Dual controllability de-differentiation of functional brain networks in major depressive disorder: Insights from...

Aberrant brain state transitions are a key neuropathological feature of major depressive disorder (MDD), yet the underlying mechanisms and associated transcriptional signatures remain poorly elucidated. Here, leveraging the largest publicly available resting-state fMRI dataset (N = 1494; 778 patients, 716 controls), we applied network control theory to characterize abnormal state transition dynamics in MDD and examine their link to whole-brain gene expression. Global analyses revealed that although overall brain controllability was reduced in MDD, the spatial distribution pattern of controllability and its coupling with network topology were largely preserved relative to controls. At the network level, we identified a dual de-differentiation of controllability profiles in MDD: average controllability was significantly decreased in the visual and sensorimotor networks, accompanied by aberrant increases in the frontoparietal and salience networks. Conversely, modal controllability exhibited an inverse pattern in these networks. Integrating transcriptomic data from the human brain, we found that MDD-associated genes were significantly enriched within the transcriptional signature related to these controllability alterations. Specifically, genes linked to decreased average controllability were primarily enriched in pathways governing gene expression regulation, whereas those linked to its increase were enriched in synaptic signaling and plasticity pathways. Cell type-specific analyses further indicated that expression changes specific to excitatory and inhibitory neurons showed the strongest associations with the observed alterations in average controllability. Collectively, our findings bridge microscale molecular pathways with macroscale network dysfunction, providing a novel mechanistic framework for understanding the rigid brain dynamics in depression and may inform the identification of potential therapeutic targets.