > condition B between the two sessions. I am using spm8 and entered each pair of subject contrasts (17 total using fMRI data) into the second level and used all the defaults for a paired t-test. I have also attached the design matrix. Also, I am not sure what the contrast weights should look like to for the paired t-test design. Any help would be appreciated, also please let me know if you need more information. Thanks in advance.
>
> Nero
>
> <paired t-test design matrix.jpg>
========================================================================Date: Wed, 23 Feb 2011 15:39:06 +0000
Reply-To: Chris Filo Gorgolewski <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Chris Filo Gorgolewski <[log in to unmask]>
Subject: Re: Neuroimaging Data Processing Workshop 23-24 March 2011,
Edinburgh, UK
Comments: To: nipy-devel <[log in to unmask]>,
[log in to unmask], [log in to unmask],
[log in to unmask], freesurfer
<[log in to unmask]>,
FSL - FMRIB's Software Library <[log in to unmask]>
Comments: cc: dtc-edinburgh <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Message-ID: <[log in to unmask]>
Registration is now open.
https://www.epay.ed.ac.uk/browse/extra_info.asp?compid=1&catidB&modid=2&prodidE3&deptid$&prodvarid=0
Best regards,
Chris Gorgolewski
On 18 February 2011 17:58, Chris Filo Gorgolewski
<[log in to unmask]> wrote:
> Dear all,
>
> I am delighted to invite you to a Neuroimaging Data Processing
> Workshop which will be held on 23-24th of March 2011 at University of
> Edinburgh, UK. During the workshop we will cover neuroimaging data
> analysis and management with reusable, scalable and reproducible
> processing workflows that combine different software packages (such as
> SPM, FSL, and FreeSurfer) using NiPyPE framework. The workshop will
> also feature talks and demos covering data management, encapsulation
> and visualization using PyXNAT and the Connectome Library and Viewer.
> More details can be found at the workshop website:
> http://nipype.blogspot.com
>
> Best regards,
> Chris Gorgolewski
>
========================================================================Date: Wed, 23 Feb 2011 11:51:53 -0500
Reply-To: justine dupont <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: justine dupont <[log in to unmask]>
Subject: segmentation anomalies
Content-Type: multipart/alternative;
boundary="_0dea0aa1-ab97-4673-840d-8dde58316bbb_"
MIME-Version: 1.0
Message-ID: <[log in to unmask]>
--_0dea0aa1-ab97-4673-840d-8dde58316bbb_
Content-Type: text/plain; charset="iso-8859-1"
Content-Transfer-Encoding: quoted-printable
Dear Abela, Marko, Shail, Donald and others
surprisingly, given the consensus, the images were perfectly aligned. So the mystery remains...?
Justine
--_0dea0aa1-ab97-4673-840d-8dde58316bbb_
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<body class='hmmessage'>
Dear Abela, Marko, Shail, Donald and others<div><br></div><div>surprisingly, given the consensus, the images were perfectly aligned. So the mystery remains...? </div><div><br></div><div>Justine</div><div><br><div><br></div><div><br></div></div> </body>
</html>
--_0dea0aa1-ab97-4673-840d-8dde58316bbb_--
========================================================================Date: Wed, 23 Feb 2011 12:27:44 -0500
Reply-To: Jonathan Peelle <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Jonathan Peelle <[log in to unmask]>
Subject: Re: Segmentation anomalies
In-Reply-To: <[log in to unmask]>
Content-Type: text/plain; charset=iso-8859-1
Mime-Version: 1.0 (Apple Message framework v1082)
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
Hi Justine,
I think that Marko is correct in that it is worth investigating the image inhomogeneity and bias correction parameters. If the original alignment is ok, as you say, you may be able to try different bias correction settings. Alternatively, you can do a two-pass bias correction procedure, which is probably less elegant but often seems to work. To do this, run segmentation once and output the bias-corrected image, but no segmentations. Then run segmentation again on this bias-corrected image. For images with significant inhomogeneity this often works wonders.
Best regards,
Jonathan
On Feb 23, 2011, at 10:17 AM, Marko Wilke wrote:
> Hello Justine,
>
> this looks like the initial affine registration did not work, perhaps due to image inhomogeneity. Aligning the images nicely, for example by coregistering to a template prior to segmentation, may help, as may fiddling with the inhomogeneity parameters (bias FHWM etc.).
>
> Good luck,
> Marko
>
> justine dupont wrote:
>> Dear experts
>>
>> I recently saw something very puzzling with a VBM analysis. I have 40 T1
>> scans. They all have the same dimensions and voxel size and the raw
>> images all look consistent and normal. Segmentation (with normalisation
>> and modulation) works fine for 38 but for one the GM result is an empty
>> brain (top row of attached image) and for the second the result is a
>> kind of 'half brain' (bottom row of attached image).
>>
>> What causes this and can it be fixed?
>>
>> Many thanks
>>
>> Justine
>>
>
> --
> ____________________________________________________
> PD Dr. med. Marko Wilke
> Facharzt für Kinder- und Jugendmedizin
> Leiter, Experimentelle Pädiatrische Neurobildgebung
> Universitäts-Kinderklinik
> Abt. III (Neuropädiatrie)
>
>
> Marko Wilke, MD, PhD
> Pediatrician
> Head, Experimental Pediatric Neuroimaging
> University Children's Hospital
> Dept. III (Pediatric Neurology)
>
>
> Hoppe-Seyler-Str. 1
> D - 72076 Tübingen, Germany
> Tel. +49 7071 29-83416
> Fax +49 7071 29-5473
> [log in to unmask]
>
> http://www.medizin.uni-tuebingen.de/kinder/epn
> ____________________________________________________
========================================================================Date: Wed, 23 Feb 2011 20:20:38 +0000
Reply-To: Nero Evero <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Nero Evero <[log in to unmask]>
Subject: Re: paired t-test issues
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain; charset="UTF-8"
Message-ID: <[log in to unmask]>
Thank you Martin for your quick responses. I have another question, if anyone could help. After completing the 2nd level analysis and creating my contrast, I noticed I had no significantly activated clusters compared to the series of within-subjects contrasts I created earlier. I was curious why that may be; possibly a lack of power (i.e. only 17 subjects) to detect any significant difference between the two treatments? Again, here is a review of my design for my crossover study:
Session 1 --> Treatment 1 --> Scan (Visual paradigm: Active vs. Control)
Session 2 --> Treatment 2 --> Scan (Visual paradigm: Active vs. Control)
Also, would it be reasonable to run a one-way ANOVA for each session (as opposed to a paired t-test) and compare which regions are significantly activated. With this approach I was concerned I would not be able to infer one treatment was signifcantly different than another, would I be correct? Thanks for any comments in advance.
Nero
========================================================================Date: Thu, 24 Feb 2011 03:47:59 +0000
Reply-To: Dashjamts Jargal <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Dashjamts Jargal <[log in to unmask]>
Subject: Umodulated image analysis better in discrimination: possible
explanation?
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain; charset="UTF-8"
Message-ID: <[log in to unmask]>
Dear all,
I am novice in the SPM. There are many questions regarding the interpretation of SPM analysis results modulated versus unmodulated structural images. I am studying Alzheimer subjects compared to healthy aged controls. I have used T1 WI images of same size, orientation and scanner, applied DARTEL tool as implemented in SPM8. Proportional global normalization eliminated multiple scattered areas of interest in the unmodulated date and produced better localization of the findings into MTL structures in modulated data. But still even after proportional global normalization, my globally normalized unmodulated data show larger relevant difference areas and have higher diagnostic accuracy than the globally normalized modulated. What could explain such result? Why the unmodulated demonstrated much more affected areas than the modulated? Is the following correct according to the definition of how modulation works: "perfectly registered unmodulated data should show no difference between the groups"?Does it mean that unmodulated data have some compound of registration failures? When I am adding covariates such as TIV, age and sex to both modulated and unmodulated ANCOVA with normalization, there is only slight improvement.( I think it is improvement because after that I am loosing those few voxels which were in the deep subcortical white matter.) I am looking forward to receiving your comments and thank you for your time.
Jargal Dashjamts,
PhD-student
Kyushu University, Japan/ HSUM, Mongolia
========================================================================Date: Wed, 23 Feb 2011 16:36:13 -0800
Reply-To: Meghan Meyer <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Meghan Meyer <[log in to unmask]>
Subject: Re: Contrast Manager Error in SPM8
In-Reply-To: <[log in to unmask]>
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more info on this error: the model was not estimated bc of a problem using
'svd'....
Running 'Model estimation'
SPM8: spm_spm (v3960) 16:29:05 - 23/02/2011
======================================================================Initialising parameters :
...computingFailed 'Model estimation'
Error using ==> svd
Input to SVD must not contain NaN or Inf.
In file "/space/raid/fmri/spm8/spm_sp.m" (v1143), function "sf_set" at line
1126.
In file "/space/raid/fmri/spm8/spm_sp.m" (v1143), function "spm_sp" at line
225.
In file "/space/raid/fmri/spm8/spm_spm.m" (v3960), function "spm_spm" at
line 439.
In file "/space/raid/fmri/spm8/config/spm_run_fmri_est.m" (v3691), function
"spm_run_fmri_est" at line 33.
Running 'Contrast Manager'
Changing directory to:
/space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s
Failed 'Contrast Manager'
Error using ==> spm_run_con at 37
This model has not been estimated.
On Wed, Feb 23, 2011 at 4:11 PM, Meghan Meyer <[log in to unmask]> wrote:
> Hello,
> I'm running 1st level analyses in spm8, but for some of my subjects, i'm
> getting the following error below, but i'm not sure why the model would not
> have been estimated. the error appears for 9 of my 18 subjects. the other 9
> ran fine. Thanks!
>
> Running 'Contrast Manager'
> Changing directory to:
> /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s
> Failed 'Contrast Manager'
> Error using ==> spm_run_con at 37
> This model has not been estimated.
> In file "/space/raid/fmri/spm8/config/spm_run_con.m" (v3993), function
> "spm_run_con" at line 37.
>
> The following modules did not run:
> Failed: fMRI model specification
> Failed: Model estimation
> Failed: Contrast Manager
>
> --
> Meghan Meyer, M.A.
> Graduate Student
> Social Cognitive Neuroscience Lab
> UCLA, Psychology
>
--
Meghan Meyer, M.A.
Graduate Student
Social Cognitive Neuroscience Lab
UCLA, Psychology
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more info on this error: the model was not estimated bc of a problem using 'svd'....<br>Running 'Model estimation'<br><br>SPM8: spm_spm (v3960) 16:29:05 - 23/02/2011<br>========================================================================<br>
Initialising parameters : ...computingFailed 'Model estimation'<br>Error using ==> svd<br>Input to SVD must not contain NaN or Inf.<br>In file "/space/raid/fmri/spm8/spm_sp.m" (v1143), function "sf_set" at line 1126.<br>
In file "/space/raid/fmri/spm8/spm_sp.m" (v1143), function "spm_sp" at line 225.<br>In file "/space/raid/fmri/spm8/spm_spm.m" (v3960), function "spm_spm" at line 439.<br>In file "/space/raid/fmri/spm8/config/spm_run_fmri_est.m" (v3691), function "spm_run_fmri_est" at line 33.<br>
<br>Running 'Contrast Manager'<br> Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s<br>Failed 'Contrast Manager'<br>Error using ==> spm_run_con at 37<br>This model has not been estimated.<br>
<br><div class="gmail_quote">On Wed, Feb 23, 2011 at 4:11 PM, Meghan Meyer <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Hello, <br>I'm running 1st level analyses in spm8, but for some of my subjects, i'm getting the following error below, but i'm not sure why the model would not have been estimated. the error appears for 9 of my 18 subjects. the other 9 ran fine. Thanks!<br>
<br>Running 'Contrast Manager'<br> Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s<br>Failed 'Contrast Manager'<br>Error using ==> spm_run_con at 37<br>This model has not been estimated.<br>
In file "/space/raid/fmri/spm8/config/spm_run_con.m" (v3993), function "spm_run_con" at line 37.<br><br>The following modules did not run:<br>Failed: fMRI model specification<br>Failed: Model estimation<br>
Failed: Contrast Manager<br clear="all"><font color="#888888"><br>-- <br>Meghan Meyer, M.A.<br>Graduate Student<br>Social Cognitive Neuroscience Lab<br>UCLA, Psychology<br>
</font></blockquote></div><br><br clear="all"><br>-- <br>Meghan Meyer, M.A.<br>Graduate Student<br>Social Cognitive Neuroscience Lab<br>UCLA, Psychology<br>
--0023547c90239ad82a049cfc6633--
========================================================================Date: Thu, 24 Feb 2011 02:12:23 +0000
Reply-To: Israr Ul Haq <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Israr Ul Haq <[log in to unmask]>
Subject: fixed effects analysis across two sessions
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Dear Spm users,
I am trying to see treatment effects on a patient, by putting his pre and post treatment sessions (all the runs separately) in one fixed effects analysis, and defining post – pre contrast (t) by giving positive one to the experimental condition in the post treatment runs and negative one in the pretreatment runs. This is all being done at the ‘specify first level’ option in spm8. It made sense and the few people I did ask seem to think this was okay too. However I came across something that has me confused and it would be great to hear an explanation. I had been including the control condition in the contrast too, so for the post treatment runs it was included as a negative one and for the pretreatment runs a positive one (just how it’s supposed to be contrasted out of the experiment condition at each session level, by giving it a weightage equal and opposite to the experiment condition).
I thought I was getting a reasonable result from the contrast till I fortunately or unfortunately tried a contrast without the control condition, which is positive and negative ones only for the experimental condition, post and pretreatment respectively. This now is giving me FAR less activation then the contrast with the control condition, which is opposite to what i was expecting, since I though the whole point of including the control condition was to subtract activations unrelated to the process of interest, making the result purer so to speak. Since I have a medicine background and limited statistical knowledge, it would be great if you can point out whether this is possible and adding the control condition into each run somehow increases the t statistic when the analysis is done across sessions, or if I am doing something wrong. My t contrast for the fixed effects is:
-1 1 -1 1 1 -1 1 -1 . where the sequence of runs put into the model specification part is pre tx run1, pre tx run2, post tx run1 and post tx run2, and each run has an experimental and a control condition. Will extremely appreciate help in this matter.
Regards
Israr
========================================================================Date: Thu, 24 Feb 2011 09:35:18 +0100
Reply-To: "Kay H. Brodersen" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "Kay H. Brodersen" <[log in to unmask]>
Subject: Re: multivariate bayes analysis
Comments: To: Ciara Greene <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Message-ID: <9C3287B48E0C415E9A5E53EF766836C4@KHB>
Dear Ciara,
Great to hear you found the SPM course useful. With regard to your first
question: the priors in Multivariate Bayes have not been renamed. Instead,
the code actually contains more priors than the GUI offers. You can see this
by comparing the priors listed in spm_mvb_estimate (line 78) to those
actually supported in spm_mvb_U (starting in line 40). I would suggest
starting off by comparing those models that are directly listed in the GUI.
Your second question relates to fixed-effects versus random-effects model
comparison. SPM currently does not provide a fully automated pipeline for
comparing different MVB coding hypotheses (i.e., spatial priors). However,
this is easy to do with just a few Matlab commands. You would begin by
running MVB separately for all subjects and coding hypotheses. Given n
subjects and m models, this results in n*m different MVB.mat files. You then
extract the log model evidences from these files (F) and arrange them in an
n*m matrix M.
(i) For fixed-effects model comparison, you would then compute group Bayes
factors as sum(M,1) and select the model with the highest group Bayes
factor. This analysis relies on the assumption that the same model is best
in all subjects.
(ii) For a random-effects model comparison, you can use spm_BMS(M, ...).
This approach relaxes the assumption above and explicitly accounts for
between-subjects variability.
Let me know if this works. I'm copying this to the SPM mailing list so that
other users of MVB may benefit from our discussion.
Very best wishes
Kay
--
Kay Henning Brodersen
Department of Computer Science
Pattern Analysis and Machine Learning Group
ETH Zurich
Switzerland
[log in to unmask]
http://people.inf.ethz.ch/bkay/
From: Ciara Greene
Sent: Monday, February 21, 2011 3:34 PM
To: [log in to unmask]
Subject: multivariate bayes analysis
Hi Kay,
Thanks for the interesting practical session on multivariate analysis at the
SPM course last week, I really enjoyed it and I'm hoping to put it to good
use. I'm trying out the multivariate bayes method with a view to comparing
coding models in my data, and I had a couple of questions; I hope you don't
mind answering them!
Firstly, I'm a bit confused by the terminology for the model priors. In the
Friston et al. NeuroImage paper, the various priors were listed as spatial,
smooth, singular and support, while in the SPM GUI the options are compact,
sparse, smooth and support. I can't find a description of these new labels
anywhere, so I'm not sure if the priors have simply been renamed (e.g.
singular becoming compact???) or if these represent new models that weren't
in the old version.
My second question related to comparing models across subjects. For obvious
reasons, I'm not really interested in model comparison within a single
subject, I want to see which model seems best in my whole sample (and by
extension, in the population). I've read a bit about the new Bayesian model
selection method for DCM implemented in SPM8 which allows a random effects
comparsion of models across subjects, but I can't get that to work with the
multivariate bayes. The BMS button in the GUI allows models to be compared
to one another, but presumably that uses fixed effects, and isn't suitable
for group analysis. As far as I understand, it also doesn't allow the
results of the analyses to be saved out and carried forward to a second
level. Do you know if it's possible to use the DCM model selection method
outside DCM, or alternatively if there's another way of running a group
level analysis here?
Thanks in advance for any help you can provide!
Ciara
========================================================================Date: Wed, 23 Feb 2011 16:11:21 -0800
Reply-To: Meghan Meyer <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Meghan Meyer <[log in to unmask]>
Subject: Contrast Manager Error in SPM8
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Hello,
I'm running 1st level analyses in spm8, but for some of my subjects, i'm
getting the following error below, but i'm not sure why the model would not
have been estimated. the error appears for 9 of my 18 subjects. the other 9
ran fine. Thanks!
Running 'Contrast Manager'
Changing directory to:
/space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s
Failed 'Contrast Manager'
Error using ==> spm_run_con at 37
This model has not been estimated.
In file "/space/raid/fmri/spm8/config/spm_run_con.m" (v3993), function
"spm_run_con" at line 37.
The following modules did not run:
Failed: fMRI model specification
Failed: Model estimation
Failed: Contrast Manager
--
Meghan Meyer, M.A.
Graduate Student
Social Cognitive Neuroscience Lab
UCLA, Psychology
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Hello, <br>I'm running 1st level analyses in spm8, but for some of my subjects, i'm getting the following error below, but i'm not sure why the model would not have been estimated. the error appears for 9 of my 18 subjects. the other 9 ran fine. Thanks!<br>
<br>Running 'Contrast Manager'<br> Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s<br>Failed 'Contrast Manager'<br>Error using ==> spm_run_con at 37<br>This model has not been estimated.<br>
In file "/space/raid/fmri/spm8/config/spm_run_con.m" (v3993), function "spm_run_con" at line 37.<br><br>The following modules did not run:<br>Failed: fMRI model specification<br>Failed: Model estimation<br>
Failed: Contrast Manager<br clear="all"><br>-- <br>Meghan Meyer, M.A.<br>Graduate Student<br>Social Cognitive Neuroscience Lab<br>UCLA, Psychology<br>
--0023547c8b43a1e6d7049cfc0d30--
========================================================================Date: Thu, 24 Feb 2011 10:25:46 +0100
Reply-To: Martin Jensen Dietz <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Martin Jensen Dietz <[log in to unmask]>
Subject: Re: paired t-test issues
Comments: To: Nero Evero <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Hi Nero,
ANOVA is used in the case where you wish to test hypothesis concerning >2 beta or contrast images. Using a paired t-test on 2 contrast images, as in your case, is more straight forward. Alternatively, you could subtract your contrast images in ImCalc and perform a one-sample t-test.
Best wishes,
Martin
On Feb 23, 2011, at 9:20 PM, Nero Evero wrote:
> Thank you Martin for your quick responses. I have another question, if anyone could help. After completing the 2nd level analysis and creating my contrast, I noticed I had no significantly activated clusters compared to the series of within-subjects contrasts I created earlier. I was curious why that may be; possibly a lack of power (i.e. only 17 subjects) to detect any significant difference between the two treatments? Again, here is a review of my design for my crossover study:
>
> Session 1 --> Treatment 1 --> Scan (Visual paradigm: Active vs. Control)
>
> Session 2 --> Treatment 2 --> Scan (Visual paradigm: Active vs. Control)
>
> Also, would it be reasonable to run a one-way ANOVA for each session (as opposed to a paired t-test) and compare which regions are significantly activated. With this approach I was concerned I would not be able to infer one treatment was signifcantly different than another, would I be correct? Thanks for any comments in advance.
>
> Nero
========================================================================Date: Thu, 24 Feb 2011 12:03:13 +0100
Reply-To: guillaume auzias <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: guillaume auzias <[log in to unmask]>
Subject: DARTEL create template: minimum number of subjects?
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Hello,
I've not been able to find in the SPM list archives a clear answer to the
following questions:
Is there a minimum number of subjects required for the template creation
using DARTEL?
Does the registration of a pair of brains through a specifically created
2-brains template makes any sense?
Thanks for any input,
Guillaume
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Hello,<br><br>I've not been able to find in the SPM list archives a clear answer to the following questions:<br><br>Is there a minimum number of subjects required for the template creation using DARTEL?<br>Does the registration of a pair of brains through a specifically created 2-brains template makes any sense? <br>
<br>Thanks for any input,<br><br>Guillaume<br>
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========================================================================Date: Thu, 24 Feb 2011 19:16:50 +0800
Reply-To: li jian <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: li jian <[log in to unmask]>
Subject: Re: MATLAB Out of Memory error
Comments: To: Dennis Thompson <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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maybe you can add the virtual memory in window system.
http://www.delete-computer-history.com/increase-virtual-memory.html
Addtionally, you could used "free" command in matlab.
2011/2/19 Dennis Thompson <[log in to unmask]>
> Window 32 bit limits you to 2 GB of memory by default . There are some
> tricks to boost that to 3GB as outlined by Matlab in this link
>
>
> http://www.mathworks.com/help/techdoc/matlab_prog/brh72ex-49.html#brh72ex-67
>
>
> Here at the IRC we do all our processing on 64 bit linux computers. Have
> not had a "Out of memory error" in years.
>
>
>
> On Thu, Feb 17, 2011 at 7:32 PM, Nero Evero <[log in to unmask]> wrote:
>
>> Hello All,
>>
>> We are trying to run a fixed effects analysis using SPM8 on a Windows 7
>> 32-bit OS with 4GM of RAM. We have 120 subjects/sessions with a total of
>> 6600 scans (55/session). The analysis stops during the parameter estimation
>> and gives the following erroe message:
>>
>> Initialising parameters : ...done
>> Output images : ...initialised
>> Plane 1/46 , block 1/3 :
>> ...estimation??? Out of memory. Type HELP MEMORY for your options.
>>
>> Error in ==> spm_spm at 715
>> CY = CY + Y*Y';
>>
>> Error in ==> spm_getSPM at 233
>> SPM = spm_spm(SPM);
>>
>> Error in ==> spm_results_ui at 277
>> [SPM,xSPM] = spm_getSPM;
>>
>> ??? Error while evaluating uicontrol Callback
>>
>> I tried adding more memory to SPM (i.e. by changing defaults.stats.maxmem
>> to about 2GB), but I still received the error. Looking through the archives
>> it seems like the common solution is to change to a 64-bit OS, but I wanted
>> to know if there was any other possible solutions? Thanks in adavance.
>>
>> Nero
>>
>
>
--
À
Jian Li,
Institute of neuroinformatics,
Dalian University of Technology,
NO.2 Linggong Rd,
Dalian, P.R.China
116024
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<div>maybe you can add the virtual memory in window system.</div>
<div><a href="http://www.delete-computer-history.com/increase-virtual-memory.html">http://www.delete-computer-history.com/increase-virtual-memory.html</a></div>
<div>Addtionally, you could used "free" command in matlab.</div>
<div> </div>
<div><br><br> </div>
<div class="gmail_quote">2011/2/19 Dennis Thompson <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span><br>
<blockquote class="gmail_quote" style="PADDING-LEFT: 1ex; MARGIN: 0px 0px 0px 0.8ex; BORDER-LEFT: #ccc 1px solid">
<div>Window 32 bit limits you to 2 GB of memory by default . There are some tricks to boost that to 3GB as outlined by Matlab in this link</div>
<div><br></div><a href="http://www.mathworks.com/help/techdoc/matlab_prog/brh72ex-49.html#brh72ex-67" target="_blank">http://www.mathworks.com/help/techdoc/matlab_prog/brh72ex-49.html#brh72ex-67</a>
<div><br></div>
<div><br></div>
<div>Here at the IRC we do all our processing on 64 bit linux computers. Have not had a "Out of memory error" in years.</div>
<div>
<div></div>
<div class="h5">
<div><br></div>
<div><br><br>
<div class="gmail_quote">On Thu, Feb 17, 2011 at 7:32 PM, Nero Evero <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="PADDING-LEFT: 1ex; MARGIN: 0px 0px 0px 0.8ex; BORDER-LEFT: #ccc 1px solid">Hello All,<br><br>We are trying to run a fixed effects analysis using SPM8 on a Windows 7 32-bit OS with 4GM of RAM. We have 120 subjects/sessions with a total of 6600 scans (55/session). The analysis stops during the parameter estimation and gives the following erroe message:<br>
<br>Initialising parameters : ...done<br>Output images : ...initialised<br>Plane 1/46 , block 1/3 : ...estimation??? Out of memory. Type HELP MEMORY for your options.<br>
<br>Error in ==> spm_spm at 715<br> CY = CY + Y*Y';<br><br>Error in ==> spm_getSPM at 233<br> SPM = spm_spm(SPM);<br><br>Error in ==> spm_results_ui at 277<br> [SPM,xSPM] = spm_getSPM;<br>
<br>??? Error while evaluating uicontrol Callback<br><br>I tried adding more memory to SPM (i.e. by changing defaults.stats.maxmem to about 2GB), but I still received the error. Looking through the archives it seems like the common solution is to change to a 64-bit OS, but I wanted to know if there was any other possible solutions? Thanks in adavance.<br>
<font color="#888888"><br>Nero<br></font></blockquote></div><br></div></div></div></blockquote></div><br><br clear="all"><br>-- <br>À<br>Jian Li, <br>Institute of neuroinformatics,<br>Dalian University of Technology,<br>
NO.2 Linggong Rd,<br>Dalian, P.R.China<br>116024<br>
--0016e64bbe8ea02358049d055915--
========================================================================Date: Thu, 24 Feb 2011 12:41:49 +0100
Reply-To: "Kay H. Brodersen" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "Kay H. Brodersen" <[log in to unmask]>
Subject: Re: multivariate bayes analysis
Comments: To: MS Al-Rawi <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Dear Rawi,
Multivariate Bayes becomes accessible once you have loaded the results from
a first-level GLM analysis. Use the 'Results' button to do that, which will
make the three MVB-related buttons appear: multivariate Bayes, BMS, and
p-value. For details, see slide 38 in:
http://www.fil.ion.ucl.ac.uk/spm/course/slides11-zurich/Kay_Multivariate.pdf
Best wishes
Kay
-----Original Message-----
From: MS Al-Rawi
Sent: Thursday, February 24, 2011 12:33 PM
To: Kay H. Brodersen
Cc: [log in to unmask]
Subject: Re: [SPM] multivariate bayes analysis
Hello Kay....
I want to give MVB a try, however, I don't see anything in the SPM_GUI->MVB.
But, yet, there are mvb related functions, e.g. spm_mvb.m, etc. (SPM was
updated
using r4010).
All the best,
-Rawi
----- Original Message ----
> From: Kay H. Brodersen <[log in to unmask]>
> To: [log in to unmask]
> Sent: Thu, February 24, 2011 8:35:18 AM
> Subject: Re: [SPM] multivariate bayes analysis
>
> Dear Ciara,
>
> Great to hear you found the SPM course useful. With regard to your first
> question: the priors in Multivariate Bayes have not been renamed.
> Instead,
> the code actually contains more priors than the GUI offers. You can see
> this
> by comparing the priors listed in spm_mvb_estimate (line 78) to those
> actually supported in spm_mvb_U (starting in line 40). I would suggest
> starting off by comparing those models that are directly listed in the
> GUI.
>
> Your second question relates to fixed-effects versus random-effects model
> comparison. SPM currently does not provide a fully automated pipeline for
> comparing different MVB coding hypotheses (i.e., spatial priors).
> However,
> this is easy to do with just a few Matlab commands. You would begin by
> running MVB separately for all subjects and coding hypotheses. Given n
> subjects and m models, this results in n*m different MVB.mat files. You
> then
> extract the log model evidences from these files (F) and arrange them in
> an
> n*m matrix M.
>
> (i) For fixed-effects model comparison, you would then compute group
> Bayes
> factors as sum(M,1) and select the model with the highest group Bayes
> factor. This analysis relies on the assumption that the same model is
> best
> in all subjects.
>
> (ii) For a random-effects model comparison, you can use spm_BMS(M, ...).
> This approach relaxes the assumption above and explicitly accounts for
> between-subjects variability.
>
> Let me know if this works. I'm copying this to the SPM mailing list so
> that
> other users of MVB may benefit from our discussion.
>
> Very best wishes
> Kay
>
> --
> Kay Henning Brodersen
>
> Department of Computer Science
> Pattern Analysis and Machine Learning Group
> ETH Zurich
> Switzerland
>
> [log in to unmask]
> http://people.inf.ethz.ch/bkay/
>
>
>
>
> From: Ciara Greene
> Sent: Monday, February 21, 2011 3:34 PM
> To: [log in to unmask]
> Subject: multivariate bayes analysis
>
> Hi Kay,
>
> Thanks for the interesting practical session on multivariate analysis at
> the
> SPM course last week, I really enjoyed it and I'm hoping to put it to
> good
> use. I'm trying out the multivariate bayes method with a view to
> comparing
> coding models in my data, and I had a couple of questions; I hope you
> don't
> mind answering them!
>
> Firstly, I'm a bit confused by the terminology for the model priors. In
> the
> Friston et al. NeuroImage paper, the various priors were listed as
> spatial,
> smooth, singular and support, while in the SPM GUI the options are
> compact,
> sparse, smooth and support. I can't find a description of these new
> labels
> anywhere, so I'm not sure if the priors have simply been renamed (e.g.
> singular becoming compact???) or if these represent new models that
> weren't
> in the old version.
>
> My second question related to comparing models across subjects. For
> obvious
> reasons, I'm not really interested in model comparison within a single
> subject, I want to see which model seems best in my whole sample (and by
> extension, in the population). I've read a bit about the new Bayesian
> model
> selection method for DCM implemented in SPM8 which allows a random
> effects
> comparsion of models across subjects, but I can't get that to work with
> the
> multivariate bayes. The BMS button in the GUI allows models to be
> compared
> to one another, but presumably that uses fixed effects, and isn't
> suitable
> for group analysis. As far as I understand, it also doesn't allow the
> results of the analyses to be saved out and carried forward to a second
> level. Do you know if it's possible to use the DCM model selection method
> outside DCM, or alternatively if there's another way of running a group
> level analysis here?
>
> Thanks in advance for any help you can provide!
>
> Ciara
========================================================================Date: Thu, 24 Feb 2011 11:49:25 +0000
Reply-To: Paula Banca <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Paula Banca <[log in to unmask]>
Subject: Question regarding DCM
MIME-Version: 1.0
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Hello!
From what I read, the best method to study connectivity (when we are
dealing with fMRI data) is the DCM (dynamic causal model) which runs
in SPM.
However, this has been a problem for me because I have all my
functional data analysed by BrainVoyager and I can't use the files
from BV in SPM.
Does any of you know if there is any possiblity to export data from
BrainVoyager into SPM, namely the design matrix and the time series,
which are the two ingredients that DCM requires?
Thanks!
Best regards,
Paula Banca
========================================================================Date: Thu, 24 Feb 2011 12:14:22 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: Umodulated image analysis better in discrimination: possible
explanation?
Comments: To: Dashjamts Jargal <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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> I am novice in the SPM. There are many questions regarding the interpretation of SPM analysis results modulated versus unmodulated structural images. I am studying Alzheimer subjects compared to healthy aged controls. I have used T1 WI images of same size, orientation and scanner, applied DARTEL tool as implemented in SPM8. Proportional global normalization eliminated multiple scattered areas of interest in the unmodulated date and produced better localization of the findings into MTL structures in modulated data. But still even after proportional global normalization, my globally normalized unmodulated data show larger relevant difference areas and have higher diagnostic accuracy than the globally normalized modulated. What could explain such result?
I don't know, but there are a variety of possible explanations.:
1) In those areas, maybe Jacobian scaling is capturing more of the
within group variability, relative to the difference between the
populations. The t stat is proportional to the difference between the
groups, divided by the square root of the within-group variance.
2) Enlarged ventricles etc may result in systematic displacements of
structures, making fully accurate alignment of some regions more
difficult. Even a tiny systematic shift can result in pretty
significant t statistics.
3) The frequencies encoded by the deformation are slightly lower than
those encoded by the segmentation,so more detailed volumes are being
normalised (ie divided by) some volume measure that extends over a
larger region. With Dartel, information is lower frequency along the
direction in which the brain deforms (See eg Ashburner & Friston,
NeuroImage, in press).
4) Random statistical fluke.
5) Something else. Differences between anatomies can be described
using features other than local volumetric changes. For example,
local shearing may result in the same volume, but relatively different
lengths or areas.
> Why the unmodulated demonstrated much more affected areas than the modulated?
I'm not sure, but it is not clear to me what "unmodulated" features
actually represent quantitatively. Jacobian scaled features have a
quantitative interpretation.
> Is the following correct according to the definition of how modulation works: "perfectly registered unmodulated data should show no difference between the groups"?
If grey matter tissue classes were all exactly aligned with each
other, then they would be identical with each other.
In practice though, such perfect registration is impossible to achieve
because image registration has to be regularised. If images are
aligned using a particular form of registration approach (LDDMM or
Geodesic Shooting), the residual difference between a warped scan and
the template, scaled by the Jacobian determinant of the transform, can
have a mechanistic interpretation. It's a bit hard to explain without
resorting to the underlying maths, but the following is an example of
where it has been used:
http://www.ncbi.nlm.nih.gov/pubmed/20879441?dopt=Abstract
http://www.rrmind.research.va.gov/RRMINDRESEARCH/docs/Joshi_Workshop_2010.pdf
> Does it mean that unmodulated data have some compound of registration failures?
Not necessarily. Image registration simply can not be 100% exact.
> When I am adding covariates such as TIV, age and sex to both modulated and unmodulated ANCOVA with normalization, there is only slight improvement.( I think it is improvement because after that I am loosing those few voxels which were in the deep subcortical white matter.) I am looking forward to receiving your comments and thank you for your time.
I don't have a good answer here. Maybe someone else out there could comment.
Best regards,
-John
========================================================================Date: Thu, 24 Feb 2011 12:18:14 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: DARTEL create template: minimum number of subjects?
Comments: To: guillaume auzias <[log in to unmask]>
In-Reply-To: <AANLkTikq_aiRJEWbTkHbeXNG-zp6Do0gKGk©[log in to unmask]>
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Pairwise registration of brains this way makes perfect sense. This is
what was done for the evaluations of various registration algorithms
by Klein et al (as some collaborators objected to group-wise
registration).
Best regards,
-John
On 24 February 2011 11:03, guillaume auzias <[log in to unmask]> wrote:
> Hello,
>
> I've not been able to find in the SPM list archives a clear answer to the
> following questions:
>
> Is there a minimum number of subjects required for the template creation
> using DARTEL?
> Does the registration of a pair of brains through a specifically created
> 2-brains template makes any sense?
>
> Thanks for any input,
>
> Guillaume
>
========================================================================Date: Thu, 24 Feb 2011 09:05:43 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: fixed effects analysis across two sessions
Comments: To: Israr Ul Haq <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=windows-1252
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The control condition helps for a number of reasons:
(1) it allows you to ignore baseline shifts due to the drug (e.g. Drug
increases or decreases general neural activity;
(2) it makes it a within-subject comparison which almost always has
more power than between subjects(e.g. If A increases by 5 in one
subject and 2 in another, then you will have large variability;
however if B increases by 4 and 1, respectively, there is low
variability for A-B;
(3) without knowing your design, it's hard to interpret the implicit
baseline and whether it is tied to any cognitive state or tied to
statistics. The latter occurs when you have all your data occurring in
your two conditions. If this is the case, then the baseline is
completely arbitrary and would easily explain your results.
On Wednesday, February 23, 2011, Israr Ul Haq <[log in to unmask]> wrote:
> Dear Spm users,
>
> I am trying to see treatment effects on a patient, by putting his pre and post treatment sessions (all the runs separately) in one fixed effects analysis, and defining post – pre contrast (t) by giving positive one to the experimental condition in the post treatment runs and negative one in the pretreatment runs. This is all being done at the ‘specify first level’ option in spm8. It made sense and the few people I did ask seem to think this was okay too. However I came across something that has me confused and it would be great to hear an explanation. I had been including the control condition in the contrast too, so for the post treatment runs it was included as a negative one and for the pretreatment runs a positive one (just how it’s supposed to be contrasted out of the experiment condition at each session level, by giving it a weightage equal and opposite to the experiment condition).
> I thought I was getting a reasonable result from the contrast till I fortunately or unfortunately tried a contrast without the control condition, which is positive and negative ones only for the experimental condition, post and pretreatment respectively. This now is giving me FAR less activation then the contrast with the control condition, which is opposite to what i was expecting, since I though the whole point of including the control condition was to subtract activations unrelated to the process of interest, making the result purer so to speak. Since I have a medicine background and limited statistical knowledge, it would be great if you can point out whether this is possible and adding the control condition into each run somehow increases the t statistic when the analysis is done across sessions, or if I am doing something wrong. My t contrast for the fixed effects is:
> -1 1 -1 1 1 -1 1 -1 . where the sequence of runs put into the model specification part is pre tx run1, pre tx run2, post tx run1 and post tx run2, and each run has an experimental and a control condition. Will extremely appreciate help in this matter.
>
> Regards
> Israr
>
--
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Office: (773) 406-2464
=====================
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========================================================================Date: Thu, 24 Feb 2011 15:03:56 +0000
Reply-To: Simon Vandekar <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Simon Vandekar <[log in to unmask]>
Subject: DARTEL Flow fields problem?
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Hi John and SPMers,
I am attempting to normalise my fMRI data using the steps described in pages 440 and 441 of the spm8 manual:
1. Slice timing
2. Realign: Estimate and Reslice
3. Coregister the structural to functional images
4. Registration looks good.
5. Segment the anatomicals the SPM5 routine
6. Initial Import- use the *seg_sn files
7. Run Dartel with dependency on the gray and white matter images output by import step
8. Normalise to MNI, select the template6 from dartel output, the subject's flow field, and normalise the ra* files (slice timing, resliced).
The output I get is sdra* files. When I compare the output to the mni template it looks as if the subject's image has not been normalised. I am suspicious of the flow field because if I look at the individual subjects' uc1* image it is an empty black box.
What am I doing incorrectly here?
Thanks in advance,
Simon
========================================================================Date: Thu, 24 Feb 2011 15:43:34 +0000
Reply-To: Alexa Morcom <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Alexa Morcom <[log in to unmask]>
Subject: PhD studentships in Neuroinformatics and Computational
Neuroscience, Edinburgh
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Date: Thu, 24 Feb 2011 15:43:34 +0000
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Subject: PhD studentships in Neuroinformatics and Computational Neuroscience, Edinburgh
From: Alexa Morcom <[log in to unmask]>
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Dear all - please see below for full particulars and contact information.
Alexa
_________________________________________________
PhD studentships in Neuroinformatics and Computational Neuroscience,
Edinburgh
2011-2012 applications for fully-funded PhD studentships at the
University of Edinburgh Doctoral Training Centre (DTC) in
Neuroinformatics and Computational Neuroscience are now being
considered. The DTC is a world-class centre for research at the
interface between neuroscience and the engineering, computational, and
physical sciences.
Our four-year programme is ideal for students with strong
computational and analytical skills who want to employ cutting-edge
methodology to advance research in neuroscience and related fields, or
to apply ideas from neuroscience to computational problems. The first
year consists of courses in neuroscience and informatics, as well as
lab projects. This is followed by a three-year PhD project done in
collaboration with one of the many departments and institutes
affiliated with the DTC.
Current DTC PhD topics fall into five main areas:
* Computational neuroscience: Using analytical and computational
models, potentially supplemented with experiments, to gain
quantitative understanding of the nervous system. Many projects
focus on the development and function of sensory and motor systems
in animals, including neural coding, learning, and memory.
* Biomedical imaging algorithms and tools: Using advanced data
analysis techniques, such as machine learning and Bayesian
approaches, for imaging-based diagnosis and research.
* Cognitive science: Studying human cognitive processes and analysing
them in computational terms.
* Neuromorphic engineering: Using insights from neuroscience to help
build better hardware, such as neuromorphic VLSI circuits and robots
that perform robustly under natural conditions.
* Software systems and applications: Using discoveries from
neuroscience to develop software that can handle real-world data,
such as video, audio, or speech.
Other related areas of research may also be considered. Edinburgh has
a large, world-class research community in these areas and leads the
UK in creating a coherent programme in neuroinformatics and
computational neuroscience. Edinburgh has often been voted 'best place
to live in Britain', and has many exciting cultural and student
activities.
Students with a strong background in computer science, mathematics,
physics, or engineering are particularly encouraged to apply. Highly
motivated students with other backgrounds will also be considered.
15 full studentships (including stipend of 14,082-16,870 UK
pounds/year) are available to permanent UK residents or other EU
citizens who have been residing in the UK for the past three years
(e.g. for education); see the web site (below) for full details.
Other applicants can be accepted if they provide their own funding,
typically via a scholarship from their country of origin.
Further information and application forms can be obtained from:
http://www.anc.ed.ac.uk/dtc
For full consideration for entry in September 2011, the deadline for
complete applications is March 31st, 2011.
--
Dr. Alexa Morcom
RCUK Academic Fellow
Centre for Cognitive & Neural Systems/ Centre for Cognitive Ageing &
Cognitive Epidemiology
Psychology, University of Edinburgh
http://www.ccns.sbms.mvm.ed.ac.uk/people/academic/morcom.html
The University of Edinburgh is a charitable body, registered in Scotland,
with registration number SC005336
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Dear all - please see below for full particulars and contact information.<br><br>Alexa<br><div id=":1fe">_________________________________________________<br>
<br>
PhD studentships in Neuroinformatics and Computational Neuroscience, Edinburgh<br>
<br>
2011-2012 applications for fully-funded PhD studentships at the<br>
University of Edinburgh Doctoral Training Centre (DTC) in<br>
Neuroinformatics and Computational Neuroscience are now being<br>
considered. The DTC is a world-class centre for research at the<br>
interface between neuroscience and the engineering, computational, and<br>
physical sciences.<br>
<br>
Our four-year programme is ideal for students with strong<br>
computational and analytical skills who want to employ cutting-edge<br>
methodology to advance research in neuroscience and related fields, or<br>
to apply ideas from neuroscience to computational problems. The first<br>
year consists of courses in neuroscience and informatics, as well as<br>
lab projects. This is followed by a three-year PhD project done in<br>
collaboration with one of the many departments and institutes<br>
affiliated with the DTC.<br>
<br>
Current DTC PhD topics fall into five main areas:<br>
<br>
* Computational neuroscience: Using analytical and computational<br>
models, potentially supplemented with experiments, to gain<br>
quantitative understanding of the nervous system. Many projects<br>
focus on the development and function of sensory and motor systems<br>
in animals, including neural coding, learning, and memory.<br>
<br>
* Biomedical imaging algorithms and tools: Using advanced data<br>
analysis techniques, such as machine learning and Bayesian<br>
approaches, for imaging-based diagnosis and research.<br>
<br>
* Cognitive science: Studying human cognitive processes and analysing<br>
them in computational terms.<br>
<br>
* Neuromorphic engineering: Using insights from neuroscience to help<br>
build better hardware, such as neuromorphic VLSI circuits and robots<br>
that perform robustly under natural conditions.<br>
<br>
* Software systems and applications: Using discoveries from<br>
neuroscience to develop software that can handle real-world data,<br>
such as video, audio, or speech.<br>
<br>
<br>
Other related areas of research may also be considered. Edinburgh has<br>
a large, world-class research community in these areas and leads the<br>
UK in creating a coherent programme in neuroinformatics and<br>
computational neuroscience. Edinburgh has often been voted 'best place<br>
to live in Britain', and has many exciting cultural and student<br>
activities.<br>
<br>
Students with a strong background in computer science, mathematics,<br>
physics, or engineering are particularly encouraged to apply. Highly<br>
motivated students with other backgrounds will also be considered.<br>
<br>
15 full studentships (including stipend of 14,082-16,870 UK<br>
pounds/year) are available to permanent UK residents or other EU<br>
citizens who have been residing in the UK for the past three years<br>
(e.g. for education); see the web site (below) for full details.<br>
Other applicants can be accepted if they provide their own funding,<br>
typically via a scholarship from their country of origin.<br>
<br>
Further information and application forms can be obtained from:<br>
<a href="http://www.anc.ed.ac.uk/dtc" target="_blank">http://www.anc.ed.ac.uk/dtc</a><br>
<br>
For full consideration for entry in September 2011, the deadline for<br>
complete applications is March 31st, 2011.</div><br clear="all"><br>-- <br><font size="2">Dr. Alexa Morcom<br>RCUK Academic Fellow<br>Centre for Cognitive & Neural Systems/ Centre for Cognitive Ageing & Cognitive Epidemiology<br>
Psychology, University of Edinburgh<br><a href="http://www.ccns.sbms.mvm.ed.ac.uk/people/academic/morcom.html" target="_blank">http://www.ccns.sbms.mvm.ed.ac.uk/people/academic/morcom.html</a> <br><br>The University of Edinburgh is a charitable body, registered in Scotland, with registration number SC005336 </font><p>
</p>
<br>
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The University of Edinburgh is a charitable body, registered in
Scotland, with registration number SC005336.
------------=_1298562215-12437-60--
========================================================================Date: Thu, 24 Feb 2011 15:47:10 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: DARTEL Flow fields problem?
Comments: To: Simon Vandekar <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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It is a bit suspicious if all the u_*.nii are just zero. I'm not sure
why this would be the case. Do the template*.nii files seem to be OK?
Best regards,
John
On 24 February 2011 15:03, Simon Vandekar <[log in to unmask]> wrote:
> Hi John and SPMers,
>
> I am attempting to normalise my fMRI data using the steps described in pages 440 and 441 of the spm8 manual:
>
> 1. Slice timing
> 2. Realign: Estimate and Reslice
> 3. Coregister the structural to functional images
> 4. Registration looks good.
> 5. Segment the anatomicals the SPM5 routine
> 6. Initial Import- use the *seg_sn files
> 7. Run Dartel with dependency on the gray and white matter images output by import step
> 8. Normalise to MNI, select the template6 from dartel output, the subject's flow field, and normalise the ra* files (slice timing, resliced).
>
> The output I get is sdra* files. When I compare the output to the mni template it looks as if the subject's image has not been normalised. I am suspicious of the flow field because if I look at the individual subjects' uc1* image it is an empty black box.
>
> What am I doing incorrectly here?
>
> Thanks in advance,
> Simon
>
========================================================================Date: Thu, 24 Feb 2011 17:14:25 +0100
Reply-To: =?ISO-8859-1?Q?Rainer_Bögle?= <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: =?ISO-8859-1?Q?Rainer_Bögle?= <[log in to unmask]>
Subject: Re: Negative values in DCM.A
Comments: To: Maria Dauvermann <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Hello Maria,
as far as I understand this, connection parameters in DCM are change rates,
i.e. area 1 reduces the activity in area 2 (if a21 < 0).
As stated in Friston 2003, connection parameters are always relative to
parameters of self connections (which have to be negative to ensure a stable
system).
Regards,
Rainer
On Thu, Feb 24, 2011 at 8:58 AM, Maria Dauvermann <
[log in to unmask]> wrote:
> Hello,
>
> I had a look at the values in DCM.A after I have estimated the DCMs.
>
> What does a negative value in the intrinsic connection mean? How do I
> interprete this result?
>
> Thanks for your help.
>
> BW, Maria
>
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Hello Maria,<br><br>as far as I understand this, connection parameters in DCM are change rates, i.e. area 1 reduces the activity in area 2 (if a21 < 0).<br><br>As stated in Friston 2003, connection parameters are always relative to parameters of self connections (which have to be negative to ensure a stable system).<br>
<br>Regards,<br>Rainer<br>
<br><br><br><div class="gmail_quote">On Thu, Feb 24, 2011 at 8:58 AM, Maria Dauvermann <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
Hello,<br>
<br>
I had a look at the values in DCM.A after I have estimated the DCMs.<br>
<br>
What does a negative value in the intrinsic connection mean? How do I interprete this result?<br>
<br>
Thanks for your help.<br>
<br>
BW, Maria<br>
</blockquote></div><br>
--20cf3054a719d2a53a049d0981e7--
========================================================================Date: Thu, 24 Feb 2011 11:27:25 -0500
Reply-To: simon vandekar <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: simon vandekar <[log in to unmask]>
Subject: Re: DARTEL Flow fields problem?
Comments: To: John Ashburner <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/mixed; boundarye6ba4fc3661fad66049d09bbb3
Message-ID: <[log in to unmask]>
--90e6ba4fc3661fad66049d09bbb3
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--90e6ba4fc3661fad59049d09bbb1
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Thanks for the reply John,
As far as I can tell the template*.nii files look good, they are gray matter
images. There is also a u_rc1*_Template.nii in the same folder as my
template*.nii files, I've attached the image for that. Is that what my flow
fields are suppose to look like?
Thank you,
Simon
On Thu, Feb 24, 2011 at 10:47 AM, John Ashburner <[log in to unmask]>wrote:
> It is a bit suspicious if all the u_*.nii are just zero. I'm not sure
> why this would be the case. Do the template*.nii files seem to be OK?
>
> Best regards,
> John
>
> On 24 February 2011 15:03, Simon Vandekar <[log in to unmask]> wrote:
> > Hi John and SPMers,
> >
> > I am attempting to normalise my fMRI data using the steps described in
> pages 440 and 441 of the spm8 manual:
> >
> > 1. Slice timing
> > 2. Realign: Estimate and Reslice
> > 3. Coregister the structural to functional images
> > 4. Registration looks good.
> > 5. Segment the anatomicals the SPM5 routine
> > 6. Initial Import- use the *seg_sn files
> > 7. Run Dartel with dependency on the gray and white matter images output
> by import step
> > 8. Normalise to MNI, select the template6 from dartel output, the
> subject's flow field, and normalise the ra* files (slice timing, resliced).
> >
> > The output I get is sdra* files. When I compare the output to the mni
> template it looks as if the subject's image has not been normalised. I am
> suspicious of the flow field because if I look at the individual subjects'
> uc1* image it is an empty black box.
> >
> > What am I doing incorrectly here?
> >
> > Thanks in advance,
> > Simon
> >
>
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Thanks for the reply John,<br><br>As far as I can tell the template*.nii files look good, they are gray matter images. There is also a u_rc1*_Template.nii in the same folder as my template*.nii files, I've attached the image for that. Is that what my flow fields are suppose to look like?<br>
<br>Thank you,<br>Simon<br><br><div class="gmail_quote">On Thu, Feb 24, 2011 at 10:47 AM, John Ashburner <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
It is a bit suspicious if all the u_*.nii are just zero. I'm not sure<br>
why this would be the case. Do the template*.nii files seem to be OK?<br>
<br>
Best regards,<br>
John<br>
<br>
On 24 February 2011 15:03, Simon Vandekar <<a href="mailto:[log in to unmask]">[log in to unmask]</a>> wrote:<br>
> Hi John and SPMers,<br>
><br>
> I am attempting to normalise my fMRI data using the steps described in pages 440 and 441 of the spm8 manual:<br>
><br>
> 1. Slice timing<br>
> 2. Realign: Estimate and Reslice<br>
> 3. Coregister the structural to functional images<br>
> 4. Registration looks good.<br>
> 5. Segment the anatomicals the SPM5 routine<br>
> 6. Initial Import- use the *seg_sn files<br>
> 7. Run Dartel with dependency on the gray and white matter images output by import step<br>
> 8. Normalise to MNI, select the template6 from dartel output, the subject's flow field, and normalise the ra* files (slice timing, resliced).<br>
><br>
> The output I get is sdra* files. When I compare the output to the mni template it looks as if the subject's image has not been normalised. I am suspicious of the flow field because if I look at the individual subjects' uc1* image it is an empty black box.<br>
><br>
> What am I doing incorrectly here?<br>
><br>
> Thanks in advance,<br>
> Simon<br>
><br>
</blockquote></div><br>
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========================================================================Date: Thu, 24 Feb 2011 11:35:22 -0500
Reply-To: Ghazi Saidi Ladan <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Ghazi Saidi Ladan <[log in to unmask]>
Subject: deactivation
MIME-Version: 1.0
Content-Type: text/plain; charset="iso-8859-1"
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Message-ID: <[log in to unmask]>
Dear All,
could you please explain to me what it means to have a deactivation and what it means to have more deactivation in the second phase of learning than in the first?
I would also appreciate some relevant references,
thank you,
Ladan
Ladan Ghazisaidi
Étudiante au Doctorat en Sciences Biomédicales
Université de Montréal,
Centre de recherche Institut universitaire de gériatrie de Montréal
4545 Ch. Queen Mary Montréal, Québec H3W 1W5
tél. 514 340-3540 poste 4700
e-mail: [log in to unmask]
========================================================================Date: Thu, 24 Feb 2011 12:08:38 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: deactivation
Comments: To: Ghazi Saidi Ladan <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
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Deactivation means that you have less activity than baseline. I am not
sure that I have seen studies where deactivation increases with
learning. In repetition paradigms, the deactivation is less with each
repetition.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
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On Thu, Feb 24, 2011 at 11:35 AM, Ghazi Saidi Ladan
<[log in to unmask]> wrote:
> Dear All,
> could you please explain to me what it means to have a deactivation and what it means to have more deactivation in the second phase of learning than in the first?
> I would also appreciate some relevant references,
> thank you,
> Ladan
>
>
> Ladan Ghazisaidi
> Étudiante au Doctorat en Sciences Biomédicales
> Université de Montréal,
> Centre de recherche Institut universitaire de gériatrie de Montréal
> 4545 Ch. Queen Mary Montréal, Québec H3W 1W5
> tél. 514 340-3540 poste 4700
> e-mail: [log in to unmask]
>
========================================================================Date: Thu, 24 Feb 2011 17:28:27 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: DARTEL Flow fields problem?
Comments: To: simon vandekar <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
The flow field looks pretty OK to me. I thought that you were
referring to something that was uniformly grey. This is the x
component. The other components can be viewed by looking at
u_rc1*_Template.nii,2 and u_rc1*_Template.nii,3 (by changing the 1 in
the file selector to 1:3).
Also note that the main objective of Dartel is to align all the scans
in the study together. When you normalise to MNI space, the images
that are closely aligned within study are all affine transformed to
MNI space. The version of MNI space is only really defined for affine
aligned brain images (ie the rather blurred averages). You should
note that the single subject brain that people often overlay their
results on does not define MNI space. It is just the scan of a single
individual that has been affine registered to MNI space.
Best regards,
-John
On 24 February 2011 16:27, simon vandekar <[log in to unmask]> wrote:
> Thanks for the reply John,
>
> As far as I can tell the template*.nii files look good, they are gray matter
> images. There is also a u_rc1*_Template.nii in the same folder as my
> template*.nii files, I've attached the image for that. Is that what my flow
> fields are suppose to look like?
>
> Thank you,
> Simon
>
> On Thu, Feb 24, 2011 at 10:47 AM, John Ashburner <[log in to unmask]>
> wrote:
>>
>> It is a bit suspicious if all the u_*.nii are just zero. I'm not sure
>> why this would be the case. Do the template*.nii files seem to be OK?
>>
>> Best regards,
>> John
>>
>> On 24 February 2011 15:03, Simon Vandekar <[log in to unmask]> wrote:
>> > Hi John and SPMers,
>> >
>> > I am attempting to normalise my fMRI data using the steps described in
>> > pages 440 and 441 of the spm8 manual:
>> >
>> > 1. Slice timing
>> > 2. Realign: Estimate and Reslice
>> > 3. Coregister the structural to functional images
>> > 4. Registration looks good.
>> > 5. Segment the anatomicals the SPM5 routine
>> > 6. Initial Import- use the *seg_sn files
>> > 7. Run Dartel with dependency on the gray and white matter images output
>> > by import step
>> > 8. Normalise to MNI, select the template6 from dartel output, the
>> > subject's flow field, and normalise the ra* files (slice timing, resliced).
>> >
>> > The output I get is sdra* files. When I compare the output to the mni
>> > template it looks as if the subject's image has not been normalised. I am
>> > suspicious of the flow field because if I look at the individual subjects'
>> > uc1* image it is an empty black box.
>> >
>> > What am I doing incorrectly here?
>> >
>> > Thanks in advance,
>> > Simon
>> >
>
>
========================================================================Date: Thu, 24 Feb 2011 17:35:45 +0000
Reply-To: Chien-Ho Lin <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Chien-Ho Lin <[log in to unmask]>
Subject: script to flip MR images left to right (or vice versa)
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain; charset="UTF-8"
Message-ID: <[log in to unmask]>
Hi,
I stuides patients with stroke doing some behavior paradigm in fMRI.
Patients always used their affected limb for the task so some patients used left hand some used right hand.
Now I would like to flip the left and right of the images so patients' lesional hemisphere are on the same side.
Is there any script in spm 5 or 8 can help to accomplish this goal?
I found a m file called spm_flip.m but it seems support only spm2.
Your assistance are well appreciated.
Thank you so much.
Janice
========================================================================Date: Thu, 24 Feb 2011 18:03:09 +0000
Reply-To: Vy Dinh <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vy Dinh <[log in to unmask]>
Subject: Re: Contrast Manager Error in SPM8
Comments: To: Meghan Meyer <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
Content-Type: multipart/alternative;
boundary="_000_242AD0D3D1EDCE48AA6268ABC41C044D434FEDEBRUKWEXMAIL001ru_"
MIME-Version: 1.0
Message-ID: <[log in to unmask]>
--_000_242AD0D3D1EDCE48AA6268ABC41C044D434FEDEBRUKWEXMAIL001ru_
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Hi Meghan,
I also came across the error message "Input to SVD..." yesterday. After some debugging, we learned that our BETA values had NaNs for some voxels. If you do a search in the forum for betas and NaNs, you will come across a post that discusses how extra brain voxels in a ROI can result in NaNs. Although I encountered my error during VOI extraction (and not model estimation), I think that this may also be your problem. Did you specify an explicit mask for the model estimation? If so, it may mean that your mask may too big. I also had this issue in the past where my model estimation failed and I resolved it by using a smaller explicity mask AND downloading the 4010 version of SPM8. Hope that helps.
Best,
Vy T.U. Dinh
Research Assistant, Neurological Sciences
Rush University Medical Center
Phone: (312) 563-3853
Fax: (312) 563-4660
Email: [log in to unmask]<mailto:[log in to unmask]>
________________________________
From: SPM (Statistical Parametric Mapping) [[log in to unmask]] on behalf of Meghan Meyer [[log in to unmask]]
Sent: Wednesday, February 23, 2011 6:36 PM
To: [log in to unmask]
Subject: Re: [SPM] Contrast Manager Error in SPM8
more info on this error: the model was not estimated bc of a problem using 'svd'....
Running 'Model estimation'
SPM8: spm_spm (v3960) 16:29:05 - 23/02/2011
========================================================================
Initialising parameters : ...computingFailed 'Model estimation'
Error using ==> svd
Input to SVD must not contain NaN or Inf.
In file "/space/raid/fmri/spm8/spm_sp.m" (v1143), function "sf_set" at line 1126.
In file "/space/raid/fmri/spm8/spm_sp.m" (v1143), function "spm_sp" at line 225.
In file "/space/raid/fmri/spm8/spm_spm.m" (v3960), function "spm_spm" at line 439.
In file "/space/raid/fmri/spm8/config/spm_run_fmri_est.m" (v3691), function "spm_run_fmri_est" at line 33.
Running 'Contrast Manager'
Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s
Failed 'Contrast Manager'
Error using ==> spm_run_con at 37
This model has not been estimated.
On Wed, Feb 23, 2011 at 4:11 PM, Meghan Meyer <[log in to unmask]<mailto:[log in to unmask]>> wrote:
Hello,
I'm running 1st level analyses in spm8, but for some of my subjects, i'm getting the following error below, but i'm not sure why the model would not have been estimated. the error appears for 9 of my 18 subjects. the other 9 ran fine. Thanks!
Running 'Contrast Manager'
Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s
Failed 'Contrast Manager'
Error using ==> spm_run_con at 37
This model has not been estimated.
In file "/space/raid/fmri/spm8/config/spm_run_con.m" (v3993), function "spm_run_con" at line 37.
The following modules did not run:
Failed: fMRI model specification
Failed: Model estimation
Failed: Contrast Manager
--
Meghan Meyer, M.A.
Graduate Student
Social Cognitive Neuroscience Lab
UCLA, Psychology
--
Meghan Meyer, M.A.
Graduate Student
Social Cognitive Neuroscience Lab
UCLA, Psychology
--_000_242AD0D3D1EDCE48AA6268ABC41C044D434FEDEBRUKWEXMAIL001ru_
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<div style="direction: ltr;font-family: Tahoma;color: #000000;font-size: 13px;">
<div style="">Hi Meghan,</div>
<div style=""><br>
</div>
<div style="">I also came across the error message "Input to SVD..." yesterday. After some debugging, we learned that our BETA values had NaNs for some voxels. If you do a search in the forum for betas and NaNs, you will come across a post that discusses how
extra brain voxels in a ROI can result in NaNs. Although I encountered my error during VOI extraction (and not model estimation), I think that this may also be your problem. Did you specify an explicit mask for the model estimation? If so, it may mean that
your mask may too big. I also had this issue in the past where my model estimation failed and I resolved it by using a smaller explicity mask AND downloading the 4010 version of SPM8. Hope that helps.</div>
<div style=""><br>
</div>
<div style="">Best,</div>
<div><br>
<div style="font-family:Tahoma; font-size:13px"><span class="Apple-style-span" style="font-family:arial; font-size:small">Vy T.U. Dinh<br>
Research Assistant, Neurological Sciences<br>
Rush University Medical Center<br>
Phone: (312) 563-3853<br>
Fax: (312) 563-4660<br>
Email: <a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a></span>
<div style="font-family:Tahoma; font-size:13px"></div>
</div>
</div>
<div style="font-family: Times New Roman; color: #000000; font-size: 16px">
<hr tabindex="-1">
<div id="divRpF938411" style="direction: ltr; "><font face="Tahoma" size="2" color="#000000"><b>From:</b> SPM (Statistical Parametric Mapping) [[log in to unmask]] on behalf of Meghan Meyer [[log in to unmask]]<br>
<b>Sent:</b> Wednesday, February 23, 2011 6:36 PM<br>
<b>To:</b> [log in to unmask]<br>
<b>Subject:</b> Re: [SPM] Contrast Manager Error in SPM8<br>
</font><br>
</div>
<div></div>
<div>more info on this error: the model was not estimated bc of a problem using 'svd'....<br>
Running 'Model estimation'<br>
<br>
SPM8: spm_spm (v3960) 16:29:05 - 23/02/2011<br>
========================================================================<br>
Initialising parameters : ...computingFailed 'Model estimation'<br>
Error using ==> svd<br>
Input to SVD must not contain NaN or Inf.<br>
In file "/space/raid/fmri/spm8/spm_sp.m" (v1143), function "sf_set" at line 1126.<br>
In file "/space/raid/fmri/spm8/spm_sp.m" (v1143), function "spm_sp" at line 225.<br>
In file "/space/raid/fmri/spm8/spm_spm.m" (v3960), function "spm_spm" at line 439.<br>
In file "/space/raid/fmri/spm8/config/spm_run_fmri_est.m" (v3691), function "spm_run_fmri_est" at line 33.<br>
<br>
Running 'Contrast Manager'<br>
Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s<br>
Failed 'Contrast Manager'<br>
Error using ==> spm_run_con at 37<br>
This model has not been estimated.<br>
<br>
<div class="gmail_quote">On Wed, Feb 23, 2011 at 4:11 PM, Meghan Meyer <span dir="ltr">
<<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="border-left:1px solid rgb(204,204,204); margin:0pt 0pt 0pt 0.8ex; padding-left:1ex">
Hello, <br>
I'm running 1st level analyses in spm8, but for some of my subjects, i'm getting the following error below, but i'm not sure why the model would not have been estimated. the error appears for 9 of my 18 subjects. the other 9 ran fine. Thanks!<br>
<br>
Running 'Contrast Manager'<br>
Changing directory to: /space/raid8/data/lieber/SWM/SWM08/analysis/swm_ppi_delay6s<br>
Failed 'Contrast Manager'<br>
Error using ==> spm_run_con at 37<br>
This model has not been estimated.<br>
In file "/space/raid/fmri/spm8/config/spm_run_con.m" (v3993), function "spm_run_con" at line 37.<br>
<br>
The following modules did not run:<br>
Failed: fMRI model specification<br>
Failed: Model estimation<br>
Failed: Contrast Manager<br clear="all">
<font color="#888888"><br>
-- <br>
Meghan Meyer, M.A.<br>
Graduate Student<br>
Social Cognitive Neuroscience Lab<br>
UCLA, Psychology<br>
</font></blockquote>
</div>
<br>
<br clear="all">
<br>
-- <br>
Meghan Meyer, M.A.<br>
Graduate Student<br>
Social Cognitive Neuroscience Lab<br>
UCLA, Psychology<br>
</div>
</div>
</div>
</body>
</html>
--_000_242AD0D3D1EDCE48AA6268ABC41C044D434FEDEBRUKWEXMAIL001ru_--
========================================================================Date: Thu, 24 Feb 2011 18:26:08 +0000
Reply-To: Meng-Chuan Lai <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Meng-Chuan Lai <[log in to unmask]>
Subject: "proportional scaling" in model specification - questions for the
batch itself
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain; charset="UTF-8"
Message-ID: <[log in to unmask]>
Dear John and SPM designers/users,
I have 2 questions regarding how the batch for 2nd level analysis actually works:
1. I've run a VBM analysis using factorial design. I selected at the end of batch "Global normalisation - Normalisation - proportional" since I would like to model GLM on GM regional volume relative to total GM volume (my inputs are modulated GM images). I learned that this setting will adjust individual map voxel values by dividing them by the sum (or mean?) of the individual image, is this the case? Could you please clarify how the individual-level re-scaling is actually done, in terms of what are the scaling factors (sum or mean or others) etc?
2. In the batch I also select absolute threshold masking. Could you please clarify if the individual scaling factor (mean or sum or others of the individual map) is from the original input image, or the image thresholded by this absolute threshold masking?
I asked these because I would need to get the rescaled voxel values and do some post-hoc analysis, but this cannot be done directly since the rescaling is run inside the batch and no extra rescaled maps are generated. I would need to calculate it outside SPM so the clarifications will be very helpful. Thank you very much!!
regards,
Meng-chuan
========================================================================Date: Thu, 24 Feb 2011 19:54:52 +0000
Reply-To: Israr Ul Haq <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Israr Ul Haq <[log in to unmask]>
Subject: Re: fixed effects analysis across two sessions
Comments: To: Donald McLaren <[log in to unmask]>
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain
Message-ID: <[log in to unmask]>
Thanks a lot, This makes sense, particularly the second explanation. although the runs1 and 2 I specified were from the same subject and the whole analysis is being done for each subject separately but I suppose in a way its the same thing and is taking into account the variability across runs. My model is actually an overt naming task, the experimental condition includes a semantic process of interest whereas the control condition has all but that.
Thinking about this though, how is the direction of the change from pre to post treatment (in a voxel) taken into account? I computed a pre - post treatment contrast to check whether it will give me the same voxels (worried that the areas included both positive and negative change over the two sessions in my first contrast) and was relieved to see different voxels. However, if its not too cumbersome, can you please explain how the eventual t statistic is being computed in such a multi session analysis with two independent conditions? Heres what I understand; for each voxel, a correlation coefficient (r) is calculated for each stimulus of a specified condition, and all the r values in a run give a distribution for that condition, with a mean and standard deviation. These are then utilized in the t tests of no significant difference, where the linear t contrasts between two conditions are setup such that whether for that particular voxel, one condition had significantly different signal than the other, using the mean and sd of the r values. What i am trying to wrap my head around is how based on the weightings that we specificy (i.e +1 or -1), the t statistic is calculated for exp > control vs control > exp, since in both cases, what we are essentially inputting is the difference between the means. Intuitively it would seem that it would give a voxel as significant only if the condition weighted +1 also has the higher mean of the two conditions being contrasted. But if so, how would this apply to multiple runs and sessions, or more specifically what would be the sequence of computations in my design? Theres a plus one weighting in both pre and post treatment runs, albeit for different conditions.
Thanks
Israr
========================================================================Date: Thu, 24 Feb 2011 15:27:00 -0500
Reply-To: simon vandekar <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: simon vandekar <[log in to unmask]>
Subject: Re: DARTEL Flow fields problem?
Comments: To: John Ashburner <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/mixed; boundary¼aec53f398fec3f19049d0d0af8
Message-ID: <[log in to unmask]>
--bcaec53f398fec3f19049d0d0af8
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Hi John,
Thanks again for your help and sorry for all the emails. The flow fields
that 'Normalise to MNI' asks for are the individual subjects' flow fields
'u_c1*.nii which look like the 3rd image selected in the attached picture.
Also included in this picture are a template, and subject's functional scan
after the 'normalise to MNI' step.
Thank you,
Simon
On Thu, Feb 24, 2011 at 12:28 PM, John Ashburner <[log in to unmask]>wrote:
> The flow field looks pretty OK to me. I thought that you were
> referring to something that was uniformly grey. This is the x
> component. The other components can be viewed by looking at
> u_rc1*_Template.nii,2 and u_rc1*_Template.nii,3 (by changing the 1 in
> the file selector to 1:3).
>
> Also note that the main objective of Dartel is to align all the scans
> in the study together. When you normalise to MNI space, the images
> that are closely aligned within study are all affine transformed to
> MNI space. The version of MNI space is only really defined for affine
> aligned brain images (ie the rather blurred averages). You should
> note that the single subject brain that people often overlay their
> results on does not define MNI space. It is just the scan of a single
> individual that has been affine registered to MNI space.
>
> Best regards,
> -John
>
> On 24 February 2011 16:27, simon vandekar <[log in to unmask]> wrote:
> > Thanks for the reply John,
> >
> > As far as I can tell the template*.nii files look good, they are gray
> matter
> > images. There is also a u_rc1*_Template.nii in the same folder as my
> > template*.nii files, I've attached the image for that. Is that what my
> flow
> > fields are suppose to look like?
> >
> > Thank you,
> > Simon
> >
> > On Thu, Feb 24, 2011 at 10:47 AM, John Ashburner <[log in to unmask]>
> > wrote:
> >>
> >> It is a bit suspicious if all the u_*.nii are just zero. I'm not sure
> >> why this would be the case. Do the template*.nii files seem to be OK?
> >>
> >> Best regards,
> >> John
> >>
> >> On 24 February 2011 15:03, Simon Vandekar <[log in to unmask]> wrote:
> >> > Hi John and SPMers,
> >> >
> >> > I am attempting to normalise my fMRI data using the steps described in
> >> > pages 440 and 441 of the spm8 manual:
> >> >
> >> > 1. Slice timing
> >> > 2. Realign: Estimate and Reslice
> >> > 3. Coregister the structural to functional images
> >> > 4. Registration looks good.
> >> > 5. Segment the anatomicals the SPM5 routine
> >> > 6. Initial Import- use the *seg_sn files
> >> > 7. Run Dartel with dependency on the gray and white matter images
> output
> >> > by import step
> >> > 8. Normalise to MNI, select the template6 from dartel output, the
> >> > subject's flow field, and normalise the ra* files (slice timing,
> resliced).
> >> >
> >> > The output I get is sdra* files. When I compare the output to the mni
> >> > template it looks as if the subject's image has not been normalised. I
> am
> >> > suspicious of the flow field because if I look at the individual
> subjects'
> >> > uc1* image it is an empty black box.
> >> >
> >> > What am I doing incorrectly here?
> >> >
> >> > Thanks in advance,
> >> > Simon
> >> >
> >
> >
>
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Hi John,<br><br>Thanks again for your help and sorry for all the emails. The flow fields that 'Normalise to MNI' asks for are the individual subjects' flow fields 'u_c1*.nii which look like the 3rd image selected in the attached picture. Also included in this picture are a template, and subject's functional scan after the 'normalise to MNI' step.<br>
<br>Thank you,<br>Simon<br>
<br><br><div class="gmail_quote">On Thu, Feb 24, 2011 at 12:28 PM, John Ashburner <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
The flow field looks pretty OK to me. I thought that you were<br>
referring to something that was uniformly grey. This is the x<br>
component. The other components can be viewed by looking at<br>
u_rc1*_Template.nii,2 and u_rc1*_Template.nii,3 (by changing the 1 in<br>
the file selector to 1:3).<br>
<br>
Also note that the main objective of Dartel is to align all the scans<br>
in the study together. When you normalise to MNI space, the images<br>
that are closely aligned within study are all affine transformed to<br>
MNI space. The version of MNI space is only really defined for affine<br>
aligned brain images (ie the rather blurred averages). You should<br>
note that the single subject brain that people often overlay their<br>
results on does not define MNI space. It is just the scan of a single<br>
individual that has been affine registered to MNI space.<br>
<br>
Best regards,<br>
<font color="#888888">-John<br>
</font><div><div></div><div><br>
On 24 February 2011 16:27, simon vandekar <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>> wrote:<br>
> Thanks for the reply John,<br>
><br>
> As far as I can tell the template*.nii files look good, they are gray matter<br>
> images. There is also a u_rc1*_Template.nii in the same folder as my<br>
> template*.nii files, I've attached the image for that. Is that what my flow<br>
> fields are suppose to look like?<br>
><br>
> Thank you,<br>
> Simon<br>
><br>
> On Thu, Feb 24, 2011 at 10:47 AM, John Ashburner <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>><br>
> wrote:<br>
>><br>
>> It is a bit suspicious if all the u_*.nii are just zero. I'm not sure<br>
>> why this would be the case. Do the template*.nii files seem to be OK?<br>
>><br>
>> Best regards,<br>
>> John<br>
>><br>
>> On 24 February 2011 15:03, Simon Vandekar <<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>> wrote:<br>
>> > Hi John and SPMers,<br>
>> ><br>
>> > I am attempting to normalise my fMRI data using the steps described in<br>
>> > pages 440 and 441 of the spm8 manual:<br>
>> ><br>
>> > 1. Slice timing<br>
>> > 2. Realign: Estimate and Reslice<br>
>> > 3. Coregister the structural to functional images<br>
>> > 4. Registration looks good.<br>
>> > 5. Segment the anatomicals the SPM5 routine<br>
>> > 6. Initial Import- use the *seg_sn files<br>
>> > 7. Run Dartel with dependency on the gray and white matter images output<br>
>> > by import step<br>
>> > 8. Normalise to MNI, select the template6 from dartel output, the<br>
>> > subject's flow field, and normalise the ra* files (slice timing, resliced).<br>
>> ><br>
>> > The output I get is sdra* files. When I compare the output to the mni<br>
>> > template it looks as if the subject's image has not been normalised. I am<br>
>> > suspicious of the flow field because if I look at the individual subjects'<br>
>> > uc1* image it is an empty black box.<br>
>> ><br>
>> > What am I doing incorrectly here?<br>
>> ><br>
>> > Thanks in advance,<br>
>> > Simon<br>
>> ><br>
><br>
><br>
</div></div></blockquote></div><br>
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--bcaec53f398fec3f19049d0d0af8--
========================================================================Date: Thu, 24 Feb 2011 15:42:24 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: fixed effects analysis across two sessions
Comments: To: Israr Ul Haq <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
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Israr,
Let me start of on the issue of means, sd, r, and t-statistics (basic ideas).
(1) beta ~ weight of a particular IV (e.g. column)
(2) sd ~ weight of overall variance based on covariance of betas
(3) beta=sd(XY)/var(X); r=beta*sd(x)/sd(y) {for linear regression, for
multiple linear regression and general linear models, you compute
partial correlations}
(4) r=sqrt(t^2/(t^2+df) OR t=beta-0/sd
The beta and residual of the model form the basis of the r, not the
other way around. Everything is test against a mean of 0. Since fMRI
values are non-zero, a constant is included to account for the
non-zero mean of the data. It is the betas (not r) that are compared
statistically.
Contrasts:
Run1-C1 Run1-C2 Run2-C1 Run2-C2 Run3-C1 Run3-C2 Run4-C1 Run4-C2
1 -1 1 -1 0 0 0 0 tests C1>C2 for pretreatment
0 0 0 0 1 -1 1 -1 tests C1>C2 for postreatment
1 -1 1 -1 -1 1 -1 1 tests (C1>C2 for pretreatment) > (C1>C2 for postreatment)
1 means you add the beta, -1 means you subtract the beta, AND it is
the sum of the added and subtracted betas that give you the value to
compare against 0.
T-statistic (matrix notation not included):
T=Contrast*beta/(ResMS*Contrasts*covbeta*Contrasts)
which can be thought of as effect-mean (effect-0) divided by the variance.
Now, if you have multiple subjects, you take the Contrast*beta part of
the T-statistic(con_* images) to the second level modelling.
Let me know if that clarified the issue.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain
PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
and which is intended only for the use of the individual or entity
named above. If the reader of the e-mail is not the intended recipient
or the employee or agent responsible for delivering it to the intended
recipient, you are hereby notified that you are in possession of
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disclosure, copying or the taking of any action in reliance on the
contents of this information is strictly prohibited and may be
unlawful. If you have received this e-mail unintentionally, please
immediately notify the sender via telephone at (773) 406-2464 or
email.
On Thu, Feb 24, 2011 at 2:54 PM, Israr Ul Haq <[log in to unmask]> wrote:
> Thanks a lot, This makes sense, particularly the second explanation. although the runs1 and 2 I specified were from the same subject and the whole analysis is being done for each subject separately but I suppose in a way its the same thing and is taking into account the variability across runs. My model is actually an overt naming task, the experimental condition includes a semantic process of interest whereas the control condition has all but that.
>
> Thinking about this though, how is the direction of the change from pre to post treatment (in a voxel) taken into account? I computed a pre - post treatment contrast to check whether it will give me the same voxels (worried that the areas included both positive and negative change over the two sessions in my first contrast) and was relieved to see different voxels. However, if its not too cumbersome, can you please explain how the eventual t statistic is being computed in such a multi session analysis with two independent conditions? Heres what I understand; for each voxel, a correlation coefficient (r) is calculated for each stimulus of a specified condition, and all the r values in a run give a distribution for that condition, with a mean and standard deviation. These are then utilized in the t tests of no significant difference, where the linear t contrasts between two conditions are setup such that whether for that particular voxel, one condition had significantly different signal than the other, using the mean and sd of the r values. What i am trying to wrap my head around is how based on the weightings that we specificy (i.e +1 or -1), the t statistic is calculated for exp > control vs control > exp, since in both cases, what we are essentially inputting is the difference between the means. Intuitively it would seem that it would give a voxel as significant only if the condition weighted +1 also has the higher mean of the two conditions being contrasted. But if so, how would this apply to multiple runs and sessions, or more specifically what would be the sequence of computations in my design? Theres a plus one weighting in both pre and post treatment runs, albeit for different conditions.
>
> Thanks
> Israr
>
>
>
>
========================================================================Date: Thu, 24 Feb 2011 20:51:15 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: DARTEL Flow fields problem?
Comments: To: simon vandekar <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
Something has certainly gone wrong here. One possibility (which was
my fault - sorry) is that you are using an early version of SPM5,
which had a bug in the normalise to MNI option. This was fixed in a
later set of SPM8 updates. The sequence of procedures that you
described in your earlier email all look perfectly correct.
You could narrow down the possible causes of the problem by using the
normalise option of Dartel (not the normalise to MNI option), which
should bring your fMRI and anatomical scans into alignment with the
Template_6.nii.
Bye for now,
-John
On 24 February 2011 20:27, simon vandekar <[log in to unmask]> wrote:
> Hi John,
>
> Thanks again for your help and sorry for all the emails. The flow fields
> that 'Normalise to MNI' asks for are the individual subjects' flow fields
> 'u_c1*.nii which look like the 3rd image selected in the attached picture.
> Also included in this picture are a template, and subject's functional scan
> after the 'normalise to MNI' step.
>
> Thank you,
> Simon
>
>
> On Thu, Feb 24, 2011 at 12:28 PM, John Ashburner <[log in to unmask]>
> wrote:
>>
>> The flow field looks pretty OK to me. I thought that you were
>> referring to something that was uniformly grey. This is the x
>> component. The other components can be viewed by looking at
>> u_rc1*_Template.nii,2 and u_rc1*_Template.nii,3 (by changing the 1 in
>> the file selector to 1:3).
>>
>> Also note that the main objective of Dartel is to align all the scans
>> in the study together. When you normalise to MNI space, the images
>> that are closely aligned within study are all affine transformed to
>> MNI space. The version of MNI space is only really defined for affine
>> aligned brain images (ie the rather blurred averages). You should
>> note that the single subject brain that people often overlay their
>> results on does not define MNI space. It is just the scan of a single
>> individual that has been affine registered to MNI space.
>>
>> Best regards,
>> -John
>>
>> On 24 February 2011 16:27, simon vandekar <[log in to unmask]> wrote:
>> > Thanks for the reply John,
>> >
>> > As far as I can tell the template*.nii files look good, they are gray
>> > matter
>> > images. There is also a u_rc1*_Template.nii in the same folder as my
>> > template*.nii files, I've attached the image for that. Is that what my
>> > flow
>> > fields are suppose to look like?
>> >
>> > Thank you,
>> > Simon
>> >
>> > On Thu, Feb 24, 2011 at 10:47 AM, John Ashburner <[log in to unmask]>
>> > wrote:
>> >>
>> >> It is a bit suspicious if all the u_*.nii are just zero. I'm not sure
>> >> why this would be the case. Do the template*.nii files seem to be OK?
>> >>
>> >> Best regards,
>> >> John
>> >>
>> >> On 24 February 2011 15:03, Simon Vandekar <[log in to unmask]> wrote:
>> >> > Hi John and SPMers,
>> >> >
>> >> > I am attempting to normalise my fMRI data using the steps described
>> >> > in
>> >> > pages 440 and 441 of the spm8 manual:
>> >> >
>> >> > 1. Slice timing
>> >> > 2. Realign: Estimate and Reslice
>> >> > 3. Coregister the structural to functional images
>> >> > 4. Registration looks good.
>> >> > 5. Segment the anatomicals the SPM5 routine
>> >> > 6. Initial Import- use the *seg_sn files
>> >> > 7. Run Dartel with dependency on the gray and white matter images
>> >> > output
>> >> > by import step
>> >> > 8. Normalise to MNI, select the template6 from dartel output, the
>> >> > subject's flow field, and normalise the ra* files (slice timing,
>> >> > resliced).
>> >> >
>> >> > The output I get is sdra* files. When I compare the output to the mni
>> >> > template it looks as if the subject's image has not been normalised.
>> >> > I am
>> >> > suspicious of the flow field because if I look at the individual
>> >> > subjects'
>> >> > uc1* image it is an empty black box.
>> >> >
>> >> > What am I doing incorrectly here?
>> >> >
>> >> > Thanks in advance,
>> >> > Simon
>> >> >
>> >
>> >
>
>
========================================================================Date: Thu, 24 Feb 2011 15:16:32 -0800
Reply-To: Michael T Rubens <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Michael T Rubens <[log in to unmask]>
Subject: Re: script to flip MR images left to right (or vice versa)
Comments: To: Chien-Ho Lin <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--0023547c93373a30ff049d0f6945
Content-Type: text/plain; charset=UTF-8
how about this:
%%%
v = spm_vol('/image_path/brain.img');
x = spm_read_vols(v);
x = x(size(x,1):-1:1,:,:);
[path file] = fileparts(v.fname);
v.fname = fullfile(path,['flipped_' file');
spm_write_vol(v,x)
%%%
cheers,
michael
On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin <[log in to unmask]> wrote:
> Hi,
>
> I stuides patients with stroke doing some behavior paradigm in fMRI.
> Patients always used their affected limb for the task so some patients used
> left hand some used right hand.
> Now I would like to flip the left and right of the images so patients'
> lesional hemisphere are on the same side.
>
> Is there any script in spm 5 or 8 can help to accomplish this goal?
>
> I found a m file called spm_flip.m but it seems support only spm2.
>
> Your assistance are well appreciated.
>
> Thank you so much.
>
> Janice
>
--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco
--0023547c93373a30ff049d0f6945
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how about this:<div>%%%</div><div><br></div><div>v = spm_vol('/image_path/brain.img');</div><div>x = spm_read_vols(v);</div><div><br></div><div>x = x(size(x,1):-1:1,:,:);</div><div><br></div><div>[path file] = fileparts(v.fname);</div>
<div>v.fname = fullfile(path,['flipped_' file');</div><div><br></div><div>spm_write_vol(v,x)<br><br></div><div>%%%</div><div><br></div><div><br></div><div>cheers,</div><div>michael</div><div><br><div class="gmail_quote">
On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
Hi,<br>
<br>
I stuides patients with stroke doing some behavior paradigm in fMRI.<br>
Patients always used their affected limb for the task so some patients used left hand some used right hand.<br>
Now I would like to flip the left and right of the images so patients' lesional hemisphere are on the same side.<br>
<br>
Is there any script in spm 5 or 8 can help to accomplish this goal?<br>
<br>
I found a m file called spm_flip.m but it seems support only spm2.<br>
<br>
Your assistance are well appreciated.<br>
<br>
Thank you so much.<br>
<br>
Janice<br>
</blockquote></div><br><br clear="all"><br>-- <br>Research Associate<br>Gazzaley Lab<br>Department of Neurology<br>University of California, San Francisco<br>
</div>
--0023547c93373a30ff049d0f6945--
========================================================================Date: Thu, 24 Feb 2011 17:28:03 -0600
Reply-To: Pilar Archila-Suerte <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Pilar Archila-Suerte <[log in to unmask]>
Subject: Order of subject files in ANOVA batch
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--0016e64c1cf0cd62ce049d0f91a3
Content-Type: text/plain; charset=UTF-8
Dear SPM list,
In setting ANOVAs in SPM, do the subject files need to be in the same order
under each cell?
I just ran two trial trial batches, one with the same order of subjects and
one with a different order. The areas of activity are very similar but the
order in which the areas show up as more or less intensive did vary.
Any insight as to how SPM does this? should I stick to the same order of
subjects for clarity?
Thanks,
Pilar A .
--0016e64c1cf0cd62ce049d0f91a3
Content-Type: text/html; charset=UTF-8
Content-Transfer-Encoding: quoted-printable
Dear SPM list,Â
<div><span class="Apple-style-span" style="font-family: arial, sans-serif; font-size: 13px; ">In setting ANOVAs in SPM, do the subject files need to be in the same order under each cell?</span></div><div><font class="Apple-style-span" face="arial, sans-serif"><br>
</font></div><div><font class="Apple-style-span" face="arial, sans-serif">I just ran two trial trial batches, one with the same order of subjects and one with a different order. The areas of activity are very similar but the order in which the areas show up as more or less intensive did vary.</font></div>
<div><font class="Apple-style-span" face="arial, sans-serif"><br></font></div><div><font class="Apple-style-span" face="arial, sans-serif">Any insight as to how SPM does this? should I stick to the same order of subjects for clarity?</font></div>
<div><font class="Apple-style-span" face="arial, sans-serif"><br></font></div><div><font class="Apple-style-span" face="arial, sans-serif">Thanks,</font></div><div><font class="Apple-style-span" face="arial, sans-serif">Pilar A .</font></div>
--0016e64c1cf0cd62ce049d0f91a3--
========================================================================Date: Thu, 24 Feb 2011 18:38:16 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: Order of subject files in ANOVA batch
Comments: To: Pilar Archila-Suerte <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Message-ID: <[log in to unmask]>
You should use the flexible factorial for repeated measure designs
with factors for subject and condition in your design.
Best Regards, Donald McLaren
================D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
====================This e-mail contains CONFIDENTIAL INFORMATION which may contain
PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
and which is intended only for the use of the individual or entity
named above. If the reader of the e-mail is not the intended recipient
or the employee or agent responsible for delivering it to the intended
recipient, you are hereby notified that you are in possession of
confidential and privileged information. Any unauthorized use,
disclosure, copying or the taking of any action in reliance on the
contents of this information is strictly prohibited and may be
unlawful. If you have received this e-mail unintentionally, please
immediately notify the sender via telephone at (773) 406-2464 or
email.
On Thu, Feb 24, 2011 at 6:28 PM, Pilar Archila-Suerte
<[log in to unmask]> wrote:
> Dear SPM list,
> In setting ANOVAs in SPM, do the subject files need to be in the same order
> under each cell?
> I just ran two trial trial batches, one with the same order of subjects and
> one with a different order. The areas of activity are very similar but the
> order in which the areas show up as more or less intensive did vary.
> Any insight as to how SPM does this? should I stick to the same order of
> subjects for clarity?
> Thanks,
> Pilar A .
========================================================================Date: Thu, 24 Feb 2011 17:44:46 -0600
Reply-To: Pilar Archila-Suerte <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Pilar Archila-Suerte <[log in to unmask]>
Subject: Re: Order of subject files in ANOVA batch
Comments: To: "MCLAREN, Donald" <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--0016367b60fa2fded8049d0fcf11
Content-Type: text/plain; charset=UTF-8
Mine is actually not a repeated measures design. I'm running a full
factorial and I just want to compare activity in one group vs. another. Do
the con files need to be in the same order in each cell in this case?
Pilar
On Thu, Feb 24, 2011 at 5:38 PM, MCLAREN, Donald
<[log in to unmask]>wrote:
> You should use the flexible factorial for repeated measure designs
> with factors for subject and condition in your design.
>
> Best Regards, Donald McLaren
> ================> D.G. McLaren, Ph.D.
> Postdoctoral Research Fellow, GRECC, Bedford VA
> Research Fellow, Department of Neurology, Massachusetts General
> Hospital and Harvard Medical School
> Office: (773) 406-2464
> ====================> This e-mail contains CONFIDENTIAL INFORMATION which may contain
> PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
> and which is intended only for the use of the individual or entity
> named above. If the reader of the e-mail is not the intended recipient
> or the employee or agent responsible for delivering it to the intended
> recipient, you are hereby notified that you are in possession of
> confidential and privileged information. Any unauthorized use,
> disclosure, copying or the taking of any action in reliance on the
> contents of this information is strictly prohibited and may be
> unlawful. If you have received this e-mail unintentionally, please
> immediately notify the sender via telephone at (773) 406-2464 or
> email.
>
>
>
> On Thu, Feb 24, 2011 at 6:28 PM, Pilar Archila-Suerte
> <[log in to unmask]> wrote:
> > Dear SPM list,
> > In setting ANOVAs in SPM, do the subject files need to be in the same
> order
> > under each cell?
> > I just ran two trial trial batches, one with the same order of subjects
> and
> > one with a different order. The areas of activity are very similar but
> the
> > order in which the areas show up as more or less intensive did vary.
> > Any insight as to how SPM does this? should I stick to the same order of
> > subjects for clarity?
> > Thanks,
> > Pilar A .
>
--0016367b60fa2fded8049d0fcf11
Content-Type: text/html; charset=UTF-8
Content-Transfer-Encoding: quoted-printable
Mine is actually not a repeated measures design. I'm running a full factorial and I just want to compare activity in one group vs. another. Do the con files need to be in the same order in each cell in this case?<div><br>
</div><div>Pilar <br><br><div class="gmail_quote">On Thu, Feb 24, 2011 at 5:38 PM, MCLAREN, Donald <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
You should use the flexible factorial for repeated measure designs<br>
with factors for subject and condition in your design.<br>
<br>
Best Regards, Donald McLaren<br>
=================<br>
D.G. McLaren, Ph.D.<br>
Postdoctoral Research Fellow, GRECC, Bedford VA<br>
Research Fellow, Department of Neurology, Massachusetts General<br>
Hospital and Harvard Medical School<br>
Office: (773) 406-2464<br>
=====================<br>
This e-mail contains CONFIDENTIAL INFORMATION which may contain<br>
PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED<br>
and which is intended only for the use of the individual or entity<br>
named above. If the reader of the e-mail is not the intended recipient<br>
or the employee or agent responsible for delivering it to the intended<br>
recipient, you are hereby notified that you are in possession of<br>
confidential and privileged information. Any unauthorized use,<br>
disclosure, copying or the taking of any action in reliance on the<br>
contents of this information is strictly prohibited and may be<br>
unlawful. If you have received this e-mail unintentionally, please<br>
immediately notify the sender via telephone at (773) 406-2464 or<br>
email.<br>
<div><div></div><div class="h5"><br>
<br>
<br>
On Thu, Feb 24, 2011 at 6:28 PM, Pilar Archila-Suerte<br>
<<a href="mailto:[log in to unmask]">[log in to unmask]</a>> wrote:<br>
> Dear SPM list,<br>
> In setting ANOVAs in SPM, do the subject files need to be in the same order<br>
> under each cell?<br>
> I just ran two trial trial batches, one with the same order of subjects and<br>
> one with a different order. The areas of activity are very similar but the<br>
> order in which the areas show up as more or less intensive did vary.<br>
> Any insight as to how SPM does this? should I stick to the same order of<br>
> subjects for clarity?<br>
> Thanks,<br>
> Pilar A .</div></div></blockquote></div>
</div>
--0016367b60fa2fded8049d0fcf11--
========================================================================Date: Thu, 24 Feb 2011 15:50:51 -0800
Reply-To: J S Lee <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: J S Lee <[log in to unmask]>
Subject: Re: Can't find driving effects for PPI analysis
Comments: To: [log in to unmask]
MIME-Version: 1.0
Content-Type: multipart/mixed;
boundary="----=_NextPart_000_98E4_01CBD43A.9CB39E80"
Message-ID: <[log in to unmask]>
This is a multi-part message in MIME format.
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Hi Donald,
Thank you for your reply!
I am using SPM8.
I looked at the SPM.xX.X values for each subject. The values in the
SPM.xX.X *PSY* columns for each of my models line up perfectly across
subjects. For the different models within a subject, there also seems to
be correspondence for the PSY negative values (although there are slight
baseline shifts between different models). The values in the PPI column,
however, do not correspond so well. I don't think I would have expected
the PPI values to correspond across subjects, however, because the VOI
values differ from subject to subject, so the PPI should also differ as
it represents an interaction? The PPI values for different models in the
same subject also do not show perfect correspondence (png attached: It
shows 2 subjects' SPM.xX.X PPI values over the first 80 scans. The All
conds - control model and Cond 1 - control PPI values are plotted for
each subject. I didn't include the Cond 2 - control, Cond 3 - control,
etc. for clarity).
All models are coming from the same VOI, so I think the adjustment has
to be the same for all models (all extractions were adjusted for an F
contrast of the effects of interest at the first-level model). Is this
what you meant?
The voxels are also identical (again, coming from the same VOI I think
they have to be?).
Thank you very much for taking the time to consider my question--even
looking at the PSY and PPI columns has been useful!
Jamie
>
>Are you using SPM8? The issue of summing was fixed in one of the later
>releases of SPM5, so if you have an older version, that could explain
>some of the issue. You could check to make sure that negative aspects
>of the SPM.xX.X for the PPI term are the same for all subjects. You
>could plot them.
>Are you using the same adjustment for all models?
>Are you using exactly the same voxels for all models?
>
>On Tue, Feb 22, 2011 at 6:21 PM, J S Lee <[log in to unmask]>
wrote:
>> Dear list,
>>
>> I conducted a PPI analysis in an experiment with 6 conditions. To
replicate
>> a previous study's PPI analysis, I was interested in connectivity
>> differences between 5 of the conditions compared to the control (6th)
>> condition, so extracted my VOI (using an all effects of interest
contrast),
>> then created a PPI model with a [1 1 1 1 1 -1] weighting for the
>> psychological context regressor. I get a reasonable replication of
the same
>> PPI effects from the previous study, so the results are sensible.
>>
>> However, in that previous study, there were not enough trials of each
of the
>> 5 conditions to realistically analyze them separately, which is why I
>> collapsed across them. In this study, there are many more trials, so
I was
>> hoping to look at which of the 5 conditions were driving the original
PPI
>> results. I was given hope when the initial PPI replicated in this new
study.
>> However, when I create separate PPI models for each condition versus
control
>> (e.g., context regressors using [1 0 0 0 0 -1] for model 1, [0 1 0 0
0 -1]
>> for model 2, etc.), NONE of these analyses show the same pattern as
the 1 1
>> 1 1 1 -1 model does. Mostly there are no significant (or anywhere
near
>> significant) results, and those random speckles that do show up at
low
>> threshold are not in the same places.
>>
>> Is it theoretically possible that 5 conditions vs 1 other can produce
a PPI,
>> but that none of those conditions singly vs the 1 other can do that?
Or must
>> there be an error? I have checked the microtime onset files to make
the
>> context is specified correctly, and made sure everything matches up
in terms
>> of specifying the conditions. Everything about the models looks fine
to me.
>> I know the 5 conditions vs 1 is a bit unbalanced, but it replicates
the
>> previous study (in which the 5 vs 1 were equal in terms of number of
>> trials), and I understand that when creating the context variable one
does
>> NOT sum the vector to zero the way one would in defining a contrast
for a
>> regional activation analysis.
>>
>> Many thanks in advance for any thoughts,
>> Jamie Lee
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Hi Donald,<br>
Thank you for your reply!<br>
I am using SPM8. <br>
I looked at the SPM.xX.X values for each subject. The values in the SPM.xX.X *PSY* columns for each of my models line up perfectly across subjects. For the different models within a subject, there also seems to be correspondence for the PSY negative values (although there are slight baseline shifts between different models). The values in the PPI column, however, do not correspond so well. I don't think I would have expected the PPI values to correspond across subjects, however, because the VOI values differ from subject to subject, so the PPI should also differ as it represents an interaction? The PPI values for different models in the same subject also do not show perfect correspondence (png attached: It shows 2 subjects' SPM.xX.X PPI values over the first 80 scans. The All conds - control model and Cond 1 - control PPI values are plotted for each subject. I didn't include the Cond 2 - control, Cond 3 - control, etc. for clarity).<br>
<br>
All models are coming from the same VOI, so I think the adjustment has to be the same for all models (all extractions were adjusted for an F contrast of the effects of interest at the first-level model). Is this what you meant?<br>
The voxels are also identical (again, coming from the same VOI I think they have to be?).<br>
<br>
<br>
Thank you very much for taking the time to consider my question--even looking at the PSY and PPI columns has been useful!<br>
<br>
Jamie<br>
<br>
><br>
>Are you using SPM8? The issue of summing was fixed in one of the later<br>
>releases of SPM5, so if you have an older version, that could explain<br>
>some of the issue. You could check to make sure that negative aspects<br>
>of the SPM.xX.X for the PPI term are the same for all subjects. You<br>
>could plot them.<br>
>Are you using the same adjustment for all models?<br>
>Are you using exactly the same voxels for all models?<br>
<br>
<br>
><br>
>On Tue, Feb 22, 2011 at 6:21 PM, J S Lee <[log in to unmask]> wrote:<br>
>> Dear list,<br>
>><br>
>> I conducted a PPI analysis in an experiment with 6 conditions. To replicate<br>
>> a previous study's PPI analysis, I was interested in connectivity<br>
>> differences between 5 of the conditions compared to the control (6th)<br>
>> condition, so extracted my VOI (using an all effects of interest contrast),<br>
>> then created a PPI model with a [1 1 1 1 1 -1] weighting for the<br>
>> psychological context regressor. I get a reasonable replication of the same<br>
>> PPI effects from the previous study, so the results are sensible.<br>
>><br>
>> However, in that previous study, there were not enough trials of each of the<br>
>> 5 conditions to realistically analyze them separately, which is why I<br>
>> collapsed across them. In this study, there are many more trials, so I was<br>
>> hoping to look at which of the 5 conditions were driving the original PPI<br>
>> results. I was given hope when the initial PPI replicated in this new study.<br>
>> However, when I create separate PPI models for each condition versus control<br>
>> (e.g., context regressors using [1 0 0 0 0 -1] for model 1, [0 1 0 0 0 -1]<br>
>> for model 2, etc.), NONE of these analyses show the same pattern as the 1 1<br>
>> 1 1 1 -1 model does. Mostly there are no significant (or anywhere near<br>
>> significant) results, and those random speckles that do show up at low<br>
>> threshold are not in the same places.<br>
>><br>
>> Is it theoretically possible that 5 conditions vs 1 other can produce a PPI,<br>
>> but that none of those conditions singly vs the 1 other can do that? Or must<br>
>> there be an error? I have checked the microtime onset files to make the<br>
>> context is specified correctly, and made sure everything matches up in terms<br>
>> of specifying the conditions. Everything about the models looks fine to me.<br>
>> I know the 5 conditions vs 1 is a bit unbalanced, but it replicates the<br>
>> previous study (in which the 5 vs 1 were equal in terms of number of<br>
>> trials), and I understand that when creating the context variable one does<br>
>> NOT sum the vector to zero the way one would in defining a contrast for a<br>
>> regional activation analysis.<br>
>><br>
>> Many thanks in advance for any thoughts,<br>
>> Jamie Lee<br>
</BODY></HTML>
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------=_NextPart_000_98E4_01CBD43A.9CB39E80--
========================================================================Date: Fri, 25 Feb 2011 05:25:48 +0000
Reply-To: Israr Ul Haq <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Israr Ul Haq <[log in to unmask]>
Subject: Re: fixed effects analysis across two sessions
Comments: To: Donald McLaren <[log in to unmask]>
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Dear Donald,
I remember reading about beta and the GLM and how its calculated by using the matrix inverse, when I first started out reading about it. For some reason I formed the whole concept of individual correlations for each time point of a voxel and the subsequent distribution with its mean and sd as a way of representing what actually beta is representing, the weights of columns. Perhaps because the visualization came easier. Thankyou very much for taking time to point this out.
What I understand from your explanation is that contrast*beta is essentially the output of adding and subtracting the individual values of the betas, based on the 1's and the -1's in the contrast, 1 meaning the beta will be added and -1, it will be subtracted. I guess for my question about the direction of change in terms of betas, how does the t statistic differ between a certain value of contrast*beta and an equal but a negative value of the same. Since the absolute difference between the effect and the mean is the same, does it take into account whether the contrast*beta is a positive or a negative value?
Thanks
Israr
========================================================================Date: Fri, 25 Feb 2011 10:54:06 +0100
Reply-To: Martin Jensen Dietz <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Martin Jensen Dietz <[log in to unmask]>
Subject: Re: Order of subject files in ANOVA batch
Comments: To: Pilar Archila-Suerte <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Hi Pilar,
When comparing 2 independent groups use a two-sample t-test which is very straight forward
Martin
--
Martin Dietz
[log in to unmask]<mailto:[log in to unmask]>
On Feb 25, 2011, at 12:44 AM, Pilar Archila-Suerte wrote:
Mine is actually not a repeated measures design. I'm running a full factorial and I just want to compare activity in one group vs. another. Do the con files need to be in the same order in each cell in this case?
Pilar
On Thu, Feb 24, 2011 at 5:38 PM, MCLAREN, Donald <[log in to unmask]<mailto:[log in to unmask]>> wrote:
You should use the flexible factorial for repeated measure designs
with factors for subject and condition in your design.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
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On Thu, Feb 24, 2011 at 6:28 PM, Pilar Archila-Suerte
<[log in to unmask]<mailto:[log in to unmask]>> wrote:
> Dear SPM list,
> In setting ANOVAs in SPM, do the subject files need to be in the same order
> under each cell?
> I just ran two trial trial batches, one with the same order of subjects and
> one with a different order. The areas of activity are very similar but the
> order in which the areas show up as more or less intensive did vary.
> Any insight as to how SPM does this? should I stick to the same order of
> subjects for clarity?
> Thanks,
> Pilar A .
========================================================================Date: Fri, 25 Feb 2011 11:42:31 +0100
Reply-To: "Maarten A.S. Boksem" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "Maarten A.S. Boksem" <[log in to unmask]>
Subject: PhD position in Decision Neuroscience
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--0015174c187ee518de049d18fd88
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*PhD position in Decision Neuroscience at the Erasmus University Rotterdam*
*and Donders Institute Nijmegen*
The Erasmus Center for Neuroeconomics (ECNE) at the Rotterdam School of
Management, Erasmus University and the Donders Institute for Brain,
Cognition and Behaviour at Radboud University Nijmegen announce an opening
for a PhD researcher to join an active group of researchers in the Center in
the field of decision neuroscience/neuroeconomics/neuromarketing. This
position will be jointly supervised by Prof. Ale Smidts (Erasmus), Dr. Alan
Sanfey (Donders Institute) and Dr. Maarten Boksem (Erasmus and Donders).
We currently seek outstanding applicants whose research interests lie at the
intersection of decision-making, behavioral economics, and neuroscience and
who are interested in studying the brain mechanisms that underlie
decision-making. Particular interests of our group are the neural mechanisms
that underlie social influences on decisions, emotion regulation and
self-control involved in decision-making, and the role of risk and reward in
such decisions. We are especially interested in applicants whose research
can build bridges between existing strengths in consumer-behavior research
at the marketing department of the Rotterdam School of Management and
decision neuroscience at the Donders Institute.
The Erasmus Center for Neuroeconomics is dedicated to conducting
cutting-edge interdisciplinary research in decision neuroscience, and hosts
the Erasmus Behavioral Lab which provides an excellent infrastructure for
conducting behavioral and EEG/ERP experiments. The Donders Institute is a
leading research institute in cognitive neuroscience and provides excellent
resources for functional neuroimaging by means of two research-dedicated
fMRI scanners, an MEG scanner, and EEG and TMS facilities. Additional
facilities are available for the collection and analysis of genetic samples.
The collaboration between Erasmus University and the Donders Institute
provides an outstanding environment for studying the neural underpinnings of
decision-making behavior, and the successful applicant will have full access
to the facilities in both institutions.
*Requirements for the PhD position *
Successful candidates must have a relevant Masters degree, preferably with a
background in cognitive neuroscience, cognitive psychology, or biological
psychology. Candidates with a background in consumer behavior or economics,
with proven evidence or a strong interest in developing cognitive
neuroscience and imaging skills, are also invited to apply. A tailored PhD
course program will be developed. The PhD position is for four years. PhDs
receive a regular employment contract for a doctoral student. See the ERIM
Doctoral Program for further information on the facilities of PhD students
at Erasmus.
Preferred starting date: September 2011.
Applications, including CV, a brief summary of current and proposed
research, and at least two letters of recommendation, should be submitted
through the ERIM application website (http://www.erim.eur.nl). submit your
application preferably before April 1, 2011. PhD applicants may be requested
to provide GRE/ GMAT scores and TOEFLl /IELTS language scores. For further
information on the position, visit our website erim.nl/neuroconomics or
contact Prof. Ale Smidts ([log in to unmask]) or Dr. Maarten Boksem (
[log in to unmask]).
--
Dr. Maarten A.S. Boksem
RSM
Erasmus University
Burgemeester Oudlaan 50
3062 PA Rotterdam
The Netherlands
Donders Institute for Brain, Cognition and Behaviour
Centre for Cognitive Neuroimaging
P.O. Box 9101
6500 HB Nijmegen
The Netherlands
+31 (0) 24 36 68063
[log in to unmask]
www.Boksem.nl
--0015174c187ee518de049d18fd88
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
<p class="MsoNoSpacing" align="center" style="text-align:center"><b style="mso-bidi-font-weight:normal">PhD position in Decision Neuroscience at the
Erasmus University Rotterdam</b></p>
<p class="MsoNoSpacing" align="center" style="text-align:center"><b style="mso-bidi-font-weight:normal">and Donders Institute Nijmegen</b></p>
<p class="MsoNoSpacing" style="text-align:justify"><span style="mso-spacerun:yes"> </span></p>
<p class="MsoNoSpacing" style="text-align:justify">The Erasmus Center for
Neuroeconomics (ECNE) at the Rotterdam School of Management, Erasmus University
and the Donders Institute for Brain, Cognition and Behaviour at Radboud
University Nijmegen announce an opening for a PhD researcher to join an active group
of researchers in the Center in the field of decision neuroscience/neuroeconomics/neuromarketing.
This position will be jointly supervised by Prof. Ale Smidts (Erasmus), Dr.
Alan Sanfey (Donders Institute) and Dr. Maarten Boksem (Erasmus and Donders). </p>
<p class="MsoNoSpacing" style="text-align:justify"> </p>
<p class="MsoNoSpacing" style="text-align:justify">We currently seek outstanding
applicants whose research interests lie at the intersection of decision-making,
behavioral economics, and neuroscience and who are interested in studying the
brain mechanisms that underlie decision-making. Particular interests of our
group are the neural mechanisms that underlie social influences on decisions, emotion
regulation and self-control involved in decision-making, and the role of risk and
reward in such decisions. We are especially interested in applicants whose
research can build bridges between existing strengths in consumer-behavior
research at the marketing department of the Rotterdam School of Management and
decision neuroscience at the Donders Institute. </p>
<p class="MsoNoSpacing" style="text-align:justify"><span style="mso-spacerun:yes"> </span></p>
<p class="MsoNoSpacing" style="text-align:justify">The Erasmus Center for
Neuroeconomics is dedicated to conducting cutting-edge interdisciplinary research
in decision neuroscience, and hosts the Erasmus Behavioral Lab which provides an
excellent infrastructure for conducting behavioral and EEG/ERP experiments. The
Donders Institute is a leading research institute in cognitive neuroscience and
provides excellent resources for functional neuroimaging by means of two research-dedicated
fMRI scanners, an MEG scanner, and EEG and TMS facilities. Additional
facilities are available for the collection and analysis of genetic samples.
The collaboration between Erasmus University and the Donders Institute provides
an outstanding environment for studying the neural underpinnings of
decision-making behavior, and the successful applicant will have full access to
the facilities in both institutions. </p>
<p class="MsoNoSpacing" style="text-align:justify"><span style="mso-spacerun:yes"> </span></p>
<p class="MsoNoSpacing" style="text-align:justify"><i style="mso-bidi-font-style:
normal">Requirements for the PhD position </i></p>
<p class="MsoNoSpacing" style="text-align:justify"> </p>
<p class="MsoNoSpacing" style="text-align:justify">Successful candidates must
have a relevant Masters degree, preferably with a background in cognitive neuroscience,
cognitive psychology, or biological psychology. Candidates with a background in
consumer behavior or economics, with proven evidence or a strong interest in
developing cognitive neuroscience and imaging skills, are also invited to apply.
A tailored PhD course program will be developed. The PhD position is for four
years. PhDs receive a regular employment contract for a doctoral student. See
the ERIM Doctoral Program for further information on the facilities of PhD students
at Erasmus.<span style="mso-spacerun:yes"> </span></p>
<p class="MsoNoSpacing" style="text-align:justify"> </p>
<p class="MsoNoSpacing" style="text-align:justify">Preferred starting date: September
2011. </p>
<p class="MsoNoSpacing" style="text-align:justify"><span style="mso-spacerun:yes"> </span></p>
<p class="MsoNoSpacing" style="text-align:justify">Applications, including CV, a
brief summary of current and proposed research, and at least two letters of recommendation,
should be submitted through the ERIM application website (<a href="http://www.erim.eur.nl/">http://www.erim.eur.nl</a>). submit your
application preferably before April 1, 2011. PhD applicants may be requested to
provide GRE/ GMAT scores and TOEFLl /IELTS language scores. For further information
on the position, visit our website <a href="http://erim.nl/neuroconomics">erim.nl/neuroconomics</a> or contact Prof. Ale Smidts
(<a href="mailto:[log in to unmask]">[log in to unmask]</a>) or Dr. Maarten Boksem (<a href="mailto:[log in to unmask]">[log in to unmask]</a>).</p><br>-- <br>Dr. Maarten A.S. Boksem<div><br>RSM<br>Erasmus University</div>
<div>Burgemeester Oudlaan 50<br>3062 PA Rotterdam<br>The Netherlands<div><br></div><div>Donders Institute for Brain, Cognition and Behaviour
<br>
Centre for Cognitive Neuroimaging
<br>
P.O. Box 9101
<br>
6500 HB Nijmegen
<br>
The Netherlands</div><div><br></div><div>+31 (0) 24 36 68063<br>[log in to unmask]<br><a href="http://www.Boksem.nl" target="_blank">www.Boksem.nl</a></div></div><br>
--0015174c187ee518de049d18fd88--
========================================================================Date: Fri, 25 Feb 2011 22:39:16 +1100
Reply-To: Richard Morris <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Richard Morris <[log in to unmask]>
Subject: Re: script to flip MR images left to right (or vice versa)
In-Reply-To: <[log in to unmask]>
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boundary="_000_61FCD4EC1B8E4B6FBEE491DCCD0A91DAunsweduau_"
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I'm not sure, but I think you can use the image calculator in SPM, with the following function:
f = 'Inv(i1)'
On 25/02/2011, at 10:16 AM, Michael T Rubens wrote:
how about this:
%%%
v = spm_vol('/image_path/brain.img');
x = spm_read_vols(v);
x = x(size(x,1):-1:1,:,:);
[path file] = fileparts(v.fname);
v.fname = fullfile(path,['flipped_' file');
spm_write_vol(v,x)
%%%
cheers,
michael
On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin <[log in to unmask]<mailto:[log in to unmask]>> wrote:
Hi,
I stuides patients with stroke doing some behavior paradigm in fMRI.
Patients always used their affected limb for the task so some patients used left hand some used right hand.
Now I would like to flip the left and right of the images so patients' lesional hemisphere are on the same side.
Is there any script in spm 5 or 8 can help to accomplish this goal?
I found a m file called spm_flip.m but it seems support only spm2.
Your assistance are well appreciated.
Thank you so much.
Janice
--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco
--_000_61FCD4EC1B8E4B6FBEE491DCCD0A91DAunsweduau_
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<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">I'm not sure, but I think you can use the image calculator in SPM, with the following function:<div><br></div><div>f = 'Inv(i1)'</div><div><br></div><div><br><div><div>On 25/02/2011, at 10:16 AM, Michael T Rubens wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite">how about this:<div>%%%</div><div><br></div><div>v = spm_vol('/image_path/brain.img');</div><div>x = spm_read_vols(v);</div><div><br></div><div>x = x(size(x,1):-1:1,:,:);</div><div><br></div><div>[path file] = fileparts(v.fname);</div>
<div>v.fname = fullfile(path,['flipped_' file');</div><div><br></div><div>spm_write_vol(v,x)<br><br></div><div>%%%</div><div><br></div><div><br></div><div>cheers,</div><div>michael</div><div><br><div class="gmail_quote">
On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
Hi,<br>
<br>
I stuides patients with stroke doing some behavior paradigm in fMRI.<br>
Patients always used their affected limb for the task so some patients used left hand some used right hand.<br>
Now I would like to flip the left and right of the images so patients' lesional hemisphere are on the same side.<br>
<br>
Is there any script in spm 5 or 8 can help to accomplish this goal?<br>
<br>
I found a m file called spm_flip.m but it seems support only spm2.<br>
<br>
Your assistance are well appreciated.<br>
<br>
Thank you so much.<br>
<br>
Janice<br>
</blockquote></div><br><br clear="all"><br>-- <br>Research Associate<br>Gazzaley Lab<br>Department of Neurology<br>University of California, San Francisco<br>
</div>
</blockquote></div><br></div></body></html>
--_000_61FCD4EC1B8E4B6FBEE491DCCD0A91DAunsweduau_--
========================================================================Date: Fri, 25 Feb 2011 11:54:19 +0000
Reply-To: Rosa Sanchez Panchuelo <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Rosa Sanchez Panchuelo <[log in to unmask]>
Subject: Re: script to flip MR images left to right (or vice versa)
Comments: To: Richard Morris <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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You can do it easily with fsl:
fslswapdim input_image.img -x outpu_image.img
-x will flip the image in the RL direction.
On 25/02/2011 11:39, Richard Morris wrote:
> I'm not sure, but I think you can use the image calculator in SPM,
> with the following function:
>
> f = 'Inv(i1)'
>
>
> On 25/02/2011, at 10:16 AM, Michael T Rubens wrote:
>
>> how about this:
>> %%%
>>
>> v = spm_vol('/image_path/brain.img');
>> x = spm_read_vols(v);
>>
>> x = x(size(x,1):-1:1,:,:);
>>
>> [path file] = fileparts(v.fname);
>> v.fname = fullfile(path,['flipped_' file');
>>
>> spm_write_vol(v,x)
>>
>> %%%
>>
>>
>> cheers,
>> michael
>>
>> On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin <[log in to unmask]
>> <mailto:[log in to unmask]>> wrote:
>>
>> Hi,
>>
>> I stuides patients with stroke doing some behavior paradigm in fMRI.
>> Patients always used their affected limb for the task so some
>> patients used left hand some used right hand.
>> Now I would like to flip the left and right of the images so
>> patients' lesional hemisphere are on the same side.
>>
>> Is there any script in spm 5 or 8 can help to accomplish this goal?
>>
>> I found a m file called spm_flip.m but it seems support only spm2.
>>
>> Your assistance are well appreciated.
>>
>> Thank you so much.
>>
>> Janice
>>
>>
>>
>>
>> --
>> Research Associate
>> Gazzaley Lab
>> Department of Neurology
>> University of California, San Francisco
>
--
Rosa Maria Sanchez Panchuelo
Post-doctoral Research fellow
Sir Peter Mansfield Magnetic Resonance Centre
University of Nottingham
University Park
Nottingham, NG7 2RD
United Kingdom
+44 115 84 66003
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You can do it easily with fsl:<br>
fslswapdim input_image.img -x outpu_image.img<br>
-x will flip the image in the RL direction. <br>
<br>
On 25/02/2011 11:39, Richard Morris wrote:
<blockquote cite="mid:[log in to unmask]"
type="cite">I'm not sure, but I think you can use the image calculator
in SPM, with the following function:
<div><br>
</div>
<div>f = 'Inv(i1)'</div>
<div><br>
</div>
<div><br>
<div>
<div>On 25/02/2011, at 10:16 AM, Michael T Rubens wrote:</div>
<br class="Apple-interchange-newline">
<blockquote type="cite">how about this:
<div>%%%</div>
<div><br>
</div>
<div>v = spm_vol('/image_path/brain.img');</div>
<div>x = spm_read_vols(v);</div>
<div><br>
</div>
<div>x = x(size(x,1):-1:1,:,:);</div>
<div><br>
</div>
<div>[path file] = fileparts(v.fname);</div>
<div>v.fname = fullfile(path,['flipped_' file');</div>
<div><br>
</div>
<div>spm_write_vol(v,x)<br>
<br>
</div>
<div>%%%</div>
<div><br>
</div>
<div><br>
</div>
<div>cheers,</div>
<div>michael</div>
<div><br>
<div class="gmail_quote">On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho
Lin <span dir="ltr"><<a moz-do-not-send="true"
href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span>
wrote:<br>
<blockquote class="gmail_quote"
style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">Hi,<br>
<br>
I stuides patients with stroke doing some behavior paradigm in fMRI.<br>
Patients always used their affected limb for the task so some patients
used left hand some used right hand.<br>
Now I would like to flip the left and right of the images so patients'
lesional hemisphere are on the same side.<br>
<br>
Is there any script in spm 5 or 8 can help to accomplish this goal?<br>
<br>
I found a m file called spm_flip.m but it seems support only spm2.<br>
<br>
Your assistance are well appreciated.<br>
<br>
Thank you so much.<br>
<br>
Janice<br>
</blockquote>
</div>
<br>
<br clear="all">
<br>
-- <br>
Research Associate<br>
Gazzaley Lab<br>
Department of Neurology<br>
University of California, San Francisco<br>
</div>
</blockquote>
</div>
<br>
</div>
</blockquote>
<br>
<br>
<pre class="moz-signature" cols="72">--
Rosa Maria Sanchez Panchuelo
Post-doctoral Research fellow
Sir Peter Mansfield Magnetic Resonance Centre
University of Nottingham
University Park
Nottingham, NG7 2RD
United Kingdom
+44 115 84 66003
</pre>
<br/>
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--------------040105050906060404020507--
========================================================================Date: Fri, 25 Feb 2011 08:19:45 -0600
Reply-To: Michael Harms <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Michael Harms <[log in to unmask]>
Subject: Re: script to flip MR images left to right (or vice versa)
Comments: To: Rosa Sanchez Panchuelo <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
Content-Type: text/plain
Mime-Version: 1.0
Content-Transfer-Encoding: 7bit
Message-ID: <[log in to unmask]>
A note of caution about using fslswapdim in this manner however:
If you do that, the data order and the header information stored in the
sform/qform will no longer be in sync. So, what the sform/qform defines
to be "left" will no longer be the subject's true left (assuming the
labels in the original data were correct)!
Read the following FSL page if you want all the details:
http://www.fmrib.ox.ac.uk/fsl/avwutils/index.html
Best,
-MH
On Fri, 2011-02-25 at 11:54 +0000, Rosa Sanchez Panchuelo wrote:
> You can do it easily with fsl:
> fslswapdim input_image.img -x outpu_image.img
> -x will flip the image in the RL direction.
>
> On 25/02/2011 11:39, Richard Morris wrote:
> > I'm not sure, but I think you can use the image calculator in SPM,
> > with the following function:
> >
> >
> > f = 'Inv(i1)'
> >
> >
> >
> > On 25/02/2011, at 10:16 AM, Michael T Rubens wrote:
> >
> > > how about this:
> > > %%%
> > >
> > >
> > > v = spm_vol('/image_path/brain.img');
> > > x = spm_read_vols(v);
> > >
> > >
> > > x = x(size(x,1):-1:1,:,:);
> > >
> > >
> > > [path file] = fileparts(v.fname);
> > > v.fname = fullfile(path,['flipped_' file');
> > >
> > >
> > > spm_write_vol(v,x)
> > >
> > >
> > > %%%
> > >
> > >
> > >
> > >
> > > cheers,
> > > michael
> > >
> > > On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin
> > > <[log in to unmask]> wrote:
> > > Hi,
> > >
> > > I stuides patients with stroke doing some behavior
> > > paradigm in fMRI.
> > > Patients always used their affected limb for the task so
> > > some patients used left hand some used right hand.
> > > Now I would like to flip the left and right of the images
> > > so patients' lesional hemisphere are on the same side.
> > >
> > > Is there any script in spm 5 or 8 can help to accomplish
> > > this goal?
> > >
> > > I found a m file called spm_flip.m but it seems support
> > > only spm2.
> > >
> > > Your assistance are well appreciated.
> > >
> > > Thank you so much.
> > >
> > > Janice
> > >
> > >
> > >
> > > --
> > > Research Associate
> > > Gazzaley Lab
> > > Department of Neurology
> > > University of California, San Francisco
> > >
> >
> >
>
>
> --
> Rosa Maria Sanchez Panchuelo
> Post-doctoral Research fellow
> Sir Peter Mansfield Magnetic Resonance Centre
> University of Nottingham
> University Park
> Nottingham, NG7 2RD
> United Kingdom
> +44 115 84 66003
>
>
>
>
>
> This message and any attachment are intended solely for the addressee
> and may contain confidential information. If you have received this
> message in error, please send it back to me, and immediately delete
> it. Please do not use, copy or disclose the information contained in
> this message or in any attachment. Any views or opinions expressed by
> the author of this email do not necessarily reflect the views of the
> University of Nottingham.
>
> This message has been checked for viruses but the contents of an
> attachment may still contain software viruses which could damage your
> computer system: you are advised to perform your own checks. Email
> communications with the University of Nottingham may be monitored as
> permitted by UK legislation.
>
========================================================================Date: Fri, 25 Feb 2011 15:23:11 +0100
Reply-To: =?ISO-8859-1?Q?Rainer_Bögle?= <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: =?ISO-8859-1?Q?Rainer_Bögle?= <[log in to unmask]>
Subject: Re: Negative values in DCM.A
Comments: To: Maria Dauvermann <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--00504502e3d0ea2d57049d1c1166
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Dear Maria,
to clarify a little:
-self connections need to be negative or the activation in this region would
go to infinity (and with that the whole network would become unstable)
-all connections are (defined) relative to self-connections because the
self-connections are 'fixed' to mean=-1 with priors for their mean and
variance such that the probability of the system becoming unstable being
very small. (This leaves a renormalization parameters corresponding to a
decay rate, fixed to 1s.)
-connections in DCM are change rates, i.e. a12 tells you how much of the
current state/activation (on neuronal level) at time t of region 2 is added
to the change in region 1 (at time t). There might be influences from other
regions on region 2 which have to be considered, but if if a12 is the sole
connection, we can say:
*** a12=0.1 means 10% of the state in region 2 is added to the state
(activity) of region 1
and analog for a12=-0.1, this corresponds to a subtraction by 10% of
the state in region 2 from the state in region 1.
Regards,
Rainer
On Fri, Feb 25, 2011 at 8:48 AM, Maria Dauvermann <
[log in to unmask]> wrote:
> Dear Rainer,
>
> thank you very much for your reply. Now I understand why there are negative
> values in the self connections but I also have positive values in some DCMs.
> What does that mean?
>
> BW, Maria
>
>
>
>
> On 24 February 2011 16:14, Rainer Bögle <[log in to unmask]>wrote:
>
>> Hello Maria,
>>
>> as far as I understand this, connection parameters in DCM are change
>> rates, i.e. area 1 reduces the activity in area 2 (if a21 < 0).
>>
>> As stated in Friston 2003, connection parameters are always relative to
>> parameters of self connections (which have to be negative to ensure a stable
>> system).
>>
>> Regards,
>> Rainer
>>
>>
>>
>> On Thu, Feb 24, 2011 at 8:58 AM, Maria Dauvermann <
>> [log in to unmask]> wrote:
>>
>>> Hello,
>>>
>>> I had a look at the values in DCM.A after I have estimated the DCMs.
>>>
>>> What does a negative value in the intrinsic connection mean? How do I
>>> interprete this result?
>>>
>>> Thanks for your help.
>>>
>>> BW, Maria
>>>
>>
>>
>
--00504502e3d0ea2d57049d1c1166
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Dear Maria,<br><br>to clarify a little:<br><br>-self connections need to be negative or the activation in this region would go to infinity (and with that the whole network would become unstable)<br>-all connections are (defined) relative to self-connections because the self-connections are 'fixed' to mean=-1 with priors for their mean and variance such that the probability of the system becoming unstable being very small. (This leaves a renormalization parameters corresponding to a decay rate, fixed to 1s.) <br>
-connections in DCM are change rates, i.e. a12 tells you how much of the current state/activation (on neuronal level) at time t of region 2 is added to the change in region 1 (at time t). There might be influences from other regions on region 2 which have to be considered, but if if a12 is the sole connection, we can say:<br>
*** a12=0.1 means 10% of the state in region 2 is added to the state (activity) of region 1<br> and analog for a12=-0.1, this corresponds to a subtraction by 10% of the state in region 2 from the state in region 1.<br>
<br>Regards,<br>Rainer<br><br><div class="gmail_quote">On Fri, Feb 25, 2011 at 8:48 AM, Maria Dauvermann <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">Dear Rainer,<br><br>thank you very much for your reply. Now I understand why there are negative values in the self connections but I also have positive values in some DCMs. What does that mean?<br>
<br>BW, Maria<div><div></div><div class="h5"><br><br><br>
<br><div class="gmail_quote">On 24 February 2011 16:14, Rainer Bögle <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
Hello Maria,<br><br>as far as I understand this, connection parameters in DCM are change rates, i.e. area 1 reduces the activity in area 2 (if a21 < 0).<br><br>As stated in Friston 2003, connection parameters are always relative to parameters of self connections (which have to be negative to ensure a stable system).<br>
<br>Regards,<br>Rainer<br>
<br><br><br><div class="gmail_quote">On Thu, Feb 24, 2011 at 8:58 AM, Maria Dauvermann <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
Hello,<br>
<br>
I had a look at the values in DCM.A after I have estimated the DCMs.<br>
<br>
What does a negative value in the intrinsic connection mean? How do I interprete this result?<br>
<br>
Thanks for your help.<br>
<br>
BW, Maria<br>
</blockquote></div><br>
</blockquote></div><br>
</div></div></blockquote></div><br>
--00504502e3d0ea2d57049d1c1166--
========================================================================Date: Fri, 25 Feb 2011 10:13:26 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: Can't find driving effects for PPI analysis
Comments: To: J S Lee <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
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Message-ID: <[log in to unmask]>
As you can see the *PSY* negative columns do line up well together,
but the positives do not. Samething with the PPI, where you have the
control condition, the curves line up. Now, if we think about the
concept of the the general linear model (Y=BX)...
Trying to fit the all-control vector to the data is much different
than fitting the condition1-control vector. I think this reiterates
the point I'm making in a paper I'm about to submit on PPI. That is
PPI as it is currently implemented fits these vectors above, rather
than fitting a vector for condition1, condition2, .., and control. In
splitting them up, one is then estimating the relationship of each
component, rather than the joint relationship. My hunch is that if you
were to separate them conditions, you would either eliminate the
average effect OR more likely find out which one is driving the
effect. When you only model 2 conditions, there is a chance that you
attribute the variance of the data to the wrong factor or it ends up
in the error term. Also, when you only model 2 conditions, you are
only modelling the activitation effect of those 2 conditions.
Modelling has shown that the individual model fit is improved when you
separate the conditions.
I'm adding some comments to the code for splitting the vectors and
have termed the approach "a generalizable form of PPI (gPPI)" and
hopefully can provide you with the code later today.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain
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email.
On Thu, Feb 24, 2011 at 6:50 PM, J S Lee <[log in to unmask]> wrote:
> Hi Donald,
> Thank you for your reply!
> I am using SPM8.
> I looked at the SPM.xX.X values for each subject. The values in the SPM.xX.X
> *PSY* columns for each of my models line up perfectly across subjects. For
> the different models within a subject, there also seems to be correspondence
> for the PSY negative values (although there are slight baseline shifts
> between different models). The values in the PPI column, however, do not
> correspond so well. I don't think I would have expected the PPI values to
> correspond across subjects, however, because the VOI values differ from
> subject to subject, so the PPI should also differ as it represents an
> interaction? The PPI values for different models in the same subject also do
> not show perfect correspondence (png attached: It shows 2 subjects' SPM.xX.X
> PPI values over the first 80 scans. The All conds - control model and Cond 1
> - control PPI values are plotted for each subject. I didn't include the Cond
> 2 - control, Cond 3 - control, etc. for clarity).
>
> All models are coming from the same VOI, so I think the adjustment has to be
> the same for all models (all extractions were adjusted for an F contrast of
> the effects of interest at the first-level model). Is this what you meant?
> The voxels are also identical (again, coming from the same VOI I think they
> have to be?).
>
>
> Thank you very much for taking the time to consider my question--even
> looking at the PSY and PPI columns has been useful!
>
> Jamie
>
>>
>>Are you using SPM8? The issue of summing was fixed in one of the later
>>releases of SPM5, so if you have an older version, that could explain
>>some of the issue. You could check to make sure that negative aspects
>>of the SPM.xX.X for the PPI term are the same for all subjects. You
>>could plot them.
>>Are you using the same adjustment for all models?
>>Are you using exactly the same voxels for all models?
>
>
>>
>>On Tue, Feb 22, 2011 at 6:21 PM, J S Lee <[log in to unmask]> wrote:
>>> Dear list,
>>>
>>> I conducted a PPI analysis in an experiment with 6 conditions. To
>>> replicate
>>> a previous study's PPI analysis, I was interested in connectivity
>>> differences between 5 of the conditions compared to the control (6th)
>>> condition, so extracted my VOI (using an all effects of interest
>>> contrast),
>>> then created a PPI model with a [1 1 1 1 1 -1] weighting for the
>>> psychological context regressor. I get a reasonable replication of the
>>> same
>>> PPI effects from the previous study, so the results are sensible.
>>>
>>> However, in that previous study, there were not enough trials of each of
>>> the
>>> 5 conditions to realistically analyze them separately, which is why I
>>> collapsed across them. In this study, there are many more trials, so I
>>> was
>>> hoping to look at which of the 5 conditions were driving the original PPI
>>> results. I was given hope when the initial PPI replicated in this new
>>> study.
>>> However, when I create separate PPI models for each condition versus
>>> control
>>> (e.g., context regressors using [1 0 0 0 0 -1] for model 1, [0 1 0 0 0
>>> -1]
>>> for model 2, etc.), NONE of these analyses show the same pattern as the 1
>>> 1
>>> 1 1 1 -1 model does. Mostly there are no significant (or anywhere near
>>> significant) results, and those random speckles that do show up at low
>>> threshold are not in the same places.
>>>
>>> Is it theoretically possible that 5 conditions vs 1 other can produce a
>>> PPI,
>>> but that none of those conditions singly vs the 1 other can do that? Or
>>> must
>>> there be an error? I have checked the microtime onset files to make the
>>> context is specified correctly, and made sure everything matches up in
>>> terms
>>> of specifying the conditions. Everything about the models looks fine to
>>> me.
>>> I know the 5 conditions vs 1 is a bit unbalanced, but it replicates the
>>> previous study (in which the 5 vs 1 were equal in terms of number of
>>> trials), and I understand that when creating the context variable one
>>> does
>>> NOT sum the vector to zero the way one would in defining a contrast for a
>>> regional activation analysis.
>>>
>>> Many thanks in advance for any thoughts,
>>> Jamie Lee
>
> _______________________________________________________________
> Get the Free email that has everyone talking at http://www.mail2world.com
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========================================================================Date: Fri, 25 Feb 2011 15:26:24 +0000
Reply-To: "Stephen J. Fromm" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "Stephen J. Fromm" <[log in to unmask]>
Subject: Re: Can't find driving effects for PPI analysis
Comments: To: Donald McLaren <[log in to unmask]>
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain; charset="UTF-8"
Message-ID: <[log in to unmask]>
Donald,
You wrote,
"The issue of summing was fixed in one of the later releases of SPM5,..."
Are you saying that it's now OK if the weights used sum to zero, when before they shouldn't?
Best,
S
========================================================================Date: Fri, 25 Feb 2011 16:23:05 +0000
Reply-To: "Stephen J. Fromm" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "Stephen J. Fromm" <[log in to unmask]>
Subject: DCM: significance and graph connectivity
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain; charset="UTF-8"
Message-ID: <[log in to unmask]>
I'm working on a DCM project. So far the best fitting model looks like this:
DI--->R1 ==> R2 ==> R3
^ ^
| |
MI1 MI2
That is:
* there are three regions, with intrinsic connections from R1 to R2 and R2 to R3
* there is one driving input DI acting on R1
* there are two modulatory connections MI1 and MI2 acting on the two between-regions intrinsic connections
(The experimental variables for MI1 and MI2 are identical.)
At the group level, the second connection (R2 ==> R3) is significant, but the first (R1 ==> R2) isn't. Conceptually, this doesn't make sense, insofar as the only way the system perturbations introduced by the DI can get to (R2 ==> R3) is through (R1 ==> R2).
Does this reduce the credibility of the model? Or is it alone not enough to do that because of the possible vagaries of what is significant?
TIA,
S
========================================================================Date: Fri, 25 Feb 2011 11:38:07 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: Can't find driving effects for PPI analysis
Comments: To: "Stephen J. Fromm" <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Message-ID: <[log in to unmask]>
I am saying two things:
(1) You are contrasting the positive weight conditions against the
negative weighted conditions in such a manner that the connectivity of
the positive weights (1s) are assumed to be the same (e.g. if you have
3 conditions and a control AND contrast conditions versus control, you
assume that the connectivity during the conditions is the same) and
the same is true for the negatives (-1s). They should sum to 0 unless
there are 2 conditions OR you think the connectivity is different
between the positive and negative conditions, but assumptions like
this are bad.
(1b) Along those same lines, if you have 2 conditions plus fixation,
then you are assuming that connectivity for yoru conditions is less
than fixation in one condition and greater than fixation in the other.
(2) gPPI -- model each condition as a separate interaction. Now if you
have 3 conditions and fixation, you will get three PPI values - one
for each condition. Then, you can compare the three conditions to each
other as you do in fMRI activation analyses. Additionally, if you were
to add the vectors for each condition together with the weights used
in step 1, you would recover the same joint vector. The critical
difference is the joint vector has imposed a relationship on your data
that may or may not be true more complex designs.
Hope that clarifies my comment from earlier. Let me know if it didn't.
Best Regards, Donald McLaren
================D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
====================This e-mail contains CONFIDENTIAL INFORMATION which may contain
PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
and which is intended only for the use of the individual or entity
named above. If the reader of the e-mail is not the intended recipient
or the employee or agent responsible for delivering it to the intended
recipient, you are hereby notified that you are in possession of
confidential and privileged information. Any unauthorized use,
disclosure, copying or the taking of any action in reliance on the
contents of this information is strictly prohibited and may be
unlawful. If you have received this e-mail unintentionally, please
immediately notify the sender via telephone at (773) 406-2464 or
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On Fri, Feb 25, 2011 at 10:26 AM, Stephen J. Fromm <[log in to unmask]> wrote:
> Donald,
>
> You wrote,
>
> "The issue of summing was fixed in one of the later releases of SPM5,..."
>
> Are you saying that it's now OK if the weights used sum to zero, when before they shouldn't?
>
> Best,
>
> S
>
>
>
========================================================================Date: Fri, 25 Feb 2011 16:44:41 +0000
Reply-To: Mohamed Seghier <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Mohamed Seghier <[log in to unmask]>
Subject: Re: DCM: significance and graph connectivity
Comments: To: "Stephen J. Fromm" <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8; format=flowed
Content-Transfer-Encoding: 7bit
Message-ID: <[log in to unmask]>
Hi Stephen,
Are you using DCM8 or DCM10?
If your connection R1==>R2 is not significant at the group level, then
it is possible that (1) your modulatory parameter MI1 is significant,
which may suggest that the inter-regional interaction between R1 and R2
increased "specifically" during your context MI1; or (2) you have a
large inter-subject variability on this connection. For a similar
situation, see Figure 24 in Karl's 2003 paper (Friston 2003 p1298:
connection V1-to-V5 not significant but motion modulation was
significant)...
I hope this helps,
Mohamed
On 25/02/2011 16:23, Stephen J. Fromm wrote:
> I'm working on a DCM project. So far the best fitting model looks like this:
>
>
> DI--->R1 ==> R2 ==> R3
> ^ ^
> | |
> MI1 MI2
>
> That is:
> * there are three regions, with intrinsic connections from R1 to R2 and R2 to R3
> * there is one driving input DI acting on R1
> * there are two modulatory connections MI1 and MI2 acting on the two between-regions intrinsic connections
>
> (The experimental variables for MI1 and MI2 are identical.)
>
> At the group level, the second connection (R2 ==> R3) is significant, but the first (R1 ==> R2) isn't. Conceptually, this doesn't make sense, insofar as the only way the system perturbations introduced by the DI can get to (R2 ==> R3) is through (R1 ==> R2).
>
> Does this reduce the credibility of the model? Or is it alone not enough to do that because of the possible vagaries of what is significant?
>
> TIA,
>
> S
>
>
========================================================================Date: Fri, 25 Feb 2011 10:47:46 -0600
Reply-To: Darren Gitelman <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Darren Gitelman <[log in to unmask]>
Subject: Re: DCM: significance and graph connectivity
Comments: To: Mohamed Seghier <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Mohamed
Would your response below differ depending on whether he was using DCM8 vs.
DCM10?
Darren
On Fri, Feb 25, 2011 at 10:44 AM, Mohamed Seghier <
[log in to unmask]> wrote:
> Hi Stephen,
>
> Are you using DCM8 or DCM10?
> If your connection R1==>R2 is not significant at the group level, then it
> is possible that (1) your modulatory parameter MI1 is significant, which may
> suggest that the inter-regional interaction between R1 and R2 increased
> "specifically" during your context MI1; or (2) you have a large
> inter-subject variability on this connection. For a similar situation, see
> Figure 24 in Karl's 2003 paper (Friston 2003 p1298: connection V1-to-V5 not
> significant but motion modulation was significant)...
>
> I hope this helps,
>
> Mohamed
>
>
>
> On 25/02/2011 16:23, Stephen J. Fromm wrote:
>
>> I'm working on a DCM project. So far the best fitting model looks like
>> this:
>>
>>
>> DI--->R1 ==> R2 ==> R3
>> ^ ^
>> | |
>> MI1 MI2
>>
>> That is:
>> * there are three regions, with intrinsic connections from R1 to R2 and R2
>> to R3
>> * there is one driving input DI acting on R1
>> * there are two modulatory connections MI1 and MI2 acting on the two
>> between-regions intrinsic connections
>>
>> (The experimental variables for MI1 and MI2 are identical.)
>>
>> At the group level, the second connection (R2 ==> R3) is significant, but
>> the first (R1 ==> R2) isn't. Conceptually, this doesn't make sense,
>> insofar as the only way the system perturbations introduced by the DI can
>> get to (R2 ==> R3) is through (R1 ==> R2).
>>
>> Does this reduce the credibility of the model? Or is it alone not enough
>> to do that because of the possible vagaries of what is significant?
>>
>> TIA,
>>
>> S
>>
>>
>>
--
Darren Gitelman, MD
Northwestern University
710 N. Lake Shore Dr., 1122
Chicago, IL 60611
Ph: (312) 908-8614
Fax: (312) 908-5073
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Mohamed<br><br>Would your response below differ depending on whether he was using DCM8 vs. DCM10?<br><br>Darren<br><br><div class="gmail_quote">On Fri, Feb 25, 2011 at 10:44 AM, Mohamed Seghier <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">Hi Stephen,<br>
<br>
Are you using DCM8 or DCM10?<br>
If your connection R1==>R2 is not significant at the group level, then it is possible that (1) your modulatory parameter MI1 is significant, which may suggest that the inter-regional interaction between R1 and R2 increased "specifically" during your context MI1; or (2) you have a large inter-subject variability on this connection. For a similar situation, see Figure 24 in Karl's 2003 paper (Friston 2003 p1298: connection V1-to-V5 not significant but motion modulation was significant)...<br>
<br>
I hope this helps,<br>
<br>
Mohamed<br>
<br>
<br>
<br>
On 25/02/2011 16:23, Stephen J. Fromm wrote:<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;">
I'm working on a DCM project. So far the best fitting model looks like this:<br>
<br>
<br>
DI--->R1 ==> R2 ==> R3<br>
^ ^<br>
| |<br>
MI1 MI2<br>
<br>
That is:<br>
* there are three regions, with intrinsic connections from R1 to R2 and R2 to R3<br>
* there is one driving input DI acting on R1<br>
* there are two modulatory connections MI1 and MI2 acting on the two between-regions intrinsic connections<br>
<br>
(The experimental variables for MI1 and MI2 are identical.)<br>
<br>
At the group level, the second connection (R2 ==> R3) is significant, but the first (R1 ==> R2) isn't. Conceptually, this doesn't make sense, insofar as the only way the system perturbations introduced by the DI can get to (R2 ==> R3) is through (R1 ==> R2).<br>
<br>
Does this reduce the credibility of the model? Or is it alone not enough to do that because of the possible vagaries of what is significant?<br>
<br>
TIA,<br>
<br>
S<br>
<br>
<br>
</blockquote>
</blockquote></div><br><br clear="all"><br>-- <br>Darren Gitelman, MD<br>Northwestern University<br>710 N. Lake Shore Dr., 1122<br>Chicago, IL 60611<br>Ph: (312) 908-8614<br>Fax: (312) 908-5073<br>
--20cf30434548f1250a049d1e16a0--
========================================================================Date: Fri, 25 Feb 2011 11:47:39 -0500
Reply-To: "Fromm, Stephen (NIH/NIMH) [C]" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "Fromm, Stephen (NIH/NIMH) [C]" <[log in to unmask]>
Subject: Re: DCM: significance and graph connectivity
Comments: To: Mohamed Seghier <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
Content-Type: text/plain; charset="us-ascii"
Content-Transfer-Encoding: quoted-printable
MIME-Version: 1.0
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Dear Mohamed,
Thanks for the quick, informative reply. I'm using DCM8, and probably not the newest version of SPM8 either (ie not 4010).
I'll look at the example you cite from the seminal DCM paper.
Best,
Stephen J. Fromm, PhD
Contractor, NIMH/MAP
(301) 451--9265
________________________________________
From: Mohamed Seghier [[log in to unmask]]
Sent: Friday, February 25, 2011 11:44 AM
To: Fromm, Stephen (NIH/NIMH) [C]
Cc: [log in to unmask]
Subject: Re: [SPM] DCM: significance and graph connectivity
Hi Stephen,
Are you using DCM8 or DCM10?
If your connection R1==>R2 is not significant at the group level, then
it is possible that (1) your modulatory parameter MI1 is significant,
which may suggest that the inter-regional interaction between R1 and R2
increased "specifically" during your context MI1; or (2) you have a
large inter-subject variability on this connection. For a similar
situation, see Figure 24 in Karl's 2003 paper (Friston 2003 p1298:
connection V1-to-V5 not significant but motion modulation was
significant)...
I hope this helps,
Mohamed
On 25/02/2011 16:23, Stephen J. Fromm wrote:
> I'm working on a DCM project. So far the best fitting model looks like this:
>
>
> DI--->R1 ==> R2 ==> R3
> ^ ^
> | |
> MI1 MI2
>
> That is:
> * there are three regions, with intrinsic connections from R1 to R2 and R2 to R3
> * there is one driving input DI acting on R1
> * there are two modulatory connections MI1 and MI2 acting on the two between-regions intrinsic connections
>
> (The experimental variables for MI1 and MI2 are identical.)
>
> At the group level, the second connection (R2 ==> R3) is significant, but the first (R1 ==> R2) isn't. Conceptually, this doesn't make sense, insofar as the only way the system perturbations introduced by the DI can get to (R2 ==> R3) is through (R1 ==> R2).
>
> Does this reduce the credibility of the model? Or is it alone not enough to do that because of the possible vagaries of what is significant?
>
> TIA,
>
> S
>
>========================================================================Date: Fri, 25 Feb 2011 16:55:34 +0000
Reply-To: Artur <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Artur <[log in to unmask]>
Subject: Re: deactivation
Comments: To: Ladan Ghazi Saidi <[log in to unmask]>
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Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain; charset="UTF-8"
Message-ID: <[log in to unmask]>
Hi,
It depends on the locus of "deactivation" spot and the type of learning. E.g. one may see a decrease in hippocampal BOLD signal accompanied by increase in implicit memory performance.
For more details check out Russ Poldrack's and others' papers on hippocampus-caudate interactions. Email me if you want a package of papers on the topic.
Best regards,
Art
========================================================================Date: Fri, 25 Feb 2011 17:29:44 +0000
Reply-To: Mohamed Seghier <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Mohamed Seghier <[log in to unmask]>
Subject: Re: DCM: significance and graph connectivity
Comments: To: Darren Gitelman <[log in to unmask]>
In-Reply-To: <AANLkTinyS0KxkJVZQseLm¯[log in to unmask]>
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Hi Darren,
A short response: yes it is also valid for DCM10... But....
My understanding is that, in DCM8, any "posterior" effects in intrinsic
connectivity is expressed within the inter-regional connections (the
off-diagonal of the A matrix, as all self-connections were fixed to
"-1", although used as a scaling factor). In DCM10, the self-connections
are allowed to deviate/vary from "-1" and thus inter-regional
interactions that were significant in DCM8 may be different in DCM10
(i.e. as these effects can now be expressed in the self-connections). In
other words, the generic model is more "flexible" in DCM10 than DCM8...
I hope this makes sense...
Note also that in DCM10 you have the option to mean-centre your inputs
which makes the intrinsic (endogenous) connectivity parameters equal to
the "average" connectivity across all conditions of your driving
input(s). This is different from the previous interpretation of a
"baseline" connectivity that was considered in DCM8...
For more details, see Klaas's email of 22/11/2010...
I hope this helps,
Mohamed
On 25/02/2011 16:47, Darren Gitelman wrote:
> Mohamed
>
> Would your response below differ depending on whether he was using
> DCM8 vs. DCM10?
>
> Darren
>
> On Fri, Feb 25, 2011 at 10:44 AM, Mohamed Seghier
> <[log in to unmask] <mailto:[log in to unmask]>> wrote:
>
> Hi Stephen,
>
> Are you using DCM8 or DCM10?
> If your connection R1==>R2 is not significant at the group level,
> then it is possible that (1) your modulatory parameter MI1 is
> significant, which may suggest that the inter-regional interaction
> between R1 and R2 increased "specifically" during your context
> MI1; or (2) you have a large inter-subject variability on this
> connection. For a similar situation, see Figure 24 in Karl's 2003
> paper (Friston 2003 p1298: connection V1-to-V5 not significant but
> motion modulation was significant)...
>
> I hope this helps,
>
> Mohamed
>
>
>
> On 25/02/2011 16:23, Stephen J. Fromm wrote:
>
> I'm working on a DCM project. So far the best fitting model
> looks like this:
>
>
> DI--->R1 ==> R2 ==> R3
> ^ ^
> | |
> MI1 MI2
>
> That is:
> * there are three regions, with intrinsic connections from R1
> to R2 and R2 to R3
> * there is one driving input DI acting on R1
> * there are two modulatory connections MI1 and MI2 acting on
> the two between-regions intrinsic connections
>
> (The experimental variables for MI1 and MI2 are identical.)
>
> At the group level, the second connection (R2 ==> R3) is
> significant, but the first (R1 ==> R2) isn't. Conceptually,
> this doesn't make sense, insofar as the only way the system
> perturbations introduced by the DI can get to (R2 ==> R3) is
> through (R1 ==> R2).
>
> Does this reduce the credibility of the model? Or is it alone
> not enough to do that because of the possible vagaries of what
> is significant?
>
> TIA,
>
> S
>
>
>
>
>
> --
> Darren Gitelman, MD
> Northwestern University
> 710 N. Lake Shore Dr., 1122
> Chicago, IL 60611
> Ph: (312) 908-8614
> Fax: (312) 908-5073
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http-equiv="Content-Type">
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<body bgcolor="#ffffff" text="#000000">
<br>
Hi Darren,<br>
<br>
A short response: yes it is also valid for DCM10... But....<br>
My understanding is that, in DCM8, any "posterior" effects in
intrinsic connectivity is expressed within the inter-regional
connections (the off-diagonal of the A matrix, as all
self-connections were fixed to "-1", although used as a scaling
factor). In DCM10, the self-connections are allowed to deviate/vary
from "-1" and thus inter-regional interactions that were significant
in DCM8 may be different in DCM10 (i.e. as these effects can now be
expressed in the self-connections). In other words, the generic
model is more "flexible" in DCM10 than DCM8... I hope this makes
sense...<br>
Note also that in DCM10 you have the option to mean-centre your
inputs which makes the intrinsic (endogenous) connectivity
parameters equal to the “average” connectivity across all conditions
of your driving input(s). This is different from the previous
interpretation of a "baseline" connectivity that was considered in
DCM8...<br>
For more details, see Klaas's email of 22/11/2010...<br>
<br>
I hope this helps,<br>
<br>
Mohamed<br>
<br>
<br>
<br>
<br>
On 25/02/2011 16:47, Darren Gitelman wrote:
<blockquote
cite="mid:AANLkTinyS0KxkJVZQseLm¯[log in to unmask]"
type="cite">Mohamed<br>
<br>
Would your response below differ depending on whether he was using
DCM8 vs. DCM10?<br>
<br>
Darren<br>
<br>
<div class="gmail_quote">On Fri, Feb 25, 2011 at 10:44 AM, Mohamed
Seghier <span dir="ltr"><<a moz-do-not-send="true"
href="mailto:[log in to unmask]">[log in to unmask]</a>></span>
wrote:<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt
0.8ex; border-left: 1px solid rgb(204, 204, 204);
padding-left: 1ex;">Hi Stephen,<br>
<br>
Are you using DCM8 or DCM10?<br>
If your connection R1==>R2 is not significant at the group
level, then it is possible that (1) your modulatory parameter
MI1 is significant, which may suggest that the inter-regional
interaction between R1 and R2 increased "specifically" during
your context MI1; or (2) you have a large inter-subject
variability on this connection. For a similar situation, see
Figure 24 in Karl's 2003 paper (Friston 2003 p1298: connection
V1-to-V5 not significant but motion modulation was
significant)...<br>
<br>
I hope this helps,<br>
<br>
Mohamed<br>
<br>
<br>
<br>
On 25/02/2011 16:23, Stephen J. Fromm wrote:<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt
0.8ex; border-left: 1px solid rgb(204, 204, 204);
padding-left: 1ex;">
I'm working on a DCM project. So far the best fitting model
looks like this:<br>
<br>
<br>
DI--->R1 ==> R2 ==> R3<br>
^ ^<br>
| |<br>
MI1 MI2<br>
<br>
That is:<br>
* there are three regions, with intrinsic connections from
R1 to R2 and R2 to R3<br>
* there is one driving input DI acting on R1<br>
* there are two modulatory connections MI1 and MI2 acting on
the two between-regions intrinsic connections<br>
<br>
(The experimental variables for MI1 and MI2 are identical.)<br>
<br>
At the group level, the second connection (R2 ==> R3) is
significant, but the first (R1 ==> R2) isn't.
Conceptually, this doesn't make sense, insofar as the only
way the system perturbations introduced by the DI can get to
(R2 ==> R3) is through (R1 ==> R2).<br>
<br>
Does this reduce the credibility of the model? Or is it
alone not enough to do that because of the possible vagaries
of what is significant?<br>
<br>
TIA,<br>
<br>
S<br>
<br>
<br>
</blockquote>
</blockquote>
</div>
<br>
<br clear="all">
<br>
-- <br>
Darren Gitelman, MD<br>
Northwestern University<br>
710 N. Lake Shore Dr., 1122<br>
Chicago, IL 60611<br>
Ph: (312) 908-8614<br>
Fax: (312) 908-5073<br>
</blockquote>
<br>
</body>
</html>
--------------080208090307080100090302--
========================================================================Date: Fri, 25 Feb 2011 20:33:16 +0100
Reply-To: SALEM BOUSSIDA <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: SALEM BOUSSIDA <[log in to unmask]>
Subject: Mixed effects group analysis questions
MIME-Version: 1.0
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Dear SPMers,
I have a group of 7 subjects (rats) and I would like to perform a group
analysis (mixed effects).
I have done the 1st level analysis (fixed effects) for each subject and
I have two con_001 and con_002 images, wich are respectively the
contrast image for a positive effect (activation) and for a negative
effect (deactivation) of one condition (current stimulation) .
Then, I have done a one-sample-t-test : I feed all the con_001 of each
subject into the 2nd level analysis , then I entered a T contrast [1]
and SPM displayed the thresholded T-statistic image. But when I entered
a F contrast [1], I have got this error:
"
??? Inf computed by model function, fitting cannot continue.
Try using or tightening upper and lower bounds on coefficients.
"
Based on the procedure described in SPM8 manuel concerning the "face
group data", It is mentionned that I have to enter a F contrast.
I am confused by this and I have some questions:
(1) which contrast should I enter, F or T contrast? and what is the
differenece between them?
(2) Is the one-sample-t-test, the appropriate one to run mixed effects
analysis?
(3) Do I have to run two separate "one-sample-t-test" for
positive(activation) and negative-deactivation) effects?
Thank you for your help.
Best regards,
Salem
========================================================================Date: Fri, 25 Feb 2011 17:13:46 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: Mixed effects group analysis questions
Comments: To: SALEM BOUSSIDA <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
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Salem,
This isn't a mixed effects design as you only have one group and only
one condition. What you want to do is to use the con_001 images in a
one-sample t-test. DO NOT USE the con_002 images in the model. I
suspect that might have been what happened.
Your design (SPM.xX.X)hould have a size of [7 1]; load SPM.mat and
then use size(SPM.xX.X) to find the answer.
A t-contrast of 1 will find the regions that are activated. A
t-contrast of -1 will find regions that are deactivated. An F-contrast
is simply the square of the 2 t-tests and does not have a direction.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain
PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
and which is intended only for the use of the individual or entity
named above. If the reader of the e-mail is not the intended recipient
or the employee or agent responsible for delivering it to the intended
recipient, you are hereby notified that you are in possession of
confidential and privileged information. Any unauthorized use,
disclosure, copying or the taking of any action in reliance on the
contents of this information is strictly prohibited and may be
unlawful. If you have received this e-mail unintentionally, please
immediately notify the sender via telephone at (773) 406-2464 or
email.
On Fri, Feb 25, 2011 at 2:33 PM, SALEM BOUSSIDA
<[log in to unmask]> wrote:
> Dear SPMers,
>
> I have a group of 7 subjects (rats) and I would like to perform a group
> analysis (mixed effects).
> I have done the 1st level analysis (fixed effects) for each subject and I
> have two con_001 and con_002 images, wich are respectively the contrast
> image for a positive effect (activation) and for a negative effect
> (deactivation) of one condition (current stimulation) .
> Then, I have done a one-sample-t-test : I feed all the con_001 of each
> subject into the 2nd level analysis , then I entered a T contrast [1] and
> SPM displayed the thresholded T-statistic image. But when I entered a F
> contrast [1], I have got this error:
> "
>
> ??? Inf computed by model function, fitting cannot continue.
> Try using or tightening upper and lower bounds on coefficients.
>
> "
>
> Based on the procedure described in SPM8 manuel concerning the "face group
> data", It is mentionned that I have to enter a F contrast.
> I am confused by this and I have some questions:
> (1) which contrast should I enter, F or T contrast? and what is the
> differenece between them?
> (2) Is the one-sample-t-test, the appropriate one to run mixed effects
> analysis?
> (3) Do I have to run two separate "one-sample-t-test" for
> positive(activation) and negative-deactivation) effects?
>
>
> Thank you for your help.
>
> Best regards,
>
> Salem
>
========================================================================Date: Fri, 25 Feb 2011 17:25:10 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: fixed effects analysis across two sessions
Comments: To: Israr Ul Haq <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
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Message-ID: <[log in to unmask]>
If contrast*beta>0, then the t-statistic is positive.
If contrast*beta<0, then the t-statistic is negative.
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain
PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
and which is intended only for the use of the individual or entity
named above. If the reader of the e-mail is not the intended recipient
or the employee or agent responsible for delivering it to the intended
recipient, you are hereby notified that you are in possession of
confidential and privileged information. Any unauthorized use,
disclosure, copying or the taking of any action in reliance on the
contents of this information is strictly prohibited and may be
unlawful. If you have received this e-mail unintentionally, please
immediately notify the sender via telephone at (773) 406-2464 or
email.
On Fri, Feb 25, 2011 at 12:25 AM, Israr Ul Haq <[log in to unmask]> wrote:
> Dear Donald,
>
> I remember reading about beta and the GLM and how its calculated by using the matrix inverse, when I first started out reading about it. For some reason I formed the whole concept of individual correlations for each time point of a voxel and the subsequent distribution with its mean and sd as a way of representing what actually beta is representing, the weights of columns. Perhaps because the visualization came easier. Thankyou very much for taking time to point this out.
>
> What I understand from your explanation is that contrast*beta is essentially the output of adding and subtracting the individual values of the betas, based on the 1's and the -1's in the contrast, 1 meaning the beta will be added and -1, it will be subtracted. I guess for my question about the direction of change in terms of betas, how does the t statistic differ between a certain value of contrast*beta and an equal but a negative value of the same. Since the absolute difference between the effect and the mean is the same, does it take into account whether the contrast*beta is a positive or a negative value?
>
> Thanks
> Israr
>
>
========================================================================Date: Fri, 25 Feb 2011 17:47:20 -0500
Reply-To: John Fredy <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Fredy <[log in to unmask]>
Subject: comparing blocks with different durations
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--0016368e2380d64648049d231c4f
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Hello all, I like to know if is possible compare two conditions with
different durations, that is, task with duration of 40 s and control with
duration of 20 s.
Thanks in advance
John Ochoa
Universidad de Antioquia
--0016368e2380d64648049d231c4f
Content-Type: text/html; charset=ISO-8859-1
Hello all, I like to know if is possible compare two conditions with different durations, that is, task with duration of 40 s and control with duration of 20 s.<div><br></div><div>Thanks in advance</div><div><br></div><div>
John Ochoa</div><div>Universidad de Antioquia</div>
--0016368e2380d64648049d231c4f--
========================================================================Date: Sat, 26 Feb 2011 01:02:42 +0100
Reply-To: Salem Boussida <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Salem Boussida <[log in to unmask]>
Subject: Re: Mixed effects group analysis questions
Comments: To: "MCLAREN, Donald" <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Dear Donald,
Thank you for your help. I am still confused about some details:
"MCLAREN, Donald" <[log in to unmask]> a écrit :
> Salem,
>
> This isn't a mixed effects design as you only have one group and only
> one condition. What you want to do is to use the con_001 images in a
> one-sample t-test. DO NOT USE the con_002 images in the model. I
> suspect that might have been what happened.
I am still confused about "mixed effects analysis"and random effects
analysis. In prevoius discussions from SPMlist users and the notes
from Terry Oakes page
(http://psyphz.psych.wisc.edu/~oakes/spm/spm_random_effects.html), it
is mentioned that "Random-Effects" analysis is also referred to as a
"Mixed Effects" analysis, since it considers both within- and
between-subject variance. Also, in SPM manual, the one-sample-t-test
is considered as a "random effects analysis".
Any comments about this?
Which test (in SPM) should I use to consider both fixed and random effects?
>
> Your design (SPM.xX.X)hould have a size of [7 1]; load SPM.mat and
> then use size(SPM.xX.X) to find the answer.
The "SPM.xX.X" command gives me : SPM.xX.X =
1
1
1
1
1
1
1
Is it right?
>
> A t-contrast of 1 will find the regions that are activated. A
> t-contrast of -1 will find regions that are deactivated. An F-contrast
> is simply the square of the 2 t-tests and does not have a direction.
>
Thank you again for your help.
Best regards,
Salem
> Best Regards, Donald McLaren
> =================
> D.G. McLaren, Ph.D.
> Postdoctoral Research Fellow, GRECC, Bedford VA
> Research Fellow, Department of Neurology, Massachusetts General
> Hospital and Harvard Medical School
> Office: (773) 406-2464
> =====================
> This e-mail contains CONFIDENTIAL INFORMATION which may contain
> PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
> and which is intended only for the use of the individual or entity
> named above. If the reader of the e-mail is not the intended recipient
> or the employee or agent responsible for delivering it to the intended
> recipient, you are hereby notified that you are in possession of
> confidential and privileged information. Any unauthorized use,
> disclosure, copying or the taking of any action in reliance on the
> contents of this information is strictly prohibited and may be
> unlawful. If you have received this e-mail unintentionally, please
> immediately notify the sender via telephone at (773) 406-2464 or
> email.
>
>
>
> On Fri, Feb 25, 2011 at 2:33 PM, SALEM BOUSSIDA
> <[log in to unmask]> wrote:
>> Dear SPMers,
>>
>> I have a group of 7 subjects (rats) and I would like to perform a group
>> analysis (mixed effects).
>> I have done the 1st level analysis (fixed effects) for each subject and  I
>> have two con_001 and con_002 images, wich are respectively the contrast
>> image for a positive effect (activation) and for a negative effect
>> (deactivation) of one condition (current stimulation) .
>> Then, I have done a one-sample-t-test : I feed all the con_001 of each
>> subject into the 2nd level analysis , then I entered a T contrast [1] Â and
>> SPM displayed the thresholded T-statistic image. But when I entered a F
>> contrast [1], I have got this error:
>> "
>>
>> ??? Inf computed by model function, fitting cannot continue.
>> Try using or tightening upper and lower bounds on coefficients.
>>
>> "
>>
>> Based on the procedure described in SPM8 manuel concerning the "face group
>> data", It is mentionned that I have to enter a F contrast.
>> I am  confused by this and I have some questions:
>> (1) which contrast should I enter, F or T contrast? and what is the
>> differenece between them?
>> (2) Is the one-sample-t-test, the appropriate one to run mixed effects
>> analysis?
>> (3) Do I have to run two separate "one-sample-t-test" for
>> positive(activation) and negative-deactivation) effects?
>>
>>
>> Thank you for your help.
>>
>> Best regards,
>>
>> Salem
>>
>
========================================================================Date: Fri, 25 Feb 2011 22:04:49 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: Mixed effects group analysis questions
Comments: To: Salem Boussida <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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On Fri, Feb 25, 2011 at 7:02 PM, Salem Boussida
<[log in to unmask]> wrote:
> Dear Donald,
>
> Thank you for your help. I am still confused about some details:
>
>
> "MCLAREN, Donald" <[log in to unmask]> a écrit :
>
>> Salem,
>>
>> This isn't a mixed effects design as you only have one group and only
>> one condition. What you want to do is to use the con_001 images in a
>> one-sample t-test. DO NOT USE the con_002 images in the model. I
>> suspect that might have been what happened.
>
> I am still confused about "mixed effects analysis"and random effects
> analysis. In prevoius discussions from SPMlist users and the notes from
> Terry Oakes page
> (http://psyphz.psych.wisc.edu/~oakes/spm/spm_random_effects.html), it is
> mentioned that "Random-Effects" analysis is also referred to as a "Mixed
> Effects" analysis, since it considers both within- and between-subject
> variance. Also, in SPM manual, the one-sample-t-test is considered as a
> "random effects analysis".
> Any comments about this?
> Which test (in SPM) should I use to consider both fixed and random effects?
The whole discussion of fixed effects and random effects are best left
to the field of statistician. If you want the details of why it could
be considered either, see Ch.12 in Human Brain Function.
It is better to think of the analysis as within-subject factor and
within-subject errors OR between-subject factors and between errors.
In group comparison or comparing the group against zero, you have
between subject errors (1 sample t-test, 2-sample t-test, ANOVA).
Paired t-tests and repeated measures ANOVAs have within-subject error
terms. And then, you can have models with both within-subject and
between-subject errors.
>
>
>
>>
>> Your design (SPM.xX.X)hould have a size of [7 1]; load SPM.mat and
>> then use size(SPM.xX.X) to find the answer.
>
> The "SPM.xX.X" command gives me : SPM.xX.X =
> 1
> 1
> 1
> 1
> 1
> 1
> 1
> Is it right?
Yes.
>
>>
>> A t-contrast of 1 will find the regions that are activated. A
>> t-contrast of -1 will find regions that are deactivated. An F-contrast
>> is simply the square of the 2 t-tests and does not have a direction.
>>
> Thank you again for your help.
>
> Best regards,
>
> Salem
>
>
>> Best Regards, Donald McLaren
>> =================
>> D.G. McLaren, Ph.D.
>> Postdoctoral Research Fellow, GRECC, Bedford VA
>> Research Fellow, Department of Neurology, Massachusetts General
>> Hospital and Harvard Medical School
>> Office: (773) 406-2464
>> =====================
>> This e-mail contains CONFIDENTIAL INFORMATION which may contain
>> PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
>> and which is intended only for the use of the individual or entity
>> named above. If the reader of the e-mail is not the intended recipient
>> or the employee or agent responsible for delivering it to the intended
>> recipient, you are hereby notified that you are in possession of
>> confidential and privileged information. Any unauthorized use,
>> disclosure, copying or the taking of any action in reliance on the
>> contents of this information is strictly prohibited and may be
>> unlawful. If you have received this e-mail unintentionally, please
>> immediately notify the sender via telephone at (773) 406-2464 or
>> email.
>>
>>
>>
>> On Fri, Feb 25, 2011 at 2:33 PM, SALEM BOUSSIDA
>> <[log in to unmask]> wrote:
>>>
>>> Dear SPMers,
>>>
>>> I have a group of 7 subjects (rats) and I would like to perform a group
>>> analysis (mixed effects).
>>> I have done the 1st level analysis (fixed effects) for each subject and
>>> I
>>> have two con_001 and con_002 images, wich are respectively the contrast
>>> image for a positive effect (activation) and for a negative effect
>>> (deactivation) of one condition (current stimulation) .
>>> Then, I have done a one-sample-t-test : I feed all the con_001 of each
>>> subject into the 2nd level analysis , then I entered a T contrast [1]
>>> and
>>> SPM displayed the thresholded T-statistic image. But when I entered a F
>>> contrast [1], I have got this error:
>>> "
>>>
>>> ??? Inf computed by model function, fitting cannot continue.
>>> Try using or tightening upper and lower bounds on coefficients.
>>>
>>> "
>>>
>>> Based on the procedure described in SPM8 manuel concerning the "face
>>> group
>>> data", It is mentionned that I have to enter a F contrast.
>>> I am confused by this and I have some questions:
>>> (1) which contrast should I enter, F or T contrast? and what is the
>>> differenece between them?
>>> (2) Is the one-sample-t-test, the appropriate one to run mixed effects
>>> analysis?
>>> (3) Do I have to run two separate "one-sample-t-test" for
>>> positive(activation) and negative-deactivation) effects?
>>>
>>>
>>> Thank you for your help.
>>>
>>> Best regards,
>>>
>>> Salem
>>>
>>
>
>
>
>
>
========================================================================Date: Fri, 25 Feb 2011 23:37:15 -0500
Reply-To: Jonathan Peelle <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Jonathan Peelle <[log in to unmask]>
Subject: Re: Mixed effects group analysis questions
In-Reply-To: <[log in to unmask]>
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Dear Salem,
> The whole discussion of fixed effects and random effects are best left
> to the field of statistician. If you want the details of why it could
> be considered either, see Ch.12 in Human Brain Function.
To follow on what Donald said, a couple of further reference that may be of interest regarding mixed effects and group studies:
Friston KJ, Stephan KE, Lund TE, Morcom A, Kiebel S (2005) Mixed-effects and fMRI studies. NeuroImage 24:244-252.
Mumford JA, Nichols T (2009) Simple group fMRI modeling and inference. NeuroImage 47:1469-1475.
Best regards,
Jonathan
--
Dr. Jonathan Peelle
Department of Neurology
University of Pennsylvania
3 West Gates
3400 Spruce Street
Philadelphia, PA 19104
USA
http://jonathanpeelle.net/
========================================================================Date: Sat, 26 Feb 2011 15:25:26 +1100
Reply-To: =?ISO-8859-1?Q?Kristian_Löwe?= <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: =?ISO-8859-1?Q?Kristian_Löwe?= <[log in to unmask]>
Subject: spm_hrf: delay of response and resulting peak
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Dear SPM'ers,
playing around with the function spm_hrf, I noticed the following:
The resulting hrf peaks one second earlier than I would expect given the
specified delay of response (relative to onset), i.e. p(1).
E.g., issueing the following command, shows the above mentioned.
figure; hold on; TR = 1; peak = 6; plot(0:32, spm_hrf(TR, [peak 16 1 1 6
0 32])); plot([peak peak],[-1 1],'--r'); grid on; xlim([-1 35]);
I was under the impression, that the delay of response effectively is
the peak of the hrf. Probably I'm missing something obvious here, I
would be thankful for any guidance on this.
Cheers,
Kristian
========================================================================Date: Sat, 26 Feb 2011 19:35:04 +0100
Reply-To: Stefan =?iso-8859-1?b?S2z2cHBlbA==? <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Stefan =?iso-8859-1?b?S2z2cHBlbA==? <[log in to unmask]>
Subject: PhD position in Freiburg: Clinical applications of pattern
recognition methods
In-Reply-To: <[log in to unmask]>
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A 3-years doctoral position (50% TV-L 13) is available from April
2011 at the Freiburg Brain Imaging Center (FBI,
http://www.uniklinik-freiburg.de/fbi/live/index_en.html) in Germany.
The candidate can obtain a PhD in the field of Psychology, Medicine or
Biology
The candidate will apply pattern recognition methods to multivariate
data (e.g. imaging, neuropsychological test results) to improve our
understanding of neurodegenerative diseases
(http://www.uniklinik-freiburg.de/fbi/live/forschung/AutomatedDiagnosing_en.html).
We offer
The FBI (http://www.uniklinik-freiburg.de/fbi/live/index_en.html)
combins efforts in neuroimaging research of the departments of
Psychiatry/Psychotherapie, Neurology, Neuroradiology and Neurosurgery.
It is closely collaborating with the Center for Data Analyses and
Modelling
(http://www.fdm.uni-freiburg.de/projects/dynamic-processes-in-life-sciences)
and the Department for Pattern recognition methods
(http://lmb.informatik.uni-freiburg.de/index.en.html). Support for MRI
sequence development is provided by the Department of MR-Physics, that
is also part of the FBI. Starting April 2011, we will begin a
continuous education program in the field of neuroimaging.
We require
Applicants must hold a master degree or equivalent in medicine,
psychology or a related field. Previous programming experience (e.g.
Matlab) is advantageous but not required. Sound knowledge of
statistics as well as good IT skills are essential.
Disabled applicants are preferred if qualification is equal.
Please send applications to Dr. Stefan Klöppel. For informal enquiries
call +49 761 270 66400 or email [log in to unmask] .
http://www.uniklinik-freiburg.de/fbi/live/members/kloeppel_en.html
========================================================================Date: Sun, 27 Feb 2011 20:16:32 +1100
Reply-To: Bill Budd <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Bill Budd <[log in to unmask]>
Organization: University of Newcastle
Subject: Re: script to flip MR images left to right (or vice versa)
Comments: To: Michael Harms <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-version: 1.0
Content-transfer-encoding: 7BIT
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Message-ID: <[log in to unmask]>
Another note of caution is that brains are not entirely symmetrical,
structurally or functionally. So flipping MRI images may not result in a
spatially homogenous data set for your analysis even though they are
consistent in terms of the lesioned hemisphere. One option to address the
lack of anatomical asymmetry is to flip epi images prior to normalisation
and then normalise using symmetrical priors. (See Didolet et al, 2010 for an
example creating a symmetrical template). Others on this list might have
more detailed information but this approach may create additional problems
with normalisation routines and anatomical labelling/ROI analyses and
doesn't address the issue of functional asymmetries.
Cheers
-Bill
> -----Original Message-----
> From: SPM (Statistical Parametric Mapping) [mailto:[log in to unmask]]
> On Behalf Of Michael Harms
> Sent: Saturday, 26 February 2011 1:20 AM
> To: [log in to unmask]
> Subject: Re: [SPM] script to flip MR images left to right (or vice
> versa)
>
> A note of caution about using fslswapdim in this manner however:
> If you do that, the data order and the header information stored in the
> sform/qform will no longer be in sync. So, what the sform/qform defines
> to be "left" will no longer be the subject's true left (assuming the
> labels in the original data were correct)!
>
> Read the following FSL page if you want all the details:
> http://www.fmrib.ox.ac.uk/fsl/avwutils/index.html
>
> Best,
> -MH
>
> On Fri, 2011-02-25 at 11:54 +0000, Rosa Sanchez Panchuelo wrote:
> > You can do it easily with fsl:
> > fslswapdim input_image.img -x outpu_image.img
> > -x will flip the image in the RL direction.
> >
> > On 25/02/2011 11:39, Richard Morris wrote:
> > > I'm not sure, but I think you can use the image calculator in SPM,
> > > with the following function:
> > >
> > >
> > > f = 'Inv(i1)'
> > >
> > >
> > >
> > > On 25/02/2011, at 10:16 AM, Michael T Rubens wrote:
> > >
> > > > how about this:
> > > > %%%
> > > >
> > > >
> > > > v = spm_vol('/image_path/brain.img');
> > > > x = spm_read_vols(v);
> > > >
> > > >
> > > > x = x(size(x,1):-1:1,:,:);
> > > >
> > > >
> > > > [path file] = fileparts(v.fname);
> > > > v.fname = fullfile(path,['flipped_' file');
> > > >
> > > >
> > > > spm_write_vol(v,x)
> > > >
> > > >
> > > > %%%
> > > >
> > > >
> > > >
> > > >
> > > > cheers,
> > > > michael
> > > >
> > > > On Thu, Feb 24, 2011 at 9:35 AM, Chien-Ho Lin
> > > > <[log in to unmask]> wrote:
> > > > Hi,
> > > >
> > > > I stuides patients with stroke doing some behavior
> > > > paradigm in fMRI.
> > > > Patients always used their affected limb for the task so
> > > > some patients used left hand some used right hand.
> > > > Now I would like to flip the left and right of the images
> > > > so patients' lesional hemisphere are on the same side.
> > > >
> > > > Is there any script in spm 5 or 8 can help to accomplish
> > > > this goal?
> > > >
> > > > I found a m file called spm_flip.m but it seems support
> > > > only spm2.
> > > >
> > > > Your assistance are well appreciated.
> > > >
> > > > Thank you so much.
> > > >
> > > > Janice
> > > >
> > > >
> > > >
> > > > --
> > > > Research Associate
> > > > Gazzaley Lab
> > > > Department of Neurology
> > > > University of California, San Francisco
> > > >
> > >
> > >
> >
> >
> > --
> > Rosa Maria Sanchez Panchuelo
> > Post-doctoral Research fellow
> > Sir Peter Mansfield Magnetic Resonance Centre
> > University of Nottingham
> > University Park
> > Nottingham, NG7 2RD
> > United Kingdom
> > +44 115 84 66003
> >
> >
> >
> >
> >
> > This message and any attachment are intended solely for the addressee
> > and may contain confidential information. If you have received this
> > message in error, please send it back to me, and immediately delete
> > it. Please do not use, copy or disclose the information contained in
> > this message or in any attachment. Any views or opinions expressed by
> > the author of this email do not necessarily reflect the views of the
> > University of Nottingham.
> >
> > This message has been checked for viruses but the contents of an
> > attachment may still contain software viruses which could damage your
> > computer system: you are advised to perform your own checks. Email
> > communications with the University of Nottingham may be monitored as
> > permitted by UK legislation.
> >
========================================================================Date: Sun, 27 Feb 2011 20:12:49 +0800
Reply-To: =?UTF-8?B?6aOe6bif?= <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: =?UTF-8?B?6aOe6bif?= <[log in to unmask]>
Subject: Re: Negative values in DCM.A
Comments: To: Maria Dauvermann <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Dear Maria,
Once I have asked a similar question as yours.
In the situation of DCM for EEG/MEG, the number in DCM.A means that the coupling strength is exp(number) times the prior coupling. As for the fmri, I'm not very clear either.
I Hope this helps.
Haoran.
At 2011-02-24 15:58:16，"Maria Dauvermann" <[log in to unmask]> wrote:
>Hello,
>
>I had a look at the values in DCM.A after I have estimated the DCMs.
>
>What does a negative value in the intrinsic connection mean? How do I interprete this result?
>
>Thanks for your help.
>
>BW, Maria
========================================================================Date: Sun, 27 Feb 2011 20:27:05 +0800
Reply-To: =?GBK?B?t8nE8Q==?= <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: =?GBK?B?t8nE8Q==?= <[log in to unmask]>
Subject: [SPM DCM] how to specify the modulation?
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========================================================================Date: Sun, 27 Feb 2011 15:13:43 +0000
Reply-To: Nynke L <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Nynke L <[log in to unmask]>
Subject: Second level t-test: test against other value than zero
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain; charset="UTF-8"
Message-ID: <[log in to unmask]>
Hello all,
I have a particular group level analysis for which I want to use spm. However, till now I don't manage to so I hope you have suggestions for me.
I use fMRI for the prediction of a certain binary outcome. Therefore, I use a different classification software package that computes prediction-accuracies for all voxels. For each subject, I have a .nii file with as values the accuracies (range between 0 and 1). So, this is a brain map just like you usually feed to the second level.
What I want to test in the second level analysis is which brain areas have accuracies significantly higher than 0.5. So I want to test against the null-hypothesis that accuracies are 0.5 or lower. Now the problem is that the 1 sample t-test in spm on default tests against the null-hypothesis that the values are zero or higher.... So my first question is whether it is possible to set a custum value for the t-test to test against a manually set value?
I already tried a different approach, namely subtracting 0.5 from the values in my input images (to put them in the default spm t-test). However, when I trie to do this with the image calculator of spm (with as expression i1 - 0.5) I do not get correct results: I do not get a map with exactly 0.5 subtracted from each value but a range between 0.4-0.6. It seems that spm-image-calc is also doing something else with the image... So my second question is: how can I make image-calc subtract exactly 0.5 from all values in the image?
Thanks in advance!
Best regards,
Nynke van der Laan
========================================================================Date: Sun, 27 Feb 2011 07:22:05 -0800
Reply-To: Faezeh Vedaei <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Faezeh Vedaei <[log in to unmask]>
Subject: display result on template MRI
MIME-Version: 1.0
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I am working with some data by SPM8,as a question how can I display my brain
glass result on multiple slices of a template MRI to view regions of
hyperperfusion and hypoperfusion?
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<html><head><style type="text/css"><!-- DIV {margin:0px;} --></style></head><body><div style="font-family:times new roman, new york, times, serif;font-size:18pt;color:#000000;"><DIV>I am working with some data by SPM8,as a question how can I display my brain glass result on multiple slices of a template MRI to view regions of hyperperfusion and hypoperfusion?</DIV></div><br>
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========================================================================Date: Sun, 27 Feb 2011 07:25:39 -0800
Reply-To: Faezeh Vedaei <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Faezeh Vedaei <[log in to unmask]>
Subject: display result on template MRI
MIME-Version: 1.0
Content-Type: multipart/alternative; boundary="0-2098163962-1298820339=:20497"
Message-ID: <[log in to unmask]>
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I am working with some data by SPM8,as a question how can I display my brain
glass result on multiple slices of a template MRI to view regions of
hyperperfusion and hypoperfusion?
--0-2098163962-1298820339=:20497
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<html><head><style type="text/css"><!-- DIV {margin:0px;} --></style></head><body><div style="font-family:times new roman, new york, times, serif;font-size:18pt"><DIV><BR></DIV>
<DIV style="FONT-FAMILY: times new roman, new york, times, serif; FONT-SIZE: 18pt"><BR>
<DIV style="FONT-FAMILY: times new roman, new york, times, serif; FONT-SIZE: 12pt">
<DIV style="FONT-FAMILY: times new roman, new york, times, serif; COLOR: #000000; FONT-SIZE: 18pt">
<DIV>I am working with some data by SPM8,as a question how can I display my brain glass result on multiple slices of a template MRI to view regions of hyperperfusion and hypoperfusion?</DIV></DIV><BR></DIV></DIV></div><br>
</body></html>
--0-2098163962-1298820339=:20497--
========================================================================Date: Sun, 27 Feb 2011 20:41:27 +0100
Reply-To: Leonhard Schilbach <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Leonhard Schilbach <[log in to unmask]>
Subject: Call for Papers; Frontiers Special Topic: Toward a neuroscience
of social interaction
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Message-ID: <[log in to unmask]>
Dear colleagues,
this is to draw your attention toward and invite submissions for an upcoming Frontiers in Neuroscience Special Topic entitled:
"Toward a neuroscience of social interaction"
Please find more details below and on the Frontiers website:
http://www.frontiersin.org/Human%20Neuroscience/specialtopics/towards_a_neuroscience_of_soci/211
-Deadline for submission of abstracts: 30 Sept 2011
-Deadline for submission of full manuscripts: 31 Dec 2011
Looking forward to your contributions!
Best regards,
Leo Schilbach
--
Towards a neuroscience of social interaction
Hosted By:
Ulrich Pfeiffer, University Hospital Cologne, Germany
Bert Timmermans, University Hospital Cologne, Germany
Kai Vogeley, University Hospital Cologne, Germany
Chris Frith, Wellcome Trust Centre for Neuroimaging at University College London, United Kingdom
Leonhard Schilbach, Max-Planck-Institute for Neurological Research, Germany
The burgeoning field of social neuroscience has begun to illuminate the complex biological bases of human social cognitive abilities. However, in spite of being based on the premise of investigating the neural bases of interacting minds, the majority of studies have focused on studying brains in isolation using paradigms that investigate offline social cognition, i.e. social cognition from a detached observer's point of view, asking study participants to read out the mental states of others without being engaged in interaction with them. Consequently, the neural correlates of real-time social interaction have remained elusive and may —paradoxically— represent the 'dark matter' of social neuroscience.
More recently, a growing number of researchers have begun to study online social cognition, i.e. social cognition from a participant's point of view, based on the assumption that there is something fundamentally different when we are actively engaged with others in real-time social interaction as compared to when we merely observe them. Whereas, for offline social cognition, interaction and feedback are merely a way of gathering data about the other person that feeds into processing algorithms 'inside’ the agent, it has been proposed that in online social cognition the knowledge of the other —at least in part— resides in the interaction dynamics ‘between’ the agents. Furthermore being a participant in an ongoing interaction may entail a commitment toward being responsive created by important differences in the motivational foundations of online and offline social cognition.
In order to promote the development of the neuroscientific investigation of online social cognition, this Frontiers Special Topic aims at bringing together contributions from researchers in social neuroscience and related fields, whose work involves the study of at least two individuals and sometimes two brains, rather than single individuals and brains responding to a social context. Specifically, this special issue will adopt an interdisciplinary perspective on what it is that separates online from offline social cognition and the putative differences in the recruitment of underlying processes and mechanisms. Here, an important focal point will be to address the various roles of social interaction in contributing to and —at times— constituting our awareness of other minds. For this Special Topic, we, therefore, solicit reviews, original research articles, opinion and method papers, which address the investigation of social interaction and go beyond traditional concepts and ways of experimentation in doing so. While focusing on work in the neurosciences, this Special Topic also welcomes contributions in the form of behavioral studies, psychophysiological investigations, methodological innovations, computational approaches, developmental and patient studies.
By focusing on cutting-edge research in social neuroscience and related fields, this Frontiers Special Issue will create new insights concerning the neurobiology of social interaction and holds the promise of helping social neuroscience to really go social.
--
Leonhard Schilbach, MD
Department of Psychiatry and Psychotherapy
University of Cologne, Germany
[log in to unmask]
&
Max-Planck-Institute for Neurological Research
Cologne, Germany
[log in to unmask]: Sun, 27 Feb 2011 17:17:44 -0500
Reply-To: John Fredy <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Fredy <[log in to unmask]>
Subject: Re: display result on template MRI
Comments: To: Faezeh Vedaei <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--0016368e2380ab9416049d4aeebc
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Hello Faezeh, I use xjview ( http://www.alivelearn.net/xjview8/ )
Regards
On Sun, Feb 27, 2011 at 10:25 AM, Faezeh Vedaei <[log in to unmask]> wrote:
>
>
> I am working with some data by SPM8,as a question how can I display my
> brain glass result on multiple slices of a template MRI to view regions of
> hyperperfusion and hypoperfusion?
>
>
>
--0016368e2380ab9416049d4aeebc
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Hello Faezeh, I use xjview ( <a href="http://www.alivelearn.net/xjview8/">http://www.alivelearn.net/xjview8/</a> )<br><br>Regards<br><br><div class="gmail_quote">On Sun, Feb 27, 2011 at 10:25 AM, Faezeh Vedaei <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;"><div><div></div><div class="h5"><div><div style="font-family: times new roman,new york,times,serif; font-size: 18pt;">
<div><br></div>
<div style="font-family: times new roman,new york,times,serif; font-size: 18pt;"><br>
<div style="font-family: times new roman,new york,times,serif; font-size: 12pt;">
<div style="font-family: times new roman,new york,times,serif; color: rgb(0, 0, 0); font-size: 18pt;">
<div>I am working with some data by SPM8,as a question how can I display my brain glass result on multiple slices of a template MRI to view regions of hyperperfusion and hypoperfusion?</div></div><br></div></div></div><br>
</div></div></div></blockquote></div><br>
--0016368e2380ab9416049d4aeebc--
========================================================================Date: Sun, 27 Feb 2011 17:22:30 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: display result on template MRI
Comments: To: Faezeh Vedaei <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID: <[log in to unmask]>
Click Overlay --> sections OR Overlay --> slices
MRIcron is also useful
Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
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On Sun, Feb 27, 2011 at 10:22 AM, Faezeh Vedaei <[log in to unmask]> wrote:
> I am working with some data by SPM8,as a question how can I display my brain
> glass result on multiple slices of a template MRI to view regions of
> hyperperfusion and hypoperfusion?
>
========================================================================Date: Sun, 27 Feb 2011 14:25:32 -0800
Reply-To: Vladimir Bogdanov <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Bogdanov <[log in to unmask]>
Subject: Re: display result on template MRI
Comments: To: Faezeh Vedaei <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundary="0-1112837336-1298845532=:86375"
Message-ID: <[log in to unmask]>
--0-1112837336-1298845532=:86375
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Content-Transfer-Encoding: quoted-printable
When you explore your results, in the window "Results" click on "overlays" in the group "Display". Here you can select "sections". In the menu wich is opened by this action you can select either a normalized individual structural image form the proper folder of your subject or a T1 image from SPM8\canonical.
I hope this is helpful.
Vladimir
--- On Sun, 2/27/11, Faezeh Vedaei <[log in to unmask]> wrote:
From: Faezeh Vedaei <[log in to unmask]>
Subject: [SPM] display result on template MRI
To: [log in to unmask]
Date: Sunday, February 27, 2011, 4:25 PM
I am working with some data by SPM8,as a question how can I display my brain glass result on multiple slices of a template MRI to view regions of hyperperfusion and hypoperfusion?
--0-1112837336-1298845532=:86375
Content-Type: text/html; charset=iso-8859-1
Content-Transfer-Encoding: quoted-printable
<table cellspacing="0" cellpadding="0" border="0" ><tr><td valign="top" style="font: inherit;">When you explore your results, in the window "Results" click on "overlays" in the group "Display". Here you can select "sections". In the menu wich is opened by this action you can select either a normalized individual structural image form the proper folder of your subject or a T1 image from SPM8\canonical.<br><br>I hope this is helpful.<br><br>Vladimir<br><br>--- On <b>Sun, 2/27/11, Faezeh Vedaei <i><[log in to unmask]></i></b> wrote:<br><blockquote style="border-left: 2px solid rgb(16, 16, 255); margin-left: 5px; padding-left: 5px;"><br>From: Faezeh Vedaei <[log in to unmask]><br>Subject: [SPM] display result on template MRI<br>To: [log in to unmask]<br>Date: Sunday, February 27, 2011, 4:25 PM<br><br><div id="yiv357382596"><style type="text/css"><!--#yiv357382596 DIV {margin:0px;}--></style><div style="font-family: times new roman,new
york,times,serif; font-size: 18pt;"><div><br></div>
<div style="font-family: times new roman,new york,times,serif; font-size: 18pt;"><br>
<div style="font-family: times new roman,new york,times,serif; font-size: 12pt;">
<div style="font-family: times new roman,new york,times,serif; color: rgb(0, 0, 0); font-size: 18pt;">
<div>I am working with some data by SPM8,as a question how can I display my brain glass result on multiple slices of a template MRI to view regions of hyperperfusion and hypoperfusion?</div></div><br></div></div></div><br>
</div></blockquote></td></tr></table><br>
--0-1112837336-1298845532=:86375--
========================================================================Date: Sun, 27 Feb 2011 19:55:57 -0500
Reply-To: Yune Lee <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Yune Lee <[log in to unmask]>
Subject: normalization failure (anterior posterior mismatch)
MIME-Version: 1.0
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Dear SPM experts,
I've encountered a normalization failure, such that there is a mismatch of
anterior-posterior between a template (EPI.nii) and a source image
(meanbold.nii)
This is clearly shown in the attached PDF file.
Any help wold be greatly appreciated.
Thanks in advance,
YSL
--001636283ab280d804049d4d24ab
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<br> Dear SPM experts, <br> I've encountered a normalization failure, such that there is a mismatch of anterior-posterior between a template (EPI.nii) and a source image (meanbold.nii) <br> This is clearly shown in the attached PDF file. <br>
Any help wold be greatly appreciated. <br><br> Thanks in advance, <br> YSL<br><br><br><br>
--001636283ab280d804049d4d24ab--
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========================================================================Date: Sun, 27 Feb 2011 20:56:27 -0500
Reply-To: Glen Lee <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Glen Lee <[log in to unmask]>
Subject: SPM8 error
MIME-Version: 1.0
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Hello SPMers,
Does anybody help me figuring out what this error message (Error using ==>
< a href="matlab: opentonline ('cfg_util.m',808,0)"> cfg_util at 808 </a>)
mean and how i can resolve this issue?
I get this same error no matter what I try (e.g., realign, normalize, etc)
in SPM8 (also see attached)
Let me know. Thanks in advance.
Glen
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Hello SPMers, <br>Does anybody help me figuring out what this error message (Error using ==> < a href="matlab: opentonline ('cfg_util.m',808,0)"> cfg_util at 808 </a>) mean and how i can resolve this issue? <br>
I get this same error no matter what I try (e.g., realign, normalize, etc) in SPM8 (also see attached)<br>Let me know. Thanks in advance. <br><br>Glen <br><br><br>
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--20cf3071c9bae64ca3049d4dfcd2--
========================================================================Date: Sun, 27 Feb 2011 22:30:29 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: Second level t-test: test against other value than zero
Comments: To: Nynke L <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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--20cf3054a2f72ca08e049d4f4d70
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Set the interpolation option to nearest neighbor. That should make it exact.
Also, you might want to think about other transforms since the data is
likely not to be normally distributed.
Best Regards, Donald McLaren
================D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Office: (773) 406-2464
====================This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED
HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is
intended only for the use of the individual or entity named above. If the
reader of the e-mail is not the intended recipient or the employee or agent
responsible for delivering it to the intended recipient, you are hereby
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information. Any unauthorized use, disclosure, copying or the taking of any
action in reliance on the contents of this information is strictly
prohibited and may be unlawful. If you have received this e-mail
unintentionally, please immediately notify the sender via telephone at (773)
406-2464 or email.
On Sun, Feb 27, 2011 at 10:13 AM, Nynke L
<[log in to unmask]>wrote:
> Hello all,
>
> I have a particular group level analysis for which I want to use spm.
> However, till now I don't manage to so I hope you have suggestions for me.
>
> I use fMRI for the prediction of a certain binary outcome. Therefore, I use
> a different classification software package that computes
> prediction-accuracies for all voxels. For each subject, I have a .nii file
> with as values the accuracies (range between 0 and 1). So, this is a brain
> map just like you usually feed to the second level.
>
> What I want to test in the second level analysis is which brain areas have
> accuracies significantly higher than 0.5. So I want to test against the
> null-hypothesis that accuracies are 0.5 or lower. Now the problem is that
> the 1 sample t-test in spm on default tests against the null-hypothesis that
> the values are zero or higher.... So my first question is whether it is
> possible to set a custum value for the t-test to test against a manually set
> value?
>
> I already tried a different approach, namely subtracting 0.5 from the
> values in my input images (to put them in the default spm t-test). However,
> when I trie to do this with the image calculator of spm (with as expression
> i1 - 0.5) I do not get correct results: I do not get a map with exactly 0.5
> subtracted from each value but a range between 0.4-0.6. It seems that
> spm-image-calc is also doing something else with the image... So my second
> question is: how can I make image-calc subtract exactly 0.5 from all values
> in the image?
>
> Thanks in advance!
>
> Best regards,
> Nynke van der Laan
>
--20cf3054a2f72ca08e049d4f4d70
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Set the interpolation option to nearest neighbor. That should make it exact.<br><br>Also, you might want to think about other transforms since the data is likely not to be normally distributed.<br clear="all"><br>Best Regards, Donald McLaren<br>
=================<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, GRECC, Bedford VA<br>Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School<br>Office: (773) 406-2464<br>
=====================<br>This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email.<br>
<br><br><div class="gmail_quote">On Sun, Feb 27, 2011 at 10:13 AM, Nynke L <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Hello all,<br>
<br>
I have a particular group level analysis for which I want to use spm. However, till now I don't manage to so I hope you have suggestions for me.<br>
<br>
I use fMRI for the prediction of a certain binary outcome. Therefore, I use a different classification software package that computes prediction-accuracies for all voxels. For each subject, I have a .nii file with as values the accuracies (range between 0 and 1). So, this is a brain map just like you usually feed to the second level.<br>
<br>
What I want to test in the second level analysis is which brain areas have accuracies significantly higher than 0.5. So I want to test against the null-hypothesis that accuracies are 0.5 or lower. Now the problem is that the 1 sample t-test in spm on default tests against the null-hypothesis that the values are zero or higher.... So my first question is whether it is possible to set a custum value for the t-test to test against a manually set value?<br>
<br>
I already tried a different approach, namely subtracting 0.5 from the values in my input images (to put them in the default spm t-test). However, when I trie to do this with the image calculator of spm (with as expression i1 - 0.5) I do not get correct results: I do not get a map with exactly 0.5 subtracted from each value but a range between 0.4-0.6. It seems that spm-image-calc is also doing something else with the image... So my second question is: how can I make image-calc subtract exactly 0.5 from all values in the image?<br>
<br>
Thanks in advance!<br>
<br>
Best regards,<br>
Nynke van der Laan<br>
</blockquote></div><br>
--20cf3054a2f72ca08e049d4f4d70--
========================================================================Date: Mon, 28 Feb 2011 09:55:35 +0100
Reply-To: Marko Wilke <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Marko Wilke <[log in to unmask]>
Subject: Re: SPM8 error
Comments: To: Glen Lee <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Hi Glen,
this could be an installation problem, perhaps try using a
newly-downloaded SPM, install the latest updates on top of it and
install it into a directory without spaces (say, 'C:\Test\SPM8'), and
then only add this directory to the Matlab path.
Cheers,
Marko
Glen Lee wrote:
> Hello SPMers,
> Does anybody help me figuring out what this error message (Error using
> ==> < a href="matlab: opentonline ('cfg_util.m',808,0)"> cfg_util at 808
> </a>) mean and how i can resolve this issue?
> I get this same error no matter what I try (e.g., realign, normalize,
> etc) in SPM8 (also see attached)
> Let me know. Thanks in advance.
>
> Glen
>
>
--
____________________________________________________
PD Dr. med. Marko Wilke
Facharzt für Kinder- und Jugendmedizin
Leiter, Experimentelle Pädiatrische Neurobildgebung
Universitäts-Kinderklinik
Abt. III (Neuropädiatrie)
Marko Wilke, MD, PhD
Pediatrician
Head, Experimental Pediatric Neuroimaging
University Children's Hospital
Dept. III (Pediatric Neurology)
Hoppe-Seyler-Str. 1
D - 72076 Tübingen, Germany
Tel. +49 7071 29-83416
Fax +49 7071 29-5473
[log in to unmask]
http://www.medizin.uni-tuebingen.de/kinder/epn
____________________________________________________
========================================================================Date: Mon, 28 Feb 2011 09:06:14 +0000
Reply-To: John Ashburner <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Ashburner <[log in to unmask]>
Subject: Re: normalization failure (anterior posterior mismatch)
Comments: To: Yune Lee <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Before running the spatial normalisation, you'll need to ensure that
the image is in approximate alignment with the templates (Check Reg
button). For these images, it appears that they are either rotated by
180 degrees (about the z axis) or flipped in the anterior posterior
direction.
Best regards,
-John
On 28 February 2011 00:55, Yune Lee <[log in to unmask]> wrote:
>
> Dear SPM experts,
> I've encountered a normalization failure, such that there is a mismatch of
> anterior-posterior between a template (EPI.nii) and a source image
> (meanbold.nii)
> This is clearly shown in the attached PDF file.
> Any help wold be greatly appreciated.
>
> Thanks in advance,
> YSL
>
>
>
>
========================================================================Date: Mon, 28 Feb 2011 14:41:37 +0530
Reply-To: sarika cherodath <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: sarika cherodath <[log in to unmask]>
Subject: question on VOI analysis
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--00248c05002ff8481b049d5412cf
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Hi SPMers,
I have been trying to perform VOI analysis to obtain signal changes
at a particular area for individual subjects in the dataset, so as to make
correlations with behavioral scores. When i tried to calculate the values,
SPM returns the same value for all subjects! Can anybody explain why this
might be? The dataset has been analysed at second level and then moved to
another directory (along with first level data). Is it possible that SPM is
not able to access the data because of the path change??
Thanks in advance,
--
Sarika Cherodath
Graduate Student
National Brain Research Centre
Manesar, Gurgaon -122050
India
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<div dir="ltr"><br clear="all">Hi SPMers,<div><br></div><div> I have been trying to perform VOI analysis to obtain signal changes at a particular area for individual subjects in the dataset, so as to make correlations with behavioral scores. When i tried to calculate the values, SPM returns the same value for all subjects! Can anybody explain why this might be? The dataset has been analysed at second level and then moved to another directory (along with first level data). Is it possible that SPM is not able to access the data because of the path change??</div>
<div><br></div><div>Thanks in advance,<br>-- <br>Sarika Cherodath<br>Graduate Student<br>National Brain Research Centre<br>Manesar, Gurgaon -122050<br>India<br><br>
</div></div>
--00248c05002ff8481b049d5412cf--
========================================================================Date: Mon, 28 Feb 2011 10:13:17 +0100
Reply-To: Michels Lars <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Michels Lars <[log in to unmask]>
Subject: Postdoctoral Fellowship in multimodal developmental neuroimaging
Comments: To: [log in to unmask]
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Applications are invited for an 18 month postdoctoral fellowship in the human multimodal neuroimaging project "Linking the major system markers for typical and atypical brain development: a multimodal imaging and spectroscopy study" (http://www.zihp.uzh.ch/1610.php#45) funded by the Zürich Institute of Human Physiology.
This study will investigate the major physiological markers of brain development, using a combination of multimodal brain imaging (e.g., simultaneous EEG-fMRI, see Lüchinger et al., 2011, NeuroImage, in press) and MR-spectroscopy methods (i.e., GABA, see O'Gorman et al., 2011, J. of Mag. Reson. Img., in press). The initial phase of the study will establish baseline neurotransmitter levels, cerebral blood flow (e.g., perfusion MRI) and EEG frequency and power at rest in children, adolescents, and adults. Examining the interactions between these markers and the changes they demonstrate with age and hormone levels will allow to better understanding the global and regional processes underlying brain maturation. In addition, we will investigate changes in these physiological markers with (a) memory tasks (see Michels et al., 2010, PLoS ONE) and (b) attention deficit hyperactivity disorder (ADHD, see Doehnert et al., Biol Psychiatry, 2010). The starting date of the position is May 2011. Our department is equipped with 64-channel fMRI-compatible EEG equipment and a 3 Tesla GE scanner, which is mainly dedicated for research questions.
The successful applicant will have a PhD research background in Cognitive Neuroscience, Neurophysiology, Psychology, Neuropsychology, or related fields. Fluency in English and the ability to work within a multidisciplinary team are essential. Applicants must be experienced at conducting fMRI and/or EEG studies -demonstrated by at least 2 first author publications in international peer-reviewed journals- and be familiar with analysis software such as SPM/Matlab, BrainVoyager and/or FSL. Experience with stimulus presentation software (such as Presentation), UNIX, and programming languages a plus.
Salaries are in accordance with the Swiss National Research Foundation.
APPLICATION INSTRUCTIONS: To apply, please send a curriculum vitae, a personal statement describing research interests, 3 letters of recommendations, and up to 3 article reprints/preprints (max. 2 MB!!!) to:
Dr Lars Michels
[log in to unmask]
MR-Zentrum
University Children's Hospital
Steinwiesstrasse 75
Zürich 8032
Switzerland
Reviews of applications will begin on the 1st of March and will continue until the position is filled.
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<div class=Section1>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'>Applications are invited for an 18
month </span></font><font size=3 color=black face="Times New Roman"><span
lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman";color:black'>postdoctoral
fellowship </span></font><font size=3 color=black face="Times New Roman"><span
lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman";color:black'>in
the human multimodal neuroimaging project “Linking the major system
markers for typical and atypical brain development: a multimodal imaging and
spectroscopy study” <b><span style='font-weight:bold'>(http://www.zihp.uzh.ch/1610.php#45)</span></b>
funded by the Zürich Institute of Human Physiology. <o:p></o:p></span></font></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'>This study will investigate the
major physiological markers of brain development, using a combination of multimodal
brain imaging (e.g., simultaneous </span></font><font size=3 color=black
face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;font-family:
"Times New Roman";color:black'>EEG-fMRI, see Lüchinger et al., 2011, NeuroImage,
in press</span></font><font size=3 color=black face="Times New Roman"><span
lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman";color:black'>)
and MR-spectroscopy methods (i.e., GABA, see O’Gorman et al., 2011, J. of
Mag. Reson. Img., in press). The initial phase of the study will establish
baseline neurotransmitter levels, cerebral blood flow (e.g., perfusion MRI) and
EEG frequency and power at rest in children, adolescents, and adults. Examining
the interactions between these markers and the changes they demonstrate with
age and hormone levels will allow to better understanding the global and
regional processes underlying brain maturation. In addition, we will
investigate changes in these physiological markers with (a) memory tasks (see
Michels et al., 2010, PLoS ONE) and (b) attention deficit hyperactivity disorder
(ADHD, see </span></font><font size=3 face="Times New Roman"><span lang=EN-GB
style='font-size:12.0pt;font-family:"Times New Roman"'>Doehnert et al., <span
class=jrnl>Biol Psychiatry</span>, 2010</span><font color=black><span
style='color:black'>). The<i><span style='font-style:italic'> </span></i>starting
date of the position is<i><span style='font-style:italic'> May 2011</span></i>.
Our department is equipped with 64-channel fMRI-compatible EEG equipment and a
3 Tesla GE scanner, which is mainly dedicated for research questions.</span></font></span></font><font
size=3 color=black face="Times New Roman"><span lang=EN-GB style='font-size:
12.0pt;font-family:"Times New Roman";color:black'><o:p></o:p></span></font></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'><o:p> </o:p></span></font></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-US style='font-size:12.0pt;
font-family:"Times New Roman";color:black'>The successful applicant will have a
PhD research background in Cognitive Neuroscience, Neurophysiology, Psychology,
Neuropsychology, or related fields</span></font><font size=3 color=black
face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;font-family:
"Times New Roman";color:black'>. Fluency in English </span></font><font size=3
color=black face="Times New Roman"><span lang=EN-US style='font-size:12.0pt;
font-family:"Times New Roman";color:black'>and the ability to work within a
multidisciplinary team are essential. Applicants <u>must</u> be experienced at
conducting fMRI and/or EEG studies –demonstrated by at least <u>2 first
author</u> publications in international peer-reviewed journals– and be
familiar with analysis software such as SPM/Matlab, BrainVoyager and/or
FSL. Experience with stimulus presentation software (such as Presentation),
UNIX, and programming languages a plus. </span></font><font size=3 color=black
face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;font-family:
"Times New Roman";color:black'><o:p></o:p></span></font></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'><o:p> </o:p></span></font></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'>Salaries are in accordance with the
Swiss National Research Foundation.<o:p></o:p></span></font></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'><o:p> </o:p></span></font></p>
<p class=MsoNormal style='text-align:justify'><font size=3 color=black
face="Times New Roman"><span lang=EN-US style='font-size:12.0pt;font-family:
"Times New Roman";color:black'>APPLICATION INSTRUCTIONS: </span></font><font
size=3 color=black face="Times New Roman"><span lang=EN-US style='font-size:
12.0pt;font-family:"Times New Roman";color:black'>To apply, please send a
curriculum vitae, a personal statement describing research interests, 3 letters
of recommendations, and up to 3 article reprints/preprints (max. 2 MB!!!) to:<o:p></o:p></span></font></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-US style='font-size:12.0pt;
font-family:"Times New Roman";color:black'><o:p> </o:p></span></font></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'>Dr Lars Michels<o:p></o:p></span></font></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><b><font
size=3 color=black face="Times New Roman"><span lang=EN-GB style='font-size:
12.0pt;font-family:"Times New Roman";color:black;font-weight:bold'>[log in to unmask]<o:p></o:p></span></font></b></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'>MR-Zentrum<o:p></o:p></span></font></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span style='font-size:12.0pt;font-family:
"Times New Roman";color:black'>University Children’s Hospital<o:p></o:p></span></font></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span style='font-size:12.0pt;font-family:
"Times New Roman";color:black'>Steinwiesstrasse 75<o:p></o:p></span></font></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span style='font-size:12.0pt;font-family:
"Times New Roman";color:black'>Zürich 8032<o:p></o:p></span></font></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span style='font-size:12.0pt;font-family:
"Times New Roman";color:black'>Switzerland<o:p></o:p></span></font></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span style='font-size:12.0pt;font-family:
"Times New Roman";color:black'><o:p> </o:p></span></font></p>
<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'>Reviews of applications will begin
on the <b><span style='font-weight:bold'>1<sup>st</sup> of March</span></b> and
will continue until the position is filled.<o:p></o:p></span></font></p>
<p class=MsoNormal><font size=2 face=Arial><span lang=EN-GB style='font-size:
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========================================================================Date: Mon, 28 Feb 2011 09:48:36 +0000
Reply-To: Vincent Koppelmans <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vincent Koppelmans <[log in to unmask]>
Subject: Adjusting Dartel for ICV: RC volumes or C volumes
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Message-ID: <[log in to unmask]>
Dear SPM experts,
If you adjust for intra cranial volume in a dartel analyses on gray matter, would it be best to have your ICV calculated from the RC files or the C files. I have read that C files are a little bit more accurate than the RC files. Then again, the dartel analysis uses RC files, therefore volumes calculated from RC files would match the data best, right? Kind regards,
Vincent
========================================================================Date: Mon, 28 Feb 2011 11:43:44 +0100
Reply-To: =?ISO-8859-1?Q?Gemma_Monté?= <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: =?ISO-8859-1?Q?Gemma_Monté?= <[log in to unmask]>
Subject: Re: Adjusting Dartel for ICV: RC volumes or C volumes
Comments: To: Vincent Koppelmans <[log in to unmask]>
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Hi Vincent,
I suggest you use the c*.nii files. They are the tissue classes in native
space.
You are right, DARTEL takes information encoded in the rc*.nii files.
However, the flow fields u_rc1*.nii files are applied to the c1*.nii to
create the warped files. So finally, you obtain files in the form
swmc1*.nii, either in the DARTEL space or in the MNI space. You can check
that the volumes of the GM Jacobian scaled images (without smoothing) by
DARTEL are the same than the volume of the GM segments in native space
(c1*.nii files).
Hope this helps,
Gemma
On 28 February 2011 10:48, Vincent Koppelmans <[log in to unmask]>wrote:
> Dear SPM experts,
>
> If you adjust for intra cranial volume in a dartel analyses on gray matter,
> would it be best to have your ICV calculated from the RC files or the C
> files. I have read that C files are a little bit more accurate than the RC
> files. Then again, the dartel analysis uses RC files, therefore volumes
> calculated from RC files would match the data best, right? Kind regards,
>
> Vincent
>
<[log in to unmask]>
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Hi Vincent,<br>I suggest you use the c*.nii files. They are the tissue classes in native space. <br><br>You are right, DARTEL takes information encoded in the rc*.nii files. However, the flow fields u_rc1*.nii files are applied to the c1*.nii to create the warped files. So finally, you obtain files in the form swmc1*.nii, either in the DARTEL space or in the MNI space. You can check that the volumes of the GM Jacobian scaled images (without smoothing) by DARTEL are the same than the volume of the GM segments in native space (c1*.nii files).<br>
<br>Hope this helps,<br>Gemma<br><br><div class="gmail_quote">
On 28 February 2011 10:48, Vincent Koppelmans <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Dear SPM experts,<br>
<br>
If you adjust for intra cranial volume in a dartel analyses on gray matter, would it be best to have your ICV calculated from the RC files or the C files. I have read that C files are a little bit more accurate than the RC files. Then again, the dartel analysis uses RC files, therefore volumes calculated from RC files would match the data best, right? Kind regards,<br>
<font color="#888888"><br>
Vincent<br>
</font></blockquote></div><div style="font-family: arial,helvetica,sans-serif;"><font color="#999999"><font style="color: rgb(51, 51, 51);" size="1"><br></font><font size="1"><a href="mailto:[log in to unmask]" target="_blank"><br>
</a></font></font></div><br>
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========================================================================Date: Mon, 28 Feb 2011 11:50:23 +0100
Reply-To: michel grothe <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: michel grothe <[log in to unmask]>
Subject: Re: Adjusting Dartel for ICV: RC volumes or C volumes
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In-Reply-To: <[log in to unmask]>
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Dear Vincent,
the rc*-files are rigid-body (6-P) transformed versions of the c*-files and total tissue calculations should therefore be exactly the same between the two files. Given that modulation aims to preserve the native amount of tissue, totals calculated from mwrc*-files should also be equal to the ones calculated from c*- or rc*-files, although they might differ slightly because of interpolation issues. Do totals calculated from the different image-types differ in your case?
Best regards,
Michel
> Date: Mon, 28 Feb 2011 09:48:36 +0000
> From: [log in to unmask]
> Subject: [SPM] Adjusting Dartel for ICV: RC volumes or C volumes
> To: [log in to unmask]
>
> Dear SPM experts,
>
> If you adjust for intra cranial volume in a dartel analyses on gray matter, would it be best to have your ICV calculated from the RC files or the C files. I have read that C files are a little bit more accurate than the RC files. Then again, the dartel analysis uses RC files, therefore volumes calculated from RC files would match the data best, right? Kind regards,
>
> Vincent
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Dear Vincent,<br><br>the rc*-files are rigid-body (6-P) transformed versions of the c*-files and total tissue calculations should therefore be exactly the same between the two files. Given that modulation aims to preserve the native amount of tissue, totals calculated from mwrc*-files should also be equal to the ones calculated from c*- or rc*-files, although they might differ slightly because of interpolation issues. Do totals calculated from the different image-types differ in your case? <br><br>Best regards,<br>Michel<br><br>> Date: Mon, 28 Feb 2011 09:48:36 +0000<br>> From: [log in to unmask]<br>> Subject: [SPM] Adjusting Dartel for ICV: RC volumes or C volumes<br>> To: [log in to unmask]<br>> <br>> Dear SPM experts,<br>> <br>> If you adjust for intra cranial volume in a dartel analyses on gray matter, would it be best to have your ICV calculated from the RC files or the C files. I have read that C files are a little bit more accurate than the RC files. Then again, the dartel analysis uses RC files, therefore volumes calculated from RC files would match the data best, right? Kind regards,<br>> <br>> Vincent<br> </body>
</html>
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========================================================================Date: Mon, 28 Feb 2011 10:59:22 +0000
Reply-To: Vladimir Litvak <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Litvak <[log in to unmask]>
Subject: Re: [SPM DCM] how to specify the modulation?
Comments: To: =?UTF-8?B?6aOe6bif?= <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Dear Haoran,
2011/2/27 飞鸟 <[log in to unmask]>:
> Hello all,
> 　　When you analysis your data with the method of DCM, have you ever met the
> problem about how to specify the modulation? Now I choose 7 basic models,
> and then assume all the connections in each basic model are modulated by the
> experimental manipulation. What I want to know is that whether or not this
> is reasonable
Usually one would have a hypothesis about which specific connections
are affected by the experimental condition.
>In addition, is it possible that the connections
> between two sources could include forward connectivity, backward
> connectivity and lateral connectivity at the same time?
No. Every connection can only have one type.
Vladimir
> 　　Any help will be grateful!
>
> --
> Haoran LI (MS)
> Brain Imaging Lab,
> Research Center for Learning Science,
> Southeast University
> 2 Si Pai Lou , Nanjing, 210096, P.R.China
>
>
========================================================================Date: Mon, 28 Feb 2011 12:02:19 +0100
Reply-To: Marko Wilke <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Marko Wilke <[log in to unmask]>
Subject: F-values
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Dear All,
at the risk of (once again) exhibiting my statistical ignorance, I
wanted to ask for feedback re: an effect I see when doing F-tests. I am
comparing sessions with different numbers of covariates and compute an
omnibus F-test in SPM (for the covariates only) and sum the resulting
F-values. As expected, I see that there seems to be an optimum number of
covariates as the sum of F-values first increases, then decreases when
adding more covariates. I expected this to be a function of the degrees
of freedom but I cannot seem to find the piece of code where these are
taken into account. Any input is appreciated.
Cheers,
Marko
--
____________________________________________________
PD Dr. med. Marko Wilke
Facharzt für Kinder- und Jugendmedizin
Leiter, Experimentelle Pädiatrische Neurobildgebung
Universitäts-Kinderklinik
Abt. III (Neuropädiatrie)
Marko Wilke, MD, PhD
Pediatrician
Head, Experimental Pediatric Neuroimaging
University Children's Hospital
Dept. III (Pediatric Neurology)
Hoppe-Seyler-Str. 1
D - 72076 Tübingen, Germany
Tel. +49 7071 29-83416
Fax +49 7071 29-5473
[log in to unmask]
http://www.medizin.uni-tuebingen.de/kinder/epn
____________________________________________________
========================================================================Date: Mon, 28 Feb 2011 14:12:21 +0000
Reply-To: SUBSCRIBE FSL Patricia Pires <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: SUBSCRIBE FSL Patricia Pires <[log in to unmask]>
Subject: DTI with SPM
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Hello,
i am new in the SPM forum and i will be glad if somebody could help me. My question is concerning to preocess DTI images with SPM. I have processed DTI images (FA; MD, RD...) with FSL. Could anyone tell me some lecture to find how process (e.g. FA images) with SPM or where to find information concernig SPM and FSL procedures softwares differences?
Thank you very much,
Patricia.
========================================================================Date: Mon, 28 Feb 2011 15:25:26 +0100
Reply-To: Janani Dhinakaran <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Janani Dhinakaran <[log in to unmask]>
Subject: Unsubscribe me please
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Thank you.
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Thank you.<br>
--20cf3079b77ea0b2a8049d5874c7--
========================================================================Date: Mon, 28 Feb 2011 15:29:46 +0100
Reply-To: Wolfgang Weber-Fahr <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Wolfgang Weber-Fahr <[log in to unmask]>
Subject: Job Anouncement ZI-Mannheim, Germany
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The Central Institute of Mental Health (State Foundation) is an
internationally
renowned research institute in the field of psychiatry and neuroscience,
home
of the Department of Psychiatry of the Medical Faculty Mannheim of the
University of Heidelberg as well as psychiatric hospital with 255 inpatient
and 52 day-hospital beds.
Within several new projects funded by the European Union and the Federal
Ministry
of Education and Research we offer
2 Positions for PostDocs and PhD-Students
in the area of rodent magnetic resonance functional-, perfusion- and
diffusion tensor (DTI)
imaging at 9.4 Tesla.
The Central Institute of Mental Health is equipped with a latest
generation 210 mm
horizontalbore Bruker BioSpec animal system and additional cryogenic MR
coils for
1H and 13C detection in the mouse brain as well as two 3 Tesla human
whole body MR-scanners.
The positions are for two years initially and located in the research
group Translational Imaging.
Potential applicants should have a diploma or master in physics,
physical chemistry or neuroscience,
or an equivalent field. The ideal candidate from the quantitative
sciences has a solid background
in NMR physics, signal detection and processing theory, and is familiar
with the
Bruker Paravision environment. Experience in other computer languages,
Matlab or IDL,
is desirable.
We have openings in these primary fields of research:
• Establishment of manganese enhanced magnetic resonance imaging (MEMRI)
for a the longitudinal assessment of connectivity and neuronal activity
in experimental animals.
• Development of methods for the acquisition and post processing of
functional
BOLD imaging data with optogenetics for the identification and
investigation of brain networks.
• Diffusion Tensor Imaging for the investigation of structural integrity
in different
psychiatric mouse-models.
The candidate would also collaborate on several ongoing animal studies
of schizophrenia, depression,
substance abuse and other psychiatric disorders in mouse and rat models.
The assistance in the general supervision of the animal scanner would
also be part of the position.
More information can be found at
http://www.zi-mannheim.de/transl_imaging.html.
We offer an interesting job in a leading research hospital with a strong
emphasis on MR-Imaging,
salary is according to the German TV-L pay scale, including the social
benefits of the public service sector.
For further information please contact Dr. Wolfgang Weber-Fahr, Tel. +49
621 1703-2961,
E-Mail: [log in to unmask]
Homepage: www.zi-mannheim.de/start_en.html
----------------------------------------------------------
Wolfgang Weber-Fahr, Dr.rer.nat.
Head RG Translational Imaging
Neuroimaging Department
Central Institute of Mental Health
J5
68072 Mannheim
Germany
email:[log in to unmask]
phone: ++49 621 1703 2961
========================================================================Date: Mon, 28 Feb 2011 10:36:54 -0500
Reply-To: zao liu <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: zao liu <[log in to unmask]>
Subject: regions of brain corresponding to VBM coordinates
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*Dear All,*
**
*I did VBM analysis on our T1-w images and would like to know if there is a
way to get the names of regions of activation in the brain from the
coordinates (which comes as a report at the end of VBM, i.e voxelwise,
cluster wise and set level reports). One more clarification the coordinates
reported are in MNI right and are mm not voxel coordinates right.*
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<div><font size="2" face="Arial"><em>Dear All,</em></font></div>
<div><font size="2" face="Arial"><em></em></font> </div>
<div><font size="2" face="Arial"><em>I did VBM analysis on our T1-w images and would like to know if there is a way to get the names of regions of activation in the brain from the coordinates (which comes as a report at the end of VBM, i.e voxelwise, cluster wise and set level reports). One more clarification the coordinates reported are in MNI right and are mm not voxel coordinates right.</em></font></div>
--0015174bf21c068a29049d5973e3--
========================================================================Date: Mon, 28 Feb 2011 16:35:44 +0000
Reply-To: Richard Binney <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Richard Binney <[log in to unmask]>
Subject: PPI: adjust for effects of interest
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Hi Darren G/ Karl F/other PPI-ers,
I can't find any posts on this and wondered if you can clear it up for me.
In extracting the principal eigenvariate from your VOI, you are asked if you
want to adjust the extracted timecourse. When should you adjust and when
should you not worry? what is the impact of adjusting?
My impression was that the raw timecourse would be extracted always. The
option to adjust suggests this is not always true. Do you use this option
(only?) when you have time or dispersion derivatives and/or motion
regressors?
I have a parametric design with two conditions (tasks) and two parametric
modulations per condition. The design matrix therefore has 6 regressors of
interest. Motion regressors are also included. In extracting the timecourse
should I adjust using an F-contrast spaning the first 6 columns only ([1 1 1
1 1 1]->right-padded with zeros)?? Is it problematic if I have not done
this? What are the consequences?
What about if I were only interested in the parametric modulations of the
first condition in the PPI analysis? Should I adjust for the first 3 columns
only ([1 1 1 0 0 0])?
All your comments will be greatly appreciated.
Richard
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<div>Hi Darren G/ Karl F/other PPI-ers,</div>
<div> </div>
<div>I can't find any posts on this and wondered if you can clear it up for me.</div>
<div> </div>
<div>In extracting the principal eigenvariate from your VOI, you are asked if you want to adjust the extracted timecourse. When should you adjust and when should you not worry? what is the impact of adjusting?</div>
<div> </div>
<div>My impression was that the raw timecourse would be extracted always. The option to adjust suggests this is not always true. Do you use this option (only?) when you have time or dispersion derivatives and/or motion regressors?</div>
<div> </div>
<div>I have a parametric design with two conditions (tasks) and two parametric modulations per condition. The design matrix therefore has 6 regressors of interest. Motion regressors are also included. In extracting the timecourse should I adjust using an F-contrast spaning the first 6 columns only ([1 1 1 1 1 1]->right-padded with zeros)?? Is it problematic if I have not done this? What are the consequences?</div>
<div> </div>
<div>What about if I were only interested in the parametric modulations of the first condition in the PPI analysis? Should I adjust for the first 3 columns only ([1 1 1 0 0 0])?</div>
<div> </div>
<div>All your comments will be greatly appreciated.</div>
<div> </div>
<div>Richard</div>
--90e6ba1efe9a7601f6049d5a45df--
========================================================================Date: Mon, 28 Feb 2011 11:42:00 -0500
Reply-To: "MCLAREN, Donald" <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: "MCLAREN, Donald" <[log in to unmask]>
Subject: Re: PPI: adjust for effects of interest
Comments: To: Richard Binney <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
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Richard,
I'd use an F-contrast that is one row per condition/modulator. In your case:
1 0 0 0 0 0 ...
0 1 0 0 0 0 ...
0 0 1 0 0 0 ...
0 0 0 1 0 0 ...
0 0 0 0 1 0 ...
0 0 0 0 0 1 ...
The goal of the adjustment is to extract only the BOLD signal related to
neural activity and eliminate the activity due to motion.
Best Regards, Donald McLaren
================D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Office: (773) 406-2464
====================This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED
HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is
intended only for the use of the individual or entity named above. If the
reader of the e-mail is not the intended recipient or the employee or agent
responsible for delivering it to the intended recipient, you are hereby
notified that you are in possession of confidential and privileged
information. Any unauthorized use, disclosure, copying or the taking of any
action in reliance on the contents of this information is strictly
prohibited and may be unlawful. If you have received this e-mail
unintentionally, please immediately notify the sender via telephone at (773)
406-2464 or email.
On Mon, Feb 28, 2011 at 11:35 AM, Richard Binney <
[log in to unmask]> wrote:
> Hi Darren G/ Karl F/other PPI-ers,
>
> I can't find any posts on this and wondered if you can clear it up for me.
>
> In extracting the principal eigenvariate from your VOI, you are asked if
> you want to adjust the extracted timecourse. When should you adjust and when
> should you not worry? what is the impact of adjusting?
>
> My impression was that the raw timecourse would be extracted always. The
> option to adjust suggests this is not always true. Do you use this option
> (only?) when you have time or dispersion derivatives and/or motion
> regressors?
>
> I have a parametric design with two conditions (tasks) and two parametric
> modulations per condition. The design matrix therefore has 6 regressors of
> interest. Motion regressors are also included. In extracting the timecourse
> should I adjust using an F-contrast spaning the first 6 columns only ([1 1 1
> 1 1 1]->right-padded with zeros)?? Is it problematic if I have not done
> this? What are the consequences?
>
> What about if I were only interested in the parametric modulations of the
> first condition in the PPI analysis? Should I adjust for the first 3 columns
> only ([1 1 1 0 0 0])?
>
> All your comments will be greatly appreciated.
>
> Richard
>
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<div>Richard,</div><div><br></div>I'd use an F-contrast that is one row per condition/modulator. In your case:<div>1 0 0 0 0 0 ...</div><div>0 1 0 0 0 0 ...</div><div>0 0 1 0 0 0 ...</div><div>0 0 0 1 0 0 ...</div><div>
0 0 0 0 1 0 ...</div><div>0 0 0 0 0 1 ...</div><div><br></div><div>The goal of the adjustment is to extract only the BOLD signal related to neural activity and eliminate the activity due to motion. <br clear="all"><br>Best Regards, Donald McLaren<br>
=================<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, GRECC, Bedford VA<br>Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School<br>Office: (773) 406-2464<br>
=====================<br>This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email.<br>
<br><br><div class="gmail_quote">On Mon, Feb 28, 2011 at 11:35 AM, Richard Binney <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
<div>Hi Darren G/ Karl F/other PPI-ers,</div>
<div> </div>
<div>I can't find any posts on this and wondered if you can clear it up for me.</div>
<div> </div>
<div>In extracting the principal eigenvariate from your VOI, you are asked if you want to adjust the extracted timecourse. When should you adjust and when should you not worry? what is the impact of adjusting?</div>
<div> </div>
<div>My impression was that the raw timecourse would be extracted always. The option to adjust suggests this is not always true. Do you use this option (only?) when you have time or dispersion derivatives and/or motion regressors?</div>
<div> </div>
<div>I have a parametric design with two conditions (tasks) and two parametric modulations per condition. The design matrix therefore has 6 regressors of interest. Motion regressors are also included. In extracting the timecourse should I adjust using an F-contrast spaning the first 6 columns only ([1 1 1 1 1 1]->right-padded with zeros)?? Is it problematic if I have not done this? What are the consequences?</div>
<div> </div>
<div>What about if I were only interested in the parametric modulations of the first condition in the PPI analysis? Should I adjust for the first 3 columns only ([1 1 1 0 0 0])?</div>
<div> </div>
<div>All your comments will be greatly appreciated.</div>
<div> </div><font color="#888888">
<div>Richard</div>
</font></blockquote></div><br></div>
--20cf3054a4b5dbb06a049d5a5beb--
========================================================================Date: Mon, 28 Feb 2011 11:31:33 -0600
Reply-To: Pilar Archila-Suerte <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Pilar Archila-Suerte <[log in to unmask]>
Subject: Art repair - mean signal change
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SPM Users,
When using the "scaling to percent signal change" option in ArtRepair,
sometimes I get what I want from the files selected and sometimes I don't
(depending on which files I select).
Why would ArtRepair give this answer:
*Direct calls to spm_defauts are deprecated.*
*Please use spm('Defaults',modality) or spm_get_defaults instead.*
*Automatically estimated peak and contrast scaling.*
* Normalizing by beta_0008.img*
*Peak value = 4.97*
*Contrast sum = 0.996*
*Mean value = NaN*
*(peak/contrast_sum)*100/bmean = **NaN*
*
*
*ans =*
*
*
* 4.9700 0.9960 ** NaN*
All files follow the same path so I don't think that's the problem. Does
anybody know why this is happening and how I can fix this?
Thank you,
Pilar A.
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<div><div><div><div>SPM Users,</div></div></div></div><div>When using the "scaling to percent signal change" option in ArtRepair, sometimes I get what I want from the files selected and sometimes I don't (depending on which files I select).</div>
<div><br></div><div>Why would ArtRepair give this answer:</div><div><br></div><div><div><i>Direct calls to spm_defauts are deprecated.</i></div><div><i>Please use spm('Defaults',modality) or spm_get_defaults instead.</i></div>
<div><i>Automatically estimated peak and contrast scaling.</i></div><div><i> Normalizing by beta_0008.img</i></div><div><i>Peak value   = 4.97</i></div><div><i>Contrast sum  = 0.996</i></div><div><i>Mean value   = NaN</i></div>
<div><i>(peak/contrast_sum)*100/bmean  = </i><font class="Apple-style-span" color="#CC0000"><b><i>NaN</i></b></font></div><div><i><br></i></div><div><i>ans =</i></div><div><i><br></i></div><div><i>   4.9700   0.9960   </i><b><font class="Apple-style-span" color="#FF0000"><i>  NaN</i></font></b></div>
</div><br><div> All files follow the same path so I don't think that's the problem. Does anybody know why this is happening and how I can fix this?</div><div><br></div><div>Thank you,</div><div>Pilar A. </div>
--0015177fcec43a8612049d5b0ea2--
========================================================================Date: Mon, 28 Feb 2011 12:36:59 -0500
Reply-To: Chris Watson <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Chris Watson <[log in to unmask]>
Subject: Re: Art repair - mean signal change
Comments: To: Pilar Archila-Suerte <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset="UTF-8"; format=flowed
Content-Transfer-Encoding: 7bit
Message-ID: <[log in to unmask]>
I think that was a bug in an older version of ArtRepair. Do you have the
latest version?
Pilar Archila-Suerte wrote:
> SPM Users,
> When using the "scaling to percent signal change" option in ArtRepair,
> sometimes I get what I want from the files selected and sometimes I
> don't (depending on which files I select).
>
> Why would ArtRepair give this answer:
>
> /Direct calls to spm_defauts are deprecated./
> /Please use spm('Defaults',modality) or spm_get_defaults instead./
> /Automatically estimated peak and contrast scaling./
> / Normalizing by beta_0008.img/
> /Peak value = 4.97/
> /Contrast sum = 0.996/
> /Mean value = NaN/
> /(peak/contrast_sum)*100/bmean = /*/NaN/*
> /
> /
> /ans =/
> /
> /
> / 4.9700 0.9960 /*/ NaN/*
>
> All files follow the same path so I don't think that's the
> problem. Does anybody know why this is happening and how I can fix this?
>
> Thank you,
> Pilar A.
========================================================================Date: Mon, 28 Feb 2011 11:38:50 -0600
Reply-To: Pilar Archila-Suerte <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Pilar Archila-Suerte <[log in to unmask]>
Subject: Re: Art repair - mean signal change
Comments: To: Chris Watson <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]>
--00163683195045f1c4049d5b2847
Content-Type: text/plain; charset=UTF-8
I have ArtRepair v4. Isn't this the latest version?
On Mon, Feb 28, 2011 at 11:36 AM, Chris Watson <
[log in to unmask]> wrote:
> I think that was a bug in an older version of ArtRepair. Do you have the
> latest version?
>
>
> Pilar Archila-Suerte wrote:
>
>> SPM Users,
>> When using the "scaling to percent signal change" option in ArtRepair,
>> sometimes I get what I want from the files selected and sometimes I don't
>> (depending on which files I select).
>>
>> Why would ArtRepair give this answer:
>>
>> /Direct calls to spm_defauts are deprecated./
>> /Please use spm('Defaults',modality) or spm_get_defaults instead./
>> /Automatically estimated peak and contrast scaling./
>> / Normalizing by beta_0008.img/
>> /Peak value = 4.97/
>> /Contrast sum = 0.996/
>> /Mean value = NaN/
>> /(peak/contrast_sum)*100/bmean = /*/NaN/*
>> /
>> /
>> /ans =/
>> /
>> /
>> / 4.9700 0.9960 /*/ NaN/*
>>
>> All files follow the same path so I don't think that's the problem. Does
>> anybody know why this is happening and how I can fix this?
>>
>> Thank you,
>> Pilar A.
>>
>
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I have ArtRepair v4. Isn't this the latest version?<br><br><div class="gmail_quote">On Mon, Feb 28, 2011 at 11:36 AM, Chris Watson <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">I think that was a bug in an older version of ArtRepair. Do you have the latest version?<div><div></div><div class="h5">
<br>
<br>
Pilar Archila-Suerte wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
SPM Users,<br>
When using the "scaling to percent signal change" option in ArtRepair, sometimes I get what I want from the files selected and sometimes I don't (depending on which files I select).<br>
<br>
Why would ArtRepair give this answer:<br>
<br>
/Direct calls to spm_defauts are deprecated./<br>
/Please use spm('Defaults',modality) or spm_get_defaults instead./<br>
/Automatically estimated peak and contrast scaling./<br>
/ Normalizing by beta_0008.img/<br>
/Peak value   = 4.97/<br>
/Contrast sum  = 0.996/<br>
/Mean value   = NaN/<br>
/(peak/contrast_sum)*100/bmean  = /*/NaN/*<br>
/<br>
/<br>
/ans =/<br>
/<br>
/<br>
/ Â Â 4.9700 Â Â 0.9960 Â Â /*/ Â NaN/*<br>
<br>
 All files follow the same path so I don't think that's the problem. Does anybody know why this is happening and how I can fix this?<br>
<br>
Thank you,<br>
Pilar A. <br>
</blockquote>
</div></div></blockquote></div><br><br clear="all"><div><div><br><div><div><br></div></div></div></div><br>
--00163683195045f1c4049d5b2847--
========================================================================Date: Mon, 28 Feb 2011 12:47:00 -0500
Reply-To: Jason Steffener <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Jason Steffener <[log in to unmask]>
Subject: matlabbatch "decode dependencies"
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Message-ID: <[log in to unmask]>
Dear All,
Hello all.
I am trying to create some provenance info from an SPM batch file. So
I would like to take an SPM job file and read through it to pull out
the different steps, the data and the parameters. I have no problem
doing this EXCEPT where I have specified some dependencies. I am
having trouble decoding what the dependencies refer to.
Essentially if I have the following steps:
Realign << Input data
Reslice >> Output data
smooth << Output data from reslice
I would like the following:
Step1 Realign
input data: FILENAME
parameters :XX
Step2 Reslice
input data: output from Step 1
output data: rFILENAME
parameters: XX
Step3 Smooth
input data: rFILENAME
output data: srFILENAME
parameters: XX
And ideas? Or if someone can point me to the code that translates the
dependencies in the job file into something I can figure out, that
would also be great.
Thank you,
Jason
--
Jason Steffener, Ph.D.
Department of Neurology
Columbia University
http://www.cogneurosci.org/steffener.html
========================================================================Date: Mon, 28 Feb 2011 14:08:11 +0100
Reply-To: Alexander Hammers <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Alexander Hammers <[log in to unmask]>
Subject: Re: question on VOI analysis
Comments: To: sarika cherodath <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
Mime-Version: 1.0 (Apple Message framework v1082)
Content-Type: multipart/mixed; boundary=Apple-Mail-79-616227633
Message-ID: <[log in to unmask]>
--Apple-Mail-79-616227633
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charset=us-ascii
Dear Sarika,
How did you define the VOI(s)? If they are in MNI space as your second level implies, then you're applying one VOI to all the subjects... and this will unsurprisingly have the same volume in all.
I don't know what kind of data you have - it sounds like fMRI. An approximation of individual volumetrics would be to backtransform your standard space VOIs into individual space - if you've used Unified Segmentation for your MRI spatial normalisation, then the *inv_seg_sn.mat file can accomplish this.
Such a procedure's accuracy will be limited by 1) the quality and provenience of your standard space VOI (see our recent thread on single-subject vs multi-subject atlases) and 2) the low-ish number of degrees of freedom of the transformation for standard normalisation and Unified Segmentation. Point #2 should be improve-able if you use DARTEL.
The gold standard for just one particular area would be to devise a protocol, check the reliability of results and ideally their veracity, and then outline those areas by hand in your subjects.
Hope this helps,
Best wishes,
Alexander
-----------------------------
Alexander Hammers, MD PhD
--Apple-Mail-79-616227633
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Chair in Functional Neuroimaging
Neurodis Foundation
http://www.fondation-neurodis.org/
Postal Address:
CERMEP – Imagerie du Vivant
Hôpital Neurologique Pierre Wertheimer
59 Boulevard Pinel, 69003 Lyon, France
Telephone +33-(0)4-72 68 86 34
Fax +33-(0)4-72 68 86 10
Email [log in to unmask];[log in to unmask]
---------------------------------
Other affiliations:
Visiting Reader; Honorary Consultant Neurologist
Division of Neuroscience and Mental Health, Faculty of Medicine
Imperial College London, UK
---------------------------------
Honorary Reader in Neurology; Honorary Consultant Neurologist
Department of Clinical and Experimental Epilepsy
National Hospital for Neurology and Neurosurgery/ Institute of Neurology, University College London, UK
On 28 Feb 2011, at 10:11, sarika cherodath wrote:
>
> Hi SPMers,
>
> I have been trying to perform VOI analysis to obtain signal changes at a particular area for individual subjects in the dataset, so as to make correlations with behavioral scores. When i tried to calculate the values, SPM returns the same value for all subjects! Can anybody explain why this might be? The dataset has been analysed at second level and then moved to another directory (along with first level data). Is it possible that SPM is not able to access the data because of the path change??
>
> Thanks in advance,
> --
> Sarika Cherodath
> Graduate Student
> National Brain Research Centre
> Manesar, Gurgaon -122050
> India
>
--Apple-Mail-79-616227633--
========================================================================Date: Mon, 28 Feb 2011 15:14:22 -0500
Reply-To: Jeffrey West <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Jeffrey West <[log in to unmask]>
Subject: question regarding flexible factorial model
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Hello.
I have question relating to setting up a flexible factorial model with 2 groups, each group has 4 conditions.
I have 3 factors: 1 = subject (independent, variance equal), 2 = group (independent, variance not equal), 3 = condition (not independent, variance equal)
For the subject level, I entered in 20 subjects, for each subject, I entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2 3 4. Once all 20 subjects were completed, I entered 3 main effects, and interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2 3.
The model did not run and I got the following error message:
Running job #2
-----------------------------------------------------------------------
Running 'Factorial design specification'
Failed 'Factorial design specification'
Index exceeds matrix dimensions.
In file "C:\Documents and Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067), function "spm_run_factorial_design" at line 482.
The following modules did not run:
Failed: Factorial design specification
I feel like the error is in the subject level: either the scan or the conditions. I do not feel like the conditions step is correct. Can anyone please explain what I did wrong setting up my model.
Thank you for any suggestions.
Jef
Jeffrey West, M.A.
Research Analyst
Maryland Psychiatric Research Center
Baltimore, Maryland 21228-0247
Phone: 410-402-6018
email: [log in to unmask]
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<BODY style="MARGIN: 4px 4px 1px; FONT: 10pt Tahoma">
<DIV>Hello.</DIV>
<DIV> </DIV>
<DIV>I have question relating to setting up a flexible factorial model with 2 groups, each group has 4 conditions.</DIV>
<DIV> </DIV>
<DIV>I have 3 factors: 1 = subject (independent, variance equal), 2 = group (independent, variance not equal), 3 = condition (not independent, variance equal)</DIV>
<DIV> </DIV>
<DIV>For the subject level, I entered in 20 subjects, for each subject, I entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2 3 4. Once all 20 subjects were completed, I entered 3 main effects, and interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2 3. </DIV>
<DIV> </DIV>
<DIV>The model did not run and I got the following error message:</DIV>
<DIV> </DIV>
<DIV><STRONG>Running job #2<BR>-----------------------------------------------------------------------<BR>Running 'Factorial design specification'<BR>Failed 'Factorial design specification'<BR>Index exceeds matrix dimensions.<BR>In file "C:\Documents and Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067), function "spm_run_factorial_design" at line 482.</STRONG></DIV>
<DIV> </DIV>
<DIV><STRONG>The following modules did not run:<BR>Failed: Factorial design specification</STRONG></DIV>
<DIV><STRONG></STRONG> </DIV>
<DIV>I feel like the error is in the subject level: either the scan or the conditions. I do not feel like the conditions step is correct. Can anyone please explain what I did wrong setting up my model. </DIV>
<DIV> </DIV>
<DIV>Thank you for any suggestions.</DIV>
<DIV> </DIV>
<DIV>Jef</DIV>
<DIV> </DIV>
<DIV> </DIV>
<DIV>Jeffrey West, M.A.<BR>Research Analyst<BR>Maryland Psychiatric Research Center<BR>Baltimore, Maryland 21228-0247<BR>Phone: 410-402-6018<BR>email: <A href="mailto:[log in to unmask]">[log in to unmask]</A> </DIV>
<DIV> </DIV>
<DIV> </DIV></BODY></HTML>
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========================================================================Date: Tue, 1 Mar 2011 09:17:07 +1300
Reply-To: Ehsan Negahbani <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Ehsan Negahbani <[log in to unmask]>
Subject: Unsubscribe me please
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Ehsan
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<br clear="all"><br>-- <br><span style="color:rgb(102, 102, 102)">Ehsan </span><br>
--0022150475f729946e049d5d5d2c--
========================================================================Date: Mon, 28 Feb 2011 22:28:55 +0000
Reply-To: Vladimir Litvak <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Litvak <[log in to unmask]>
Subject: Re: spm_eeg_convert2scalp
Comments: To: Erick Britis Ortiz <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Message-ID: <AANLkTiÃ[log in to unmask]>
Hi Erick,
SPM uses the same algorithm as Fieldtrip to generate a layout from the
sensor array so it should be quite similar, just the convention for
representing it is slightly different. I don't know what exactly the
problem is so it's hard for me to advise you. In principle you can use
the GUI functionality in Prepare or your own script to generate any
layout you like and then load it as a channel template file via
Prepare (this is a mat-file, there is an example for CTF in
EEGTemplates).
Best,
Vladimir
On Mon, Feb 28, 2011 at 10:22 PM, Erick Britis Ortiz
<[log in to unmask]> wrote:
>
> Hi Vladimir,
>
> I have been using spm_eeg_convert2images to transform my MEG frequency
> analysis results to image volumes and run statistics. The objective
> being to determine lateralization in language (in children) by frequency.
>
> I assumed that spm_eeg_convert2scalp would use a standard 2D layout (à
> la Fieldtrip), and when I checked, this is not true. This makes
> lateralization studies, for instance, much more difficult, and I could
> think of others. What is the advantage?
>
> My guess was that setting D.channels.X_plot2D and Y_plot2D to empty
> would generate a default arrangement, but this also did not work. Could
> you shed some light at the issue?
>
> Best,
> Erick
>
>
========================================================================Date: Mon, 28 Feb 2011 23:08:58 +0000
Reply-To: Vladimir Litvak <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Vladimir Litvak <[log in to unmask]>
Subject: Re: spm_eeg_convert2scalp
Comments: To: Erick Britis Ortiz <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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These 2D locations are created by projecting 3D locations represented
in head coordinates so the differences you find are due to different
head positions of your subjects in the helmet. This is by design, but
you also can load a standard channel template file for all your
subjects.
Vladimir
On Mon, Feb 28, 2011 at 10:55 PM, Erick Britis Ortiz
<[log in to unmask]> wrote:
>
> Thank you as always for the prompt response!
>
> For you to know at least what I am talking about, a picture is attached
> with the projected MEG sensors (colored by region) in my 22 subjects.
> Left is greenish, right is reddish.
>
> The problem is that I see no reason for the MEG sensors not to have
> simply a standard position. In the Fieldtrip plots, they have (for
> example, in CTF151.lay). I will follow your advice, no matter. But this
> is a bit dangerous in the general case, don't you think? As I said, I
> just do not know if it is by design, else I could "fix" it.
>
> Best,
> Erick
>
> On 2011-02-28 23:28, Vladimir Litvak wrote:
>> Hi Erick,
>>
>> SPM uses the same algorithm as Fieldtrip to generate a layout from the
>> sensor array so it should be quite similar, just the convention for
>> representing it is slightly different. I don't know what exactly the
>> problem is so it's hard for me to advise you. In principle you can use
>> the GUI functionality in Prepare or your own script to generate any
>> layout you like and then load it as a channel template file via
>> Prepare (this is a mat-file, there is an example for CTF in
>> EEGTemplates).
>>
>> Best,
>>
>> Vladimir
>>
>>
>>
>> On Mon, Feb 28, 2011 at 10:22 PM, Erick Britis Ortiz
>> <[log in to unmask]> wrote:
>>>
>>> Hi Vladimir,
>>>
>>> I have been using spm_eeg_convert2images to transform my MEG frequency
>>> analysis results to image volumes and run statistics. The objective
>>> being to determine lateralization in language (in children) by frequency.
>>>
>>> I assumed that spm_eeg_convert2scalp would use a standard 2D layout (à
>>> la Fieldtrip), and when I checked, this is not true. This makes
>>> lateralization studies, for instance, much more difficult, and I could
>>> think of others. What is the advantage?
>>>
>>> My guess was that setting D.channels.X_plot2D and Y_plot2D to empty
>>> would generate a default arrangement, but this also did not work. Could
>>> you shed some light at the issue?
>>>
>>> Best,
>>> Erick
>>>
>>>
>>
>
>
========================================================================Date: Mon, 28 Feb 2011 18:34:38 -0500
Reply-To: Pieter van de Vijver <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Pieter van de Vijver <[log in to unmask]>
Subject: Re: question regarding flexible factorial model
Comments: To: Jeffrey West <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
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Hi Jeff,
You should enter conditions for subjects in a nscans x factor matrix. The
rows are your scans (4 in your case) and the columns indicate the factors
(2, one for group and one for condition, subject is automatically
modelled).
So for a subject in group 2 with scans for all four conditions you would
need:
[2 1
2 2
2 3
2 4]
Make sure your scans are entered in the right order!
Also, main effect for subjects doesn't need to be specified, this is done
automatically (this is because you chose 'Subjects' at the 'Specify Subjects
or all Scans & Factors').
Good luck,
Pieter
On Mon, Feb 28, 2011 at 3:14 PM, Jeffrey West <[log in to unmask]>wrote:
> Hello.
>
> I have question relating to setting up a flexible factorial model with 2
> groups, each group has 4 conditions.
>
> I have 3 factors: 1 = subject (independent, variance equal), 2 = group
> (independent, variance not equal), 3 = condition (not independent, variance
> equal)
>
> For the subject level, I entered in 20 subjects, for each subject, I
> entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2
> 3 4. Once all 20 subjects were completed, I entered 3 main effects, and
> interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2
> 3.
>
> The model did not run and I got the following error message:
>
> *Running job #2
> -----------------------------------------------------------------------
> Running 'Factorial design specification'
> Failed 'Factorial design specification'
> Index exceeds matrix dimensions.
> In file "C:\Documents and
> Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067),
> function "spm_run_factorial_design" at line 482.*
>
> *The following modules did not run:
> Failed: Factorial design specification*
> **
> I feel like the error is in the subject level: either the scan or the
> conditions. I do not feel like the conditions step is correct. Can anyone
> please explain what I did wrong setting up my model.
>
> Thank you for any suggestions.
>
> Jef
>
>
> Jeffrey West, M.A.
> Research Analyst
> Maryland Psychiatric Research Center
> Baltimore, Maryland 21228-0247
> Phone: 410-402-6018
> email: [log in to unmask]
>
>
>
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Hi Jeff, <div><br></div><div>You should enter conditions for subjects in a nscans x factor matrix. The rows are your scans (4 in your case) and the columns indicate the factors (2, one for group and one for condition, subject is automatically modelled). </div>
<div>So for a subject in group 2 with scans for all four conditions you would need:</div><div>[2 1<br>2 2 <br>2 3</div><div>2 4]</div><div>Make sure your scans are entered in the right order!</div><div><br></div><div>Also, main effect for subjects doesn't need to be specified, this is done automatically (this is because you chose 'Subjects' at the 'Specify Subjects or all Scans & Factors').</div>
<div><br></div><div>Good luck, </div><div><br></div><div>Pieter</div><div><br></div><div><br><div class="gmail_quote">On Mon, Feb 28, 2011 at 3:14 PM, Jeffrey West <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
<div style="margin:4px 4px 1px;font:10pt Tahoma">
<div>Hello.</div>
<div> </div>
<div>I have question relating to setting up a flexible factorial model with 2 groups, each group has 4 conditions.</div>
<div> </div>
<div>I have 3 factors: 1 = subject (independent, variance equal), 2 = group (independent, variance not equal), 3 = condition (not independent, variance equal)</div>
<div> </div>
<div>For the subject level, I entered in 20 subjects, for each subject, I entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2 3 4. Once all 20 subjects were completed, I entered 3 main effects, and interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2 3. </div>
<div> </div>
<div>The model did not run and I got the following error message:</div>
<div> </div>
<div><strong>Running job #2<br>-----------------------------------------------------------------------<br>Running 'Factorial design specification'<br>Failed 'Factorial design specification'<br>Index exceeds matrix dimensions.<br>
In file "C:\Documents and Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067), function "spm_run_factorial_design" at line 482.</strong></div>
<div> </div>
<div><strong>The following modules did not run:<br>Failed: Factorial design specification</strong></div>
<div><strong></strong> </div>
<div>I feel like the error is in the subject level: either the scan or the conditions. I do not feel like the conditions step is correct. Can anyone please explain what I did wrong setting up my model. </div>
<div> </div>
<div>Thank you for any suggestions.</div>
<div> </div>
<div>Jef</div>
<div> </div>
<div> </div>
<div>Jeffrey West, M.A.<br>Research Analyst<br>Maryland Psychiatric Research Center<br>Baltimore, Maryland 21228-0247<br>Phone: <a href="tel:410-402-6018" target="_blank">410-402-6018</a><br>email: <a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a> </div>
<div> </div>
<div> </div></div>
</blockquote></div><br></div>
--bcaec51a894a8f7f93049d601f97--
========================================================================Date: Mon, 28 Feb 2011 18:38:06 -0500
Reply-To: Pieter van de Vijver <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: Pieter van de Vijver <[log in to unmask]>
Subject: Re: question regarding flexible factorial model
Comments: To: Jeffrey West <[log in to unmask]>
In-Reply-To: <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundarye6ba6e8996ee354c049d602b47
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Sorry, small correction. Main effect for subjects DOES need to be
specified.
Pieter
On Mon, Feb 28, 2011 at 6:34 PM, Pieter van de Vijver <[log in to unmask]>wrote:
> Hi Jeff,
>
> You should enter conditions for subjects in a nscans x factor matrix. The
> rows are your scans (4 in your case) and the columns indicate the factors
> (2, one for group and one for condition, subject is automatically
> modelled).
> So for a subject in group 2 with scans for all four conditions you would
> need:
> [2 1
> 2 2
> 2 3
> 2 4]
> Make sure your scans are entered in the right order!
>
> Also, main effect for subjects doesn't need to be specified, this is done
> automatically (this is because you chose 'Subjects' at the 'Specify Subjects
> or all Scans & Factors').
>
> Good luck,
>
> Pieter
>
>
> On Mon, Feb 28, 2011 at 3:14 PM, Jeffrey West <[log in to unmask]>wrote:
>
>> Hello.
>>
>> I have question relating to setting up a flexible factorial model with 2
>> groups, each group has 4 conditions.
>>
>> I have 3 factors: 1 = subject (independent, variance equal), 2 = group
>> (independent, variance not equal), 3 = condition (not independent, variance
>> equal)
>>
>> For the subject level, I entered in 20 subjects, for each subject, I
>> entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2
>> 3 4. Once all 20 subjects were completed, I entered 3 main effects, and
>> interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2
>> 3.
>>
>> The model did not run and I got the following error message:
>>
>> *Running job #2
>> -----------------------------------------------------------------------
>> Running 'Factorial design specification'
>> Failed 'Factorial design specification'
>> Index exceeds matrix dimensions.
>> In file "C:\Documents and
>> Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067),
>> function "spm_run_factorial_design" at line 482.*
>>
>> *The following modules did not run:
>> Failed: Factorial design specification*
>> **
>> I feel like the error is in the subject level: either the scan or the
>> conditions. I do not feel like the conditions step is correct. Can anyone
>> please explain what I did wrong setting up my model.
>>
>> Thank you for any suggestions.
>>
>> Jef
>>
>>
>> Jeffrey West, M.A.
>> Research Analyst
>> Maryland Psychiatric Research Center
>> Baltimore, Maryland 21228-0247
>> Phone: <410-402-6018>410-402-6018
>> email: [log in to unmask]
>>
>>
>>
>
>
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Sorry, small correction. Main effect for subjects DOES need to be specified. <div><br></div><div>Pieter<br><br><div class="gmail_quote">On Mon, Feb 28, 2011 at 6:34 PM, Pieter van de Vijver <span dir="ltr"><<a href="mailto:[log in to unmask]">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">Hi Jeff, <div><br></div><div>You should enter conditions for subjects in a nscans x factor matrix. The rows are your scans (4 in your case) and the columns indicate the factors (2, one for group and one for condition, subject is automatically modelled). </div>
<div>So for a subject in group 2 with scans for all four conditions you would need:</div><div>[2 1<br>2 2 <br>2 3</div><div>2 4]</div><div>Make sure your scans are entered in the right order!</div><div><br></div><div>Also, main effect for subjects doesn't need to be specified, this is done automatically (this is because you chose 'Subjects' at the 'Specify Subjects or all Scans & Factors').</div>
<div><br></div><div>Good luck, </div><div><br></div><font color="#888888"><div>Pieter</div></font><div><div></div><div class="h5"><div><br></div><div><br><div class="gmail_quote">On Mon, Feb 28, 2011 at 3:14 PM, Jeffrey West <span dir="ltr"><<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div style="margin:4px 4px 1px;font:10pt Tahoma">
<div>Hello.</div>
<div> </div>
<div>I have question relating to setting up a flexible factorial model with 2 groups, each group has 4 conditions.</div>
<div> </div>
<div>I have 3 factors: 1 = subject (independent, variance equal), 2 = group (independent, variance not equal), 3 = condition (not independent, variance equal)</div>
<div> </div>
<div>For the subject level, I entered in 20 subjects, for each subject, I entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2 3 4. Once all 20 subjects were completed, I entered 3 main effects, and interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2 3. </div>
<div> </div>
<div>The model did not run and I got the following error message:</div>
<div> </div>
<div><strong>Running job #2<br>-----------------------------------------------------------------------<br>Running 'Factorial design specification'<br>Failed 'Factorial design specification'<br>Index exceeds matrix dimensions.<br>
In file "C:\Documents and Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067), function "spm_run_factorial_design" at line 482.</strong></div>
<div> </div>
<div><strong>The following modules did not run:<br>Failed: Factorial design specification</strong></div>
<div><strong></strong> </div>
<div>I feel like the error is in the subject level: either the scan or the conditions. I do not feel like the conditions step is correct. Can anyone please explain what I did wrong setting up my model. </div>
<div> </div>
<div>Thank you for any suggestions.</div>
<div> </div>
<div>Jef</div>
<div> </div>
<div> </div>
<div>Jeffrey West, M.A.<br>Research Analyst<br>Maryland Psychiatric Research Center<br>Baltimore, Maryland 21228-0247<br>Phone: <a href="tel:410-402-6018" target="_blank"></a><a href="tel:410-402-6018" target="_blank">410-402-6018</a><br>
email: <a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a> </div>
<div> </div>
<div> </div></div>
</blockquote></div><br></div>
</div></div></blockquote></div><br></div>
--90e6ba6e8996ee354c049d602b47--
========================================================================Date: Mon, 28 Feb 2011 18:44:34 -0500
Reply-To: John Fredy <[log in to unmask]>
Sender: "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From: John Fredy <[log in to unmask]>
Subject: sparse data analysis
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Hello all, I have recorded 80 volumes in a block design experiment with a TR
of 6 seconds where 3 seconds are silence, the machine is silence, and the
other 3 seconds are used for the adquisition. In the 3 seconds of machine
silence I present a stimulus, 0.5 seconds later of the begins of the
silence, with an aproximated duration of 1.5 seconds
What is the best strategy for processing this data?
Regards
John Ochoa
Universidad de Antioquia
--0016364991e7155337049d6043e2
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Hello all, I have recorded 80 volumes in a block design experiment with a TR of 6 seconds where 3 seconds are silence, the machine is silence, and the other 3 seconds are used for the adquisition. In the 3 seconds of machine silence I present a stimulus, 0.5 seconds later of the begins of the silence, with an aproximated duration of 1.5 seconds<div>
<br></div><div>What is the best strategy for processing this data?</div><div><br></div><div>Regards</div><div><br></div><div>John Ochoa</div><div>Universidad de Antioquia</div>
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