Reproducible sex differences in personalised functional network topography in youth.

Abstract

A key step toward understanding psychiatric disorders that disproportionately impact female mental health is delineating the emergence of sex-specific patterns of brain organisation at the critical transition from childhood to adolescence. Prior work suggests that individual differences in the spatial organisation of functional brain networks across the cortex are associated with psychopathology and differ systematically by sex. We aimed to evaluate the impact of sex on the spatial organisation of person-specific functional brain networks. We leveraged person-specific atlases of functional brain networks, defined using non-negative matrix factorisation, in a sample of <i>n</i> = 6437 youths from the Adolescent Brain Cognitive Development Study. Across independent discovery and replication samples, we used generalised additive models to uncover associations between sex and the spatial layout (topography) of personalised functional networks (PFNs). We also trained support vector machines to classify participants' sex from multivariate patterns of PFN topography. Sex differences in PFN topography were greatest in association networks including the frontoparietal, ventral attention and default mode networks. Machine learning models trained on participants' PFNs were able to classify participant sex with high accuracy. Sex differences in PFN topography are robust, and replicate across large-scale samples of youth. These results suggest a potential contributor to the female-biased risk in depressive and anxiety disorders that emerge at the transition from childhood to adolescence.