PMC

The population-informed prior enables population studies of multi-fascicle models from single-shell HARDI data. (a–b) The first study reveals significantly decreased FA related to autism in the left arcuate fasciculus (*p<.05,**p<.01). (c–d) The second study reveals a cluster of significantly higher f_iso. (d) Average f_iso in the cluster.

(Left) Incorporating the prior in the estimation significantly improves the accuracy of the estimated model under the three simulated scenarios and for all five comparison metrics (distributions are shown for 20 datasets simulated for each set of parameters).(Right) The better accuracy mostly affects the diffusion properties of tensors (other than their directions), as predicted by .

(a) Infinitely many models produce the same diffusion signal at a given b-value and form a manifold. The manifolds for different b-values intersect at the true underlying model. (b) For N-fascicle models (here N =3), manifolds are (N−1)-dimensional hypersurfaces that intersect tangentially, making the estimation sensitive to noise. (c) The population-informed prior assigns different probabilities to models on the manifold.

(a) Incorporating prior knowledge significantly improves the quality of the model estimation for all five metrics and for both healthy controls and ASD patients. This improvement implies that (b) the extracellular water fraction can be visualized with more contrast and less noise in smaller details of the white matter up to its boundary with the grey matter, and (c) properties of the fascicles in crossing areas (shown is the corona radiata) are better represented and do not suffer the arbitrary choice of a model from .