I did a test on small subset (approx 10 subjects) and coefficients of variation of sum of weights in both standard whole-brain post-SIFT2 and combined whole-braint + targetted post-SIFT2 approaches are similarly bad = about 100%.
I am quite confused here. Not sure I understand the terminologic difference (pathway vs connection) but mainly the offer of FBA for this study case what is confusing me.
My understanding was that if there is a hypothesis on a connectivity of specific pathway which can be defined by starting and ending gray matter mask(s) (which I think is my case), then individual tracking, SIFT2 and sum of the weights of the streamlines connecting nodes of interest is always the method of choice.
In contrast, FBA is method of choice when there is not pathway-specific hypothesis and one wants to test whole-brain fixel-wise. Maybe also in the (rare) cases when in some pathologies only part of the pathway along its length is affected, or some macroscopic fibre bundle containing many different pathways is of interest, one can think of restriction of FBA to specific set of streamlines, as discussed in this post. However, also in this post, a question was raised whether single scalar measure is not a better option.
So do you see any benefit in doing FBA in case where hypothesis is concerning a connectivity of specific pathway which can be defined by gray-matter nodes the pathway involves? Do you think that one could gain a better sensitivity using FBA (with restriction of the fixel mask to the pathway of interest) when assessing a less identifiable pathway by avoiding the individual subject-wise noisy tracking? In FBA, the tracking would be performed only once and on less noisy FOD template. This approach would probably lower specificity to attribute the potentially observed effect to the particular pathway, but could it enhance statistical sensitivity to observe any effect in comparison to subject-wise tracking, SIFT2 and extracting edge-wise sum of streamline weights?
Antonin