Hi Gari,

Some points:

I read that after normalization and SIFT, I can use the fiber count directly

Essentially. By doing the intensity normalisation / group average response function steps in pre-processing, you guarantee that âone unit of AFDâ is equivalent across subjects. However here we actually want to make âone unit of *connection density*â equivalent between subjects. Matching the total number of reconstructed streamlines across subjects (in combination with the AFD normalisation) is *adequate* for this; at least until I eventually demonstrate properly in an article how it should be done (and therefore *you* donât get stuck trying to justify it in your own manuscript).

Sorry, I should have published that one a *long* time agoâŚ

But, with SIFT2, should I do a weighted sum?

Yep. Well, I prefer âsum of weightsâ rather than weighted sum. Recall that each streamline âweightâ is something proportional to cross-sectional area, so it makes sense to add these up for the total pathway. With âstreamline countâ, itâs essentially the same process thatâs happening, only that each streamline has unity weight.

In this scenario, what would be your recommendation for the tckgen -num option? I was using 500,000 but I think it is not enough for what I am readingâŚ

This is always a difficult one: Iâve given a similar answer a few times, but Iâm not sure I should be adding it to the documentation FAQ given itâs really an experimental question rather than a software question (maybe we need a forum FAQ?)

The streamlines tractography reconstruction is stochastic and discrete, and therefore repeating tracking and SIFT2 on the same image data will give slightly results. The less streamlines you generate, the more variance that stochastic behaviour will contribute to your result, and the more the fact we have discrete streamlines rather than a continuous field of connectivity contributes to the quantisation of the result.

E.g. If you get 10 streamlines in one subject, and 12 in another, do you trust that to be a genuine difference? What about 1,000 v.s. 1,200?

Personally the only recommendation I can make is to actually *do* this experiment, see how much variance you get from repeating the tracking on the same image data, and ideally contrast this against scan-rescan variance if possible. If thereâs computation time limitations as well, incorporate that into the final decision as you see fit. But itâs better to have some understanding of the influence of such parameters, rather than picking a number and hoping for the best.

Cheers

Rob