Help interpreting FBA results


Hello MRtrixers,

I used the FBA pipeline to analyze a specific white matter tract, and I need help understanding what the results (FD/FC/FDC) really mean on a microstructural and/or morphological level.

So for the tract I’m analyzing, I got a significant difference between a control group and a patient group for some metrics but not others. The FD was lower in the patient group, which I take it means the fiber density of that specific tract is significantly lower in the patient group. The FC showed no significant difference between the 2 groups, which is also easily understandable, the tract cross section shows no difference in size between the 2 groups. Finally, the FDC (like the FD) was significantly lower in the patient group, which is the part I’m struggling to wrap my head around. To summarize: decrease of FD and FDC, no difference of FC.

I’m having trouble interpreting what the decrease in FDC (with the no decrease in FC) says about the tract of interest. As far as I understand, FDC is basically a combination of both FD and FC, so the only interpretation that comes to mind is that perhaps the FD was much lower in the patient group that it “dragged down” the FDC enough to compensate for the lack of difference in FC. Does that make any sense? I’m not quite sure how the FDC is calculated, to be honest, so maybe I’m way off. What do you think such a result means??

As always, thanks for the help!



That seems to make perfect sense to me. The FDC measure combines both FD and FC, so is sensitive to both sources of a reduction in the overall ‘connectivity’ of the tract (for want of a better word): reduction in cross-section (smaller breadth of tract) or reduction in the density of the white matter within that tract. If the effect is genuinely a reduction in density with no impact on cross-section, then it will manifest in both FD & FDC – as is the case in your study. The reason we test these separately is that often there can be large differences in the variance between both measures, e.g. large variance in FC, but relatively low variance in FD. So this means that an effect in FD can be statistically significant, but once FD is combined with FC, this can lead to relatively large variance in FDC. This means it’s not uncommon to get significance on FD but not FDC, even though you’d think there should be an effect (since you see it in FD).

But in your case, it’s actually all self-consistent, and all points at a reduction in FD with little or no change in FC.


Great! That’s exactly what I was thinking, just needed confirmation :sweat_smile:

Small follow-up question, if you don’t mind :sweat_smile:

To analyze the WM tract I’m interested in, I first “extracted” the tract from the population template SIFT file using tckedit, then I created a fixel mask from the track using tck2fixel, and then used that fixel mask with fixelcfestats.

Is it safe to assume that the resulting fixel mask delineates the WM tract in all the population subjects, or do I need to create a separate fixel mask for each subject and then warp the separate masks to the population template?

Thanks for the help! (hope I’m not pushing my luck with all the questions :sweat_smile:)



As far as I can tell, that seems correct. The concept of restricting the stats to a tract of interest has come up a few times before, and I think this was more or less what was recommended at the time. Maybe @rsmith or @ThijsDhollander might be in a position to confirm…?


Is it safe to assume that the resulting fixel mask delineates the WM tract in all the population subjects, or do I need to create a separate fixel mask for each subject and then warp the separate masks to the population template?

Assuming the registration is correct, and the overall WM crossing-fibre geometry isn’t grossly variable, then the former is fine. The latter leaves some slight ambiguity: if you delineate the bundle individually for each subject, and then warp those masks to template space, how would you plan to combine the masks across subjects? You could e.g. take the intersection, and then you’d only be including template fixels that you are confident lie within the bundle of interest (for all subjects*; but that’s not the only option.

The other thing you can do is simply feed the fixel data to mrstats along with the fixel mask via the -mask option. This will give you e.g. the mean of your quantitative measure within the mask, to which you can then apply more conventional statistical algorithms. This would lose the potential for identifying effects only specifically within a subset of the cross-section of a bundle and/or only a segment of its length, but may have improved statistical power and may in fact be a more direct realisation of your intended hypothesis test. This could theoretically be done either in subject or template space, but that choice (as with any other) has consequences in terms of how subject differences manifest in the outcomes and what measures are available (e.g. the FC metric is not typically quantified in subject space).


Yep, all of this makes sense. I’m not so worried about a few “reasonable” misalignments here: they should just add a bit of variance; so if you still find statistically significant differences, then that should be quite a reliable indicator of an actual effect being present. Note that FBA itself also fully (if not much more!) relies on a “decent enough” alignment. Using the either the same fixel (“same” being the outcome from fixelcorrespondence) or the same (fixel-)ROI / mask is the closest, most unassuming, approach to comparing “like with like”. …and, potentially more importantly even, it avoids all the hassle of…

…or other more and less similar strategies you might think of to overcome this. No strategy is perfect in this regard, and relying on the registration rather than other more manual interventions (or automated ones driven by tractography, which I’d trust a whole lot less than registration) is, as I mentioned, probably erring more on the unassuming (unbiased) side.

This is indeed what I’d also mostly recommend for a post-hoc analysis (using a mask from significant fixels from another FBA), but also quite often for a hypothesis-driven analysis of a (set of) tract(s). It may be wise to include a control tract as well, where you do not expect or hypothesise to find a difference.

Finally, if you take the mean of metrics for a tract, and you’re wondering whether to go for FC or log(FC), the answer is always log(FC). Thought I’d just add that already; it’s a question that often comes up for these (fixel) ROI-wise analyses.