Population-based microstructural quantile profile

In vivo fiber tractography is a 3D reconstruction technique to assess neural tracts using data collected by dMRI. The fiber tract obtained from the technique can be used for studying the brain’s anatomy and its associations to function of interest covariates. Recent machine learning methods can efficiently identify subject-level white matter tracts. However, analyzing the relationship between the scalar clinical/psychological factors (e.g., cognitive score) and fiber tracts is not easy. The current methods rely on scalar summary statistics of fiber tract (e.g., FA mean), and thus, the investigation of associations is based on traditional regression models. In this paper, we find that the FA quantiles over the points of a fiber tract, known as the microstructural quantile profile, can be used to differentiate the effect of function of interest covariates. We adopted and further developed the quantile regression methodology with clustered data to infer the relationship between microstructural quantile profile and scalar clinical/psychological factors.
The research made use of dMRI data and neuropsychological evaluations from the HCP comprising 382 males and 427 females. As a result, each participant had 58 identifiable white matter tracts. Among the identified white matter tracts, we focus on the following ones to demonstrate our proposed new method: Arcuate Fasciculus (AF), Uncinate Fasciculus (UF), Cingulum (CB), and corticospinal tract (CST). Insightful regional findings were provided via our new approach. Compared to other methods, The method is more robust in identifying the relationship between fiber tract and scalar clinical/psychological factors.