Hi MRtrix team!
I am trying to use clusters from a Neurosynth metaanalysis map as connectome targets. I currently have the following resources at my disposal:
- Subject-space tractograms (10M streamlines)
- SIFT2 weights for these tractograms
- A high-resolution MNI FA template (provided with TractSeg) and subject-space FA maps (from FSL DTIFIT)
- A MNI-to-subject space transformation matrix calculated by FLIRT. e.g.:
flirt -ref $sub_fa -in $MNI_template -omat MNI_2_sub.mat \ -dof 6 -cost mutualinfo -searchcost mutualinfo
- Clusters from the metaanlysis map, each saved as a separate .nii.gz. Neurosynth produces these maps in MNI space.
I imagine it would be best to keep the tractograms in their native space as opposed to transforming them to MNI, since information about streamline length would be lost and these files are computationally expensive to work with. So, I anticipate my next steps would be something along the lines of:
- Put clusters into a single file (just fslmerge?)
- Convert this file to MNI space, something along the lines of:
flirt -ref $sub_fa -in $neurosynth_clusters -out clusters_sub_space.nii.gz \ -applyxfm -init MNI_2_sub.mat -dof 6 -interp nearestneighbour
- Create nodes.mif from this (this is primarily where I am unsure)
- Make connectomes, e.g.
tck2connectome tracks.tck nodes.mif connectome.csv -tck_weights_in sift2_weights.csv -out_assignments assignments.txt -scale_invnodevol
$ tcksample tracks.tck $sub_fa mean_FA_per_streamline.csv -stat_tck mean; $ tck2connectome tracks.tck nodes.mif mean_FA_connectome.csv \ -scale_file mean_FA_per_streamline.csv -stat_edge mean -tck_weights_in sift2_weights.csv
- Perform analyses in “connectome-space”
As suggested by the title, I am unsure what nodes.mif is supposed to be and how I would create it from the individual clusters. Would all the files go into a single volume with “intensities” corresponding to different labels (similar to a color look-up table), or could I merge the files into a 4d nifti, where each volume is a different cluster?
Thank you in advance!