Edge-Centric Brain Connectome Representations Reveal Increased Brain Functional Diversity of Reward Circuit in Patients With Major Depressive Disorder.

Background: Major depressive disorder (MDD) has been increasingly understood as a disorder of network-level functional dysconnectivity. However, previous brain connectome studies have primarily relied on node-centric approaches, neglecting critical edge-edge interactions that may capture essential features of network dysfunction.

Methods: This study included resting-state functional magnetic resonance imaging data from 838 patients with MDD and 881 healthy control (HC) participants across 23 sites. We applied a novel edge-centric connectome model to estimate edge functional connectivity and identify overlapping network communities. Regional functional diversity was quantified via normalized entropy based on community overlap patterns. Neurobiological decoding was performed to map brain-wide relationships between functional diversity alterations and patterns of gene expression and neurotransmitter distribution. Comparative machine learning analyses further evaluated the diagnostic utility of edge-centric versus node-centric connectome representations.

Results: Compared with HC participants, patients with MDD exhibited significantly increased functional diversity within the prefrontal-striatal-thalamic reward circuit. Neurobiological decoding analysis revealed that functional diversity alterations in MDD were spatially associated with transcriptional patterns enriched for inflammatory processes, as well as distribution of 5-HT_1B receptors. Machine learning analyses demonstrated superior classification performance of edge-centric models over traditional node-centric approaches in distinguishing patients with MDD from HC participants at the individual level.

Conclusions: Our findings highlight that abnormal functional diversity within the reward processing system might underlie multilevel neurobiological mechanisms of MDD. The edge-centric connectome approach offers a valuable tool for identifying disease biomarkers, characterizing individual variation and advancing current understanding of complex network configuration in psychiatric disorders.