I have generated tracks connecting two manually delineated ROIs in a group of subjects (disease and control). I’d like to assess how individual differences in participants’ track integrity (track weighted FA / MD, and fiber density, or AFD) relate to behavioral measures. I would really appreciate your advise with regards to the pipeline I’m planning to use to pull those numbers.
Track-weighted FA / MD. I have done a full brain 10M tck and ran SIFT2 on those for each subject. I have created whole brain track weighted FA and MD maps using my “sifted” 10M tck. Now, I have generated my ROI-based tracks via tckgen, selecting equal number of streamlines connecting two ROIs in each subject. I was wondering if I should convert this ROI-based track into a simple ROI (binarize it? can I do it with MTRrix based commands (I could not find anything suitable)), so I can pull the FA / MD numbers for this track of interest from the whole brain track weighted FA / MD? Alternatively, I could go the simpler route and just run tcksample using my ROI-based tracks of interest and FA / MD maps that were not track weighted with whole brain tck. Which method would you recommend and why?
Since I am not planning to do voxel-based group comparisons, do I still need to work with normalized FODs (and group RF to compute FODs) to build both whole brain tractogram and ROI-based tracks? Both 10M tracks and ROI-based ones were done on non-normalized individual RF FODs. After re-reading original Raffelt 2012 paper, I recon that for the track-weighted FA / MD this probably will not matter much, but for the fiber density measure I want to obtain next I am in trouble , even being interested in individual differences. Would like to hear your thoughts on this, please!
Now, the most interesting question. In order to get fiber density for my ROI-based track of interest, I have used tck2fixel (after having computed fod2fixel for the whole brain on NON-normalized individual RF FODs), and then mrstats to get the numbers. However, when comparing to other literature using MRTrix, my numbers for the mean pulled by this method (~0.4) are not up the par, and medians are 0. I wonder if this is because it doesn’t “remove” the rest of the brain’s fixels when selects the fixels for the track of interest, but kind of “zeros” them out? (sorry if this is completely incorrect understanding, I would really appreciate your explanation!). I found afdconnectivity command, and I wonder if this is the right way to go to compute AFD for my ROI-based tracks of interest? To make sure I understand correctly: in order to use wbft function, I need to generate my ROI-based tracks using tckedit command, correct? I have played with it a bit, and the number of streamlines generated for each subject with tckedit are very different. I wonder if this is because I was using individual RF non-normalized FODs -based whole brain tracks? If afdconnectivity is not the right command, could you please advise the correct route to compute this?
Thanks much for all your support and for this great tool!