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