Hey!
I’m planning to use subject-specific freesurfer parcellations, run as part of fmriprep, as ROIs for a task-based analysis, but I think I’m missing something in creating these ROI masks.
I’ve been extracting the masks from the freesurfer output using mri_binarize, e.g. for left amygdala:
mri_binarize --i ${freesurfer_dir}/${subj}/mri/aparc.a2009s+aseg.mgz \
--match 18 \
--o ${masks_dir}/${subj}_left-amygdala_raw.nii.gz
Then, I had then been resampling to BOLD (MNI) space as follows:
3dresample -master ${preprocessed_dir}/${subj}/func/${subj}_${ses}_${task}_bold.nii.gz \
-input ${masks_dir}/${subj}_left-amygdala_raw.nii.gz\
-prefix ${masks_dir}/${subj}_left-amygdala_resampled.nii.gz
Before using 3dmaskave to extract the beta-weights.
However, I think I may be missing something as when I try to view these masks they are not in the correct location in MNI space.
So, my questions are:
- As far as I can tell, the freesurfer parcellations are in T1 space, is that correct?
- If so, then is there a way to re-run fMRIPrep or freesurfer so that they’re output in MNI space, or do I need to extract as above but transform the into MNI space using the transformation calculated by fmriprep (instead of the 3dresample command above)?
- Finally, what is the best way to do this? I’ve seen various methods, but mri_label2vol seems most common. Is that what would be recommended?
Hopefully that all makes sense, please let me know if any clarification needed!