We are doing dMRI acquisitions with a SSFP sequence on a post-mortem human brainstem at 11.7T and we want to study the connectivity between different nucleus of this region.
We are going to generate a tractogram using the brainstem as seed (we can’t do whole-brain tractography because we only have the brainstem).
Is it coherent to apply SIFT on this tractogram? I think the estimated density of each fibre population in every voxel of the image will be proportionally reconstructed by streamlines and so, SIFT can be applicable?
Hi @Dianna_Le_Coz ,
SIFT in an of itself should definitely still be applicable; however, there’s 2 other concerns for this particular region, that might subsequently render your connectome less meaningful (even when SIFT is used):
I suppose using anatomically constrained tractography (ACT) will be difficult, if not impossible, for this purpose? The problem will be that, even of your SIFTed tractogram, (large) amounts of tracks will be unassigned to your connectome’s nodes. So the actual tracks that will make up the connectome, will be featuring an incomplete subset of the SIFTed tracks; i.e., they themselves will not constitute a SIFTed set. This is tricky though: do you expect tracks actually ending in regions in the brainstem (rather than just passing through?). If they just pass through, then you could use ACT with a “GM” region at the top and bottom of the brainstem, and all the rest simply being white matter. If that makes sense, biologically, then this point might not be an issue after all…
Tractography in general in this region, for the accuracy you require (connectivity between nuclei), might be suffering from a lot of false positives; I can image this region is a (almost the ultimate) challenge due to many, many, “kissing” tracts. In the “kissing” scenario, there’s a lot of ambiguity still remaining in the diffusion data; often too much for post-processing to reliably undo, without making very particular/severe assumptions…
My anatomical knowledge of the brainstem is slightly limited though, so it’d be interesting to hear other peoples’ opinions on this.