Large-scale Normative Modeling of Brain Microstructure

Normative models of brain metrics based on large populations are extremely valuable for detecting brain abnormalities in patients with dementia, psychiatric, or developmental conditions. Here we present the first large-scale normative model of the brain's white matter (WM) microstructure derived from 18 international diffusion MRI (dMRI) datasets covering almost the entire lifespan (totaling N=51,830 individuals; age: 3-80 years). We extracted regional diffusion tensor imaging (DTI) metrics using a standardized analysis and quality control protocol, and used Hierarchical Bayesian Regression (HBR) to model the statistical distribution of derived WM metrics as a function of age and sex, while modeling the site effect. HBR overcomes known weaknesses of some data harmonization methods that simply scale and shift residual distributions at each site. To illustrate the method, we applied it to detect and visualize profiles of WM microstructural deviations in cohorts of patients with Alzheimer's disease, mild cognitive impairment, Parkinson's disease and in carriers of 22q11.2 copy number variants, a rare neurogenetic condition that confers increased risk for psychosis. The resulting large-scale model offers a common reference to identify disease effects in individuals or groups, as well as to compare disorders and discover factors that influence these abnormalities.