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>>



    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
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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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