I have a couple quick questions with regards to my ACT pipeline. I have produced streamline count connectomes based on the AAL atlas which im confident of but now would like to check the last steps of my pipeline. These will be fed first into NBS for basic statistics (along with fMRI and MEG using the same ROIs) and then given to someone much smarter than me for machine learning purposes.
My original last step for the streamline count connectomes (based on the BATMAN tutorial and Andy’s Brain Book):
tck2connectome -symmetric -zero_diagonal -scale_invnodevol -tck_weights_in sift_1M.txt tracks_10M.tck parcels_coreg_AAL.mif parcels_coreg_AAL.csv -out_assignment assignments_coreg_AAL.csv
Now I want to produce connectomes using FA,MD,AD and RD. I have produced the scalar maps in FSL and upsampled them to 1.25 mm isotropic (I noticed now this isn’t necessary, but for now it’s for the sake of consistency). Based on the post here (FA, AD, RD connectomes) and the MRtrix documentation, I used:
tcksample tracks_10M.tck FA.mif mean_FA_per_streamline.csv -stat_tck mean
tck2connectome -symmetric -zero_diagonal -tck_weights_in sift_1M.txt tracks_10M.tck parcels_coreg_AAL.mif parcels_coreg_AAL_FA.csv -scale_file mean_FA_per_streamline.csv -stat_edge mean -out_assignment assignments_coreg_AAL_FA.csv
Essentially in the last step I removed -scale_invnodevo and added -scale_file and -stat_edge while keeping everything else the same. Numerically the connectomes make sense and visually they look accurate, but I just wanted to confirm as we are moving to a very large data cohort.
Lastly, in some of the the steps to bring the AAL ROI’s into diffusion space I noticed many tutorials work off only a linear registration. I added in a nonlinear registration to improve several steps (using nearest neighbour interpolation for applywarp) and the results look great. Is there any reason this step isn’t added in many tutorials beyond computation time and potential for problems?