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
Dear Doug,
Thanks. According to your suggestions, I would extract fmri time course from
an individual paracellation with subcortical regions (e.g. caudate,
thalamus...). Would you like to help me to make sure whether the following
commands are correct. Here the filtered_fun_data.nii.gz did not be
That looks right, though mri_segstats might want a summary file. If so
, just added --sum junk
doug
liang wang wrote:
Dear Doug,
Thanks. According to your suggestions, I would extract fmri time
course from an individual paracellation with subcortical regions (e.g.
caudate, thalamus...).
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
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
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
