Edge-centric brain connectomes reveal two molecularly distinct neurobiological subtypes of adolescent major depressive disorder.

Adolescent major depressive disorder (MDD) is a heterogeneous disorder that complicates diagnosis and treatment. However, the mechanisms underlying this heterogeneity are still unclear. This study investigated the neural and molecular bases of individual differences in adolescent MDD patients by integrating a novel edge-centric brain connectome with transcriptomic and neurotransmitter profiles. Using individualized edge-centric brain functional networks, we computed the nodal entropy for each brain region and identified potential adolescent MDD subtypes via a data-driven clustering algorithm. Machine learning models were constructed to assess whether subtype division could enhance discriminability. Furthermore, connectome‒transcriptome association analysis was performed to investigate the subtype-specific molecular bases underlying nodal entropy changes. We also analyzed the spatial associations between nodal entropy changes and neurotransmitter distributions. This study identified two robust adolescent MDD subtypes: subtype 1 was characterized by lower nodal entropy in the visual and somatomotor networks and significantly greater nodal entropy in the ventral attention, frontoparietal, and default mode networks, whereas subtype 2 showed a contrasting pattern. Subtype-based models significantly improved case‒control discriminability, with the two subtypes characterized by distinct core predictive factors. Nodal entropy patterns of the two subtypes are differentially linked to distinct molecular signatures involving synapse function, development, immune/inflammatory pathways, metabolic/endocrine pathways, and neurochemical modulation profiles. Collectively, our study revealed two adolescent MDD subtypes with distinct neural patterns and specific molecular signaling mechanisms, thereby elucidating the potential mechanisms underlying adolescent MDD heterogeneity.