Electroconvulsive Therapy Regulates Brain Connectome Dynamics in Patients With Major Depressive Disorder

• Published in: Biological psychiatry (1969) 2024-12, Vol.96 (12), p.929-939
• Main Authors: Guo, Yuanyuan, Xia, Mingrui, Ye, Rong, Bai, Tongjian, Wu, Yue, Ji, Yang, Yu, Yue, Ji, Gong-Jun, Wang, Kai, He, Yong, Tian, Yanghua
• Format: Article
• Language: English
• Subjects: Adult; Brain - diagnostic imaging; Brain - physiopathology; Connectome; Connectomics; Depressive Disorder, Major - diagnostic imaging; Depressive Disorder, Major - physiopathology; Depressive Disorder, Major - therapy; ECT; Electroconvulsive Therapy; Female; Gyrus Cinguli - diagnostic imaging; Gyrus Cinguli - physiopathology; Humans; Magnetic Resonance Imaging; Male; Middle Aged; Modularity; Nerve Net - diagnostic imaging; Nerve Net - physiopathology; Network dynamics; Resting-state fMRI; Suicidal Ideation; Treatment Outcome
• ISSN: 0006-3223; 1873-2402; 1873-2402
• DOI: 10.1016/j.biopsych.2024.03.012

Electroconvulsive therapy (ECT) is an effective treatment for patients with major depressive disorder (MDD), but its underlying neural mechanisms remain largely unknown. The aim of this study was to identify changes in brain connectome dynamics after ECT in MDD and to explore their associations with treatment outcome. We collected longitudinal resting-state functional magnetic resonance imaging data from 80 patients with MDD (50 with suicidal ideation [MDD-SI] and 30 without [MDD-NSI]) before and after ECT and 37 age- and sex-matched healthy control participants. A multilayer network model was used to assess modular switching over time in functional connectomes. Support vector regression was used to assess whether pre-ECT network dynamics could predict treatment response in terms of symptom severity. At baseline, patients with MDD had lower global modularity and higher modular variability in functional connectomes than control participants. Network modularity increased and network variability decreased after ECT in patients with MDD, predominantly in the default mode and somatomotor networks. Moreover, ECT was associated with decreased modular variability in the left dorsal anterior cingulate cortex of MDD-SI but not MDD-NSI patients, and pre-ECT modular variability significantly predicted symptom improvement in the MDD-SI group but not in the MDD-NSI group. We highlight ECT-induced changes in MDD brain network dynamics and their predictive value for treatment outcome, particularly in patients with SI. This study advances our understanding of the neural mechanisms of ECT from a dynamic brain network perspective and suggests potential prognostic biomarkers for predicting ECT efficacy in patients with MDD.