Abstract

Increasing sharing initiatives on neuroimaging data have provided unprecedented opportunities to study brain disorders. Standardized approaches for capturing reproducible and comparable biomarkers are greatly needed. Here, we propose a method, NeuroMark, which leverages a priori-driven independent component analysis to effectively extract functional brain network biomarkers from functional magnetic resonance imaging (fMRI) data. NeuroMark automatically estimates features adaptable to each individual and comparable across subjects by taking advantage of the replicated brain network templates extracted from 1828 healthy controls as guidance to initialize the individual-level networks. Four studies including 2454 subjects were conducted spanning six brain disorders (schizophrenia, autism spectrum disorder, depression, bipolar disorder, mild cognitive impairment and Alzheimer's disease) to evaluate the proposed method from different perspectives (replication, cross-study comparison, subtle difference identification, and multi-disorder classification). Results demonstrate the great potential of NeuroMark in its feasibility to link different datasets/studies/disorders and enhance sensitivity in identifying biomarkers for patients with challenging mental illnesses.