Hi Doug, it would be great if you could give me some further advise on the group analysis of functional connectivity maps. Specifically, I am trying to get PCC maps for certain seeds, and am not planning any comparison between groups. Following your previous advise, I am running isxconcat-sess with -m pcc to get the PCC maps. I would then run
mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname
*Could you please provide some more detail on what kind of analysis is performed when I provide pcc.nii as an input for mri_glmfit? Is it a t-test of the Fisher-transformed r-values against 0? *Is the average r-value or z-value saved somewhere? *Do you take the autocorrelation into account (as in Vincent JL et al., 2007. Intrinsic functional architecture in the anaesthetized monkey brain. Nature. 447:83-86)? I'd also be happy to look this up but I'd need to know where I can find this information.
Thanks, Caspar
On 10/01/2013 01:13 PM, Caspar M. Schwiedrzik wrote:
Hi Doug, it would be great if you could give me some further advise on the group analysis of functional connectivity maps. Specifically, I am trying to get PCC maps for certain seeds, and am not planning any comparison between groups. Following your previous advise, I am running isxconcat-sess with -m pcc to get the PCC maps. I would then run
mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname
*Could you please provide some more detail on what kind of analysis is performed when I provide pcc.nii as an input for mri_glmfit? Is it a t-test of the Fisher-transformed r-values against 0?
I just run a t-test of the r-values. I don't have a program to convert them to z-values, however, there are z-values that are created in the first level analysis. These are generated from the p-values but I bet it would give you the same thing. Use -m z with isxconcat-sess if you want to use the z.
*Is the average r-value or z-value saved somewhere?
Which level? For mri_glmfit, they are not, but it is not hard to get them with matlab.
*Do you take the autocorrelation into account (as in Vincent JL et al., 2007. Intrinsic functional architecture in the anaesthetized monkey brain. Nature. 447:83-86)?
Not usually, but it could be done by not including -no-whiten when you run mkanalysis-sess. I usually use the regression coefficients instead of correlation coefficients because that they are at least unbiased with respect to noise level and autocorrelation. doug
I'd also be happy to look this up but I'd need to know where I can find this information.
Thanks, Caspar
Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
Hi Doug, when I run a two-tailed t-test against 0 in Matlab on the Rs in pcc.nii that I get out of isxconcat-sess with -m pcc, and DOF from ffxdof.dat, I get different -log10(p) values than the ones that come out of mri_glmfit. I am not sure why this is happening. In principle, I just want pcc maps as final output to show them on the surface (instead of p-values). So I'd be happy to follow your advice regarding the biasing effects of noise and autocorrelation and use the regression coefficients. However, mri_glmfit (v5.1) does not seem to output pcc maps of the contrasts (contrary to selxavg3-sess on the single subject level). How would I get those?
Thanks, Caspar
2013/10/1 Douglas N Greve greve@nmr.mgh.harvard.edu
On 10/01/2013 01:13 PM, Caspar M. Schwiedrzik wrote:
Hi Doug, it would be great if you could give me some further advise on the group analysis of functional connectivity maps. Specifically, I am trying to get PCC maps for certain seeds, and am not planning any comparison between groups. Following your previous advise, I am running isxconcat-sess with -m pcc to get the PCC maps. I would then run
mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname
*Could you please provide some more detail on what kind of analysis is performed when I provide pcc.nii as an input for mri_glmfit? Is it a t-test of the Fisher-transformed r-values against 0?
I just run a t-test of the r-values. I don't have a program to convert them to z-values, however, there are z-values that are created in the first level analysis. These are generated from the p-values but I bet it would give you the same thing. Use -m z with isxconcat-sess if you want to use the z.
*Is the average r-value or z-value saved somewhere?
Which level? For mri_glmfit, they are not, but it is not hard to get them with matlab.
*Do you take the autocorrelation into account (as in Vincent JL et al., 2007. Intrinsic functional architecture in the anaesthetized monkey brain. Nature. 447:83-86)?
Not usually, but it could be done by not including -no-whiten when you run mkanalysis-sess. I usually use the regression coefficients instead of correlation coefficients because that they are at least unbiased with respect to noise level and autocorrelation. doug
I'd also be happy to look this up but I'd need to know where I can find this information.
Thanks, Caspar
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-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
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On 10/03/2013 10:39 AM, Caspar M. Schwiedrzik wrote:
Hi Doug, when I run a two-tailed t-test against 0 in Matlab on the Rs in pcc.nii that I get out of isxconcat-sess with -m pcc, and DOF from ffxdof.dat, I get different -log10(p) values than the ones that come out of mri_glmfit.
I don't understand what you mean. Can you elaborate?
I am not sure why this is happening. In principle, I just want pcc maps as final output to show them on the surface (instead of p-values). So I'd be happy to follow your advice regarding the biasing effects of noise and autocorrelation and use the regression coefficients. However, mri_glmfit (v5.1) does not seem to output pcc maps of the contrasts (contrary to selxavg3-sess on the single subject level). How would I get those?
Thanks, Caspar
2013/10/1 Douglas N Greve <greve@nmr.mgh.harvard.edu mailto:greve@nmr.mgh.harvard.edu>
On 10/01/2013 01:13 PM, Caspar M. Schwiedrzik wrote: > Hi Doug, > it would be great if you could give me some further advise on the > group analysis of functional connectivity maps. > Specifically, I am trying to get PCC maps for certain seeds, and am > not planning any comparison between groups. > Following your previous advise, I am running isxconcat-sess with -m > pcc to get the PCC maps. > I would then run > > mri_glmfit \ > --surf averagesubject hemisphere \ > --y pcc.nii \ > --no-cortex \ > --osgm \ > --glmdir analysisname > > *Could you please provide some more detail on what kind of analysis is > performed when I provide pcc.nii as an input for mri_glmfit? Is it a > t-test of the Fisher-transformed r-values against 0? I just run a t-test of the r-values. I don't have a program to convert them to z-values, however, there are z-values that are created in the first level analysis. These are generated from the p-values but I bet it would give you the same thing. Use -m z with isxconcat-sess if you want to use the z. > *Is the average r-value or z-value saved somewhere? Which level? For mri_glmfit, they are not, but it is not hard to get them with matlab. > *Do you take the autocorrelation into account (as in Vincent JL et > al., 2007. Intrinsic functional architecture in the anaesthetized > monkey brain. Nature. 447:83-86)? Not usually, but it could be done by not including -no-whiten when you run mkanalysis-sess. I usually use the regression coefficients instead of correlation coefficients because that they are at least unbiased with respect to noise level and autocorrelation. doug > I'd also be happy to look this up but I'd need to know where I can > find this information. > > Thanks, Caspar > > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer -- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> Phone Number: 617-724-2358 <tel:617-724-2358> Fax: 617-726-7422 <tel:617-726-7422> Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/ _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
Hi Doug, I loaded the pcc.nii file that I got from isxconcat-sess into Matlab and then ran a t-test against 0 over the 4th dimension. I converted the resulting p-values to -log10 and then compared them to the output of mri_glmfit, namely sig.vol. This was the mri_glmfit command: mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname I was expecting the p-values to be the same, which apparently is not the case, unless I am doing/understanding something wrong.
By now, I am actually more inclined to use the regression coefficients instead. However, I'd still like to get pcc maps from them, if there is a way to do so in FSFAST. Thanks, Caspar
2013/10/3 Douglas N Greve greve@nmr.mgh.harvard.edu
On 10/03/2013 10:39 AM, Caspar M. Schwiedrzik wrote:
Hi Doug,
when I run a two-tailed t-test against 0 in Matlab on the Rs in pcc.nii that I get out of isxconcat-sess with -m pcc, and DOF from ffxdof.dat, I get different -log10(p) values than the ones that come out of mri_glmfit.
I don't understand what you mean. Can you elaborate?
I am not sure why this is happening.
In principle, I just want pcc maps as final output to show them on the surface (instead of p-values). So I'd be happy to follow your advice regarding the biasing effects of noise and autocorrelation and use the regression coefficients. However, mri_glmfit (v5.1) does not seem to output pcc maps of the contrasts (contrary to selxavg3-sess on the single subject level). How would I get those?
Thanks, Caspar
2013/10/1 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto: greve@nmr.mgh.harvard.**edu greve@nmr.mgh.harvard.edu>>
On 10/01/2013 01:13 PM, Caspar M. Schwiedrzik wrote: > Hi Doug, > it would be great if you could give me some further advise on the > group analysis of functional connectivity maps. > Specifically, I am trying to get PCC maps for certain seeds, and am > not planning any comparison between groups. > Following your previous advise, I am running isxconcat-sess with -m > pcc to get the PCC maps. > I would then run > > mri_glmfit \ > --surf averagesubject hemisphere \ > --y pcc.nii \ > --no-cortex \ > --osgm \ > --glmdir analysisname > > *Could you please provide some more detail on what kind of analysis is > performed when I provide pcc.nii as an input for mri_glmfit? Is it a > t-test of the Fisher-transformed r-values against 0? I just run a t-test of the r-values. I don't have a program to convert them to z-values, however, there are z-values that are created in the first level analysis. These are generated from the p-values but I bet it would give you the same thing. Use -m z with isxconcat-sess if you want to use the z. > *Is the average r-value or z-value saved somewhere? Which level? For mri_glmfit, they are not, but it is not hard to get them with matlab. > *Do you take the autocorrelation into account (as in Vincent JL et > al., 2007. Intrinsic functional architecture in the anaesthetized > monkey brain. Nature. 447:83-86)? Not usually, but it could be done by not including -no-whiten when you run mkanalysis-sess. I usually use the regression coefficients instead of correlation coefficients because that they are at least unbiased with respect to noise level and autocorrelation. doug > I'd also be happy to look this up but I'd need to know where I can > find this information. > > Thanks, Caspar > > > ______________________________**_________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.**harvard.edu<Freesurfer@nmr.mgh.harvard.edu>> https://mail.nmr.mgh.harvard.**edu/mailman/listinfo/**freesurfer<https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer> -- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu>Phone Number: 617-724-2358 <tel:617-724-2358> Fax: 617-726-7422 <tel:617-726-7422> Bugs: surfer.nmr.mgh.harvard.edu/**fswiki/BugReporting<http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> <http://surfer.nmr.mgh.**harvard.edu/fswiki/**BugReporting<http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting>FileDrop: https://gate.nmr.mgh.harvard.**edu/filedrop2<https://gate.nmr.mgh.harvard.edu/filedrop2> www.nmr.mgh.harvard.edu/**facility/filedrop/index.html<http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> <http://www.nmr.mgh.harvard.**edu/facility/filedrop/index.**html<http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html>Outgoing: ftp://surfer.nmr.mgh.harvard.**edu/transfer/outgoing/flat/**greve/<ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/> ______________________________**_________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.**harvard.edu Freesurfer@nmr.mgh.harvard.edu>
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Bugs: surfer.nmr.mgh.harvard.edu/**fswiki/BugReportinghttp://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.**edu/filedrop2https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/**facility/filedrop/index.htmlhttp://www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.**edu/transfer/outgoing/flat/** greve/ ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
It sounds like two issues: 1. p-values not consistent with your program. What did you use to compute? Did you do a two-sided (which is what fsfast uses)? 2. Using pcc maps. Why not use -m pcc?
doug
On 10/03/2013 01:10 PM, Caspar M. Schwiedrzik wrote:
Hi Doug, I loaded the pcc.nii file that I got from isxconcat-sess into Matlab and then ran a t-test against 0 over the 4th dimension. I converted the resulting p-values to -log10 and then compared them to the output of mri_glmfit, namely sig.vol. This was the mri_glmfit command: mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname I was expecting the p-values to be the same, which apparently is not the case, unless I am doing/understanding something wrong.
By now, I am actually more inclined to use the regression coefficients instead. However, I'd still like to get pcc maps from them, if there is a way to do so in FSFAST. Thanks, Caspar
2013/10/3 Douglas N Greve <greve@nmr.mgh.harvard.edu mailto:greve@nmr.mgh.harvard.edu>
On 10/03/2013 10:39 AM, Caspar M. Schwiedrzik wrote: Hi Doug, when I run a two-tailed t-test against 0 in Matlab on the Rs in pcc.nii that I get out of isxconcat-sess with -m pcc, and DOF from ffxdof.dat, I get different -log10(p) values than the ones that come out of mri_glmfit. I don't understand what you mean. Can you elaborate? I am not sure why this is happening. In principle, I just want pcc maps as final output to show them on the surface (instead of p-values). So I'd be happy to follow your advice regarding the biasing effects of noise and autocorrelation and use the regression coefficients. However, mri_glmfit (v5.1) does not seem to output pcc maps of the contrasts (contrary to selxavg3-sess on the single subject level). How would I get those? Thanks, Caspar 2013/10/1 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> On 10/01/2013 01:13 PM, Caspar M. Schwiedrzik wrote: > Hi Doug, > it would be great if you could give me some further advise on the > group analysis of functional connectivity maps. > Specifically, I am trying to get PCC maps for certain seeds, and am > not planning any comparison between groups. > Following your previous advise, I am running isxconcat-sess with -m > pcc to get the PCC maps. > I would then run > > mri_glmfit \ > --surf averagesubject hemisphere \ > --y pcc.nii \ > --no-cortex \ > --osgm \ > --glmdir analysisname > > *Could you please provide some more detail on what kind of analysis is > performed when I provide pcc.nii as an input for mri_glmfit? Is it a > t-test of the Fisher-transformed r-values against 0? I just run a t-test of the r-values. I don't have a program to convert them to z-values, however, there are z-values that are created in the first level analysis. These are generated from the p-values but I bet it would give you the same thing. Use -m z with isxconcat-sess if you want to use the z. > *Is the average r-value or z-value saved somewhere? Which level? For mri_glmfit, they are not, but it is not hard to get them with matlab. > *Do you take the autocorrelation into account (as in Vincent JL et > al., 2007. Intrinsic functional architecture in the anaesthetized > monkey brain. Nature. 447:83-86)? Not usually, but it could be done by not including -no-whiten when you run mkanalysis-sess. I usually use the regression coefficients instead of correlation coefficients because that they are at least unbiased with respect to noise level and autocorrelation. doug > I'd also be happy to look this up but I'd need to know where I can > find this information. > > Thanks, Caspar > > > _______________________________________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> <mailto:Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu>> > https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer -- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> Phone Number: 617-724-2358 <tel:617-724-2358> <tel:617-724-2358 <tel:617-724-2358>> Fax: 617-726-7422 <tel:617-726-7422> <tel:617-726-7422 <tel:617-726-7422>> Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/ _______________________________________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> <mailto:Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu>> https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail. -- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> Phone Number: 617-724-2358 <tel:617-724-2358> Fax: 617-726-7422 <tel:617-726-7422> Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
Hi Doug,
On Thursday, October 3, 2013, Douglas N Greve wrote:
It sounds like two issues:
- p-values not consistent with your program. What did you use to compute?
Did you do a two-sided (which is what fsfast uses)?
I used ttest in Matlab, two sided.
2. Using pcc maps. Why not use -m pcc?
Isn't that giving me a map per subject? How do I get the group map that is consistent with the results of mri_glmfit run on ces.nii?
Thanks, Caspar
doug
On 10/03/2013 01:10 PM, Caspar M. Schwiedrzik wrote:
Hi Doug, I loaded the pcc.nii file that I got from isxconcat-sess into Matlab and then ran a t-test against 0 over the 4th dimension. I converted the resulting p-values to -log10 and then compared them to the output of mri_glmfit, namely sig.vol. This was the mri_glmfit command: mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname I was expecting the p-values to be the same, which apparently is not the case, unless I am doing/understanding something wrong.
By now, I am actually more inclined to use the regression coefficients instead. However, I'd still like to get pcc maps from them, if there is a way to do so in FSFAST. Thanks, Caspar
2013/10/3 Douglas N Greve <greve@nmr.mgh.harvard.edu mailto: greve@nmr.mgh.harvard.edu>
On 10/03/2013 10:39 AM, Caspar M. Schwiedrzik wrote: Hi Doug, when I run a two-tailed t-test against 0 in Matlab on the Rs in pcc.nii that I get out of isxconcat-sess with -m pcc, and DOF from ffxdof.dat, I get different -log10(p) values than the ones that come out of mri_glmfit. I don't understand what you mean. Can you elaborate? I am not sure why this is happening. In principle, I just want pcc maps as final output to show them on the surface (instead of p-values). So I'd be happy to follow your advice regarding the biasing effects of noise and autocorrelation and use the regression coefficients. However, mri_glmfit (v5.1) does not seem to output pcc maps of the contrasts (contrary to selxavg3-sess on the single subject level). How would I get those? Thanks, Caspar 2013/10/1 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> On 10/01/2013 01:13 PM, Caspar M. Schwiedrzik wrote: > Hi Doug, > it would be great if you could give me some further advise on the > group analysis of functional connectivity maps. > Specifically, I am trying to get PCC maps for certain seeds, and am > not planning any comparison between groups. > Following your previous advise, I am running isxconcat-sess with -m > pcc to get the PCC maps. > I would then run > > mri_glmfit \ > --surf averagesubject hemisphere \ > --y pcc.nii \ > --no-cortex \ > --osgm \ > --glmdir analysisname > > *Could you please provide some more detail on what kind of analysis is > performed when I provide pcc.nii as an input for mri_glmfit? Is it a > t-test of the Fisher-transformed r-values against 0? I just run a t-test of the r-values. I don't have a program to convert them to z-values, however, there are z-values that are created in the first level analysis. These are generated from the p-values but I bet it would give you the same thing. Use -m z with isxconcat-sess if you want to use the z. > *Is the average r-value or z-value saved somewhere? Which level? For mri_glmfit, they are not, but it is not hard to get them with matlab. > *Do you take the autocorrelation into account (as in Vincent JL et > al., 2007. Intrinsic functional architecture in the anaesthetized > monkey brain. Nature. 447:83-86)? Not usually, but it could be done by not including -no-whiten when you run mkanalysis-sess. I usually use the regression coefficients instead of correlation coefficients because that they are at least unbiased with respect to noise level and autocorrelation. doug > I'd also be happy to look this up but I'd need to know where I can > find this information. > > Thanks, Caspar > > > ______________________________**_________________ > Freesurfer mailing list > Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> <mailto:Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu>> > https://mail.nmr.mgh.harvard.**edu/mailman/listinfo/**freesurfer https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer
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______________________________**_________________ Freesurfer mailing list Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu> <mailto:Freesurfer@nmr.mgh.harvard.edu <mailto:Freesurfer@nmr.mgh.harvard.edu>> https://mail.nmr.mgh.harvard.**edu/mailman/listinfo/**freesurfer<https://mail.nmr.mgh.harvard.edu/mailman/listinfo/freesurfer> The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/**complianceline<http://www.partners.org/complianceline>. If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail. -- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> Phone Number: 617-724-2358 <tel:617-724-2358> Fax: 617-726-7422 <tel:617-726-7422> Bugs: surfer.nmr.mgh.harvard.edu/**fswiki/BugReporting<http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> <http://surfer.nmr.mgh.**harvard.edu/fswiki/**BugReporting<http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting>FileDrop: https://gate.nmr.mgh.harvard.**edu/filedrop2<https://gate.nmr.mgh.harvard.edu/filedrop2> www.nmr.mgh.harvard.edu/**facility/filedrop/index.html<http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> <http://www.nmr.mgh.harvard.**edu/facility/filedrop/index.**html<http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html>Outgoing: ftp://surfer.nmr.mgh.harvard.**edu/transfer/outgoing/flat/**greve/<ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/>-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/**fswiki/BugReportinghttp://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.**edu/filedrop2https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/**facility/filedrop/index.htmlhttp://www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.**edu/transfer/outgoing/flat/** greve/ ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
Hi Doug, I guess it boils down to the question how to get a group PCC map after a RFX GLM? Using -m PCC seems to only give me a map per subject. Are you calculating PCC from the t- values? Thanks, Caspar
On Thursday, October 3, 2013, Caspar M. Schwiedrzik wrote:
Hi Doug,
On Thursday, October 3, 2013, Douglas N Greve wrote:
It sounds like two issues:
- p-values not consistent with your program. What did you use to
compute? Did you do a two-sided (which is what fsfast uses)?
I used ttest in Matlab, two sided.
- Using pcc maps. Why not use -m pcc?
Isn't that giving me a map per subject? How do I get the group map that is consistent with the results of mri_glmfit run on ces.nii?
Thanks, Caspar
doug
On 10/03/2013 01:10 PM, Caspar M. Schwiedrzik wrote:
Hi Doug, I loaded the pcc.nii file that I got from isxconcat-sess into Matlab and then ran a t-test against 0 over the 4th dimension. I converted the resulting p-values to -log10 and then compared them to the output of mri_glmfit, namely sig.vol. This was the mri_glmfit command: mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname I was expecting the p-values to be the same, which apparently is not the case, unless I am doing/understanding something wrong.
By now, I am actually more inclined to use the regression coefficients instead. However, I'd still like to get pcc maps from them, if there is a way to do so in FSFAST. Thanks, Caspar
2013/10/3 Douglas N Greve <greve@nmr.mgh.harvard.edu mailto: greve@nmr.mgh.harvard.edu>
On 10/03/2013 10:39 AM, Caspar M. Schwiedrzik wrote: Hi Doug, when I run a two-tailed t-test against 0 in Matlab on the Rs in pcc.nii that I get out of isxconcat-sess with -m pcc, and DOF from ffxdof.dat, I get different -log10(p) values than the ones that come out of mri_glmfit. I don't understand what you mean. Can you elaborate? I am not sure why this is happening. In principle, I just want pcc maps as final output to show them on the surface (instead of p-values). So I'd be happy to follow your advice regarding the biasing effects of noise and autocorrelation and use the regression coefficients. However, mri_glmfit (v5.1) does not seem to output pcc maps of the contrasts (contrary to selxavg3-sess on the single subject level). How would I get those? Thanks, Caspar 2013/10/1 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> On 10/01/2013 01:13 PM, Caspar M. Schwiedrzik wrote: > Hi Doug, > it would be great if you could give me some further advise on the > group analysis of functional connectivity maps. > Specifically, I am trying to get PCC maps for certain seeds, and am > not planning any comparison between groups. > Following your previous advise, I am running isxconcat-sess with -m > pcc to get the PCC maps. > I would then run > > mri_glmfit \ > --surf averagesubject hemisphere \ > --y pcc.nii \ > --no-cortex \ > --osgm \ > --glmdir analysisname > > *Could you please provide some more detail on what kind of analysis is > performed when I provide pcc.nii as an input for mri_glmfit? Is it a > t-test of the Fisher-transformed r-values against 0? I just run a t-test of the r-values. I don't have a program to convert them to z-values, however, there are z-values that are created in the first level analysis. These are generated from the p-values but I bet it would give you the same thing. Use -m z with isxconcat-sess if you want to use the z. > *Is the average r-value or z-value saved somewhere? Which level? For mri_glmfit, they are not, but it is not hard to get them with matlab. > *Do you take the autocorrelation into account (as in Vincent JL et > al., 2007. Intrinsic functional architecture in the anaesthetized > monkey brain. Nature. 447:83-86)? Not usually, but it could be done by not including -no-whiten when you run mkanalysis-sess. I usually use the regression coefficients instead of correlation coefficients because that they are at least unbiased with respect to noise level and autocorrelation. doug > I'd also be happy to look this up but I'd need to know where I can
I think you are conflating the 1st level and the 2nd level. You could get pcc out of the 2nd level regardless of what you are using for the input from the first level. I've attached a matlab script that will compute the pcc for mri_glmfit output
doug
On 10/04/2013 03:33 PM, Caspar M. Schwiedrzik wrote:
Hi Doug, I guess it boils down to the question how to get a group PCC map after a RFX GLM? Using -m PCC seems to only give me a map per subject. Are you calculating PCC from the t- values? Thanks, Caspar
On Thursday, October 3, 2013, Caspar M. Schwiedrzik wrote:
Hi Doug, On Thursday, October 3, 2013, Douglas N Greve wrote: It sounds like two issues: 1. p-values not consistent with your program. What did you use to compute? Did you do a two-sided (which is what fsfast uses)? I used ttest in Matlab, two sided. 2. Using pcc maps. Why not use -m pcc? Isn't that giving me a map per subject? How do I get the group map that is consistent with the results of mri_glmfit run on ces.nii? Thanks, Caspar doug On 10/03/2013 01:10 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I loaded the pcc.nii file that I got from isxconcat-sess into Matlab and then ran a t-test against 0 over the 4th dimension. I converted the resulting p-values to -log10 and then compared them to the output of mri_glmfit, namely sig.vol. This was the mri_glmfit command: mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname I was expecting the p-values to be the same, which apparently is not the case, unless I am doing/understanding something wrong. By now, I am actually more inclined to use the regression coefficients instead. However, I'd still like to get pcc maps from them, if there is a way to do so in FSFAST. Thanks, Caspar 2013/10/3 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> On 10/03/2013 10:39 AM, Caspar M. Schwiedrzik wrote: Hi Doug, when I run a two-tailed t-test against 0 in Matlab on the Rs in pcc.nii that I get out of isxconcat-sess with -m pcc, and DOF from ffxdof.dat, I get different -log10(p) values than the ones that come out of mri_glmfit. I don't understand what you mean. Can you elaborate? I am not sure why this is happening. In principle, I just want pcc maps as final output to show them on the surface (instead of p-values). So I'd be happy to follow your advice regarding the biasing effects of noise and autocorrelation and use the regression coefficients. However, mri_glmfit (v5.1) does not seem to output pcc maps of the contrasts (contrary to selxavg3-sess on the single subject level). How would I get those? Thanks, Caspar 2013/10/1 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> On 10/01/2013 01:13 PM, Caspar M. Schwiedrzik wrote: > Hi Doug, > it would be great if you could give me some further advise on the > group analysis of functional connectivity maps. > Specifically, I am trying to get PCC maps for certain seeds, and am > not planning any comparison between groups. > Following your previous advise, I am running isxconcat-sess with -m > pcc to get the PCC maps. > I would then run > > mri_glmfit \ > --surf averagesubject hemisphere \ > --y pcc.nii \ > --no-cortex \ > --osgm \ > --glmdir analysisname > > *Could you please provide some more detail on what kind of analysis is > performed when I provide pcc.nii as an input for mri_glmfit? Is it a > t-test of the Fisher-transformed r-values against 0? I just run a t-test of the r-values. I don't have a program to convert them to z-values, however, there are z-values that are created in the first level analysis. These are generated from the p-values but I bet it would give you the same thing. Use -m z with isxconcat-sess if you want to use the z. > *Is the average r-value or z-value saved somewhere? Which level? For mri_glmfit, they are not, but it is not hard to get them with matlab. > *Do you take the autocorrelation into account (as in Vincent JL et > al., 2007. Intrinsic functional architecture in the anaesthetized > monkey brain. Nature. 447:83-86)? Not usually, but it could be done by not including -no-whiten when you run mkanalysis-sess. I usually use the regression coefficients instead of correlation coefficients because that they are at least unbiased with respect to noise level and autocorrelation. doug > I'd also be happy to look this up but I'd need to know where I can
Hi Doug, thank you very much for sending the Matlab function. When I run this, it creates a pcc.mgh file for my osgm contrast. However, the values seem strange. They range from -630 to 36 for my particular dataset. I was expecting something between -1 and 1. Caspar
2013/10/4 Douglas N Greve greve@nmr.mgh.harvard.edu
I think you are conflating the 1st level and the 2nd level. You could get pcc out of the 2nd level regardless of what you are using for the input from the first level. I've attached a matlab script that will compute the pcc for mri_glmfit output
doug
On 10/04/2013 03:33 PM, Caspar M. Schwiedrzik wrote:
Hi Doug, I guess it boils down to the question how to get a group PCC map after a RFX GLM? Using -m PCC seems to only give me a map per subject. Are you calculating PCC from the t- values? Thanks, Caspar
On Thursday, October 3, 2013, Caspar M. Schwiedrzik wrote:
Hi Doug, On Thursday, October 3, 2013, Douglas N Greve wrote: It sounds like two issues: 1. p-values not consistent with your program. What did you use to compute? Did you do a two-sided (which is what fsfast uses)? I used ttest in Matlab, two sided. 2. Using pcc maps. Why not use -m pcc? Isn't that giving me a map per subject? How do I get the group map that is consistent with the results of mri_glmfit run on ces.nii? Thanks, Caspar doug On 10/03/2013 01:10 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I loaded the pcc.nii file that I got from isxconcat-sess into Matlab and then ran a t-test against 0 over the 4th dimension. I converted the resulting p-values to -log10 and then compared them to the output of mri_glmfit, namely sig.vol. This was the mri_glmfit command: mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname I was expecting the p-values to be the same, which apparently is not the case, unless I am doing/understanding something wrong. By now, I am actually more inclined to use the regression coefficients instead. However, I'd still like to get pcc maps from them, if there is a way to do so in FSFAST. Thanks, Caspar 2013/10/3 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu>On 10/03/2013 10:39 AM, Caspar M. Schwiedrzik wrote: Hi Doug, when I run a two-tailed t-test against 0 in Matlab on the Rs in pcc.nii that I get out of isxconcat-sess with -m pcc, and DOF from ffxdof.dat, I get different -log10(p) values than the ones that come out of mri_glmfit. I don't understand what you mean. Can you elaborate? I am not sure why this is happening. In principle, I just want pcc maps as final output to show them on the surface (instead of p-values). So I'd be happy to follow your advice regarding the biasing effects of noise and autocorrelation and use the regression coefficients. However, mri_glmfit (v5.1) does not seem to output pcc maps of the contrasts (contrary to selxavg3-sess on the single subject level). How would I get those? Thanks, Caspar 2013/10/1 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu>On 10/01/2013 01:13 PM, Caspar M. Schwiedrzik wrote: > Hi Doug, > it would be great if you could give me some further advise on the > group analysis of functional connectivity maps. > Specifically, I am trying to get PCC maps for certain seeds, and am > not planning any comparison between groups. > Following your previous advise, I am running isxconcat-sess with -m > pcc to get the PCC maps. > I would then run > > mri_glmfit \ > --surf averagesubject hemisphere \ > --y pcc.nii \ > --no-cortex \ > --osgm \ > --glmdir analysisname > > *Could you please provide some more detail on what kind of analysis is > performed when I provide pcc.nii as an input for mri_glmfit? Is it a > t-test of the Fisher-transformed r-values against 0? I just run a t-test of the r-values. I don't have a program to convert them to z-values, however, there are z-values that are created in the first level analysis. These are generated from the p-values but I bet it would give you the same thing. Use -m z with isxconcat-sess if you want to use the z. > *Is the average r-value or z-value saved somewhere? Which level? For mri_glmfit, they are not, but it is not hard to get them with matlab. > *Do you take the autocorrelation into account (as in Vincent JL et > al., 2007. Intrinsic functional architecture in the anaesthetized > monkey brain. Nature. 447:83-86)? Not usually, but it could be done by not including -no-whiten when you run mkanalysis-sess. I usually use the regression coefficients instead of correlation coefficients because that they are at least unbiased with respect to noise level and autocorrelation. doug > I'd also be happy to look this up but I'd need to know where I can-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/**fswiki/BugReportinghttp://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.**edu/filedrop2https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/**facility/filedrop/index.htmlhttp://www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.**edu/transfer/outgoing/flat/** greve/ ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/**compliancelinehttp://www.partners.org/complianceline. If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
Can you tar up you glmfit dir and drop it to me on our file drop?
On 10/04/2013 05:29 PM, Caspar M. Schwiedrzik wrote:
Hi Doug, thank you very much for sending the Matlab function. When I run this, it creates a pcc.mgh file for my osgm contrast. However, the values seem strange. They range from -630 to 36 for my particular dataset. I was expecting something between -1 and 1. Caspar
2013/10/4 Douglas N Greve <greve@nmr.mgh.harvard.edu mailto:greve@nmr.mgh.harvard.edu>
I think you are conflating the 1st level and the 2nd level. You could get pcc out of the 2nd level regardless of what you are using for the input from the first level. I've attached a matlab script that will compute the pcc for mri_glmfit output doug On 10/04/2013 03:33 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I guess it boils down to the question how to get a group PCC map after a RFX GLM? Using -m PCC seems to only give me a map per subject. Are you calculating PCC from the t- values? Thanks, Caspar On Thursday, October 3, 2013, Caspar M. Schwiedrzik wrote: Hi Doug, On Thursday, October 3, 2013, Douglas N Greve wrote: It sounds like two issues: 1. p-values not consistent with your program. What did you use to compute? Did you do a two-sided (which is what fsfast uses)? I used ttest in Matlab, two sided. 2. Using pcc maps. Why not use -m pcc? Isn't that giving me a map per subject? How do I get the group map that is consistent with the results of mri_glmfit run on ces.nii? Thanks, Caspar doug On 10/03/2013 01:10 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I loaded the pcc.nii file that I got from isxconcat-sess into Matlab and then ran a t-test against 0 over the 4th dimension. I converted the resulting p-values to -log10 and then compared them to the output of mri_glmfit, namely sig.vol. This was the mri_glmfit command: mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname I was expecting the p-values to be the same, which apparently is not the case, unless I am doing/understanding something wrong. By now, I am actually more inclined to use the regression coefficients instead. However, I'd still like to get pcc maps from them, if there is a way to do so in FSFAST. Thanks, Caspar 2013/10/3 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> On 10/03/2013 10:39 AM, Caspar M. Schwiedrzik wrote: Hi Doug, when I run a two-tailed t-test against 0 in Matlab on the Rs in pcc.nii that I get out of isxconcat-sess with -m pcc, and DOF from ffxdof.dat, I get different -log10(p) values than the ones that come out of mri_glmfit. I don't understand what you mean. Can you elaborate? I am not sure why this is happening. In principle, I just want pcc maps as final output to show them on the surface (instead of p-values). So I'd be happy to follow your advice regarding the biasing effects of noise and autocorrelation and use the regression coefficients. However, mri_glmfit (v5.1) does not seem to output pcc maps of the contrasts (contrary to selxavg3-sess on the single subject level). How would I get those? Thanks, Caspar 2013/10/1 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>>> On 10/01/2013 01:13 PM, Caspar M. Schwiedrzik wrote: > Hi Doug, > it would be great if you could give me some further advise on the > group analysis of functional connectivity maps. > Specifically, I am trying to get PCC maps for certain seeds, and am > not planning any comparison between groups. > Following your previous advise, I am running isxconcat-sess with -m > pcc to get the PCC maps. > I would then run > > mri_glmfit \ > --surf averagesubject hemisphere \ > --y pcc.nii \ > --no-cortex \ > --osgm \ > --glmdir analysisname > > *Could you please provide some more detail on what kind of analysis is > performed when I provide pcc.nii as an input for mri_glmfit? Is it a > t-test of the Fisher-transformed r-values against 0? I just run a t-test of the r-values. I don't have a program to convert them to z-values, however, there are z-values that are created in the first level analysis. These are generated from the p-values but I bet it would give you the same thing. Use -m z with isxconcat-sess if you want to use the z. > *Is the average r-value or z-value saved somewhere? Which level? For mri_glmfit, they are not, but it is not hard to get them with matlab. > *Do you take the autocorrelation into account (as in Vincent JL et > al., 2007. Intrinsic functional architecture in the anaesthetized > monkey brain. Nature. 447:83-86)? Not usually, but it could be done by not including -no-whiten when you run mkanalysis-sess. I usually use the regression coefficients instead of correlation coefficients because that they are at least unbiased with respect to noise level and autocorrelation. doug > I'd also be happy to look this up but I'd need to know where I can -- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> Phone Number: 617-724-2358 <tel:617-724-2358> Fax: 617-726-7422 <tel:617-726-7422> Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/ The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.
Done. Thank you very much for looking into this. Caspar
2013/10/4 Douglas N Greve greve@nmr.mgh.harvard.edu
Can you tar up you glmfit dir and drop it to me on our file drop?
On 10/04/2013 05:29 PM, Caspar M. Schwiedrzik wrote:
Hi Doug,
thank you very much for sending the Matlab function. When I run this, it creates a pcc.mgh file for my osgm contrast. However, the values seem strange. They range from -630 to 36 for my particular dataset. I was expecting something between -1 and 1. Caspar
2013/10/4 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto: greve@nmr.mgh.harvard.**edu greve@nmr.mgh.harvard.edu>>
I think you are conflating the 1st level and the 2nd level. You could get pcc out of the 2nd level regardless of what you are using for the input from the first level. I've attached a matlab script that will compute the pcc for mri_glmfit output doug On 10/04/2013 03:33 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I guess it boils down to the question how to get a group PCC map after a RFX GLM? Using -m PCC seems to only give me a map per subject. Are you calculating PCC from the t- values? Thanks, Caspar On Thursday, October 3, 2013, Caspar M. Schwiedrzik wrote: Hi Doug, On Thursday, October 3, 2013, Douglas N Greve wrote: It sounds like two issues: 1. p-values not consistent with your program. What did you use to compute? Did you do a two-sided (which is what fsfast uses)? I used ttest in Matlab, two sided. 2. Using pcc maps. Why not use -m pcc? Isn't that giving me a map per subject? How do I get the group map that is consistent with the results of mri_glmfit run on ces.nii? Thanks, Caspar doug On 10/03/2013 01:10 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I loaded the pcc.nii file that I got from isxconcat-sess into Matlab and then ran a t-test against 0 over the 4th dimension. I converted the resulting p-values to -log10 and then compared them to the output of mri_glmfit, namely sig.vol. This was the mri_glmfit command: mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname I was expecting the p-values to be the same, which apparently is not the case, unless I am doing/understanding something wrong. By now, I am actually more inclined to use the regression coefficients instead. However, I'd still like to get pcc maps from them, if there is a way to do so in FSFAST. Thanks, Caspar 2013/10/3 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu <greve@nmr.mgh.harvard.edu>On 10/03/2013 10:39 AM, Caspar M. Schwiedrzik wrote: Hi Doug, when I run a two-tailed t-test against 0 in Matlab on the Rs in pcc.nii that I get out of isxconcat-sess with -m pcc, and DOF from ffxdof.dat, I get different -log10(p) values than the ones that come out of mri_glmfit. I don't understand what you mean. Can you elaborate? I am not sure why this is happening. In principle, I just want pcc maps as final output to show them on the surface (instead of p-values). So I'd be happy to follow your advice regarding the biasing effects of noise and autocorrelation and use the regression coefficients. However, mri_glmfit (v5.1) does not seem to output pcc maps of the contrasts (contrary to selxavg3-sess on the single subject level). How would I get those? Thanks, Caspar 2013/10/1 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu <greve@nmr.mgh.harvard.edu>>> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu <greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu <greve@nmr.mgh.harvard.edu>On 10/01/2013 01:13 PM, Caspar M. Schwiedrzik wrote: > Hi Doug, > it would be great if you could give me some further advise on the > group analysis of functional connectivity maps. > Specifically, I am trying to get PCC maps for certain seeds, and am > not planning any comparison between groups. > Following your previous advise, I am running isxconcat-sess with -m > pcc to get the PCC maps. > I would then run > > mri_glmfit \ > --surf averagesubject hemisphere \ > --y pcc.nii \ > --no-cortex \ > --osgm \ > --glmdir analysisname > > *Could you please provide some more detail on what kind of analysis is > performed when I provide pcc.nii as an input for mri_glmfit? Is it a > t-test of the Fisher-transformed r-values against 0? I just run a t-test of the r-values. I don't have a program to convert them to z-values, however, there are z-values that are created in the first level analysis. These are generated from the p-values but I bet it would give you the same thing. Use -m z with isxconcat-sess if you want to use the z. > *Is the average r-value or z-value saved somewhere? Which level? For mri_glmfit, they are not, but it is not hard to get them with matlab. > *Do you take the autocorrelation into account (as in Vincent JL et > al., 2007. Intrinsic functional architecture in the anaesthetized > monkey brain. Nature. 447:83-86)? Not usually, but it could be done by not including -no-whiten when you run mkanalysis-sess. I usually use the regression coefficients instead of correlation coefficients because that they are at least unbiased with respect to noise level and autocorrelation. doug > I'd also be happy to look this up but I'd need to know where I can -- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu>Phone Number: 617-724-2358 <tel:617-724-2358> Fax: 617-726-7422 <tel:617-726-7422> Bugs: surfer.nmr.mgh.harvard.edu/**fswiki/BugReporting<http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> <http://surfer.nmr.mgh.**harvard.edu/fswiki/**BugReporting<http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting>FileDrop: https://gate.nmr.mgh.harvard.**edu/filedrop2<https://gate.nmr.mgh.harvard.edu/filedrop2> www.nmr.mgh.harvard.edu/**facility/filedrop/index.html<http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> <http://www.nmr.mgh.harvard.**edu/facility/filedrop/index.**html<http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html>Outgoing: ftp://surfer.nmr.mgh.harvard.**edu/transfer/outgoing/flat/**greve/<ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/> The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/**complianceline<http://www.partners.org/complianceline>. If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/**fswiki/BugReportinghttp://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.**edu/filedrop2https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/**facility/filedrop/index.htmlhttp://www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.**edu/transfer/outgoing/flat/** greve/ ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
Hi Doug, did you already have a chance to look into this? Thanks, Caspar
2013/10/4 Caspar M. Schwiedrzik cschwiedrz@rockefeller.edu
Done. Thank you very much for looking into this. Caspar
2013/10/4 Douglas N Greve greve@nmr.mgh.harvard.edu
Can you tar up you glmfit dir and drop it to me on our file drop?
On 10/04/2013 05:29 PM, Caspar M. Schwiedrzik wrote:
Hi Doug,
thank you very much for sending the Matlab function. When I run this, it creates a pcc.mgh file for my osgm contrast. However, the values seem strange. They range from -630 to 36 for my particular dataset. I was expecting something between -1 and 1. Caspar
2013/10/4 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto: greve@nmr.mgh.harvard.**edu greve@nmr.mgh.harvard.edu>>
I think you are conflating the 1st level and the 2nd level. You could get pcc out of the 2nd level regardless of what you are using for the input from the first level. I've attached a matlab script that will compute the pcc for mri_glmfit output doug On 10/04/2013 03:33 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I guess it boils down to the question how to get a group PCC map after a RFX GLM? Using -m PCC seems to only give me a map per subject. Are you calculating PCC from the t- values? Thanks, Caspar On Thursday, October 3, 2013, Caspar M. Schwiedrzik wrote: Hi Doug, On Thursday, October 3, 2013, Douglas N Greve wrote: It sounds like two issues: 1. p-values not consistent with your program. What did you use to compute? Did you do a two-sided (which is what fsfast uses)? I used ttest in Matlab, two sided. 2. Using pcc maps. Why not use -m pcc? Isn't that giving me a map per subject? How do I get the group map that is consistent with the results of mri_glmfit run on ces.nii? Thanks, Caspar doug On 10/03/2013 01:10 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I loaded the pcc.nii file that I got from isxconcat-sess into Matlab and then ran a t-test against 0 over the 4th dimension. I converted the resulting p-values to -log10 and then compared them to the output of mri_glmfit, namely sig.vol. This was the mri_glmfit command: mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname I was expecting the p-values to be the same, which apparently is not the case, unless I am doing/understanding something wrong. By now, I am actually more inclined to use the regression coefficients instead. However, I'd still like to get pcc maps from them, if there is a way to do so in FSFAST. Thanks, Caspar 2013/10/3 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu <greve@nmr.mgh.harvard.edu>On 10/03/2013 10:39 AM, Caspar M. Schwiedrzik wrote: Hi Doug, when I run a two-tailed t-test against 0 in Matlab on the Rs in pcc.nii that I get out of isxconcat-sess with -m pcc, and DOF from ffxdof.dat, I get different-log10(p) values than the ones that come out of mri_glmfit.
I don't understand what you mean. Can you elaborate? I am not sure why this is happening. In principle, I just want pcc maps as final output to show them on the surface (instead of p-values). So I'd be happy to follow your advice regarding the biasing effects of noise and autocorrelation and use the regression coefficients. However, mri_glmfit (v5.1) does not seem to output pcc maps of the contrasts (contrary to selxavg3-sess on the single subject level). How would I get those? Thanks, Caspar 2013/10/1 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu <greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu <greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu <greve@nmr.mgh.harvard.edu>>
On 10/01/2013 01:13 PM, Caspar M. Schwiedrzik wrote: > Hi Doug, > it would be great if you could give me some further advise on the > group analysis of functional connectivity maps. > Specifically, I am trying to get PCC maps for certain seeds, and am > not planning any comparison between groups. > Following your previous advise, I am running isxconcat-sess with -m > pcc to get the PCC maps. > I would then run > > mri_glmfit \ > --surf averagesubject hemisphere \ > --y pcc.nii \ > --no-cortex \ > --osgm \ > --glmdir analysisname > > *Could you please provide some more detail on what kind of analysis is > performed when I provide pcc.nii as an input for mri_glmfit? Is it a > t-test of the Fisher-transformed r-values against 0? I just run a t-test of the r-values. I don't have a program to convert them to z-values, however, there are z-values that are created in the first level analysis. These are generated from the p-values but I bet it would give you the same thing. Use -m z with isxconcat-sess if you want to use the z. > *Is the average r-value or z-valuesaved somewhere? Which level? For mri_glmfit, they are not, but it is not hard to get them with matlab. > *Do you take the autocorrelation into account (as in Vincent JL et > al., 2007. Intrinsic functional architecture in the anaesthetized > monkey brain. Nature. 447:83-86)? Not usually, but it could be done by not including -no-whiten when you run mkanalysis-sess. I usually use the regression coefficients instead of correlation coefficients because that they are at least unbiased with respect to noise level and autocorrelation. doug
> I'd also be happy to look this up but I'd need to know where I can -- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu>Phone Number: 617-724-2358 <tel:617-724-2358> Fax: 617-726-7422 <tel:617-726-7422> Bugs: surfer.nmr.mgh.harvard.edu/**fswiki/BugReporting<http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> <http://surfer.nmr.mgh.**harvard.edu/fswiki/**BugReporting<http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting>FileDrop: https://gate.nmr.mgh.harvard.**edu/filedrop2<https://gate.nmr.mgh.harvard.edu/filedrop2> www.nmr.mgh.harvard.edu/**facility/filedrop/index.html<http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> <http://www.nmr.mgh.harvard.**edu/facility/filedrop/index.**html<http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html>Outgoing: ftp://surfer.nmr.mgh.harvard.**edu/transfer/outgoing/flat/**greve/<ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/> The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/**complianceline<http://www.partners.org/complianceline>. If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail.-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/**fswiki/BugReportinghttp://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.**edu/filedrop2https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/**facility/filedrop/index.htmlhttp://www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.**edu/transfer/outgoing/flat/** greve/ ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
Oh, yes, sorry. The reason that mri_glmfit_pcc is failing is because this is a one-sample group mean (design matrix a column of all 1s). What are you trying to compute the correlation between? Or do you just want to convert the p-values to a correlation coefficient?
On 10/09/2013 01:26 PM, Caspar M. Schwiedrzik wrote:
Hi Doug, did you already have a chance to look into this? Thanks, Caspar
2013/10/4 Caspar M. Schwiedrzik <cschwiedrz@rockefeller.edu mailto:cschwiedrz@rockefeller.edu>
Done. Thank you very much for looking into this. Caspar 2013/10/4 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> Can you tar up you glmfit dir and drop it to me on our file drop? On 10/04/2013 05:29 PM, Caspar M. Schwiedrzik wrote: Hi Doug, thank you very much for sending the Matlab function. When I run this, it creates a pcc.mgh file for my osgm contrast. However, the values seem strange. They range from -630 to 36 for my particular dataset. I was expecting something between -1 and 1. Caspar 2013/10/4 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> I think you are conflating the 1st level and the 2nd level. You could get pcc out of the 2nd level regardless of what you are using for the input from the first level. I've attached a matlab script that will compute the pcc for mri_glmfit output doug On 10/04/2013 03:33 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I guess it boils down to the question how to get a group PCC map after a RFX GLM? Using -m PCC seems to only give me a map per subject. Are you calculating PCC from the t- values? Thanks, Caspar On Thursday, October 3, 2013, Caspar M. Schwiedrzik wrote: Hi Doug, On Thursday, October 3, 2013, Douglas N Greve wrote: It sounds like two issues: 1. p-values not consistent with your program. What did you use to compute? Did you do a two-sided (which is what fsfast uses)? I used ttest in Matlab, two sided. 2. Using pcc maps. Why not use -m pcc? Isn't that giving me a map per subject? How do I get the group map that is consistent with the results of mri_glmfit run on ces.nii? Thanks, Caspar doug On 10/03/2013 01:10 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I loaded the pcc.nii file that I got from isxconcat-sess into Matlab and then ran a t-test against 0 over the 4th dimension. I converted the resulting p-values to -log10 and then compared them to the output of mri_glmfit, namely sig.vol. This was the mri_glmfit command: mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname I was expecting the p-values to be the same, which apparently is not the case, unless I am doing/understanding something wrong. By now, I am actually more inclined to use the regression coefficients instead. However, I'd still like to get pcc maps from them, if there is a way to do so in FSFAST. Thanks, Caspar 2013/10/3 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>>> On 10/03/2013 10:39 AM, Caspar M. Schwiedrzik wrote: Hi Doug, when I run a two-tailed t-test against 0 in Matlab on the Rs in pcc.nii that I get out of isxconcat-sess with -m pcc, and DOF from ffxdof.dat, I get different -log10(p) values than the ones that come out of mri_glmfit. I don't understand what you mean. Can you elaborate? I am not sure why this is happening. In principle, I just want pcc maps as final output to show them on the surface (instead of p-values). So I'd be happy to follow your advice regarding the biasing effects of noise and autocorrelation and use the regression coefficients. However, mri_glmfit (v5.1) does not seem to output pcc maps of the contrasts (contrary to selxavg3-sess on the single subject level). How would I get those? Thanks, Caspar 2013/10/1 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>>>> On 10/01/2013 01:13 PM, Caspar M. Schwiedrzik wrote: > Hi Doug, > it would be great if you could give me some further advise on the > group analysis of functional connectivity maps. > Specifically, I am trying to get PCC maps for certain seeds, and am > not planning any comparison between groups. > Following your previous advise, I am running isxconcat-sess with -m > pcc to get the PCC maps. > I would then run > > mri_glmfit \ > --surf averagesubject hemisphere \ > --y pcc.nii \ > --no-cortex \ > --osgm \ > --glmdir analysisname > > *Could you please provide some more detail on what kind of analysis is > performed when I provide pcc.nii as an input for mri_glmfit? Is it a > t-test of the Fisher-transformed r-values against 0? I just run a t-test of the r-values. I don't have a program to convert them to z-values, however, there are z-values that are created in the first level analysis. These are generated from the p-values but I bet it would give you the same thing. Use -m z with isxconcat-sess if you want to use the z. > *Is the average r-value or z-value saved somewhere? Which level? For mri_glmfit, they are not, but it is not hard to get them with matlab. > *Do you take the autocorrelation into account (as in Vincent JL et > al., 2007. Intrinsic functional architecture in the anaesthetized > monkey brain. Nature. 447:83-86)? Not usually, but it could be done by not including -no-whiten when you run mkanalysis-sess. I usually use the regression coefficients instead of correlation coefficients because that they are at least unbiased with respect to noise level and autocorrelation. doug > I'd also be happy to look this up but I'd need to know where I can -- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> Phone Number: 617-724-2358 <tel:617-724-2358> <tel:617-724-2358 <tel:617-724-2358>> Fax: 617-726-7422 <tel:617-726-7422> <tel:617-726-7422 <tel:617-726-7422>> Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/ The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail. -- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> Phone Number: 617-724-2358 <tel:617-724-2358> Fax: 617-726-7422 <tel:617-726-7422> Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
Hi Doug, this is coming from a seed-based resting state analysis. I would like to show effect size instead of p-values. I thought you calculate them from the slopes or the t-values? Caspar
2013/10/9 Douglas N Greve greve@nmr.mgh.harvard.edu
Oh, yes, sorry. The reason that mri_glmfit_pcc is failing is because this is a one-sample group mean (design matrix a column of all 1s). What are you trying to compute the correlation between? Or do you just want to convert the p-values to a correlation coefficient?
On 10/09/2013 01:26 PM, Caspar M. Schwiedrzik wrote:
Hi Doug, did you already have a chance to look into this? Thanks, Caspar
2013/10/4 Caspar M. Schwiedrzik <cschwiedrz@rockefeller.edu <mailto: cschwiedrz@**rockefeller.edu cschwiedrz@rockefeller.edu>>
Done. Thank you very much for looking into this. Caspar 2013/10/4 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu <greve@nmr.mgh.harvard.edu>>> Can you tar up you glmfit dir and drop it to me on our file drop? On 10/04/2013 05:29 PM, Caspar M. Schwiedrzik wrote: Hi Doug, thank you very much for sending the Matlab function. When I run this, it creates a pcc.mgh file for my osgm contrast. However, the values seem strange. They range from -630 to 36 for my particular dataset. I was expecting something between -1 and 1. Caspar 2013/10/4 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu>I think you are conflating the 1st level and the 2nd level. You could get pcc out of the 2nd level regardless of what you are using for the input from the first level. I've attached a matlab script that will compute the pcc for mri_glmfit output doug On 10/04/2013 03:33 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I guess it boils down to the question how to get a group PCC map after a RFX GLM? Using -m PCC seems to only give me a map per subject. Are you calculating PCC from the t- values? Thanks, Caspar On Thursday, October 3, 2013, Caspar M. Schwiedrzik wrote: Hi Doug, On Thursday, October 3, 2013, Douglas N Greve wrote: It sounds like two issues: 1. p-values not consistent with your program. What did you use to compute? Did you do a two-sided (which is what fsfast uses)? I used ttest in Matlab, two sided. 2. Using pcc maps. Why not use -m pcc? Isn't that giving me a map per subject? How do I get the group map that is consistent with the results of mri_glmfit run on ces.nii? Thanks, Caspar doug On 10/03/2013 01:10 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I loaded the pcc.nii file that I got from isxconcat-sess into Matlab and then ran a t-test against 0 over the 4th dimension. I converted the resulting p-values to -log10 and then compared them to the output of mri_glmfit, namely sig.vol. This was the mri_glmfit command: mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname I was expecting the p-values to be the same, which apparently is not the case, unless I am doing/understanding something wrong. By now, I am actually more inclined to use the regression coefficients instead. However, I'd still like to get pcc maps from them, if there is a way to do so in FSFAST. Thanks, Caspar 2013/10/3 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu>On 10/03/2013 10:39 AM, Caspar M. Schwiedrzik wrote: Hi Doug, when I run a two-tailed t-test against 0 in Matlab on the Rs in pcc.nii that I get out of isxconcat-sess with -m pcc, and DOF from ffxdof.dat, I get different -log10(p) values than the ones that come out of mri_glmfit. I don't understand what you mean. Can you elaborate? I am not sure why this is happening. In principle, I just want pcc maps as final output to show them on the surface (instead of p-values). So I'd be happy to follow your advice regarding the biasing effects of noise and autocorrelation and use the regression coefficients. However, mri_glmfit (v5.1) does not seem to output pcc maps of the contrasts (contrary to selxavg3-sess on the single subject level). How would I get those? Thanks, Caspar 2013/10/1 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edugreve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edugreve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edugreve@nmr.mgh.harvard.edu
<mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu>>
On 10/01/2013 01:13 PM, Caspar M. Schwiedrzik wrote: > Hi Doug, > it would be great if you could give me some further advise on the > group analysis of functional connectivity maps. > Specifically, I am trying to get PCC maps for certain seeds, and am > not planning any comparison between groups. > Following your previous advise, I am running isxconcat-sess with -m > pcc to get the PCC maps. > I would then run > > mri_glmfit \ > --surf averagesubject hemisphere \ > --y pcc.nii \ > --no-cortex \ > --osgm \ > --glmdir analysisname > > *Could you please provide some more detail on what kind of analysis is > performed when I provide pcc.nii as an input for mri_glmfit? Is it a > t-test of the Fisher-transformed r-values against 0? I just run a t-test of the r-values. I don't have a program to convert them to z-values, however, there are z-values that are created in the first level analysis. These are generated from the p-values but I bet it would give you the same thing. Use -m z with isxconcat-sess if you want to use the z. > *Is the average r-value or z-value saved somewhere? Which level? For mri_glmfit, they are not, but it is not hard to get them with matlab. > *Do you take the autocorrelation into account (as in Vincent JL et > al., 2007. Intrinsic functional architecture in the anaesthetized > monkey brain. Nature. 447:83-86)? Not usually, but it could be done by not including -no-whiten when you run mkanalysis-sess. I usually use the regression coefficients instead of correlation coefficients because that they are at least unbiased with respect to noise level and autocorrelation. doug > I'd also be happy to look this up but I'd need to know where I can -- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu>Phone Number: 617-724-2358 <tel:617-724-2358> <tel:617-724-2358 <tel:617-724-2358>> Fax: 617-726-7422 <tel:617-726-7422> <tel:617-726-7422 <tel:617-726-7422>> Bugs: surfer.nmr.mgh.harvard.edu/**fswiki/BugReporting<http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> <http://surfer.nmr.mgh.**harvard.edu/fswiki/**BugReporting<http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting><http://surfer.nmr.mgh.**harvard.edu/fswiki/**BugReporting http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> FileDrop: https://gate.nmr.mgh.harvard.**edu/filedrop2https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/**facility/filedrop/index.htmlhttp://www.nmr.mgh.harvard.edu/facility/filedrop/index.html <http://www.nmr.mgh.harvard.**edu/facility/filedrop/index.** html http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> <http://www.nmr.mgh.harvard.** edu/facility/filedrop/index.**htmlhttp://www.nmr.mgh.harvard.edu/facility/filedrop/index.html
Outgoing: ftp://surfer.nmr.mgh.harvard.**edu/transfer/outgoing/flat/**greve/ ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/**complianceline<http://www.partners.org/complianceline>. If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail. -- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu>Phone Number: 617-724-2358 <tel:617-724-2358> Fax: 617-726-7422 <tel:617-726-7422> Bugs: surfer.nmr.mgh.harvard.edu/**fswiki/BugReporting<http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> <http://surfer.nmr.mgh.**harvard.edu/fswiki/**BugReporting<http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting>FileDrop: https://gate.nmr.mgh.harvard.**edu/filedrop2<https://gate.nmr.mgh.harvard.edu/filedrop2> www.nmr.mgh.harvard.edu/**facility/filedrop/index.html<http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> <http://www.nmr.mgh.harvard.**edu/facility/filedrop/index.**html<http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html>Outgoing: ftp://surfer.nmr.mgh.harvard.**edu/transfer/outgoing/flat/**greve/ ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/**fswiki/BugReportinghttp://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.**edu/filedrop2https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/**facility/filedrop/index.htmlhttp://www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.**edu/transfer/outgoing/flat/** greve/ ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
If it is just an effect size you can compute gamma.mgh/rstd.mgh doug
On 10/09/2013 01:39 PM, Caspar M. Schwiedrzik wrote:
Hi Doug, this is coming from a seed-based resting state analysis. I would like to show effect size instead of p-values. I thought you calculate them from the slopes or the t-values? Caspar
2013/10/9 Douglas N Greve <greve@nmr.mgh.harvard.edu mailto:greve@nmr.mgh.harvard.edu>
Oh, yes, sorry. The reason that mri_glmfit_pcc is failing is because this is a one-sample group mean (design matrix a column of all 1s). What are you trying to compute the correlation between? Or do you just want to convert the p-values to a correlation coefficient? On 10/09/2013 01:26 PM, Caspar M. Schwiedrzik wrote: Hi Doug, did you already have a chance to look into this? Thanks, Caspar 2013/10/4 Caspar M. Schwiedrzik <cschwiedrz@rockefeller.edu <mailto:cschwiedrz@rockefeller.edu> <mailto:cschwiedrz@rockefeller.edu <mailto:cschwiedrz@rockefeller.edu>>> Done. Thank you very much for looking into this. Caspar 2013/10/4 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> Can you tar up you glmfit dir and drop it to me on our file drop? On 10/04/2013 05:29 PM, Caspar M. Schwiedrzik wrote: Hi Doug, thank you very much for sending the Matlab function. When I run this, it creates a pcc.mgh file for my osgm contrast. However, the values seem strange. They range from -630 to 36 for my particular dataset. I was expecting something between -1 and 1. Caspar 2013/10/4 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>>> I think you are conflating the 1st level and the 2nd level. You could get pcc out of the 2nd level regardless of what you are using for the input from the first level. I've attached a matlab script that will compute the pcc for mri_glmfit output doug On 10/04/2013 03:33 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I guess it boils down to the question how to get a group PCC map after a RFX GLM? Using -m PCC seems to only give me a map per subject. Are you calculating PCC from the t- values? Thanks, Caspar On Thursday, October 3, 2013, Caspar M. Schwiedrzik wrote: Hi Doug, On Thursday, October 3, 2013, Douglas N Greve wrote: It sounds like two issues: 1. p-values not consistent with your program. What did you use to compute? Did you do a two-sided (which is what fsfast uses)? I used ttest in Matlab, two sided. 2. Using pcc maps. Why not use -m pcc? Isn't that giving me a map per subject? How do I get the group map that is consistent with the results of mri_glmfit run on ces.nii? Thanks, Caspar doug On 10/03/2013 01:10 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I loaded the pcc.nii file that I got from isxconcat-sess into Matlab and then ran a t-test against 0 over the 4th dimension. I converted the resulting p-values to -log10 and then compared them to the output of mri_glmfit, namely sig.vol. This was the mri_glmfit command: mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname I was expecting the p-values to be the same, which apparently is not the case, unless I am doing/understanding something wrong. By now, I am actually more inclined to use the regression coefficients instead. However, I'd still like to get pcc maps from them, if there is a way to do so in FSFAST. Thanks, Caspar 2013/10/3 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>>>> On 10/03/2013 10:39 AM, Caspar M. Schwiedrzik wrote: Hi Doug, when I run a two-tailed t-test against 0 in Matlab on the Rs in pcc.nii that I get out of isxconcat-sess with -m pcc, and DOF from ffxdof.dat, I get different -log10(p) values than the ones that come out of mri_glmfit. I don't understand what you mean. Can you elaborate? I am not sure why this is happening. In principle, I just want pcc maps as final output to show them on the surface (instead of p-values). So I'd be happy to follow your advice regarding the biasing effects of noise and autocorrelation and use the regression coefficients. However, mri_glmfit (v5.1) does not seem to output pcc maps of the contrasts (contrary to selxavg3-sess on the single subject level). How would I get those? Thanks, Caspar 2013/10/1 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>>>>> On 10/01/2013 01:13 PM, Caspar M. Schwiedrzik wrote: > Hi Doug, > it would be great if you could give me some further advise on the > group analysis of functional connectivity maps. > Specifically, I am trying to get PCC maps for certain seeds, and am > not planning any comparison between groups. > Following your previous advise, I am running isxconcat-sess with -m > pcc to get the PCC maps. > I would then run > > mri_glmfit \ > --surf averagesubject hemisphere \ > --y pcc.nii \ > --no-cortex \ > --osgm \ > --glmdir analysisname > > *Could you please provide some more detail on what kind of analysis is > performed when I provide pcc.nii as an input for mri_glmfit? Is it a > t-test of the Fisher-transformed r-values against 0? I just run a t-test of the r-values. I don't have a program to convert them to z-values, however, there are z-values that are created in the first level analysis. These are generated from the p-values but I bet it would give you the same thing. Use -m z with isxconcat-sess if you want to use the z. > *Is the average r-value or z-value saved somewhere? Which level? For mri_glmfit, they are not, but it is not hard to get them with matlab. > *Do you take the autocorrelation into account (as in Vincent JL et > al., 2007. Intrinsic functional architecture in the anaesthetized > monkey brain. Nature. 447:83-86)? Not usually, but it could be done by not including -no-whiten when you run mkanalysis-sess. I usually use the regression coefficients instead of correlation coefficients because that they are at least unbiased with respect to noise level and autocorrelation. doug > I'd also be happy to look this up but I'd need to know where I can -- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> Phone Number: 617-724-2358 <tel:617-724-2358> <tel:617-724-2358 <tel:617-724-2358>> <tel:617-724-2358 <tel:617-724-2358> <tel:617-724-2358 <tel:617-724-2358>>> Fax: 617-726-7422 <tel:617-726-7422> <tel:617-726-7422 <tel:617-726-7422>> <tel:617-726-7422 <tel:617-726-7422> <tel:617-726-7422 <tel:617-726-7422>>> Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/ The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/complianceline . If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail. -- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> Phone Number: 617-724-2358 <tel:617-724-2358> <tel:617-724-2358 <tel:617-724-2358>> Fax: 617-726-7422 <tel:617-726-7422> <tel:617-726-7422 <tel:617-726-7422>> Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/ -- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> Phone Number: 617-724-2358 <tel:617-724-2358> Fax: 617-726-7422 <tel:617-726-7422> Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
Hi Doug, I would ideally like something that is as close as possible to the pcc map that comes out of selxavg3 (the single subject analysis). How is that computed? Thanks, Caspar
On Wednesday, October 9, 2013, Douglas N Greve wrote:
If it is just an effect size you can compute gamma.mgh/rstd.mgh doug
On 10/09/2013 01:39 PM, Caspar M. Schwiedrzik wrote:
Hi Doug, this is coming from a seed-based resting state analysis. I would like to show effect size instead of p-values. I thought you calculate them from the slopes or the t-values? Caspar
2013/10/9 Douglas N Greve <greve@nmr.mgh.harvard.edu mailto: greve@nmr.mgh.harvard.edu>
Oh, yes, sorry. The reason that mri_glmfit_pcc is failing is because this is a one-sample group mean (design matrix a column of all 1s). What are you trying to compute the correlation between? Or do you just want to convert the p-values to a correlation coefficient? On 10/09/2013 01:26 PM, Caspar M. Schwiedrzik wrote: Hi Doug, did you already have a chance to look into this? Thanks, Caspar 2013/10/4 Caspar M. Schwiedrzik <cschwiedrz@rockefeller.edu <mailto:cschwiedrz@rockefeller.edu> <mailto:cschwiedrz@rockefeller.edu <mailto:cschwiedrz@rockefeller.edu>>> Done. Thank you very much for looking into this. Caspar 2013/10/4 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> Can you tar up you glmfit dir and drop it to me on our file drop? On 10/04/2013 05:29 PM, Caspar M. Schwiedrzik wrote: Hi Doug, thank you very much for sending the Matlab function. When I run this, it creates a pcc.mgh file for my osgm contrast. However, the values seem strange. They range from -630 to 36 for my particular dataset. I was expecting something between -1 and 1. Caspar 2013/10/4 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>>> I think you are conflating the 1st level and the 2nd level. You could get pcc out of the 2nd level regardless of what you are using for the input from the first level. I've attached a matlab script that will compute the pcc for mri_glmfit output doug On 10/04/2013 03:33 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I guess it boils down to the question how to get a group PCC map after a RFX GLM? Using -m PCC seems to only give me a map per subject. Are you calculating PCC from the t- values? Thanks, Caspar On Thursday, October 3, 2013, Caspar M. Schwiedrzik wrote: Hi Doug, On Thursday, October 3, 2013, Douglas N Greve wrote: It sounds like two issues: 1. p-values not consistent with your program. What did you use to compute? Did you do a two-sided (which is what fsfast uses)? I used ttest in Matlab, two sided. 2. Using pcc maps. Why not use -m pcc? Isn't that giving me a map per subject? How do I get the group map that is consistent with the results of mri_glmfit run on ces.nii? Thanks, Caspar doug On 10/03/2013 01:10 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I loaded the pcc.nii file that I got from isxconcat-sess into Matlab and then ran a t-test against 0 over the 4th dimension. I converted the resulting p-values to -log10 and then compared them to the output of mri_glmfit, namely sig.vol. This was the mri_glmfit command: mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname I was expecting the p-values to be the same, which apparently is not the case, unless I am doing/understanding something wrong. By now, I am actually more inclined to use the regression coefficients instead. However, I'd still like to get pcc maps from them, if there is a way to do so in FSFAST. Thanks, Caspar 2013/10/3 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:gr <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>>>> On 10/03/2013 10:39 AM, Caspar M. Schwiedrzik wrote: Hi Doug, when I run a two-tailed t-test against 0 in Matlab on the Rs in pcc.nii that I get out of isxconcat-sess with -m pcc, and DOF from ffxdof.dat, I get different -log10(p) values than the ones that come out of mri_glmfit. I don't understand what you mean. Can you elaborate? I am not sure why this is happening. In principle, I just want pcc maps as final output to show them on the surface (instead of p-values). So I'd be happy to follow your advice regarding the biasing effects of noise and autocorrelation and use the regression coefficients. However, mri_glmfit (v5.1) does not seem to output pcc maps of the contrasts (contrary to selxavg3-sess on the single subject level). How would I get those? Thanks, Caspar 2013/10/1 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> <mailto:greve@nmr.mgh.harvard.edu <m <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> Phone Number: 617-724-2358 <tel:617-724-2358> <tel:617-724-2358 <tel:617-724-2358>> <tel:617-724-2358 <tel:617-724-2358> <tel:617-724-2358 <tel:617-724-2358>>> Fax: 617-726-7422 <tel:617-726-7422> <tel:617-726-7422 <tel:617-726-7422>> <tel:617-726-7422 <tel:617-726-7422> <tel:617-726-7422 <tel:617-726-7422>>> Bugs: surfer.nmr.mgh.harvard.edu/**fswiki/BugReporting<http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> <http://surfer.nmr.mgh.**harvard.edu/fswiki/**BugReporting<http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting><http://surfer.nmr.mgh.**harvard.edu/fswiki/**BugReporting http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> <http://surfer.nmr.mgh.** harvard.edu/fswiki/**BugReportinghttp://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting
FileDrop: https://gate.nmr.mgh.harvard.**edu/filedrop2<https://gate.nmr.mgh.harvard.edu/filedrop2> www.nmr.mgh.harvard.edu/**facility/filedrop/index.html<http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> <http://www.nmr.mgh.harvard.**edu/facility/filedrop/index.**html<http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html><http://www.nmr.mgh.harvard.**edu/facility/filedrop/index.**htmlhttp://www.nmr.mgh.harvard.edu/facility/filedrop/index.html
<http://www.nmr.mgh.harvard.**edu/facility/filedrop/index.**htmlhttp://www.nmr.mgh.harvard.edu/facility/filedrop/index.html
Outgoing: ftp://surfer.nmr.mgh.harvard.**edu/transfer/outgoing/flat/**greve/ ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
The information in this e-mail is intended only for the person to whom it is addressed. If you believe this e-mail was sent to you in error and the e-mail contains patient information, please contact the Partners Compliance HelpLine at http://www.partners.org/**complianceline<http://www.partners.org/complianceline>. If the e-mail was sent to you in error but does not contain patient information, please contact the sender and properly dispose of the e-mail. -- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> Phone Number: 617-724-2358 <tel:617-724-2358> <tel:617-724-2358 <tel:617-724-2358>> Fax: 617-726-7422 <tel:617-726-7422> <tel:617-726-7422 <tel:617-726-7422>> Bugs: surfer.nmr.mgh.harvard.edu/**fswiki/BugReporting<http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> <http://surfer.nmr.mgh.**harvard.edu/fswiki/**BugReporting<http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting><http://surfer.nmr.mgh.**harvard.edu/fswiki/**BugReporting http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> FileDrop: https://gate.nmr.mgh.harvard.**edu/filedrop2https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/**facility/filedrop/index.htmlhttp://www.nmr.mgh.harvard.edu/facility/filedrop/index.html <http://www.nmr.mgh.harvard.**edu/facility/filedrop/index.**htmlhttp://www.nmr.mgh.harvard.edu/facility/filedrop/index.html
<http://www.nmr.mgh.harvard.**edu/facility/filedrop/index.**htmlhttp://www.nmr.mgh.harvard.edu/facility/filedrop/index.html
Outgoing: ftp://surfer.nmr.mgh.harvard.**edu/transfer/outgoing/flat/**greve/ ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu-- Douglas N. Greve, Ph.D. MGH-NMR Center greve@nmr.mgh.harvard.edu Phone Number: 617-724-2358 Fax: 617-726-7422
Bugs: surfer.nmr.mgh.harvard.edu/**fswiki/BugReportinghttp://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting FileDrop: https://gate.nmr.mgh.harvard.**edu/filedrop2https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/**facility/filedrop/index.htmlhttp://www.nmr.mgh.harvard.edu/facility/filedrop/index.html Outgoing: ftp://surfer.nmr.mgh.harvard.**edu/transfer/outgoing/flat/** greve/ ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/
so you want the mean ppc over subjects? That would be gamma.mgh
On 10/09/2013 03:10 PM, Caspar M. Schwiedrzik wrote:
Hi Doug, I would ideally like something that is as close as possible to the pcc map that comes out of selxavg3 (the single subject analysis). How is that computed? Thanks, Caspar
On Wednesday, October 9, 2013, Douglas N Greve wrote:
If it is just an effect size you can compute gamma.mgh/rstd.mgh doug On 10/09/2013 01:39 PM, Caspar M. Schwiedrzik wrote: Hi Doug, this is coming from a seed-based resting state analysis. I would like to show effect size instead of p-values. I thought you calculate them from the slopes or the t-values? Caspar 2013/10/9 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> Oh, yes, sorry. The reason that mri_glmfit_pcc is failing is because this is a one-sample group mean (design matrix a column of all 1s). What are you trying to compute the correlation between? Or do you just want to convert the p-values to a correlation coefficient? On 10/09/2013 01:26 PM, Caspar M. Schwiedrzik wrote: Hi Doug, did you already have a chance to look into this? Thanks, Caspar 2013/10/4 Caspar M. Schwiedrzik <cschwiedrz@rockefeller.edu <mailto:cschwiedrz@rockefeller.edu> <mailto:cschwiedrz@rockefeller.edu <mailto:cschwiedrz@rockefeller.edu>>> Done. Thank you very much for looking into this. Caspar 2013/10/4 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> Can you tar up you glmfit dir and drop it to me on our file drop? On 10/04/2013 05:29 PM, Caspar M. Schwiedrzik wrote: Hi Doug, thank you very much for sending the Matlab function. When I run this, it creates a pcc.mgh file for my osgm contrast. However, the values seem strange. They range from -630 to 36 for my particular dataset. I was expecting something between -1 and 1. Caspar 2013/10/4 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>>> I think you are conflating the 1st level and the 2nd level. You could get pcc out of the 2nd level regardless of what you are using for the input from the first level. I've attached a matlab script that will compute the pcc for mri_glmfit output doug On 10/04/2013 03:33 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I guess it boils down to the question how to get a group PCC map after a RFX GLM? Using -m PCC seems to only give me a map per subject. Are you calculating PCC from the t- values? Thanks, Caspar On Thursday, October 3, 2013, Caspar M. Schwiedrzik wrote: Hi Doug, On Thursday, October 3, 2013, Douglas N Greve wrote: It sounds like two issues: 1. p-values not consistent with your program. What did you use to compute? Did you do a two-sided (which is what fsfast uses)? I used ttest in Matlab, two sided. 2. Using pcc maps. Why not use -m pcc? Isn't that giving me a map per subject? How do I get the group map that is consistent with the results of mri_glmfit run on ces.nii? Thanks, Caspar doug On 10/03/2013 01:10 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I loaded the pcc.nii file that I got from isxconcat-sess into Matlab and then ran a t-test against 0 over the 4th dimension. I converted the resulting p-values to -log10 and then compared them to the output of mri_glmfit, namely sig.vol. This was the mri_glmfit command: mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname I was expecting the p-values to be the same, which apparently is not the case, unless I am doing/understanding something wrong. By now, I am actually more inclined to use the regression coefficients instead. However, I'd still like to get pcc maps from them, if there is a way to do so in FSFAST. Thanks, Caspar 2013/10/3 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:gr <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>>>> On 10/03/2013 10:39 AM, Caspar M. Schwiedrzik wrote: Hi Doug, when I run a two-tailed t-test against 0 in Matlab on the Rs in pcc.nii that I get out of isxconcat-sess with -m pcc, and DOF from ffxdof.dat, I get different -log10(p) values than the ones that come out of mri_glmfit. I don't understand what you mean. Can you elaborate? I am not sure why this is happening. In principle, I just want pcc maps as final output to show them on the surface (instead of p-values). So I'd be happy to follow your advice regarding the biasing effects of noise and autocorrelation and use the regression coefficients. However, mri_glmfit (v5.1) does not seem to output pcc maps of the contrasts (contrary to selxavg3-sess on the single subject level). How would I get those? Thanks, Caspar 2013/10/1 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> <mailto:greve@nmr.mgh.harvard.edu <m <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>> Phone Number: 617-724-2358 <tel:617-724-2358> <tel:617-724-2358 <tel:617-724-2358>> <tel:617-724-2358 <tel:617-724-2358> <tel:617-724-2358 <tel:617-724-2358>>> Fax: 617-726-7422 <tel:617-726-7422> <tel:617-726-7422 <tel:617-726-7422>> <tel:617-726-7422 <tel:617-726-7422> <tel:617-726-7422 <tel:617-726-7422>>> Bugs: surfer.nmr.mgh.harvard.edu/fswiki/BugReporting <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> <http://surfer.nmr.mgh.harvard.edu/fswiki/BugReporting> FileDrop: https://gate.nmr.mgh.harvard.edu/filedrop2 www.nmr.mgh.harvard.edu/facility/filedrop/index.html <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> <http://www.nmr.mgh.harvard.edu/facility/filedrop/index.html> Outgoing: ftp://surfer.nmr.mgh.harvard.edu/transfer/outgoing/flat/greve/ The information in this e-mail is intended only for the person to whom it is addressed. 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Hi Doug, I have single subject seed-based resting state analyses, and I want to show maps of partial correlation coefficients for those, assuming that they are obtained from the correlation between the residuals of the regression of x1 on x2 and the residuals of the regression of y on x2. Is that correct?
I then want to run a group analysis on the same seeds and show a map that is comparable to the pcc maps that I am showing for the single subject analyses. If I run a RFX GLM, there will be no new pcc map, because I will just do a t-test, not compute a new regression (assuming summary statistics), correct?
Is the closest I can get the averaged pcc map?
Thanks, Caspar
2013/10/9 Douglas N Greve greve@nmr.mgh.harvard.edu
so you want the mean ppc over subjects? That would be gamma.mgh
On 10/09/2013 03:10 PM, Caspar M. Schwiedrzik wrote:
Hi Doug, I would ideally like something that is as close as possible to the pcc map that comes out of selxavg3 (the single subject analysis). How is that computed? Thanks, Caspar
On Wednesday, October 9, 2013, Douglas N Greve wrote:
If it is just an effect size you can compute gamma.mgh/rstd.mgh doug On 10/09/2013 01:39 PM, Caspar M. Schwiedrzik wrote: Hi Doug, this is coming from a seed-based resting state analysis. I would like to show effect size instead of p-values. I thought you calculate them from the slopes or the t-values? Caspar 2013/10/9 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu <greve@nmr.mgh.harvard.edu>>> Oh, yes, sorry. The reason that mri_glmfit_pcc is failing is because this is a one-sample group mean (design matrix a column of all 1s). What are you trying to compute the correlation between? Or do you just want to convert the p-values to a correlation coefficient? On 10/09/2013 01:26 PM, Caspar M. Schwiedrzik wrote: Hi Doug, did you already have a chance to look into this? Thanks, Caspar 2013/10/4 Caspar M. Schwiedrzik <cschwiedrz@rockefeller.edu <mailto:cschwiedrz@**rockefeller.edu<cschwiedrz@rockefeller.edu><mailto:cschwiedrz@**rockefeller.edu<cschwiedrz@rockefeller.edu> <mailto:cschwiedrz@**rockefeller.edu<cschwiedrz@rockefeller.edu>Done. Thank you very much for looking into this. Caspar 2013/10/4 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu>Can you tar up you glmfit dir and drop it to me on our file drop? On 10/04/2013 05:29 PM, Caspar M. Schwiedrzik wrote: Hi Doug, thank you very much for sending the Matlab function. When I run this, it creates a pcc.mgh file for my osgm contrast. However, the values seem strange. They range from -630 to 36 for my particular dataset. I was expecting something between -1 and 1. Caspar 2013/10/4 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu <greve@nmr.mgh.harvard.edu>> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu>I think you are conflating the 1st level and the 2nd level. You could get pcc out of the 2nd level regardless of what you are using for the input from the first level. I've attached a matlab script that will compute the pcc for mri_glmfit output doug On 10/04/2013 03:33 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I guess it boils down to the question how to get a group PCC map after a RFX GLM? Using -m PCC seems to only give me a map per subject. Are you calculating PCC from the t- values? Thanks, Caspar On Thursday, October 3, 2013, Caspar M. Schwiedrzik wrote: Hi Doug, On Thursday, October 3, 2013, Douglas N Greve wrote: It sounds like two issues: 1. p-values not consistent with your program. What did you use to compute? Did you do a two-sided (which is what fsfast uses)? I used ttest in Matlab, two sided. 2. Using pcc maps. Why not use -m pcc? Isn't that giving me a map per subject? How do I get the group map that is consistent with the results of mri_glmfit run on ces.nii? Thanks, Caspar doug On 10/03/2013 01:10 PM, Caspar M. Schwiedrzik wrote: Hi Doug, I loaded the pcc.nii file that I got from isxconcat-sess into Matlab and then ran a t-test against 0 over the 4th dimension. I converted the resulting p-values to -log10 and then compared them to the output of mri_glmfit, namely sig.vol. This was the mri_glmfit command: mri_glmfit \ --surf averagesubject hemisphere \ --y pcc.nii \ --no-cortex \ --osgm \ --glmdir analysisname I was expecting the p-values to be the same, which apparently is not the case, unless I am doing/understanding something wrong. By now, I am actually more inclined to use the regression coefficients instead. However, I'd still like to get pcc maps from them, if there is a way to do so in FSFAST. Thanks, Caspar 2013/10/3 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:gr <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu>>
On 10/03/2013 10:39 AM, Caspar M. Schwiedrzik wrote: Hi Doug, when I run a two-tailed t-test against 0 in Matlab on the Rs in pcc.nii that I get out of isxconcat-sess with -m pcc, and DOF from ffxdof.dat, I get different -log10(p) values than the ones that come out of mri_glmfit. I don't understand what you mean. Can you elaborate? I am not sure why this is happening. In principle, I just want pcc maps as final output to show them on the surface (instead of p-values). So I'd be happy to follow your advice regarding the biasing effects of noise and autocorrelation and use the regression coefficients. However, mri_glmfit (v5.1) does not seem to output pcc maps of the contrasts (contrary to selxavg3-sess on the single subject level). How would I get those? Thanks, Caspar 2013/10/1 Douglas N Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu><mailto:greve@nmr.mgh.harvard.**edu<greve@nmr.mgh.harvard.edu> <m <mailto:greve@nmr.mgh.harvard.**edu greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.**edugreve@nmr.mgh.harvard.edu
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