Mri binarize: Difference between revisions

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Use fscalc to produce 3 volumes like sections of a venn diagram, 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.  
Use fscalc to produce 3 volumes like sections of a venn diagram, 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.  
  fscalc T1_sig_lh.mgz add T2_sig_lh.mgz --o sig_T1_T2_lh.mgz  
  fscalc T1_sig_lh.mgz add T2_sig_lh.mgz --o sig_T1_T2_lh.mgz  
  fscalc T1_sig_rh.mgz add T2_sig_rh.mgz --o sig_T1_T2_rh.mgz  
  fscalc T1_sig_rh.mgz add T2_sig_rh.mgz --o sig_T1_T2_rh.mgz  
 


Then we convert our new volumes back to label files (can also use mri_cor2label), for both left and right hemi's.
Then we convert our new volumes back to label files (can also use mri_cor2label), for both left and right hemi's.

Revision as of 22:36, 30 March 2017

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 (mri_glmfit) 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, use the mri_binarize function to set significant voxels (specified by min) to 1 and 2 (specified by binval)

For Left and Right Hemi

mri_binarize --i lh.T1_200funclust.mgz --min 0.1 --binval 1 --o T1_sig_lh.mgz
mri_binarize --i lh.T2_200funclust.mgz --min 0.1 --binval 2 --o T2_sig_lh.mgz
mri_binarize --i rh.T1_200funclust.mgz --min 0.1 --binval 1 --o T1_sig_rh.mgz
mri_binarize --i rh.T2_200funclust.mgz --min 0.1 --binval 2 --o T2_sig_rh.mgz

Use fscalc to produce 3 volumes like sections of a venn diagram, 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.

fscalc T1_sig_lh.mgz add T2_sig_lh.mgz --o sig_T1_T2_lh.mgz 
fscalc T1_sig_rh.mgz add T2_sig_rh.mgz --o sig_T1_T2_rh.mgz 


Then we convert our new volumes back to label files (can also use mri_cor2label), for both left and right hemi's.

mris_seg2annot 
--seg sig_T1_T2_lh.mgz 
--ctab sig_T1_T2_200subclust_lh_CLUT.txt
--s fsaverage
--h lh
--o sig_T1_T2_200subclust_lh.annot
mris_seg2annot 
--seg sig_T1_T2_rh.mgz 
--ctab sig_T1_T2_200subclust_rh_CLUT.txt
--s fsaverage
--h rh
--o sig_T1_T2_200subclust_rh.annot


And, finally apply the new annot file to each subject, for Left and Right Hemi

mri_surf2surf \
--srcsubject fsaverage \
--trgsubject FS_T1_501 \
--hemi lh \
--sval-annot $SUBJECTS_DIR/fsaverage/label/sig_T1_T2_200subclust_lh.annot \
--tval $SUBJECTS_DIR/FS_T1_501/label/sig_T1_T2_200subclust_lh.annot
mri_surf2surf \
--srcsubject fsaverage \
--trgsubject FS_T1_501 \
--hemi rh \
--sval-annot $SUBJECTS_DIR/fsaverage/label/sig_T1_T2_200subclust_rh.annot \
--tval $SUBJECTS_DIR/FS_T1_501/label/sig_T1_T2_200subclust_rh.annot