I am trying to measure an inter-hemispheric commissure, whose streamlines start within one hemisphere and cross to the other hemisphere somewhere in the midline. I am interested only at the commissure itself, i.e., the midline portion of these streamlines. Outside of the commissure, the diffusion measures are probably contaminated with other white matter tracts that I am not interested in. So, my intent is to measure the strength of the commissure only.
I suspect that SIFT is still what you’re looking for (though I appreciate that processing can pose an issue; see below). I do however want to address the ‘contamination’ comment though.
For something like FA, absolutely the ‘quantitative’ value along the length of the streamlines would be affected by the properties of other pathways that occupy some of the same voxels but don’t traverse the entire pathway of interest. For AFD and SIFT though, this is dealt with intrinsically in the model. This is because AFD is additive: the fixel-wise value of AFD can be divided between pathways based on their relative volume fractions within the fixel:
Within the commissure, only those streamlines belonging to your pathway of interest are assigned to those fixels; therefore all of the AFD in each fixel is contributed from your pathway of interest.
Within the deep WM of each hemisphere, the fixels traversed by those streamlines may be traversed by many other streamlines that don’t go on to cross the commissure. Within the SIFT model, only a fraction of the AFD in each of those fixels is contributed from your pathway of interest; the rest comes from those other unrelated streamlines.
So it’s not a contamination in the way that this term is usually used in the context of diffusion measures (where ‘corruption’ would maybe be a better term); it’s purely a partial volume issue in the fixel domain. But the SIFT model deals with this, within the limitations of the tractogram it is fed.
Looks like I need to extract AFD, but I am planning to crop the streamlines to retain only the commissure part. Therefore, the length of the original streamlines will be lost. Do I still need
afdconnectivity in this scenario or is something else more appropriate (don’t know,
fod2fixel for example)?
You could use
afdconnectivity for something like this. You would use
tckedit to crop the streamlines, then
afdconnectivity to extract the connectivity value of interest: This command internally runs the same FOD segmentation algorithm provided by
fod2fixel, and would then internally divide by the mean (cropped) streamline length: Even in the cropped pathway scenario, it’s still necessary to convert AFD (which is a volume measure) into a measure of cross-sectional area in order to be interpretable as something related to ‘connectivity strength’.
The reason I avoided tcksift is that the examples I see in MRtrix decumentation are with millions of streamlines from whole brain analyses. I am working on a small number of streamlines from manual ROIs. I aim at getting 300 streamlines with maxnum = 1M, which already takes hours. Removing streamlines with SIFT will bring the number down to 30-50, which I don’t know if is a good number to get AFD from.
Particularly in situations where streamline count is an issue,
tcksift2 is worth having a look into. Some extra details on usage here. I would also suggest having a close look at Figure 10 in the SIFT2 paper and thinking about how it might be applicable in your use case