Print

Print


ponsive created by important differences in the motivational foundations of online and offline social cognition. 

In order to promote the development of the neuroscientific investigation of online social cognition, this Frontiers Special Topic aims at bringing together contributions from researchers in social neuroscience and related fields, whose work involves the study of at least two individuals and sometimes two brains, rather than single individuals and brains responding to a social context. Specifically, this special issue will adopt an interdisciplinary perspective on what it is that separates online from offline social cognition and the putative differences in the recruitment of underlying processes and mechanisms. Here, an important focal point will be to address the various roles of social interaction in contributing to and at times constituting our awareness of other minds. For this Special Topic, we, therefore, solicit reviews, original research articles, opinion and method papers, which address the investigation of social interaction and go beyond traditional concepts and ways of experimentation in doing so. While focusing on work in the neurosciences, this Special Topic also welcomes contributions in the form of behavioral studies, psychophysiological investigations, methodological innovations, computational approaches, developmental and patient studies. 

By focusing on cutting-edge research in social neuroscience and related fields, this Frontiers Special Issue will create new insights concerning the neurobiology of social interaction and holds the promise of helping social neuroscience to really go social.




--
Leonhard Schilbach, MD

Department of Psychiatry and Psychotherapy
University of Cologne, Germany

[log in to unmask]

&

Max-Planck-Institute for Neurological Research
Cologne, Germany

[log in to unmask]:         Sun, 27 Feb 2011 17:17:44 -0500
Reply-To:     John Fredy <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         John Fredy <[log in to unmask]>
Subject:      Re: display result on template MRI
Comments: To: Faezeh Vedaei <[log in to unmask]>
In-Reply-To:  <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID:  <[log in to unmask]>

--0016368e2380ab9416049d4aeebc
Content-Type: text/plain; charset=ISO-8859-1

Hello Faezeh, I use xjview ( http://www.alivelearn.net/xjview8/ )

Regards

On Sun, Feb 27, 2011 at 10:25 AM, Faezeh Vedaei <[log in to unmask]> wrote:

>
>
>  I am working with some data by SPM8,as a question how can I display my
> brain glass result on multiple slices of a template MRI to view regions of
> hyperperfusion and hypoperfusion?
>
>
>

--0016368e2380ab9416049d4aeebc
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable

Hello Faezeh, I use xjview ( <a href="http://www.alivelearn.net/xjview8/">http://www.alivelearn.net/xjview8/</a> )<br><br>Regards<br><br><div class="gmail_quote">On Sun, Feb 27, 2011 at 10:25 AM, Faezeh Vedaei <span dir="ltr">&lt;<a href="mailto:[log in to unmask]">[log in to unmask]</a>&gt;</span> wrote:<br>
<blockquote class="gmail_quote" style="margin: 0pt 0pt 0pt 0.8ex; border-left: 1px solid rgb(204, 204, 204); padding-left: 1ex;"><div><div></div><div class="h5"><div><div style="font-family: times new roman,new york,times,serif; font-size: 18pt;">
<div><br></div>
<div style="font-family: times new roman,new york,times,serif; font-size: 18pt;"><br>
<div style="font-family: times new roman,new york,times,serif; font-size: 12pt;">
<div style="font-family: times new roman,new york,times,serif; color: rgb(0, 0, 0); font-size: 18pt;">
<div>I am working with some data by SPM8,as a question how can I display my brain glass result on multiple slices of atemplate MRI to view regions of hyperperfusion and hypoperfusion?</div></div><br></div></div></div><br>








      </div></div></div></blockquote></div><br>

--0016368e2380ab9416049d4aeebc--
========================================================================Date:         Sun, 27 Feb 2011 17:22:30 -0500
Reply-To:     "MCLAREN, Donald" <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         "MCLAREN, Donald" <[log in to unmask]>
Subject:      Re: display result on template MRI
Comments: To: Faezeh Vedaei <[log in to unmask]>
In-Reply-To:  <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID:  <[log in to unmask]>

Click Overlay --> sections OR Overlay --> slices

MRIcron is also useful

Best Regards, Donald McLaren
=================
D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General
Hospital and Harvard Medical School
Office: (773) 406-2464
=====================
This e-mail contains CONFIDENTIAL INFORMATION which may contain
PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED
and which is intended only for the use of the individual or entity
named above. If the reader of the e-mail is not the intended recipient
or the employee or agent responsible for delivering it to the intended
recipient, you are hereby notified that you are in possession of
confidential and privileged information. Any unauthorized use,
disclosure, copying or the taking of any action in reliance on the
contents of this information is strictly prohibited and may be
unlawful. If you have received this e-mail unintentionally, please
immediately notify the sender via telephone at (773) 406-2464 or
email.



On Sun, Feb 27, 2011 at 10:22 AM, Faezeh Vedaei <[log in to unmask]> wrote:
> I am working with some data by SPM8,as a question how can I display my brain
> glass result on multiple slices of atemplate MRI to view regions of
> hyperperfusion and hypoperfusion?
>
========================================================================Date:         Sun, 27 Feb 2011 14:25:32 -0800
Reply-To:     Vladimir Bogdanov <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Vladimir Bogdanov <[log in to unmask]>
Subject:      Re: display result on template MRI
Comments: To: Faezeh Vedaei <[log in to unmask]>
In-Reply-To:  <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundary="0-1112837336-1298845532=:86375"
Message-ID:  <[log in to unmask]>

--0-1112837336-1298845532=:86375
Content-Type: text/plain; charset=iso-8859-1
Content-Transfer-Encoding: quoted-printable

When you explore your results, in the window "Results" click on "overlays" in the group "Display". Here you can select "sections". In the menu wich is opened by this action you can select either a normalized individual structural image form the proper folder of your subject or a T1 image from SPM8\canonical.

I hope this is helpful.

Vladimir

--- On Sun, 2/27/11, Faezeh Vedaei <[log in to unmask]> wrote:

From: Faezeh Vedaei <[log in to unmask]>
Subject: [SPM] display result on template MRI
To: [log in to unmask]
Date: Sunday, February 27, 2011, 4:25 PM







I am working with some data by SPM8,as a question how can I display my brain glass result on multiple slices of atemplate MRI to view regions of hyperperfusion and hypoperfusion?









      



--0-1112837336-1298845532=:86375
Content-Type: text/html; charset=iso-8859-1
Content-Transfer-Encoding: quoted-printable

<table cellspacing="0" cellpadding="0" border="0" ><tr><td valign="top" style="font: inherit;">When you explore your results, in the window "Results" click on "overlays" in the group "Display". Here you can select "sections". In the menu wich is opened by this action you can select either a normalized individual structural image form the proper folder of your subject or a T1 image from SPM8\canonical.<br><br>I hope this is helpful.<br><br>Vladimir<br><br>--- On <b>Sun, 2/27/11, Faezeh Vedaei <i>&lt;[log in to unmask]&gt;</i></b> wrote:<br><blockquote style="border-left: 2px solid rgb(16, 16, 255); margin-left: 5px; padding-left: 5px;"><br>From: Faezeh Vedaei &lt;[log in to unmask]&gt;<br>Subject: [SPM] display result on template MRI<br>To: [log in to unmask]<br>Date: Sunday, February 27, 2011, 4:25 PM<br><br><div id="yiv357382596"><style type="text/css"><!--#yiv357382596 DIV {margin:0px;}--></style><div style="font-family: times new roman,new
 york,times,serif; font-size: 18pt;"><div><br></div>
<div style="font-family: times new roman,new york,times,serif; font-size: 18pt;"><br>
<div style="font-family: times new roman,new york,times,serif; font-size: 12pt;">
<div style="font-family: times new roman,new york,times,serif; color: rgb(0, 0, 0); font-size: 18pt;">
<div>I am working with some data by SPM8,as a question how can I display my brain glass result on multiple slices of a&nbsp;template MRI to view regions of hyperperfusion and hypoperfusion?</div></div><br></div></div></div><br>







      </div></blockquote></td></tr></table><br>


--0-1112837336-1298845532=:86375--
========================================================================Date:         Sun, 27 Feb 2011 19:55:57 -0500
Reply-To:     Yune Lee <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Yune Lee <[log in to unmask]>
Subject:      normalization failure (anterior posterior mismatch)
MIME-Version: 1.0
Content-Type: multipart/mixed; boundaryMessage-ID:  <[log in to unmask]>

--001636283ab280d810049d4d24ad
Content-Type: multipart/alternative; boundary
--001636283ab280d804049d4d24ab
Content-Type: text/plain; charset=ISO-8859-1

 Dear SPM experts,
 I've encountered a normalization failure, such that there is a mismatch of
anterior-posterior between a template (EPI.nii) and a source image
(meanbold.nii)
 This is clearly shown in the attached PDF file.
 Any help wold be greatly appreciated.

 Thanks in advance,
 YSL

--001636283ab280d804049d4d24ab
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable

<br>Dear SPM experts, <br>I&#39;ve encountered a normalization failure, such that there is a mismatch of anterior-posterior between a template (EPI.nii) and a source image (meanbold.nii) <br>This is clearly shown in the attached PDF file. <br>
Any help wold be greatly appreciated. <br><br>Thanks in advance, <br>YSL<br><br><br><br>

--001636283ab280d804049d4d24ab--
--001636283ab280d810049d4d24ad
Content-Type: application/pdf; name="normalization_failure.pdf"
Content-Disposition: attachment; filename="normalization_failure.pdf"
Content-Transfer-Encoding: base64
X-Attachment-Id: f_gkoofn320
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--001636283ab280d810049d4d24ad--
========================================================================Date:         Sun, 27 Feb 2011 20:56:27 -0500
Reply-To:     Glen Lee <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Glen Lee <[log in to unmask]>
Subject:      SPM8 error
MIME-Version: 1.0
Content-Type: multipart/mixed; boundary cf3071c9bae64ca3049d4dfcd2
Message-ID:  <[log in to unmask]>

--20cf3071c9bae64ca3049d4dfcd2
Content-Type: multipart/alternative; boundary cf3071c9bae64c93049d4dfcd0

--20cf3071c9bae64c93049d4dfcd0
Content-Type: text/plain; charset=ISO-8859-1

Hello SPMers,
Does anybody help me figuring out what this error message (Error using ==>
< a href="matlab: opentonline ('cfg_util.m',808,0)"> cfg_util at 808 </a>)
mean and how i can resolve this issue?
I  get this same error no matter what I try (e.g., realign, normalize, etc)
in SPM8 (also see attached)
Let me know. Thanks in advance.

Glen

--20cf3071c9bae64c93049d4dfcd0
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable

Hello SPMers, <br>Does anybody help me figuring out what this error message (Error using ==&gt; &lt; a href=&quot;matlab: opentonline (&#39;cfg_util.m&#39;,808,0)&quot;&gt; cfg_util at 808 &lt;/a&gt;) mean and how i can resolve this issue? <br>
I get this same error no matter what I try (e.g., realign, normalize, etc) in SPM8 (also see attached)<br>Let me know. Thanks in advance. <br><br>Glen <br><br><br>

--20cf3071c9bae64c93049d4dfcd0--
--20cf3071c9bae64ca3049d4dfcd2
Content-Type: image/png; name="error_in_SPM8.PNG"
Content-Disposition: attachment; filename="error_in_SPM8.PNG"
Content-Transfer-Encoding: base64
X-Attachment-Id: f_gkoqhqmh0
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--20cf3071c9bae64ca3049d4dfcd2--
========================================================================Date:         Sun, 27 Feb 2011 22:30:29 -0500
Reply-To:     "MCLAREN, Donald" <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         "MCLAREN, Donald" <[log in to unmask]>
Subject:      Re: Second level t-test: test against other value than zero
Comments: To: Nynke L <[log in to unmask]>
In-Reply-To:  <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundary cf3054a2f72ca08e049d4f4d70
Message-ID:  <AANLkTi[log in to unmask]>

--20cf3054a2f72ca08e049d4f4d70
Content-Type: text/plain; charset=ISO-8859-1

Set the interpolation option to nearest neighbor. That should make it exact.

Also, you might want to think about other transforms since the data is
likely not to be normally distributed.

Best Regards, Donald McLaren
================D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Office: (773) 406-2464
====================This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED
HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is
intended only for the use of the individual or entity named above. If the
reader of the e-mail is not the intended recipient or the employee or agent
responsible for delivering it to the intended recipient, you are hereby
notified that you are in possession of confidential and privileged
information. Any unauthorized use, disclosure, copying or the taking of any
action in reliance on the contents of this information is strictly
prohibited and may be unlawful. If you have received this e-mail
unintentionally, please immediately notify the sender via telephone at (773)
406-2464 or email.


On Sun, Feb 27, 2011 at 10:13 AM, Nynke L
<[log in to unmask]>wrote:

> Hello all,
>
> I have a particular group level analysis for which I want to use spm.
> However, till now I don't manage to so I hope you have suggestions for me.
>
> I use fMRI for the prediction of a certain binary outcome. Therefore, I use
> a different classification software package that computes
> prediction-accuracies for all voxels. For each subject, I have a .nii file
> with as values the accuracies (range between 0 and 1). So, this is a brain
> map just like you usually feed to the second level.
>
> What I want to test in the second level analysis is which brain areas have
> accuracies significantly higher than 0.5. So I want to test against the
> null-hypothesis that accuracies are 0.5 or lower. Now the problem is that
> the 1 sample t-test in spm on default tests against the null-hypothesis that
> the values are zero or higher.... So my first question is whether it is
> possible to set a custum value for the t-test to test against a manually set
> value?
>
> I already tried a different approach, namely subtracting 0.5 from the
> values in my input images (to put them in the default spm t-test). However,
> when I trie to do this with the image calculator of spm (with as expression
> i1 - 0.5) I do not get correct results: I do not get a map with exactly 0.5
> subtracted from each value but a range between 0.4-0.6. It seems that
> spm-image-calc is also doing something else with the image... So my second
> question is: how can I make image-calc subtract exactly 0.5 from all values
> in the image?
>
> Thanks in advance!
>
> Best regards,
> Nynke van der Laan
>

--20cf3054a2f72ca08e049d4f4d70
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable

Set the interpolation option to nearest neighbor. That should make it exact.<br><br>Also, you might want to think about other transforms since the data is likely not to be normally distributed.<br clear="all"><br>Best Regards, Donald McLaren<br>
=================<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, GRECC, Bedford VA<br>Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School<br>Office: (773) 406-2464<br>
=====================<br>This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email.<br>

<br><br><div class="gmail_quote">On Sun, Feb 27, 2011 at 10:13 AM, Nynke L <span dir="ltr">&lt;<a href="mailto:[log in to unmask]">[log in to unmask]</a>&gt;</span> wrote:<br><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">
Hello all,<br>
<br>
I have a particular group level analysis for which I want to use spm. However, till now I don&#39;t manage to so I hope you have suggestions for me.<br>
<br>
I use fMRI for the prediction of a certain binary outcome. Therefore, I use a different classification software package that computes prediction-accuracies for all voxels. For each subject, I have a .nii file with as values the accuracies (range between 0 and 1). So, this is a brain map just like you usually feed to the second level.<br>

<br>
What I want to test in the second level analysis is which brain areas have accuracies significantly higher than 0.5. So I want to test against the null-hypothesis that accuracies are 0.5 or lower. Now the problem is that the 1 sample t-test in spm on default tests against the null-hypothesis that the values are zero or higher.... So my first question is whether it is possible to set a custum value for the t-test to test against a manually set value?<br>

<br>
I already tried a different approach, namely subtracting 0.5 from the values in my input images (to put them in the default spm t-test). However, when I trie to do this with the image calculator of spm (with as expression i1 - 0.5) I do not get correct results: I do not get a map with exactly 0.5 subtracted from each value but a range between 0.4-0.6. It seems that spm-image-calc is also doing something else with the image... So my second question is: how can I make image-calc subtract exactly 0.5 from all values in the image?<br>

<br>
Thanks in advance!<br>
<br>
Best regards,<br>
Nynke van der Laan<br>
</blockquote></div><br>

--20cf3054a2f72ca08e049d4f4d70--
========================================================================Date:         Mon, 28 Feb 2011 09:55:35 +0100
Reply-To:     Marko Wilke <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Marko Wilke <[log in to unmask]>
Subject:      Re: SPM8 error
Comments: To: Glen Lee <[log in to unmask]>
In-Reply-To:  <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1; format=flowed
Content-Transfer-Encoding: quoted-printable
Message-ID:  <[log in to unmask]>

Hi Glen,

this could be an installation problem, perhaps try using a 
newly-downloaded SPM, install the latest updates on top of it and 
install it into a directory without spaces (say, 'C:\Test\SPM8'), and 
then only add this directory to the Matlab path.

Cheers,
Marko

Glen Lee wrote:
> Hello SPMers,
> Does anybody help me figuring out what this error message (Error using
> ==> < a href="matlab: opentonline ('cfg_util.m',808,0)"> cfg_util at 808
> </a>) mean and how i can resolve this issue?
> I  get this same error no matter what I try (e.g., realign, normalize,
> etc) in SPM8 (also see attached)
> Let me know. Thanks in advance.
>
> Glen
>
>

-- 
____________________________________________________
PD Dr. med. Marko Wilke
  Facharzt fr Kinder- und Jugendmedizin
  Leiter, Experimentelle Pdiatrische Neurobildgebung
  Universitts-Kinderklinik
  Abt. III (Neuropdiatrie)


Marko Wilke, MD, PhD
  Pediatrician
  Head, Experimental Pediatric Neuroimaging
  University Children's Hospital
  Dept. III (Pediatric Neurology)


Hoppe-Seyler-Str. 1
  D - 72076 Tbingen, Germany
  Tel. +49 7071 29-83416
  Fax  +49 7071 29-5473
  [log in to unmask]

  http://www.medizin.uni-tuebingen.de/kinder/epn
____________________________________________________
========================================================================Date:         Mon, 28 Feb 2011 09:06:14 +0000
Reply-To:     John Ashburner <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         John Ashburner <[log in to unmask]>
Subject:      Re: normalization failure (anterior posterior mismatch)
Comments: To: Yune Lee <[log in to unmask]>
In-Reply-To:  <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID:  <[log in to unmask]>

Before running the spatial normalisation, you'll need to ensure that
the image is in approximate alignment with the templates (Check Reg
button).  For these images, it appears that they are either rotated by
180 degrees (about the z axis) or flipped in the anterior posterior
direction.

Best regards,
-John

On 28 February 2011 00:55, Yune Lee <[log in to unmask]> wrote:
>
> Dear SPM experts,
> I've encountered a normalization failure, such that there is a mismatch of
> anterior-posterior between a template (EPI.nii) and a source image
> (meanbold.nii)
> This is clearly shown in the attached PDF file.
> Any help wold be greatly appreciated.
>
> Thanks in advance,
> YSL
>
>
>
>
========================================================================Date:         Mon, 28 Feb 2011 14:41:37 +0530
Reply-To:     sarika cherodath <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         sarika cherodath <[log in to unmask]>
Subject:      question on VOI analysis
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID:  <[log in to unmask]>

--00248c05002ff8481b049d5412cf
Content-Type: text/plain; charset=ISO-8859-1

Hi SPMers,

        I have been trying to perform VOI analysis to obtain signal changes
at a particular area for individual subjects in the dataset, so as to make
correlations with behavioral scores. When i tried to calculate the values,
SPM returns the same value for all subjects! Can anybody explain why this
might be? The dataset has been analysed at second level and then moved to
another directory (along with first level data). Is it possible that SPM is
not able to access the data because of the path change??

Thanks in advance,
--
Sarika Cherodath
Graduate Student
National Brain Research Centre
Manesar, Gurgaon -122050
India

--00248c05002ff8481b049d5412cf
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable

<div dir="ltr"><br clear="all">Hi SPMers,<div><br></div><div>   I have been trying to perform VOI analysis to obtain signal changes at a particular area for individual subjects in the dataset, so as to make correlations with behavioral scores. When i tried to calculate the values, SPM returns the same value for all subjects! Can anybody explain why this might be? The dataset has been analysed at second level and then moved to another directory (along with first level data). Is it possible that SPM is not able to access the data because of the path change??</div>

<div><br></div><div>Thanks in advance,<br>-- <br>Sarika Cherodath<br>Graduate Student<br>National Brain Research Centre<br>Manesar, Gurgaon -122050<br>India<br><br>
</div></div>

--00248c05002ff8481b049d5412cf--
========================================================================Date:         Mon, 28 Feb 2011 10:13:17 +0100
Reply-To:     Michels Lars <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Michels Lars <[log in to unmask]>
Subject:      Postdoctoral Fellowship in multimodal developmental neuroimaging
Comments: To: [log in to unmask]
MIME-Version: 1.0
Content-Type: multipart/alternative;
              boundary="----_=_NextPart_001_01CBD727.BC9EDDD6"
Message-ID:  <[log in to unmask]>

This is a multi-part message in MIME format.

------_=_NextPart_001_01CBD727.BC9EDDD6
Content-Type: text/plain;
	charset="iso-8859-1"
Content-Transfer-Encoding: quoted-printable

Applications are invited for an 18 month postdoctoral fellowship in the human multimodal neuroimaging project "Linking the major system markers for typical and atypical brain development: a multimodal imaging and spectroscopy study" (http://www.zihp.uzh.ch/1610.php#45) funded by the Zrich Institute of Human Physiology. 

This study will investigate the major physiological markers of brain development, using a combination of multimodal brain imaging (e.g., simultaneous EEG-fMRI, see Lchinger et al., 2011, NeuroImage, in press) and MR-spectroscopy methods (i.e., GABA, see O'Gorman et al., 2011, J. of Mag. Reson. Img., in press). The initial phase of the study will establish baseline neurotransmitter levels, cerebral blood flow (e.g., perfusion MRI) and EEG frequency and power at rest in children, adolescents, and adults. Examining the interactions between these markers and the changes they demonstrate with age and hormone levels will allow to better understanding the global and regional processes underlying brain maturation. In addition, we will investigate changes in these physiological markers with (a) memory tasks (see Michels et al., 2010, PLoS ONE) and (b) attention deficit hyperactivity disorder (ADHD, see Doehnert et al., Biol Psychiatry, 2010). The starting date of the position is May 2011. Our department is equipped with 64-channel fMRI-compatible EEG equipment and a 3 Tesla GE scanner, which is mainly dedicated for research questions.

 

The successful applicant will have a PhD research background in Cognitive Neuroscience, Neurophysiology, Psychology, Neuropsychology, or related fields. Fluency in English and the ability to work within a multidisciplinary team are essential. Applicants must be experienced at conducting fMRI and/or EEG studies -demonstrated by at least 2 first author publications in international peer-reviewed journals- and be familiar with analysis software such as SPM/Matlab, BrainVoyager and/or FSL. Experience with stimulus presentation software (such as Presentation), UNIX, and programming languages a plus. 

 

Salaries are in accordance with the Swiss National Research Foundation.

 

APPLICATION INSTRUCTIONS: To apply, please send a curriculum vitae, a personal statement describing research interests, 3 letters of recommendations, and up to 3 article reprints/preprints (max. 2 MB!!!) to:

 

Dr Lars Michels

[log in to unmask]

MR-Zentrum

University Children's Hospital

Steinwiesstrasse 75

Zrich 8032

Switzerland

 

Reviews of applications will begin on the 1st of March and will continue until the position is filled.

 

------_=_NextPart_001_01CBD727.BC9EDDD6
Content-Type: text/html;
	charset="iso-8859-1"
Content-Transfer-Encoding: quoted-printable

<html xmlns:o="urn:schemas-microsoft-com:office:office" xmlns:w="urn:schemas-microsoft-com:office:word" xmlns="http://www.w3.org/TR/REC-html40">

<head>
<meta http-equiv=Content-Type content="text/html; charset=iso-8859-1">
<meta name=Generator content="Microsoft Word 11 (filtered medium)">
<style>
<!--
 /* Style Definitions */
 p.MsoNormal, li.MsoNormal, div.MsoNormal
	{margin:0cm;
	margin-bottom:.0001pt;
	font-size:11.0pt;
	font-family:Arial;}
a:link, span.MsoHyperlink
	{color:blue;
	text-decoration:underline;}
a:visited, span.MsoHyperlinkFollowed
	{color:purple;
	text-decoration:underline;}
span.E-MailFormatvorlage17
	{mso-style-type:personal-compose;
	font-family:Arial;
	color:windowtext;}
@page Section1
	{size:595.3pt 841.9pt;
	margin:70.85pt 70.85pt 2.0cm 70.85pt;}
div.Section1
	{page:Section1;}
-->
</style>

</head>

<body lang=DE-CH link=blue vlink=purple>

<div class=Section1>

<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'>Applications are invited for an 18
month </span></font><font size=3 color=black face="Times New Roman"><span
lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman";color:black'>postdoctoral
fellowship </span></font><font size=3 color=black face="Times New Roman"><span
lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman";color:black'>in
the human multimodal neuroimaging project &#8220;Linking the major system
markers for typical and atypical brain development: a multimodal imaging and
spectroscopy study&#8221; <b><span style='font-weight:bold'>(http://www.zihp.uzh.ch/1610.php#45)</span></b>
funded by the Zrich Institute of Human Physiology. <o:p></o:p></span></font></p>

<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'>This study will investigate the
major physiological markers of brain development, using a combination of multimodal
brain imaging (e.g., simultaneous </span></font><font size=3 color=black
face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;font-family:
"Times New Roman";color:black'>EEG-fMRI, see Lchinger et al., 2011, NeuroImage,
in press</span></font><font size=3 color=black face="Times New Roman"><span
lang=EN-GB style='font-size:12.0pt;font-family:"Times New Roman";color:black'>)
and MR-spectroscopy methods (i.e., GABA, see O&#8217;Gorman et al., 2011, J. of
Mag. Reson. Img., in press). The initial phase of the study will establish
baseline neurotransmitter levels, cerebral blood flow (e.g., perfusion MRI) and
EEG frequency and power at rest in children, adolescents, and adults. Examining
the interactions between these markers and the changes they demonstrate with
age and hormone levels will allow to better understanding the global and
regional processes underlying brain maturation. In addition, we will
investigate changes in these physiological markers with (a) memory tasks (see
Michels et al., 2010, PLoS ONE) and (b) attention deficit hyperactivity disorder
(ADHD, see </span></font><font size=3 face="Times New Roman"><span lang=EN-GB
style='font-size:12.0pt;font-family:"Times New Roman"'>Doehnert et al., <span
class=jrnl>Biol Psychiatry</span>, 2010</span><font color=black><span
style='color:black'>). The<i><span style='font-style:italic'> </span></i>starting
date of the position is<i><span style='font-style:italic'> May 2011</span></i>.
Our department is equipped with 64-channel fMRI-compatible EEG equipment and a
3 Tesla GE scanner, which is mainly dedicated for research questions.</span></font></span></font><font
size=3 color=black face="Times New Roman"><span lang=EN-GB style='font-size:
12.0pt;font-family:"Times New Roman";color:black'><o:p></o:p></span></font></p>

<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'><o:p>&nbsp;</o:p></span></font></p>

<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-US style='font-size:12.0pt;
font-family:"Times New Roman";color:black'>The successful applicant will have a
PhD research background in Cognitive Neuroscience, Neurophysiology, Psychology,
Neuropsychology, or related fields</span></font><font size=3 color=black
face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;font-family:
"Times New Roman";color:black'>. Fluency in English </span></font><font size=3
color=black face="Times New Roman"><span lang=EN-US style='font-size:12.0pt;
font-family:"Times New Roman";color:black'>and the ability to work within a
multidisciplinary team are essential. Applicants <u>must</u> be experienced at
conducting fMRI and/or EEG studies &#8211;demonstrated by at least <u>2 first
author</u> publications in international peer-reviewed journals&#8211; and be
familiar with analysis software such as&nbsp;SPM/Matlab, BrainVoyager and/or
FSL. Experience with stimulus presentation software (such as Presentation),
UNIX, and programming languages a plus. </span></font><font size=3 color=black
face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;font-family:
"Times New Roman";color:black'><o:p></o:p></span></font></p>

<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'><o:p>&nbsp;</o:p></span></font></p>

<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'>Salaries are in accordance with the
Swiss National Research Foundation.<o:p></o:p></span></font></p>

<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'><o:p>&nbsp;</o:p></span></font></p>

<p class=MsoNormal style='text-align:justify'><font size=3 color=black
face="Times New Roman"><span lang=EN-US style='font-size:12.0pt;font-family:
"Times New Roman";color:black'>APPLICATION INSTRUCTIONS: </span></font><font
size=3 color=black face="Times New Roman"><span lang=EN-US style='font-size:
12.0pt;font-family:"Times New Roman";color:black'>To apply, please send a
curriculum vitae, a personal statement describing research interests, 3 letters
of recommendations, and up to 3 article reprints/preprints (max. 2 MB!!!) to:<o:p></o:p></span></font></p>

<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-US style='font-size:12.0pt;
font-family:"Times New Roman";color:black'><o:p>&nbsp;</o:p></span></font></p>

<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'>Dr Lars Michels<o:p></o:p></span></font></p>

<p class=MsoNormal style='text-align:justify;text-autospace:none'><b><font
size=3 color=black face="Times New Roman"><span lang=EN-GB style='font-size:
12.0pt;font-family:"Times New Roman";color:black;font-weight:bold'>[log in to unmask]<o:p></o:p></span></font></b></p>

<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'>MR-Zentrum<o:p></o:p></span></font></p>

<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span style='font-size:12.0pt;font-family:
"Times New Roman";color:black'>University Children&#8217;s Hospital<o:p></o:p></span></font></p>

<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span style='font-size:12.0pt;font-family:
"Times New Roman";color:black'>Steinwiesstrasse 75<o:p></o:p></span></font></p>

<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span style='font-size:12.0pt;font-family:
"Times New Roman";color:black'>Zrich 8032<o:p></o:p></span></font></p>

<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span style='font-size:12.0pt;font-family:
"Times New Roman";color:black'>Switzerland<o:p></o:p></span></font></p>

<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span style='font-size:12.0pt;font-family:
"Times New Roman";color:black'><o:p>&nbsp;</o:p></span></font></p>

<p class=MsoNormal style='text-align:justify;text-autospace:none'><font size=3
color=black face="Times New Roman"><span lang=EN-GB style='font-size:12.0pt;
font-family:"Times New Roman";color:black'>Reviews of applications will begin
on the <b><span style='font-weight:bold'>1<sup>st</sup> of March</span></b> and
will continue until the position is filled.<o:p></o:p></span></font></p>

<p class=MsoNormal><font size=2 face=Arial><span lang=EN-GB style='font-size:
10.0pt'><o:p>&nbsp;</o:p></span></font></p>

</div>

</body>

</html>
------_=_NextPart_001_01CBD727.BC9EDDD6--
========================================================================Date:         Mon, 28 Feb 2011 09:48:36 +0000
Reply-To:     Vincent Koppelmans <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Vincent Koppelmans <[log in to unmask]>
Subject:      Adjusting Dartel for ICV: RC volumes or C volumes
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain; charset="UTF-8"
Message-ID:  <[log in to unmask]>

Dear SPM experts,

If you adjust for intra cranial volume in a dartel analyses on gray matter, would it be best to have your ICV calculated from the RC files or the C files. I have read that C files are a little bit more accurate than the RC files. Then again, the dartel analysis uses RC files, therefore volumes calculated from RC files would match the data best, right? Kind regards,

Vincent
========================================================================Date:         Mon, 28 Feb 2011 11:43:44 +0100
Reply-To:     =?ISO-8859-1?Q?Gemma_Mont?= <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         =?ISO-8859-1?Q?Gemma_Mont?= <[log in to unmask]>
Subject:      Re: Adjusting Dartel for ICV: RC volumes or C volumes
Comments: To: Vincent Koppelmans <[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]>

--0015175cb1445b46e5049d555ca2
Content-Type: text/plain; charset=ISO-8859-1

Hi Vincent,
I suggest you use the c*.nii files. They are the tissue classes in native
space.

You are right, DARTEL takes information encoded in the rc*.nii files.
However, the flow fields u_rc1*.nii files are applied to the c1*.nii to
create the warped files. So finally, you obtain files in the form
swmc1*.nii, either in the DARTEL space or in the MNI space. You can check
that the volumes of the GM Jacobian scaled images (without smoothing) by
DARTEL are the same than the volume of the GM segments in native space
(c1*.nii files).

Hope this helps,
Gemma

On 28 February 2011 10:48, Vincent Koppelmans <[log in to unmask]>wrote:

> Dear SPM experts,
>
> If you adjust for intra cranial volume in a dartel analyses on gray matter,
> would it be best to have your ICV calculated from the RC files or the C
> files. I have read that C files are a little bit more accurate than the RC
> files. Then again, the dartel analysis uses RC files, therefore volumes
> calculated from RC files would match the data best, right? Kind regards,
>
> Vincent
>


 <[log in to unmask]>

--0015175cb1445b46e5049d555ca2
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable

Hi Vincent,<br>I suggest you use the c*.nii files. They are the tissue classes in native space. <br><br>You are right, DARTEL takes information encoded in the rc*.nii files. However, the flow fields u_rc1*.nii files are applied to the c1*.nii to create the warped files. So finally, you obtain files in the form swmc1*.nii, either in the DARTEL space or in the MNI space. You can check that the volumes of the GM Jacobian scaled images (without smoothing) by DARTEL are the same than the volume of the GM segments in native space (c1*.nii files).<br>


<br>Hope this helps,<br>Gemma<br><br><div class="gmail_quote">
On 28 February 2011 10:48, Vincent Koppelmans <span dir="ltr">&lt;<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>&gt;</span> wrote:<br><blockquote class="gmail_quote" style="border-left: 1px solid rgb(204, 204, 204); margin: 0pt 0pt 0pt 0.8ex; padding-left: 1ex;">



Dear SPM experts,<br>
<br>
If you adjust for intra cranial volume in a dartel analyses on gray matter, would it be best to have your ICV calculated from the RC files or the C files. I have read that C files are a little bit more accurate than the RC files. Then again, the dartel analysis uses RC files, therefore volumes calculated from RC files would match the data best, right? Kind regards,<br>




<font color="#888888"><br>
Vincent<br>
</font></blockquote></div><div style="font-family: arial,helvetica,sans-serif;"><font color="#999999"><font style="color: rgb(51, 51, 51);" size="1"><br></font><font size="1"><a href="mailto:[log in to unmask]" target="_blank"><br>



</a></font></font></div><br>

--0015175cb1445b46e5049d555ca2--
========================================================================Date:         Mon, 28 Feb 2011 11:50:23 +0100
Reply-To:     michel grothe <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         michel grothe <[log in to unmask]>
Subject:      Re: Adjusting Dartel for ICV: RC volumes or C volumes
Comments: To: [log in to unmask]
In-Reply-To:  <[log in to unmask]>
Content-Type: multipart/alternative;
              boundary="_90f969e0-6ae7-4793-8d3b-790a0b77c215_"
MIME-Version: 1.0
Message-ID:  <[log in to unmask]>

--_90f969e0-6ae7-4793-8d3b-790a0b77c215_
Content-Type: text/plain; charset="iso-8859-1"
Content-Transfer-Encoding: quoted-printable


Dear Vincent,

the rc*-files are rigid-body (6-P) transformed versions of the c*-files and total tissue calculations should therefore be exactly the same between the two files. Given that modulation aims to preserve the native amount of tissue, totals calculated from mwrc*-files should also be equal to the ones calculated from c*- or rc*-files, although they might differ slightly because of interpolation issues. Do totals calculated from the different image-types differ in your case?   

Best regards,
Michel

> Date: Mon, 28 Feb 2011 09:48:36 +0000
> From: [log in to unmask]
> Subject: [SPM] Adjusting Dartel for ICV: RC volumes or C volumes
> To: [log in to unmask]
> 
> Dear SPM experts,
> 
> If you adjust for intra cranial volume in a dartel analyses on gray matter, would it be best to have your ICV calculated from the RC files or the C files. I have read that C files are a little bit more accurate than the RC files. Then again, the dartel analysis uses RC files, therefore volumes calculated from RC files would match the data best, right? Kind regards,
> 
> Vincent
 		 	   		  
--_90f969e0-6ae7-4793-8d3b-790a0b77c215_
Content-Type: text/html; charset="iso-8859-1"
Content-Transfer-Encoding: quoted-printable

<html>
<head>
<style><!--
.hmmessage P
{
margin:0px;
padding:0px
}
body.hmmessage
{
font-size: 10pt;
font-family:Tahoma
}
--></style>
</head>
<body class='hmmessage'>
Dear Vincent,<br><br>the rc*-files are rigid-body (6-P) transformed versions of the c*-files and total tissue calculations should therefore be exactly the same between the two files. Given that modulation aims to preserve the native amount of tissue, totals calculated from mwrc*-files should also be equal to the ones calculated from c*- or rc*-files, although they might differ slightly because of interpolation issues. Do totals calculated from the different image-types differ in your case?&nbsp;&nbsp; <br><br>Best regards,<br>Michel<br><br>&gt; Date: Mon, 28 Feb 2011 09:48:36 +0000<br>&gt; From: [log in to unmask]<br>&gt; Subject: [SPM] Adjusting Dartel for ICV: RC volumes or C volumes<br>&gt; To: [log in to unmask]<br>&gt; <br>&gt; Dear SPM experts,<br>&gt; <br>&gt; If you adjust for intra cranial volume in a dartel analyses on gray matter, would it be best to have your ICV calculated from the RC files or the C files. I have read that C files are a little bit more accurate than the RC files. Then again, the dartel analysis uses RC files, therefore volumes calculated from RC files would match the data best, right? Kind regards,<br>&gt; <br>&gt; Vincent<br> 		 	   		  </body>
</html>
--_90f969e0-6ae7-4793-8d3b-790a0b77c215_--
========================================================================Date:         Mon, 28 Feb 2011 10:59:22 +0000
Reply-To:     Vladimir Litvak <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Vladimir Litvak <[log in to unmask]>
Subject:      Re: [SPM DCM] how to specify the modulation?
Comments: To: =?UTF-8?B?6aOe6bif?= <[log in to unmask]>
In-Reply-To:  <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: quoted-printable
Message-ID:  <[log in to unmask]>

Dear Haoran,

2011/2/27 飞鸟 <[log in to unmask]>:
> Hello all,
>   When you analysis your data with the method of DCM, have you ever met the
> problem about how to specify the modulation? Now I choose 7 basic models,
> and then assume all the connections in each basic model are modulated by the
> experimental manipulation. What I want to know is that whether or not this
> is reasonable

Usually one would have a hypothesis about which specific connections
are affected by the experimental condition.

>In addition, is it possible that the connections
> between two sources could include forward connectivity, backward
> connectivity and lateral connectivity at the same time?

No. Every connection can only have one type.


Vladimir

>   Any help will be grateful!
>
> --
> Haoran LI (MS)
> Brain Imaging Lab,
> Research Center for Learning Science,
> Southeast University
> 2 Si Pai Lou , Nanjing, 210096, P.R.China
>
>
========================================================================Date:         Mon, 28 Feb 2011 12:02:19 +0100
Reply-To:     Marko Wilke <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Marko Wilke <[log in to unmask]>
Subject:      F-values
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1; format=flowed
Content-Transfer-Encoding: quoted-printable
Message-ID:  <[log in to unmask]>

Dear All,

at the risk of (once again) exhibiting my statistical ignorance, I 
wanted to ask for feedback re: an effect I see when doing F-tests. I am 
comparing sessions with different numbers of covariates and compute an 
omnibus F-test in SPM (for the covariates only) and sum the resulting 
F-values. As expected, I see that there seems to be an optimum number of 
covariates as the sum of F-values first increases, then decreases when 
adding more covariates. I expected this to be a function of the degrees 
of freedom but I cannot seem to find the piece of code where these are 
taken into account. Any input is appreciated.

Cheers,
Marko

-- 
____________________________________________________
PD Dr. med. Marko Wilke
  Facharzt fr Kinder- und Jugendmedizin
  Leiter, Experimentelle Pdiatrische Neurobildgebung
  Universitts-Kinderklinik
  Abt. III (Neuropdiatrie)


Marko Wilke, MD, PhD
  Pediatrician
  Head, Experimental Pediatric Neuroimaging
  University Children's Hospital
  Dept. III (Pediatric Neurology)


Hoppe-Seyler-Str. 1
  D - 72076 Tbingen, Germany
  Tel. +49 7071 29-83416
  Fax  +49 7071 29-5473
  [log in to unmask]

  http://www.medizin.uni-tuebingen.de/kinder/epn
____________________________________________________
========================================================================Date:         Mon, 28 Feb 2011 14:12:21 +0000
Reply-To:     SUBSCRIBE FSL Patricia Pires <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         SUBSCRIBE FSL Patricia Pires <[log in to unmask]>
Subject:      DTI with SPM
Mime-Version: 1.0
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain; charset="UTF-8"
Message-ID:  <[log in to unmask]>

Hello,

i am new in the SPM forum and i will be glad if somebody could help me. My question is concerning to preocess DTI images with SPM. I have processed DTI images (FA; MD, RD...) with FSL. Could anyone tell me some lecture to find how process (e.g. FA images) with SPM or where to find information concernig SPM and FSL procedures softwares differences? 

Thank you very much,

Patricia.
========================================================================Date:         Mon, 28 Feb 2011 15:25:26 +0100
Reply-To:     Janani Dhinakaran <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Janani Dhinakaran <[log in to unmask]>
Subject:      Unsubscribe me please
MIME-Version: 1.0
Content-Type: multipart/alternative; boundary cf3079b77ea0b2a8049d5874c7
Message-ID:  <[log in to unmask]>

--20cf3079b77ea0b2a8049d5874c7
Content-Type: text/plain; charset=UTF-8

Thank you.

--20cf3079b77ea0b2a8049d5874c7
Content-Type: text/html; charset=UTF-8

Thank you.<br>

--20cf3079b77ea0b2a8049d5874c7--
========================================================================Date:         Mon, 28 Feb 2011 15:29:46 +0100
Reply-To:     Wolfgang Weber-Fahr <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Wolfgang Weber-Fahr <[log in to unmask]>
Subject:      Job Anouncement ZI-Mannheim, Germany
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8; format=flowed
Content-Transfer-Encoding: quoted-printable
Message-ID:  <[log in to unmask]>

The Central Institute of Mental Health (State Foundation) is an 
internationally
renowned research institute in the field of psychiatry and neuroscience, 
home
of the Department of Psychiatry of the Medical Faculty Mannheim of the
University of Heidelberg as well as psychiatric hospital with 255 inpatient
and 52 day-hospital beds.

Within several new projects funded by the European Union and the Federal 
Ministry
of Education and Research we offer

2 Positions for PostDocs and PhD-Students

in the area of rodent magnetic resonance functional-, perfusion- and 
diffusion tensor (DTI)
imaging at 9.4 Tesla.

The Central Institute of Mental Health is equipped with a latest 
generation 210 mm
horizontalbore Bruker BioSpec animal system and additional cryogenic MR 
coils for
1H and 13C detection in the mouse brain as well as two 3 Tesla human 
whole body MR-scanners.
The positions are for two years initially and located in the research 
group Translational Imaging.
Potential applicants should have a diploma or master in physics, 
physical chemistry or neuroscience,
or an equivalent field. The ideal candidate from the quantitative 
sciences has a solid background
in NMR physics, signal detection and processing theory, and is familiar 
with the
Bruker Paravision environment. Experience in other computer languages, 
Matlab or IDL,
is desirable.

We have openings in these primary fields of research:

• Establishment of manganese enhanced magnetic resonance imaging (MEMRI)
for a the longitudinal assessment of connectivity and neuronal activity 
in experimental animals.
• Development of methods for the acquisition and post processing of 
functional
BOLD imaging data with optogenetics for the identification and 
investigation of brain networks.
• Diffusion Tensor Imaging for the investigation of structural integrity 
in different
psychiatric mouse-models.

The candidate would also collaborate on several ongoing animal studies 
of schizophrenia, depression,
substance abuse and other psychiatric disorders in mouse and rat models.
The assistance in the general supervision of the animal scanner would 
also be part of the position.
More information can be found at 
http://www.zi-mannheim.de/transl_imaging.html.

We offer an interesting job in a leading research hospital with a strong 
emphasis on MR-Imaging,
salary is according to the German TV-L pay scale, including the social 
benefits of the public service sector.

For further information please contact Dr. Wolfgang Weber-Fahr, Tel. +49 
621 1703-2961,
E-Mail: [log in to unmask]


Homepage: www.zi-mannheim.de/start_en.html

----------------------------------------------------------
Wolfgang Weber-Fahr, Dr.rer.nat.
Head RG Translational Imaging
Neuroimaging Department
Central Institute of Mental Health
J5
68072 Mannheim
Germany

email:[log in to unmask]
phone: ++49 621 1703 2961
========================================================================Date:         Mon, 28 Feb 2011 10:36:54 -0500
Reply-To:     zao liu <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         zao liu <[log in to unmask]>
Subject:      regions of brain corresponding to VBM coordinates
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID:  <[log in to unmask]>

--0015174bf21c068a29049d5973e3
Content-Type: text/plain; charset=ISO-8859-1

*Dear All,*
**
*I did VBM analysis on our T1-w images and would like to know if there is a
way to get the names of regions of activation in the brain from the
coordinates (which comes as a report at the end of VBM, i.e voxelwise,
cluster wise and set level reports). One more clarification the coordinates
reported are in MNI right and are mm not voxel coordinates right.*

--0015174bf21c068a29049d5973e3
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable

<div><font size="2" face="Arial"><em>Dear All,</em></font></div>
<div><font size="2" face="Arial"><em></em></font></div>
<div><font size="2" face="Arial"><em>I did VBM analysis on our T1-w images and would like to know if there is a way to get the names ofregions of activation in the brainfrom the coordinates (whichcomes as a report at the end of VBM, i.evoxelwise, cluster wise and set level reports). One more clarificationthe coordinates reported are inMNI rightand aremm not voxel coordinates right.</em></font></div>

--0015174bf21c068a29049d5973e3--
========================================================================Date:         Mon, 28 Feb 2011 16:35:44 +0000
Reply-To:     Richard Binney <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Richard Binney <[log in to unmask]>
Subject:      PPI: adjust for effects of interest
MIME-Version: 1.0
Content-Type: multipart/alternative; boundarye6ba1efe9a7601f6049d5a45df
Message-ID:  <[log in to unmask]>

--90e6ba1efe9a7601f6049d5a45df
Content-Type: text/plain; charset=ISO-8859-1

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

--90e6ba1efe9a7601f6049d5a45df
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable

<div>Hi Darren G/ Karl F/other PPI-ers,</div>
<div></div>
<div>I can&#39;t find any posts on this and wondered if you can clear it up for me.</div>
<div></div>
<div>In extracting the principal eigenvariate from your VOI, you are asked if you want to adjust the extracted timecourse. When should you adjust and when should you not worry? what is the impact of adjusting?</div>
<div></div>
<div>My impression was that the raw timecourse would be extracted always. The option to adjust suggests this is not always true. Do you use this option (only?)when you have time or dispersion derivatives and/or motion regressors?</div>

<div></div>
<div>I have a parametric design with two conditions (tasks)and two parametric modulations per condition. The design matrix therefore has 6 regressors of interest. Motion regressors are also included. In extracting the timecourse should I adjust using an F-contrast spaning the first 6 columns only ([1 1 1 1 1 1]-&gt;right-padded with zeros)?? Is it problematic if I have not done this? What are the consequences?</div>

<div></div>
<div>What about if I were only interested in the parametric modulations of the first condition in the PPI analysis? Should I adjust for the first 3 columns only ([1 1 1 0 0 0])?</div>
<div></div>
<div>All your comments will be greatly appreciated.</div>
<div></div>
<div>Richard</div>

--90e6ba1efe9a7601f6049d5a45df--
========================================================================Date:         Mon, 28 Feb 2011 11:42:00 -0500
Reply-To:     "MCLAREN, Donald" <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         "MCLAREN, Donald" <[log in to unmask]>
Subject:      Re: PPI: adjust for effects of interest
Comments: To: Richard Binney <[log in to unmask]>
In-Reply-To:  <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundary cf3054a4b5dbb06a049d5a5beb
Message-ID:  <[log in to unmask]>

--20cf3054a4b5dbb06a049d5a5beb
Content-Type: text/plain; charset=ISO-8859-1

Richard,

I'd use an F-contrast that is one row per condition/modulator. In your case:
1 0 0 0 0 0 ...
0 1 0 0 0 0 ...
0 0 1 0 0 0 ...
0 0 0 1 0 0 ...
0 0 0 0 1 0 ...
0 0 0 0 0 1 ...

The goal of the adjustment is to extract only the BOLD signal related to
neural activity and eliminate the activity due to motion.

Best Regards, Donald McLaren
================D.G. McLaren, Ph.D.
Postdoctoral Research Fellow, GRECC, Bedford VA
Research Fellow, Department of Neurology, Massachusetts General Hospital and
Harvard Medical School
Office: (773) 406-2464
====================This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED
HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is
intended only for the use of the individual or entity named above. If the
reader of the e-mail is not the intended recipient or the employee or agent
responsible for delivering it to the intended recipient, you are hereby
notified that you are in possession of confidential and privileged
information. Any unauthorized use, disclosure, copying or the taking of any
action in reliance on the contents of this information is strictly
prohibited and may be unlawful. If you have received this e-mail
unintentionally, please immediately notify the sender via telephone at (773)
406-2464 or email.


On Mon, Feb 28, 2011 at 11:35 AM, Richard Binney <
[log in to unmask]> wrote:

> Hi Darren G/ Karl F/other PPI-ers,
>
> I can't find any posts on this and wondered if you can clear it up for me.
>
> In extracting the principal eigenvariate from your VOI, you are asked if
> you want to adjust the extracted timecourse. When should you adjust and when
> should you not worry? what is the impact of adjusting?
>
> My impression was that the raw timecourse would be extracted always. The
> option to adjust suggests this is not always true. Do you use this option
> (only?) when you have time or dispersion derivatives and/or motion
> regressors?
>
> I have a parametric design with two conditions (tasks) and two parametric
> modulations per condition. The design matrix therefore has 6 regressors of
> interest. Motion regressors are also included. In extracting the timecourse
> should I adjust using an F-contrast spaning the first 6 columns only ([1 1 1
> 1 1 1]->right-padded with zeros)?? Is it problematic if I have not done
> this? What are the consequences?
>
> What about if I were only interested in the parametric modulations of the
> first condition in the PPI analysis? Should I adjust for the first 3 columns
> only ([1 1 1 0 0 0])?
>
> All your comments will be greatly appreciated.
>
> Richard
>

--20cf3054a4b5dbb06a049d5a5beb
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable

<div>Richard,</div><div><br></div>I&#39;d use an F-contrast that is one row per condition/modulator. In your case:<div>1 0 0 0 0 0 ...</div><div>0 1 0 0 0 0 ...</div><div>0 0 1 0 0 0 ...</div><div>0 0 0 1 0 0 ...</div><div>
0 0 0 0 1 0 ...</div><div>0 0 0 0 0 1 ...</div><div><br></div><div>The goal of the adjustment is to extract only the BOLD signal related to neural activity and eliminate the activity due to motion.<br clear="all"><br>Best Regards, Donald McLaren<br>
=================<br>D.G. McLaren, Ph.D.<br>Postdoctoral Research Fellow, GRECC, Bedford VA<br>Research Fellow, Department of Neurology, Massachusetts General Hospital and Harvard Medical School<br>Office: (773) 406-2464<br>
=====================<br>This e-mail contains CONFIDENTIAL INFORMATION which may contain PROTECTED HEALTHCARE INFORMATION and may also be LEGALLY PRIVILEGED and which is intended only for the use of the individual or entity named above. If the reader of the e-mail is not the intended recipient or the employee or agent responsible for delivering it to the intended recipient, you are hereby notified that you are in possession of confidential and privileged information. Any unauthorized use, disclosure, copying or the taking of any action in reliance on the contents of this information is strictly prohibited and may be unlawful. If you have received this e-mail unintentionally, please immediately notify the sender via telephone at (773) 406-2464 or email.<br>

<br><br><div class="gmail_quote">On Mon, Feb 28, 2011 at 11:35 AM, Richard Binney <span dir="ltr">&lt;<a href="mailto:[log in to unmask]">[log in to unmask]</a>&gt;</span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
<div>Hi Darren G/ Karl F/other PPI-ers,</div>
<div></div>
<div>I can&#39;t find any posts on this and wondered if you can clear it up for me.</div>
<div></div>
<div>In extracting the principal eigenvariate from your VOI, you are asked if you want to adjust the extracted timecourse. When should you adjust and when should you not worry? what is the impact of adjusting?</div>
<div></div>
<div>My impression was that the raw timecourse would be extracted always. The option to adjust suggests this is not always true. Do you use this option (only?)when you have time or dispersion derivatives and/or motion regressors?</div>


<div></div>
<div>I have a parametric design with two conditions (tasks)and two parametric modulations per condition. The design matrix therefore has 6 regressors of interest. Motion regressors are also included. In extracting the timecourse should I adjust using an F-contrast spaning the first 6 columns only ([1 1 1 1 1 1]-&gt;right-padded with zeros)?? Is it problematic if I have not done this? What are the consequences?</div>


<div></div>
<div>What about if I were only interested in the parametric modulations of the first condition in the PPI analysis? Should I adjust for the first 3 columns only ([1 1 1 0 0 0])?</div>
<div></div>
<div>All your comments will be greatly appreciated.</div>
<div></div><font color="#888888">
<div>Richard</div>
</font></blockquote></div><br></div>

--20cf3054a4b5dbb06a049d5a5beb--
========================================================================Date:         Mon, 28 Feb 2011 11:31:33 -0600
Reply-To:     Pilar Archila-Suerte <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Pilar Archila-Suerte <[log in to unmask]>
Subject:      Art repair - mean signal change
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID:  <[log in to unmask]>

--0015177fcec43a8612049d5b0ea2
Content-Type: text/plain; charset=UTF-8

SPM Users,
When using the "scaling to percent signal change" option in ArtRepair,
sometimes I get what I want from the files selected and sometimes I don't
(depending on which files I select).

Why would ArtRepair give this answer:

*Direct calls to spm_defauts are deprecated.*
*Please use spm('Defaults',modality) or spm_get_defaults instead.*
*Automatically estimated peak and contrast scaling.*
* Normalizing by beta_0008.img*
*Peak value    = 4.97*
*Contrast sum  = 0.996*
*Mean value    = NaN*
*(peak/contrast_sum)*100/bmean  = **NaN*
*
*
*ans =*
*
*
*    4.9700    0.9960    **   NaN*

 All files follow the same path so I don't think that's the problem. Does
anybody know why this is happening and how I can fix this?

Thank you,
Pilar A.

--0015177fcec43a8612049d5b0ea2
Content-Type: text/html; charset=UTF-8
Content-Transfer-Encoding: quoted-printable

<div><div><div><div>SPM Users,</div></div></div></div><div>When using the &quot;scaling to percent signal change&quot; option in ArtRepair, sometimes I get what I want from the files selected and sometimes I don&#39;t (depending on which files I select).</div>

<div><br></div><div>Why would ArtRepair give this answer:</div><div><br></div><div><div><i>Direct calls to spm_defauts are deprecated.</i></div><div><i>Please use spm(&#39;Defaults&#39;,modality) or spm_get_defaults instead.</i></div>

<div><i>Automatically estimated peak and contrast scaling.</i></div><div><i> Normalizing by beta_0008.img</i></div><div><i>Peak value    = 4.97</i></div><div><i>Contrast sum  = 0.996</i></div><div><i>Mean value    = NaN</i></div>

<div><i>(peak/contrast_sum)*100/bmean  = </i><font class="Apple-style-span" color="#CC0000"><b><i>NaN</i></b></font></div><div><i><br></i></div><div><i>ans =</i></div><div><i><br></i></div><div><i>    4.9700    0.9960    </i><b><font class="Apple-style-span" color="#FF0000"><i>   NaN</i></font></b></div>

</div><br><div> All files follow the same path so I don&#39;t think that&#39;s the problem. Does anybody know why this is happening and how I can fix this?</div><div><br></div><div>Thank you,</div><div>Pilar A. </div>

--0015177fcec43a8612049d5b0ea2--
========================================================================Date:         Mon, 28 Feb 2011 12:36:59 -0500
Reply-To:     Chris Watson <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Chris Watson <[log in to unmask]>
Subject:      Re: Art repair - mean signal change
Comments: To: Pilar Archila-Suerte <[log in to unmask]>
In-Reply-To:  <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset="UTF-8"; format=flowed
Content-Transfer-Encoding: 7bit
Message-ID:  <[log in to unmask]>

I think that was a bug in an older version of ArtRepair. Do you have the
latest version?

Pilar Archila-Suerte wrote:
> SPM Users,
> When using the "scaling to percent signal change" option in ArtRepair,
> sometimes I get what I want from the files selected and sometimes I
> don't (depending on which files I select).
>
> Why would ArtRepair give this answer:
>
> /Direct calls to spm_defauts are deprecated./
> /Please use spm('Defaults',modality) or spm_get_defaults instead./
> /Automatically estimated peak and contrast scaling./
> / Normalizing by beta_0008.img/
> /Peak value    = 4.97/
> /Contrast sum  = 0.996/
> /Mean value    = NaN/
> /(peak/contrast_sum)*100/bmean  = /*/NaN/*
> /
> /
> /ans =/
> /
> /
> /    4.9700    0.9960    /*/   NaN/*
>
>  All files follow the same path so I don't think that's the
> problem. Does anybody know why this is happening and how I can fix this?
>
> Thank you,
> Pilar A.
========================================================================Date:         Mon, 28 Feb 2011 11:38:50 -0600
Reply-To:     Pilar Archila-Suerte <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Pilar Archila-Suerte <[log in to unmask]>
Subject:      Re: Art repair - mean signal change
Comments: To: Chris Watson <[log in to unmask]>
In-Reply-To:  <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID:  <[log in to unmask]>

--00163683195045f1c4049d5b2847
Content-Type: text/plain; charset=UTF-8

I have ArtRepair v4. Isn't this the latest version?

On Mon, Feb 28, 2011 at 11:36 AM, Chris Watson <
[log in to unmask]> wrote:

> I think that was a bug in an older version of ArtRepair. Do you have the
> latest version?
>
>
> Pilar Archila-Suerte wrote:
>
>> SPM Users,
>> When using the "scaling to percent signal change" option in ArtRepair,
>> sometimes I get what I want from the files selected and sometimes I don't
>> (depending on which files I select).
>>
>> Why would ArtRepair give this answer:
>>
>> /Direct calls to spm_defauts are deprecated./
>> /Please use spm('Defaults',modality) or spm_get_defaults instead./
>> /Automatically estimated peak and contrast scaling./
>> / Normalizing by beta_0008.img/
>> /Peak value    = 4.97/
>> /Contrast sum  = 0.996/
>> /Mean value    = NaN/
>> /(peak/contrast_sum)*100/bmean  = /*/NaN/*
>> /
>> /
>> /ans =/
>> /
>> /
>> /    4.9700    0.9960    /*/   NaN/*
>>
>>  All files follow the same path so I don't think that's the problem. Does
>> anybody know why this is happening and how I can fix this?
>>
>> Thank you,
>> Pilar A.
>>
>

--00163683195045f1c4049d5b2847
Content-Type: text/html; charset=UTF-8
Content-Transfer-Encoding: quoted-printable

I have ArtRepair v4. Isn&#39;t this the latest version?<br><br><div class="gmail_quote">On Mon, Feb 28, 2011 at 11:36 AM, Chris Watson <span dir="ltr">&lt;<a href="mailto:[log in to unmask]">[log in to unmask]</a>&gt;</span> wrote:<br>

<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">I think that was a bug in an older version of ArtRepair. Do you have the latest version?<div><div></div><div class="h5">

<br>
<br>
Pilar Archila-Suerte wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
SPM Users,<br>
When using the &quot;scaling to percent signal change&quot; option in ArtRepair, sometimes I get what I want from the files selected and sometimes I don&#39;t (depending on which files I select).<br>
<br>
Why would ArtRepair give this answer:<br>
<br>
/Direct calls to spm_defauts are deprecated./<br>
/Please use spm(&#39;Defaults&#39;,modality) or spm_get_defaults instead./<br>
/Automatically estimated peak and contrast scaling./<br>
/ Normalizing by beta_0008.img/<br>
/Peak value    = 4.97/<br>
/Contrast sum  = 0.996/<br>
/Mean value    = NaN/<br>
/(peak/contrast_sum)*100/bmean  = /*/NaN/*<br>
/<br>
/<br>
/ans =/<br>
/<br>
/<br>
/    4.9700    0.9960    /*/   NaN/*<br>
<br>
 All files follow the same path so I don&#39;t think that&#39;s the problem. Does anybody know why this is happening and how I can fix this?<br>
<br>
Thank you,<br>
Pilar A. <br>
</blockquote>
</div></div></blockquote></div><br><br clear="all"><div><div><br><div><div><br></div></div></div></div><br>

--00163683195045f1c4049d5b2847--
========================================================================Date:         Mon, 28 Feb 2011 12:47:00 -0500
Reply-To:     Jason Steffener <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Jason Steffener <[log in to unmask]>
Subject:      matlabbatch "decode dependencies"
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Message-ID:  <[log in to unmask]>

Dear All,
Hello all.
I am trying to create some provenance info from an SPM batch file. So
I would like to take an SPM job file and read through it to pull out
the different steps, the data and the parameters. I have no problem
doing this EXCEPT where I have specified some dependencies. I am
having trouble decoding what the dependencies refer to.

Essentially if I have the following steps:
Realign << Input data
Reslice >> Output data
smooth << Output data from reslice

I would like the following:
Step1 Realign
input data: FILENAME
parameters :XX

Step2 Reslice
input data: output from Step 1
output data: rFILENAME
parameters: XX

Step3 Smooth
input data: rFILENAME
output data: srFILENAME
parameters: XX

And ideas? Or if someone can point me to the code that translates the
dependencies in the job file into something I can figure out, that
would also be great.

Thank you,
Jason


--
Jason Steffener, Ph.D.
Department of Neurology
Columbia University
http://www.cogneurosci.org/steffener.html
========================================================================Date:         Mon, 28 Feb 2011 14:08:11 +0100
Reply-To:     Alexander Hammers <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Alexander Hammers <[log in to unmask]>
Subject:      Re: question on VOI analysis
Comments: To: sarika cherodath <[log in to unmask]>
In-Reply-To:  <[log in to unmask]>
Mime-Version: 1.0 (Apple Message framework v1082)
Content-Type: multipart/mixed; boundary=Apple-Mail-79-616227633
Message-ID:  <[log in to unmask]>

--Apple-Mail-79-616227633
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain;
	charset=us-ascii

Dear Sarika,


How did you define the VOI(s)? If they are in MNI space as your second level implies, then you're applying one VOI to all the subjects... and this will unsurprisingly have the same volume in all.

I don't know what kind of data you have - it sounds like fMRI. An approximation of individual volumetrics would be to backtransform your standard space VOIs into individual space - if you've used Unified Segmentation for your MRI spatial normalisation, then the *inv_seg_sn.mat file can accomplish this.

Such a procedure's accuracy will be limited by 1) the quality and provenience of your standard space VOI (see our recent thread on single-subject vs multi-subject atlases) and 2) the low-ish number of degrees of freedom of the transformation for standard normalisation and Unified Segmentation. Point #2 should be improve-able if you use DARTEL.

The gold standard for just one particular area would be to devise a protocol, check the reliability of results and ideally their veracity, and then outline those areas by hand in your subjects.

Hope this helps,

Best wishes,

Alexander



-----------------------------
Alexander Hammers, MD PhD



--Apple-Mail-79-616227633
Content-Disposition: inline;
	filename=image.png
Content-Type: image/png;
	name="image.png"
Content-Transfer-Encoding: base64
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--Apple-Mail-79-616227633
Content-Transfer-Encoding: quoted-printable
Content-Type: text/plain;
	charset=windows-1252





Chair in Functional Neuroimaging
Neurodis Foundation
http://www.fondation-neurodis.org/
Postal Address:
CERMEP  Imagerie du Vivant
Hpital Neurologique Pierre Wertheimer
59 Boulevard Pinel, 69003 Lyon, France

Telephone	+33-(0)4-72 68 86 34
Fax			+33-(0)4-72 68 86 10
Email		[log in to unmask];[log in to unmask]
---------------------------------
Other affiliations:

Visiting Reader; Honorary Consultant Neurologist
Division of Neuroscience and Mental Health, Faculty of Medicine
Imperial College London, UK
---------------------------------
Honorary Reader in Neurology; Honorary Consultant Neurologist
Department of Clinical and Experimental Epilepsy
National Hospital for Neurology and Neurosurgery/ Institute of Neurology, University College London, UK




On 28 Feb 2011, at 10:11, sarika cherodath wrote:

> 
> Hi SPMers,
> 
>         I have been trying to perform VOI analysis to obtain signal changes at a particular area for individual subjects in the dataset, so as to make correlations with behavioral scores. When i tried to calculate the values, SPM returns the same value for all subjects! Can anybody explain why this might be? The dataset has been analysed at second level and then moved to another directory (along with first level data). Is it possible that SPM is not able to access the data because of the path change??
> 
> Thanks in advance,
> -- 
> Sarika Cherodath
> Graduate Student
> National Brain Research Centre
> Manesar, Gurgaon -122050
> India
> 


--Apple-Mail-79-616227633--
========================================================================Date:         Mon, 28 Feb 2011 15:14:22 -0500
Reply-To:     Jeffrey West <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Jeffrey West <[log in to unmask]>
Subject:      question regarding flexible factorial model
Mime-Version: 1.0
Content-Type: multipart/alternative; boundary="=__Part4569690E.0__="
Message-ID:  <[log in to unmask]>

This is a MIME message. If you are reading this text, you may want to
consider changing to a mail reader or gateway that understands how to
properly handle MIME multipart messages.

--=__Part4569690E.0__Content-Type: text/plain; charset=US-ASCII
Content-Transfer-Encoding: quoted-printable

Hello.
 
I have question relating to setting up a flexible factorial model with 2 groups, each group has 4 conditions.
 
I have 3 factors: 1 = subject (independent, variance equal), 2 = group (independent, variance not equal), 3 = condition (not independent, variance equal)
 
For the subject level, I entered in 20 subjects, for each subject, I entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2 3 4. Once all 20 subjects were completed, I entered 3 main effects, and interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2 3. 
 
The model did not run and I got the following error message:
 
Running job #2
-----------------------------------------------------------------------
Running 'Factorial design specification'
Failed  'Factorial design specification'
Index exceeds matrix dimensions.
In file "C:\Documents and Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067), function "spm_run_factorial_design" at line 482.
 
The following modules did not run:
Failed: Factorial design specification
 
I feel like the error is in the subject level: either the scan or the conditions. I do not feel like the conditions step is correct. Can anyone please explain what I did wrong setting up my model. 
 
Thank you for any suggestions.
 
Jef
 
 
Jeffrey West, M.A.
Research Analyst
Maryland Psychiatric Research Center
Baltimore, Maryland 21228-0247
Phone: 410-402-6018
email: [log in to unmask] 
 
 


--=__Part4569690E.0__Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Content-Description: HTML

<HTML><HEAD>
<META http-equiv=Content-Type content="text/html; charset=iso-8859-1">
<META content="MSHTML 6.00.6000.17095" name=GENERATOR></HEAD>
<BODY style="MARGIN: 4px 4px 1px; FONT: 10pt Tahoma">
<DIV>Hello.</DIV>
<DIV>&nbsp;</DIV>
<DIV>I have question relating to setting up a flexible factorial model with 2 groups, each group has&nbsp;4 conditions.</DIV>
<DIV>&nbsp;</DIV>
<DIV>I have 3 factors: 1 = subject (independent, variance equal), 2 = group (independent, variance not equal), 3 = condition (not independent, variance equal)</DIV>
<DIV>&nbsp;</DIV>
<DIV>For the subject level, I entered in 20 subjects, for each subject, I entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2 3 4. Once all 20 subjects were completed, I entered 3 main effects, and interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2 3. </DIV>
<DIV>&nbsp;</DIV>
<DIV>The model did not run and I got the following error message:</DIV>
<DIV>&nbsp;</DIV>
<DIV><STRONG>Running job #2<BR>-----------------------------------------------------------------------<BR>Running 'Factorial design specification'<BR>Failed&nbsp; 'Factorial design specification'<BR>Index exceeds matrix dimensions.<BR>In file "C:\Documents and Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067), function "spm_run_factorial_design" at line 482.</STRONG></DIV>
<DIV>&nbsp;</DIV>
<DIV><STRONG>The following modules did not run:<BR>Failed: Factorial design specification</STRONG></DIV>
<DIV><STRONG></STRONG>&nbsp;</DIV>
<DIV>I feel like the error is in the subject level: either the scan or the conditions. I do not feel like the conditions step is correct. Can anyone please explain what I did wrong setting up my model. </DIV>
<DIV>&nbsp;</DIV>
<DIV>Thank you for any suggestions.</DIV>
<DIV>&nbsp;</DIV>
<DIV>Jef</DIV>
<DIV>&nbsp;</DIV>
<DIV>&nbsp;</DIV>
<DIV>Jeffrey West, M.A.<BR>Research Analyst<BR>Maryland Psychiatric Research Center<BR>Baltimore, Maryland 21228-0247<BR>Phone: 410-402-6018<BR>email: <A href="mailto:[log in to unmask]">[log in to unmask]</A> </DIV>
<DIV>&nbsp;</DIV>
<DIV>&nbsp;</DIV></BODY></HTML>

--=__Part4569690E.0__=--
========================================================================Date:         Tue, 1 Mar 2011 09:17:07 +1300
Reply-To:     Ehsan Negahbani <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Ehsan Negahbani <[log in to unmask]>
Subject:      Unsubscribe me please
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID:  <[log in to unmask]>

--0022150475f729946e049d5d5d2c
Content-Type: text/plain; charset=ISO-8859-1

--
Ehsan

--0022150475f729946e049d5d5d2c
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable

<br clear="all"><br>-- <br><span style="color:rgb(102, 102, 102)">Ehsan</span><br>

--0022150475f729946e049d5d5d2c--
========================================================================Date:         Mon, 28 Feb 2011 22:28:55 +0000
Reply-To:     Vladimir Litvak <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Vladimir Litvak <[log in to unmask]>
Subject:      Re: spm_eeg_convert2scalp
Comments: To: Erick Britis Ortiz <[log in to unmask]>
In-Reply-To:  <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID:  <AANLkTi[log in to unmask]>

Hi Erick,

SPM uses the same algorithm as Fieldtrip to generate a layout from the
sensor array so it should be quite similar, just the convention for
representing it is slightly different. I don't know what exactly the
problem is so it's hard for me to advise you. In principle you can use
the GUI functionality in Prepare or your own script to generate any
layout you like and then load it as a channel template file via
Prepare  (this is a mat-file, there is an example for CTF in
EEGTemplates).

Best,

Vladimir



On Mon, Feb 28, 2011 at 10:22 PM, Erick Britis Ortiz
<[log in to unmask]> wrote:
>
> Hi Vladimir,
>
> I have been using spm_eeg_convert2images to transform my MEG frequency
> analysis results to image volumes and run statistics. The objective
> being to determine lateralization in language (in children) by frequency.
>
> I assumed that spm_eeg_convert2scalp would use a standard 2D layout (
> la Fieldtrip), and when I checked, this is not true. This makes
> lateralization studies, for instance, much more difficult, and I could
> think of others. What is the advantage?
>
> My guess was that setting D.channels.X_plot2D and Y_plot2D to empty
> would generate a default arrangement, but this also did not work. Could
> you shed some light at the issue?
>
> Best,
> Erick
>
>
========================================================================Date:         Mon, 28 Feb 2011 23:08:58 +0000
Reply-To:     Vladimir Litvak <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Vladimir Litvak <[log in to unmask]>
Subject:      Re: spm_eeg_convert2scalp
Comments: To: Erick Britis Ortiz <[log in to unmask]>
In-Reply-To:  <[log in to unmask]>
MIME-Version: 1.0
Content-Type: text/plain; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable
Message-ID:  <[log in to unmask]>

These 2D locations are created by projecting 3D locations represented
in head coordinates so the differences you find are due to different
head positions of your subjects in the helmet. This is by design, but
you also can load a standard channel template file for all your
subjects.

Vladimir

On Mon, Feb 28, 2011 at 10:55 PM, Erick Britis Ortiz
<[log in to unmask]> wrote:
>
> Thank you as always for the prompt response!
>
> For you to know at least what I am talking about, a picture is attached
> with the projected MEG sensors (colored by region) in my 22 subjects.
> Left is greenish, right is reddish.
>
> The problem is that I see no reason for the MEG sensors not to have
> simply a standard position. In the Fieldtrip plots, they have (for
> example, in CTF151.lay). I will follow your advice, no matter. But this
> is a bit dangerous in the general case, don't you think? As I said, I
> just do not know if it is by design, else I could "fix" it.
>
> Best,
> Erick
>
> On 2011-02-28 23:28, Vladimir Litvak wrote:
>> Hi Erick,
>>
>> SPM uses the same algorithm as Fieldtrip to generate a layout from the
>> sensor array so it should be quite similar, just the convention for
>> representing it is slightly different. I don't know what exactly the
>> problem is so it's hard for me to advise you. In principle you can use
>> the GUI functionality in Prepare or your own script to generate any
>> layout you like and then load it as a channel template file via
>> Prepare (this is a mat-file, there is an example for CTF in
>> EEGTemplates).
>>
>> Best,
>>
>> Vladimir
>>
>>
>>
>> On Mon, Feb 28, 2011 at 10:22 PM, Erick Britis Ortiz
>> <[log in to unmask]> wrote:
>>>
>>> Hi Vladimir,
>>>
>>> I have been using spm_eeg_convert2images to transform my MEG frequency
>>> analysis results to image volumes and run statistics. The objective
>>> being to determine lateralization in language (in children) by frequency.
>>>
>>> I assumed that spm_eeg_convert2scalp would use a standard 2D layout (
>>> la Fieldtrip), and when I checked, this is not true. This makes
>>> lateralization studies, for instance, much more difficult, and I could
>>> think of others. What is the advantage?
>>>
>>> My guess was that setting D.channels.X_plot2D and Y_plot2D to empty
>>> would generate a default arrangement, but this also did not work. Could
>>> you shed some light at the issue?
>>>
>>> Best,
>>> Erick
>>>
>>>
>>
>
>
========================================================================Date:         Mon, 28 Feb 2011 18:34:38 -0500
Reply-To:     Pieter van de Vijver <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Pieter van de Vijver <[log in to unmask]>
Subject:      Re: question regarding flexible factorial model
Comments: To: Jeffrey West <[log in to unmask]>
In-Reply-To:  <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryaec51a894a8f7f93049d601f97
Message-ID:  <[log in to unmask]>

--bcaec51a894a8f7f93049d601f97
Content-Type: text/plain; charset=ISO-8859-1

Hi Jeff,

You should enter conditions for subjects in a nscans x factor matrix. The
rows are your scans (4 in your case) and the columns indicate the factors
(2, one for group and one for condition, subject is automatically
modelled).
So for a subject in group 2 with scans for all four conditions you would
need:
[2 1
2 2
2 3
2 4]
Make sure your scans are entered in the right order!

Also, main effect for subjects doesn't need to be specified, this is done
automatically (this is because you chose 'Subjects' at the 'Specify Subjects
or all Scans & Factors').

Good luck,

Pieter


On Mon, Feb 28, 2011 at 3:14 PM, Jeffrey West <[log in to unmask]>wrote:

>  Hello.
>
> I have question relating to setting up a flexible factorial model with 2
> groups, each group has 4 conditions.
>
> I have 3 factors: 1 = subject (independent, variance equal), 2 = group
> (independent, variance not equal), 3 = condition (not independent, variance
> equal)
>
> For the subject level, I entered in 20 subjects, for each subject, I
> entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2
> 3 4. Once all 20 subjects were completed, I entered 3 main effects, and
> interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2
> 3.
>
> The model did not run and I got the following error message:
>
> *Running job #2
> -----------------------------------------------------------------------
> Running 'Factorial design specification'
> Failed  'Factorial design specification'
> Index exceeds matrix dimensions.
> In file "C:\Documents and
> Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067),
> function "spm_run_factorial_design" at line 482.*
>
> *The following modules did not run:
> Failed: Factorial design specification*
> **
> I feel like the error is in the subject level: either the scan or the
> conditions. I do not feel like the conditions step is correct. Can anyone
> please explain what I did wrong setting up my model.
>
> Thank you for any suggestions.
>
> Jef
>
>
> Jeffrey West, M.A.
> Research Analyst
> Maryland Psychiatric Research Center
> Baltimore, Maryland 21228-0247
> Phone: 410-402-6018
> email: [log in to unmask]
>
>
>

--bcaec51a894a8f7f93049d601f97
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable

Hi Jeff,<div><br></div><div>You should enter conditions for subjects in a nscans x factor matrix. The rows are your scans (4 in your case) and the columns indicate the factors (2, one for group and one for condition, subject is automatically modelled).</div>
<div>So for a subject in group 2 with scans for all four conditions you would need:</div><div>[2 1<br>2 2<br>2 3</div><div>2 4]</div><div>Make sure your scans are entered in the right order!</div><div><br></div><div>Also, main effect for subjects doesn&#39;t need to be specified, this is done automatically (this is because you chose &#39;Subjects&#39; at the &#39;Specify Subjects or all Scans &amp; Factors&#39;).</div>
<div><br></div><div>Good luck,</div><div><br></div><div>Pieter</div><div><br></div><div><br><div class="gmail_quote">On Mon, Feb 28, 2011 at 3:14 PM, Jeffrey West <span dir="ltr">&lt;<a href="mailto:[log in to unmask]">[log in to unmask]</a>&gt;</span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">


<div style="margin:4px 4px 1px;font:10pt Tahoma">
<div>Hello.</div>
<div></div>
<div>I have question relating to setting up a flexible factorial model with 2 groups, each group has4 conditions.</div>
<div></div>
<div>I have 3 factors: 1 = subject (independent, variance equal), 2 = group (independent, variance not equal), 3 = condition (not independent, variance equal)</div>
<div></div>
<div>For the subject level, I entered in 20 subjects, for each subject, I entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2 3 4. Once all 20 subjects were completed, I entered 3 main effects, and interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2 3. </div>

<div></div>
<div>The model did not run and I got the following error message:</div>
<div></div>
<div><strong>Running job #2<br>-----------------------------------------------------------------------<br>Running &#39;Factorial design specification&#39;<br>Failed &#39;Factorial design specification&#39;<br>Index exceeds matrix dimensions.<br>
In file &quot;C:\Documents and Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m&quot; (v3067), function &quot;spm_run_factorial_design&quot; at line 482.</strong></div>
<div></div>
<div><strong>The following modules did not run:<br>Failed: Factorial design specification</strong></div>
<div><strong></strong></div>
<div>I feel like the error is in the subject level: either the scan or the conditions. I do not feel like the conditions step is correct. Can anyone please explain what I did wrong setting up my model. </div>
<div></div>
<div>Thank you for any suggestions.</div>
<div></div>
<div>Jef</div>
<div></div>
<div></div>
<div>Jeffrey West, M.A.<br>Research Analyst<br>Maryland Psychiatric Research Center<br>Baltimore, Maryland 21228-0247<br>Phone: <a href="tel:410-402-6018" target="_blank">410-402-6018</a><br>email: <a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a> </div>

<div></div>
<div></div></div>
</blockquote></div><br></div>

--bcaec51a894a8f7f93049d601f97--
========================================================================Date:         Mon, 28 Feb 2011 18:38:06 -0500
Reply-To:     Pieter van de Vijver <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         Pieter van de Vijver <[log in to unmask]>
Subject:      Re: question regarding flexible factorial model
Comments: To: Jeffrey West <[log in to unmask]>
In-Reply-To:  <[log in to unmask]>
MIME-Version: 1.0
Content-Type: multipart/alternative; boundarye6ba6e8996ee354c049d602b47
Message-ID:  <[log in to unmask]>

--90e6ba6e8996ee354c049d602b47
Content-Type: text/plain; charset=ISO-8859-1

Sorry, small correction. Main effect for subjects DOES need to be
specified.

Pieter

On Mon, Feb 28, 2011 at 6:34 PM, Pieter van de Vijver <[log in to unmask]>wrote:

> Hi Jeff,
>
> You should enter conditions for subjects in a nscans x factor matrix. The
> rows are your scans (4 in your case) and the columns indicate the factors
> (2, one for group and one for condition, subject is automatically
> modelled).
> So for a subject in group 2 with scans for all four conditions you would
> need:
> [2 1
> 2 2
> 2 3
> 2 4]
> Make sure your scans are entered in the right order!
>
> Also, main effect for subjects doesn't need to be specified, this is done
> automatically (this is because you chose 'Subjects' at the 'Specify Subjects
> or all Scans & Factors').
>
> Good luck,
>
> Pieter
>
>
> On Mon, Feb 28, 2011 at 3:14 PM, Jeffrey West <[log in to unmask]>wrote:
>
>>  Hello.
>>
>> I have question relating to setting up a flexible factorial model with 2
>> groups, each group has 4 conditions.
>>
>> I have 3 factors: 1 = subject (independent, variance equal), 2 = group
>> (independent, variance not equal), 3 = condition (not independent, variance
>> equal)
>>
>> For the subject level, I entered in 20 subjects, for each subject, I
>> entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2
>> 3 4. Once all 20 subjects were completed, I entered 3 main effects, and
>> interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2
>> 3.
>>
>> The model did not run and I got the following error message:
>>
>> *Running job #2
>> -----------------------------------------------------------------------
>> Running 'Factorial design specification'
>> Failed  'Factorial design specification'
>> Index exceeds matrix dimensions.
>> In file "C:\Documents and
>> Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m" (v3067),
>> function "spm_run_factorial_design" at line 482.*
>>
>> *The following modules did not run:
>> Failed: Factorial design specification*
>> **
>> I feel like the error is in the subject level: either the scan or the
>> conditions. I do not feel like the conditions step is correct. Can anyone
>> please explain what I did wrong setting up my model.
>>
>> Thank you for any suggestions.
>>
>> Jef
>>
>>
>> Jeffrey West, M.A.
>> Research Analyst
>> Maryland Psychiatric Research Center
>> Baltimore, Maryland 21228-0247
>> Phone: <410-402-6018>410-402-6018
>> email: [log in to unmask]
>>
>>
>>
>
>

--90e6ba6e8996ee354c049d602b47
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable

Sorry, small correction. Main effect for subjects DOES need to be specified.<div><br></div><div>Pieter<br><br><div class="gmail_quote">On Mon, Feb 28, 2011 at 6:34 PM, Pieter van de Vijver <span dir="ltr">&lt;<a href="mailto:[log in to unmask]">[log in to unmask]</a>&gt;</span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">Hi Jeff,<div><br></div><div>You should enter conditions for subjects in a nscans x factor matrix. The rows are your scans (4 in your case) and the columns indicate the factors (2, one for group and one for condition, subject is automatically modelled).</div>

<div>So for a subject in group 2 with scans for all four conditions you would need:</div><div>[2 1<br>2 2<br>2 3</div><div>2 4]</div><div>Make sure your scans are entered in the right order!</div><div><br></div><div>Also, main effect for subjects doesn&#39;t need to be specified, this is done automatically (this is because you chose &#39;Subjects&#39; at the &#39;Specify Subjects or all Scans &amp; Factors&#39;).</div>

<div><br></div><div>Good luck,</div><div><br></div><font color="#888888"><div>Pieter</div></font><div><div></div><div class="h5"><div><br></div><div><br><div class="gmail_quote">On Mon, Feb 28, 2011 at 3:14 PM, Jeffrey West <span dir="ltr">&lt;<a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a>&gt;</span> wrote:<br>

<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">


<div style="margin:4px 4px 1px;font:10pt Tahoma">
<div>Hello.</div>
<div></div>
<div>I have question relating to setting up a flexible factorial model with 2 groups, each group has4 conditions.</div>
<div></div>
<div>I have 3 factors: 1 = subject (independent, variance equal), 2 = group (independent, variance not equal), 3 = condition (not independent, variance equal)</div>
<div></div>
<div>For the subject level, I entered in 20 subjects, for each subject, I entered in the the 4 scans: con_image 1,2,3,4. For conditions, I entered 1 2 3 4. Once all 20 subjects were completed, I entered 3 main effects, and interaction: main effect: 1, main effect: 2, main effect: 3, interaction: 2 3. </div>


<div></div>
<div>The model did not run and I got the following error message:</div>
<div></div>
<div><strong>Running job #2<br>-----------------------------------------------------------------------<br>Running &#39;Factorial design specification&#39;<br>Failed &#39;Factorial design specification&#39;<br>Index exceeds matrix dimensions.<br>

In file &quot;C:\Documents and Settings\JWest\Desktop\spm8\config\spm_run_factorial_design.m&quot; (v3067), function &quot;spm_run_factorial_design&quot; at line 482.</strong></div>
<div></div>
<div><strong>The following modules did not run:<br>Failed: Factorial design specification</strong></div>
<div><strong></strong></div>
<div>I feel like the error is in the subject level: either the scan or the conditions. I do not feel like the conditions step is correct. Can anyone please explain what I did wrong setting up my model. </div>
<div></div>
<div>Thank you for any suggestions.</div>
<div></div>
<div>Jef</div>
<div></div>
<div></div>
<div>Jeffrey West, M.A.<br>Research Analyst<br>Maryland Psychiatric Research Center<br>Baltimore, Maryland 21228-0247<br>Phone: <a href="tel:410-402-6018" target="_blank"></a><a href="tel:410-402-6018" target="_blank">410-402-6018</a><br>
email: <a href="mailto:[log in to unmask]" target="_blank">[log in to unmask]</a> </div>

<div></div>
<div></div></div>
</blockquote></div><br></div>
</div></div></blockquote></div><br></div>

--90e6ba6e8996ee354c049d602b47--
========================================================================Date:         Mon, 28 Feb 2011 18:44:34 -0500
Reply-To:     John Fredy <[log in to unmask]>
Sender:       "SPM (Statistical Parametric Mapping)" <[log in to unmask]>
From:         John Fredy <[log in to unmask]>
Subject:      sparse data analysis
MIME-Version: 1.0
Content-Type: multipart/alternative; boundaryMessage-ID:  <[log in to unmask]>

--0016364991e7155337049d6043e2
Content-Type: text/plain; charset=ISO-8859-1

Hello all, I have recorded 80 volumes in a block design experiment with a TR
of 6 seconds where 3 seconds are silence, the machine is silence, and the
other 3 seconds are used for the adquisition. In the 3 seconds of machine
silence I present a stimulus, 0.5 seconds later of the begins of the
silence, with an aproximated duration of 1.5 seconds

What is the best strategy for processing this data?

Regards

John Ochoa
Universidad de Antioquia

--0016364991e7155337049d6043e2
Content-Type: text/html; charset=ISO-8859-1
Content-Transfer-Encoding: quoted-printable

Hello all, I have recorded 80 volumes in a block design experiment with a TR of 6 seconds where 3 seconds are silence, the machine is silence, and the other 3 seconds are used for the adquisition. In the 3 seconds of machine silence I present a stimulus, 0.5 seconds later of the begins of the silence, with an aproximated duration of 1.5 seconds<div>
<br></div><div>What is the best strategy for processing this data?</div><div><br></div><div>Regards</div><div><br></div><div>John Ochoa</div><div>Universidad de Antioquia</div>

--0016364991e7155337049d6043e2--