A Contrastive Framework for Modeling Brain Heterogeneity in Precision Mental Health

Abstract

Precision mental health aims to enable personalized care for mental disorders via the identification of brain-behavior associations at the individual level, yet current frameworks often face challenges in generalizability, interpretability, and clinical translation. Contrastive machine learning (CML) has emerged as a promising paradigm for characterizing individual brain variations by extracting brain dimensions that capture both disease-relevant aberrations and inter-subject heterogeneity. In this Review, we synthesize the conceptual foundations and recent methodological advances of CML. We compare CML with related frameworks and illustrate how it integrates with subtyping and predictive modeling pipelines, situating it within the broader landscape of precision mental health. We then review emerging applications that use CML to link brain structure and function to cognition, emotion, and treatment response. Finally, we outline future directions of applications and methodological innovations where CML could further advance personalized diagnosis and intervention in mental health.