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>

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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--
Liang Wang, PhD
Postdoctoral Fellow
Woodward Lab
Department of Psychiatry
University of British Columbia
BC Mental Health & Addiction Services
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