Toward nonparametric diffusion- characterization of crossing fibers in the human brain

To estimate $$ for each distinct fiber population within voxels containing multiple brain tissue types.

A diffusion- $$ correlation experiment was carried out in an in vivo human brain using tensor-valued diffusion encoding and multiple repetition times. The acquired data were inverted using a Monte Carlo algorithm that retrieves nonparametric distributions $$ of diffusion tensors and longitudinal relaxation rates $$ . Orientation distribution functions (ODFs) of the highly anisotropic components of $$ were defined to visualize orientation-specific diffusion-relaxation properties. Finally, Monte Carlo density-peak clustering (MC-DPC) was performed to quantify fiber-specific features and investigate microstructural differences between white matter fiber bundles.

Parameter maps corresponding to $$ 's statistical descriptors were obtained, exhibiting the expected $$ contrast between brain tissue types. Our ODFs recovered local orientations consistent with the known anatomy and indicated differences in $$ between major crossing fiber bundles. These differences, confirmed by MC-DPC, were in qualitative agreement with previous model-based works but seem biased by the limitations of our current experimental setup.

Our Monte Carlo framework enables the nonparametric estimation of fiber-specific diffusion- $$ features, thereby showing potential for characterizing developmental or pathological changes in $$ within a given fiber bundle, and for investigating interbundle $$ differences.