Mri binarize

If you have data from different time points, as with the booth data, and want to make comparisons of ROI's across time points you'll want to be looking at the same voxels. Here seems to be the way to do that freesurfer.

At this point you should have conducted group stats (glm) and pulled out whatever ROI's of interest. Now you'll convert your group average .annot label file to a volume, so that we can use the binarize function.

For T1 & T2

mri_label2vol --annot T1_rh.200functional_subclusters.annot --temp f.nii --o rh.T1_200funclust.mgz --subject fsaverage --hemi rh --reg reg.2mm.mni152.dat
mri_label2vol --annot T1_lh.200functional_subclusters.annot --temp f.nii --o lh.T1_200funclust.mgz --subject fsaverage --hemi lh --reg reg.2mm.mni152.dat

mri_label2vol --annot T2_rh.200functional_subclusters.annot --temp f.nii --o rh.T2_200funclust.mgz --subject fsaverage --hemi rh --reg reg.2mm.mni152.dat
mri_label2vol --annot T2_lh.200functional_subclusters.annot --temp f.nii --o lh.T2_200funclust.mgz --subject fsaverage --hemi lh --reg reg.2mm.mni152.dat

Now that you have volumes, that mri_binarize function will produce 3 volumes with significant voxels set to 1 (or whatever value specified). One with sig. voxels in A but not B, one with sig. voxels in B but not A, and one with overlapping sig. voxels in both A and B.

For Left and Right Hemi

mri_binarize
mri_binarize

Then we convert our new volumes back to label files.

mri_cor2label
mri_cor2label

And, finally apply the new annot file to each subject

For Left and Right Hemi

mri_surf2surf