Dissecting heterogeneity in major depressive disorder via normative model-driven subtyping of functional brain...

Major depressive disorder (MDD) is a prevalent and intricate mental health condition characterized by a wide range of symptoms. A fundamental challenge in understanding MDD lies in elucidating the brain mechanisms underlying the complexity and diversity of these symptoms, particularly the heterogeneity reflected in individual differences and subtype variations within brain networks.

To address this problem, we explored the brain network topology using resting-state functional magnetic resonance imaging (rs-fMRI) data from a cohort of 797 MDD patients and 822 matched healthy controls (HC). Utilizing normative modeling of HC, we quantified individual deviations in brain network degree centrality among MDD patients. Through k-means clustering of these deviation profiles, we identified two clinically meaningful MDD subtypes. Moreover, we employed Neurosynth to analyze the cognitive correlates of these subtypes.

Subtype 1 exhibited positive deviations of degree centrality in the limbic (LIM), frontoparietal (FPN), and default mode networks (DMN), but negative deviations in the visual (VIS) and sensorimotor networks (SMN), positively correlating with higher cognitive functions and negatively with basic perceptual processes. In contrast, subtype 2 demonstrated opposing patterns, characterized by negative deviations in degree centrality of the LIM, FPN, and DMN and positive deviations of the VIS and SMN, along with inverse cognitive associations.

Our findings underscore the heterogeneity within MDD, revealing two distinct patterns of network topology between unimodal and transmodal networks, offering a valuable reference for personalized diagnosis and treatment strategies.