Abstract

Motivation: It is currently difficult to compute normative models for diffusion MRI metrics of the brain’s white matter across the lifespan due to scanner/protocol effects that are hard to eliminate during harmonization. Goal(s): We set out to build large-scale multi-site normative models for DTI metrics of the white matter of the human brain. Approach: Hierarchical Bayesian Regression was run on ROI metrics derived using the ENIGMA-DTI protocol to determine the age trajectory and centile curves of DTI metrics. Results: We built DTI reference models based on 52,719 subjects that allowed us to detect deviations from the norm for patients with brain diseases. Impact: These reference models are valuable for detecting microstructural deviations from the normal range, while modeling scanner, protocol and cohort effects. They will be used in our ENIGMA consortium to map profiles of microstructural anomalies in >20 neurological and psychiatric conditions.