Large-scale Normative Modeling of Brain Microstructure

Abstract

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.

Keywords: diffusion tensor imaging; hierarchical Bayesian regression; normative modeling; white matter microstructure.

Fig. 1.. Age distribution for the 18 datasets we analyzed.

The number above the boxplot is the median age for each sample and to the right is the total number subjects used for training. For the clinical samples, only control subjects are shown here as the normative model is based on controls only.

Fig. 2.

A) Fractional anisotropy (FA) results: proportion of extreme negative deviations on the left and positive extreme deviations on the right. Color bars show the proportion of subjects with extreme deviations per ROI. B) Mean diffusivity (MD) results: proportion of extreme negative deviations on the left and positive extreme deviations on the right. Color bars show the proportion of subjects with extreme deviations per ROI.