Electroconvulsive Therapy Drives Sensorimotor Network Segregation in Depression: A Multiscale Edge-Centric Connectomic Study

Background: Electroconvulsive therapy (ECT) induces widespread brain effects and remains the most effective intervention for severe major depressive disorder (MDD). However, how ECT reshapes the global organization of functional connectomes remains poorly understood. Edge-centric connectomics offers a framework for characterizing large-scale reconfiguration beyond conventional node-based analyses. Methods: Longitudinal resting-state fMRI data from a primary cohort (80 MDD patients, 75 healthy controls) and an independent validation cohort (30 MDD patients) were analyzed. Edge-centric normalized entropy was utilized to quantify connectomic topology at baseline and post-ECT. These topological changes were evaluated for clinical associations and multiscale spatial correlations encompassing cognitive dimensions, neurotransmitter maps, and transcriptomic profiles. Additionally, baseline edge-centric features were leveraged in a machine learning framework to predict treatment response. Results: At baseline, MDD patients showed increased entropy in the subcortical network and decreased entropy in the dorsal attention and sensorimotor networks. Following ECT, a further reduction in sensorimotor network (SMN) entropy was observed, which was replicated in the independent cohort. SMN reorganization was significantly associated with improvements in specific depressive symptoms. Multiscale decoding revealed that these topological shifts spatially aligned with broad monoaminergic receptor distributions and transcriptomic signatures governing neuroplasticity and specific cell types. Furthermore, baseline edge-centric features outperformed conventional fMRI metrics in predicting treatment response and maintained partial cross-site generalizability. Conclusions: ECT is associated with selective reorganization of the sensorimotor network rather than normalization of baseline abnormalities. Edge-centric connectomics combined with multiscale biological annotations provides a robust framework for characterizing therapeutic mechanisms and developing predictive biomarkers in MDD.