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
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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========================================================================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]> Content-Type: text/plain; charset=windows-1252 Mime-Version: 1.0 (Apple Message framework v1082) Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> 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 Content-Type: multipart/alternative; boundary cf3054a48fc6ab4c049c04fe03 Message-ID: <[log in to unmask]> --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 Content-Type: text/html; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printableStanford 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-- ========================================================================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 Content-Type: multipart/related; boundary="_004_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D05EMGMB6admedct_"; type="multipart/alternative" MIME-Version: 1.0 Message-ID: <[log in to unmask]> --_004_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D05EMGMB6admedct_ Content-Type: multipart/alternative; boundary="_000_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D05EMGMB6admedct_" --_000_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D05EMGMB6admedct_ Content-Type: text/plain; charset="us-ascii" Content-Transfer-Encoding: quoted-printable 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] ________________________________ 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, are obligated to maintain it in a safe, secure and confidential manner. Unauthorized redisclosure or failure to maintain confidentiality may subject you to federal and state penalties. If you are not the intended recipient, please immediately notify us by return email, and delete this message from your computer. --_000_D8D86424FFCFA144BDA7076B5BDF7BFC03163E9D05EMGMB6admedct_ Content-Type: text/html; charset="us-ascii" Content-Transfer-Encoding: quoted-printable
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
------------------------------------------------------------------------------------------------------------------------------
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]
I mistakenly read ‘\’ as ‘/’
Tony
------------------------------------------------------------------------------------------------------------------------------
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]
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
Hello SPM'ers, Can experts please advice on why I should switch from SPM8 from SPM5? Regards, Manish Dalwani Sr. PRA Dept. of Psychiatry University of Colorado |
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
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
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
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
>
=======================
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
* Docs
http://www.dipy.org/documentation.html
* Tutorials
http://www.dipy.org/examples_index.html
* Downloads
http://pypi.python.org/pypi/dipy
* 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
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
------------------------------------------------------------------------------------------------------------------------------
Tony Jiang,Ph.D.|Research Scholar|University of California, Los Angeles
Peter V. Ueberroth Building, Rm 2338C2|10945 Le Conte Avenue. Los Angeles,CA 90095Tel: (310)206-1423|Email: [log in to unmask]
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, are obligated to maintain it in a safe, secure and confidential manner. Unauthorized redisclosure or failure to maintain confidentiality may subject you to federal and state penalties. If you are not the intended recipient, please immediately notify us by return email, and delete this message from your computer.
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
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,
VladimirSent 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 -0500To: <[log in to unmask]>ReplyTo: MP <[log in to unmask]>Subject: [SPM] EEG significant clusters outside the MIP glass brainHello 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
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~
>
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 ÂOn 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,
VladimirSent 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 -0500To: <[log in to unmask]>ReplyTo: MP <[log in to unmask]>Subject: [SPM] EEG significant clusters outside the MIP glass brainHello 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- MuhammadÂ
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,
MichaelOn 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- MuhammadOn 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,
VladimirSent 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 -0500To: <[log in to unmask]>ReplyTo: MP <[log in to unmask]>Subject: [SPM] EEG significant clusters outside the MIP glass brainHello 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
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,
MichaelOn 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 ÂOn 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,
VladimirSent 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 -0500To: <[log in to unmask]>ReplyTo: MP <[log in to unmask]>Subject: [SPM] EEG significant clusters outside the MIP glass brainHello 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- MuhammadÂ--
Research Associate
Gazzaley Lab
Department of Neurology
University of California, San Francisco
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,
MichaelOn 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,
MichaelOn 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- MuhammadOn 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,
VladimirSent 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 -0500To: <[log in to unmask]>ReplyTo: MP <[log in to unmask]>Subject: [SPM] EEG significant clusters outside the MIP glass brainHello 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
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:
[log in to unmask]" type="cite">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
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
Hi all, there is an open position to fMRI-EEG, Best, Jose |
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
------------------------------------------------------------------------------------------------------------------------------
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]
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
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?
 | Â
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This message was sent to: [log in to unmask] as part of an established
business relationship with the Society for Neuroscience.
You were added to the system
August 5, 2010. For more information [log in to unmask]&cid=963dd964631f5b9b1df45416d6fa1fbc" target="_blank">click here.
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
>
>
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,RichardOn 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
>
>
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
>>> >
>>> >
>>
>
>
---------- 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 ErrorTo: 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,RichardOn 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
>>> >
>>> >
>>
>
>
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
|
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?
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]
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.
>
>
>
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
--_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 Content-Transfer-Encoding: quoted-printable Message-ID: <[log in to unmask]> You need to use a paired t-test. On Thursday, February 17, 2011, HwangArum <[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 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: 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 Content-Transfer-Encoding: quoted-printable Content-Type: text/plain Message-ID: <[log in to unmask]> 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 MIME-Version: 1.0 Content-Type: multipart/alternative; boundaryMessage-ID: <[log in to unmask]> --0016e6d7843fdfb662049c85d9f5 Content-Type: text/plain; charset=ISO-8859-1 Hello all, Is possible use the vbm8 toolbox for compare a single subject against the template Thanks in advance --0016e6d7843fdfb662049c85d9f5 Content-Type: text/html; charset=ISO-8859-1 Hello all,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/--------------020304020908000302080105-- --------------ms040708000206040902000902 Content-Type: application/pkcs7-signature; name="smime.p7s" Content-Transfer-Encoding: base64 Content-Disposition: attachment; filename="smime.p7s" Content-Description: S/MIME Cryptographic Signature MIAGCSqGSIb3DQEHAqCAMIACAQExCzAJBgUrDgMCGgUAMIAGCSqGSIb3DQEHAQAAoIIUEzCC BCEwggMJoAMCAQICAgDHMA0GCSqGSIb3DQEBBQUAMHExCzAJBgNVBAYTAkRFMRwwGgYDVQQK ExNEZXV0c2NoZSBUZWxla29tIEFHMR8wHQYDVQQLExZULVRlbGVTZWMgVHJ1c3QgQ2VudGVy MSMwIQYDVQQDExpEZXV0c2NoZSBUZWxla29tIFJvb3QgQ0EgMjAeFw0wNjEyMTkxMDI5MDBa Fw0xOTA2MzAyMzU5MDBaMFoxCzAJBgNVBAYTAkRFMRMwEQYDVQQKEwpERk4tVmVyZWluMRAw DgYDVQQLEwdERk4tUEtJMSQwIgYDVQQDExtERk4tVmVyZWluIFBDQSBHbG9iYWwgLSBHMDEw ggEiMA0GCSqGSIb3DQEBAQUAA4IBDwAwggEKAoIBAQDpm8NnhfkNrvWNVMOWUDU9YuluTO2U 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aS1Pc25hYnJ1ZWNrIFJaLUNBIEctMDAyMSMwIQYJKoZIhvcNAQkBFhRjYUB1bmktb3NuYWJy dWVjay5kZQIEEV+b7DAJBgUrDgMCGgUAoIICHDAYBgkqhkiG9w0BCQMxCwYJKoZIhvcNAQcB MBwGCSqGSIb3DQEJBTEPFw0xMTAyMTgwNzQ0MDFaMCMGCSqGSIb3DQEJBDEWBBR8bxTY+Xxd eGboO2rLdRNzUhLcqTBfBgkqhkiG9w0BCQ8xUjBQMAsGCWCGSAFlAwQBAjAKBggqhkiG9w0D BzAOBggqhkiG9w0DAgICAIAwDQYIKoZIhvcNAwICAUAwBwYFKw4DAgcwDQYIKoZIhvcNAwIC ASgwgasGCSsGAQQBgjcQBDGBnTCBmjCBkTELMAkGA1UEBhMCREUxIDAeBgNVBAoTF1VuaXZl cnNpdGFldCBPc25hYnJ1ZWNrMRYwFAYDVQQLEw1SZWNoZW56ZW50cnVtMSMwIQYDVQQDExpV bmktT3NuYWJydWVjayBSWi1DQSBHLTAwMjEjMCEGCSqGSIb3DQEJARYUY2FAdW5pLW9zbmFi cnVlY2suZGUCBBFfm+wwga0GCyqGSIb3DQEJEAILMYGdoIGaMIGRMQswCQYDVQQGEwJERTEg MB4GA1UEChMXVW5pdmVyc2l0YWV0IE9zbmFicnVlY2sxFjAUBgNVBAsTDVJlY2hlbnplbnRy dW0xIzAhBgNVBAMTGlVuaS1Pc25hYnJ1ZWNrIFJaLUNBIEctMDAyMSMwIQYJKoZIhvcNAQkB FhRjYUB1bmktb3NuYWJydWVjay5kZQIEEV+b7DANBgkqhkiG9w0BAQEFAASCAQA1TGJtBTt9 j+qz0VCYIQk+M0afRosxEvrxl7fMzQMNmGEhlCb2zr4DwU1WusqOI30XNnGZtdLZAOn6DKCl 7N4boj4jDkQT9JTVliPLVWJc1N6SAkSqRG0tesbaLUJQ+K/+n+t4x5WXFnB7G5jt0O82+uuJ dc8Sfhz46TLh6cTbLP8iNeoDIy7RMVqpN9VzMH/ABewXwUc6eDIKoaI8xkQ2Na1F2Q0eJZGi pBN0KICnvTKrQnX8UlznbSKASYlDK5jaoT/i3bcoHL3JqNV4iECw7lOcKADPGSOisZ3NPXp1 CQhKgjYVUQ9pUavpy7ehgtuigdHBIIV9hg5/Plow3nFRAAAAAAAA --------------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 Content-Type: text/plain; charset=windows-1252 Content-Transfer-Encoding: quoted-printable 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 Content-Type: text/html; charset=windows-1252 Content-Transfer-Encoding: quoted-printable Thanks!
Hi John
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.
> Is possible use the vbm8 toolbox for compare a single subject against the template
Jonathan
--
Dr. Jonathan Peelle
Department of Neurology
University of Pennsylvania
3 West Gates
3400 Spruce Street
Philadelphia, PA 19104
USA
http://jonathanpeelle.net/
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
>
>
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
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
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
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,CarltonOn 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, APS: 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 pickatlascheers,MichaelOn 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
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 forepilepsy patients, so, I will have time to explore more and more in working with roi analysis.Best wishes from AntioquiaOn 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
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
Hello SPM'ers Regards, Manish Dalwani Sr. PRA Dept. of Psychiatry University of Colorado |
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Fra: Richard Binney <[log in to unmask]>Dato: Uge:8 22. feb 2011 12.48.38 CETEmne: 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
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
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 linkhttp://www.mathworks.com/help/techdoc/matlab_prog/brh72ex-49.html#brh72ex-67Here 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
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
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
>
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
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
>> >
>
>
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
You should use the flexible factorial for repeated measure designs
with factors for subject and condition in your design.
Best Regards, Donald McLaren
=================
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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]> 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 .
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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.
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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]).
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,michaelOn 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
[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:
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.
--------------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 Content-Type: text/plain; charset=ISO-8859-1 Content-Transfer-Encoding: quoted-printable 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,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
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
[log in to unmask]" type="cite">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
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?
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:
|
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
Applications are invited for an 18
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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
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.
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
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
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 =/
/
/
/ Â Â 4.9700 Â Â 0.9960 Â Â /*/ Â 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.
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 specificationI 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.JefJeffrey West, M.A.
Research Analyst
Maryland Psychiatric Research Center
Baltimore, Maryland 21228-0247
Phone: 410-402-6018
email: [log in to unmask]
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 32 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,PieterOn 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 specificationI 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.JefJeffrey West, M.A.
Research Analyst
Maryland Psychiatric Research Center
Baltimore, Maryland 21228-0247
Phone: 410-402-6018
email: [log in to unmask]