Hi all
I am comparing a pathological group to a neurologically intact group, using some bedpostx outputs.
I understand the bedpostX algorithm uses automatic relevance determination (ARD) to fit the more complex model (say, 3 crossing fibers) and determines whether there is *evidence in the diffusion data to support this complex model. If there is no evidence, then it reduces the model (say, to 1 fiber). In addition, I can see that the dyads_${i} images show the theta&phi distributions, and the dyads_${i}_dispersion shows the dispersion of the specific fiber-crossing orientation.
My question is two-fold: 1) how is the "evidence" measured in the ARD approach, (when determining whether a diffusion data supports a complex model)? Also, how is "dispersion" measured in the dyads images? 2) more specifically, say anatomically there are two fiber populations in a voxel. After a pathology, one of those fiber directions is lost. As a result, you may expect the ARD to estimate a 1-fiber population at that voxel (which would be correct). However, would the "residual" diffusion of the now-lost second fiber population contribute to the dispersion of the still-intact first fiber population?
*In other words, how independent are the fiber orientations estimated, and, if 2nd or 3rd fiber populations are not supported, do their components get "leaked" into the uncertainty of the first fiber population?
As always, I really appreciate the time and the insight.
Respectfully,
Miguel
