Assessing structural connectivity at voxel-level within a given ROI


#1

Hi,

I would like to perform whole-brain tractography, but within one particular ROI I’d like to keep voxel-specific information on where tracts start/terminate, rather than store everything at the ROI level i.e. I’d like to perform an analysis similar to the one presented in this paper https://onlinelibrary.wiley.com/doi/abs/10.1002/hbm.21338.

Is there any way to do this within the MRTrix3 architecture, other than assigning each voxel within the target ROI as a separate, new ROI (this would be possible, but get a little messy)? If yes, how would you recommend doing this?


#2

Hi @Reinder,

A quick look at the manuscript suggests that it’s a typical connectivity-based parcellation type application. FSL’s tractography basically does this “internally”; with MRtrix3 it requires more explicit consideration of the individual steps required; but by “exposing” the internal steps there’s in fact a number of different ways that such an experiment can be done.

If you genuinely want to derive such connectivity information from “whole-brain tractography”, i.e. a whole-brain tractogram where streamlines are seeded throughout the entire brain rather than just your region of interest, then giving each voxel of interest a unique index and running tck2connectome is an option; alternatively, you can run your ROI through maskdump to get the voxel locations, then loop over those voxel locations using mredit to get a series of single-voxel ROIs that you could feed to tckedit.

If you want to seed streamlines just from the region of interest, you could use -seed_grid_per_voxel or -seed_random_per_voxel within tckgen, or you could use the maskdump | mredit trick described above to generate single-voxel ROIs that can be provided to tckgen as seeds in a loop. This is what I’ve done in a script of my own; which I’ve had a number of requests to upload…

Rob


#3

Hi Rob,

Thanks for the help. Would you consider uploading this script? :slight_smile:

If I seed streamlines from a specific region only will the SIFT2 algorithm still work to optimize the weights? I recall reading somewhere that SIFT2 only works for whole-brain tractography. If that is correct, how would you recommend analyzing streamlines from a region of interest?