Segmenting white matter tracts for FBA metric computation

Hey @Ana_Han,

Hope you’re doing well!

The questions you’re asking are indeed quite broad, and there’s many different ways to go about it. There’s certainly no single way that is best here: it’s about choosing what works best for your data, hypothesis/es and even just practically (or pragmatically).

If indeed you’re looking to get FD per tract “a priori”, you might want go get a fixel masks for each tract of your interest. Note this is a bit different from the post you link to: Remika Mito’s (@rmito) work there performed a whole-brain fixel-based analysis first: i.e. without hypothesising a set of tracts. Once the fixel-wise results were obtained, they were essentially labelled by identifying structures/tracts present in those results. So on an abstract level, it’s a bit the other way around, you could say. :slightly_smiling_face: I’ll highlight your question to Remika: she might be able to clarify some details of the atlases used. But in the end, this was quite a bit of manual work; it’s not an automated approach combining an existing atlas to get that kind of output.

So sadly, no, there’s no simple solution available: it takes some manual work. A PhD student is working on creating a fixel-wise tract atlas that might eventually serve this purpose, but it’s still a ways off.

Yes, approaches using TractSeg might help you quite a bit along the road to make a few things more automated or simpler. In the end, the challenge will still partially be that the main TractSeg output is voxel-wise maps: so you need to bridge a gap to link it to the fixels in the FOD template (and a tract shouldn’t contain all fixels in each voxel it passes through of course!). These days, TractSeg also outputs “tract orientation maps” ("TOM"s): I can imagine with a slight bit of trickery you could use that output and correspond it with the fixel in your fixel analysis mask, as derived from the FOD template. @jpalm, how did you go about it in practice? (again, I can imagine some alternatives, but would be great to hear some experiences!)

@Ana_Han, there was another recent question on this topic you might’ve missed: see also some of the things I wrote over here, as well as the follow-up replies. That might also inspire a few options. :wink: @jpalm, take a look as well: might be interesting to see how it relates to your approach to deal with this!

Cheers & take care,
Thijs

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