My apologies, I’m flat out with the last minute preparations for the ISMRM and next week’s workshop…
To briefly answer your question, there’s several conceivable approaches to do this:
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You could perform this in native space per subject, by defining some robust ROIs to delineate the pathway in some template space, and warping these ROIs onto each subject independently. Then extract the pathway of interest with sufficient density, but also perform a whole-brain tractogram, and merge the two using
tckedit. At this point, you can runtcksift2on the combined tractogram to obtain the streamline weights, and then usetck2connectometo extract the sum of streamline weights for the pathway of interest (by defining relevant ROIs as ‘nodes’). At this point, the sum of streamlines weights should represent the information you’re after. That way, you can also use the-actoption intckgento help remove false positives. -
The other way is more in line with what you suggest. There is a command in MRtrix3 called afdconnectivity, which superficially does exactly what you want. But there is a very real possibility that the voxels from which the AFD is estimated also contain contributions from other pathways, which would contaminate the estimate. There is a new
-wbftoption to that command that might help here, if you can produce a whole-brain tractogram and extract your pathway of interest from that – I’m not familiar with how it works exactly, but at a guess it would require a dense whole-brain tractogram, filtered usingtcksift, and thentckeditto extract the subset of interest representing your pathway. This could all be performed in template space, which makes things easier but also adds the potential confound of interpolation errors during the registration, and would require modulation of the FODs during warping (@maxpietsch, is this still possible…?).
I could probably help a bit more in a couple of weeks, but that’s all I have time for right this minute – sorry!