Hi @gabemarx,
It depends on the specific pathology. For instance:
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If every pathway in the brain were to reduce in density by 50%, but tractography otherwise behaved identically. By using a fixed number of streamlines per subject, your experiment would be completely oblivious to the difference.
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If only a single pathway were reduced in density, then (hopefully) SIFT will reveal this. However using an identical number of streamlines per subject would (in theory) marginally reduce the magnitude of the effect in that pathway, and yield a marginal increase in density in all other brain pathways. In reality though, for a single affected pathway this effect is unlikely to be detectable due to the poor reproducibility of tractography as a whole.
This issue has in fact been raised many times on the forum, they’re just difficult threads to find as everybody describes the issue in their own unique way. I refer to this as inter-subject connection density normalisation, and it’s comparable to the inter-subject intensity normalisation issues inherent to performing an AFD analysis. These threads also invariably contain me saying something along the lines of “I really need to write that paper…”.
I really need to write that paper…
For most applications, fixing the number of streamlines across subjects is in fact a perfectly reasonable mechanism of inter-subject connection density normalisation. Additionally, some connectomic measures should be invariant under global scaling of all values within the matrix, which renders density normalisation redundant. However quantifying specific connection densities in the presence of significant atrophy is the case where this really breaks down.
It is possible to perform an appropriate scaling of connection densities across subjects; it involves a combination of AFD-like intensity normalisation, and consideration of the SIFT proportionality coefficient. However as I’ve said to other users, one of the goals of my paper is to justify why that scaling is appropriate; my concern is that without that paper in the literature, use of such scaling in an applications paper may not be convincing to reviewers / readers. Indeed dividing all connection densities by TIV may be more passable; whether or not this will have the appropriate effect however depends on whether the atrophy is microscopic or macroscopic, and hence whether it affects TIV.
So, summary: If the atrophy is mild / specific, I’d be tempted to ignore it. If it’s significant, you can consider either dividing by TIV or including it as a nuisance regressor. If you really feel as though your experiment needs the best solution and think you can sufficiently justify it in your manuscript, the solution is out there; but I’d really prefer that you don’t exploit the assistance and try to claim novelty for the mechanism before I get the chance to explain / justify it properly (if it weren’t for forum obligations I’d probably have published it by now!).
Cheers
Rob