Mri binarize: Difference between revisions

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Now that you have volumes, that mri_binarize function will 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.
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
For Left and Right Hemi
  mri_binarize
  mri_binarize --i lh.T1_200funclust.mgz --min 0.1 --binval 1 --o T1_sig_lh.mgz
  mri_binarize
  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.
Then we convert our new volumes back to label files (can also use mri_cor2label), for both left and right hemi's.
  mri_cor2label
mris_seg2annot
  mri_cor2label
--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




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  --trgsubject FS_T1_501 \
  --trgsubject FS_T1_501 \
  --hemi lh \
  --hemi lh \
  --sval-annot $SUBJECTS_DIR/fsaverage/label/lh.200functional_subclusters.annot \
  --sval-annot $SUBJECTS_DIR/fsaverage/label/sig_T1_T2_200subclust_lh.annot \
  --tval $SUBJECTS_DIR/FS_T1_501/label/lh.200functional_subclusters.annot
  --tval $SUBJECTS_DIR/FS_T1_501/label/sig_T1_T2_200subclust_lh.annot


  mri_surf2surf \
  mri_surf2surf \
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  --trgsubject FS_T1_501 \
  --trgsubject FS_T1_501 \
  --hemi rh \
  --hemi rh \
  --sval-annot $SUBJECTS_DIR/fsaverage/label/rh.200functional_subclusters.annot \
  --sval-annot $SUBJECTS_DIR/fsaverage/label/sig_T1_T2_200subclust_rh.annot \
  --tval $SUBJECTS_DIR/FS_T1_501/label/rh.200functional_subclusters.annot
  --tval $SUBJECTS_DIR/FS_T1_501/label/sig_T1_T2_200subclust_rh.annot

Revision as of 22:34, 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