2010/7/19 Douglas N Greve <greve@nmr.mgh.harvard.edu>

mapping the time courses to the average subject does not solve this problem, it just hides it. I'm not sure why this would be of a concern anyway. There might be a tiny difference in the expected variance for each subject, but, unless the region is very small, the differences won't amount for much.

doug

liang wang wrote:

Dear Doug,

Thanks for your comments. However, I am worried about the different number of voxels within each parcellation between subjects, if extracting fmri time course from individual parcellation result.

Also, I am using the following combined commands to carry out the steps. Do you think it's correct?
reg-feat2anat --feat SUBID/bold.feat --subject SUBID
aseg2feat --feat SUBID/bold.feat --aseg aparcs+aseg
mri_segstats --seg SUBID/bold.feat/reg/freesurfer/aparc.a2009s+aseg.nii.gz --in filtered_fun_data.nii.gz -avgwf file.dat

Thanks,
Liang

2010/7/18 Doug Greve <greve@nmr.mgh.harvard.edu <mailto:greve@nmr.mgh.harvard.edu>>

definitely use the individuals. Use mri_vol2surf to convert the motion
corrected (and otherwise unsmoothed) fmri time courses to the
surface. Then use mri_segstats with the --avgwf and --annot options
to compute the mean time course over the parcellations.

doug

On Sat, 17 Jul 2010, liang wang wrote:

Hi,

Does anyone know one of studies which uses the parcellations
(aparc or
aparc.a2009s) Freesurfer provides to extract fMRI time course
from each
subject's fMRI data. Reading the instructions in Freesurfer
Wiki, I know how
to do that. However, I am not sure whether I should use the
parcellations
from the average output (obtained from make_average_subject),
or the
parcellations based on each subject. I would next compute
functional
connectivity between these regions. I am appreciated for any
suggestions.

Liang

-- Douglas N. Greve, Ph.D.
MGH-NMR Center
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