Changes in brain structural connectivity networks are associated with electroconvulsive therapy efficacy in adolescents with major depressive disorder and suicidal ideation.
Authors
Affiliations (6)
Affiliations (6)
- College of Artificial Intelligence Medicine, Chongqing Medical University, Chongqing, 400016, China.
- Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
- Department of Clinical Neurosciences, University of Cambridge, CB2 1TN, United Kingdom.
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati College of Medicine, Cincinnati, 45219, OH, USA.
- College of Artificial Intelligence Medicine, Chongqing Medical University, Chongqing, 400016, China; College of Biomedical Engineering, Chongqing Medical University, Chongqing, 400016, China. Electronic address: [email protected].
- College of Artificial Intelligence Medicine, Chongqing Medical University, Chongqing, 400016, China. Electronic address: [email protected].
Abstract
Suicide is a leading cause of death among adolescents with major depressive disorder (MDD). Electroconvulsive therapy (ECT) effectively alleviates depressive symptoms, yet its underlying neural mechanisms remain unclear. 27 adolescents with MDD and SI and 32 demographically matched healthy controls were included. This study used structural magnetic resonance imaging and graph theory analyses to examine group differences in the global and nodal network topology at baseline and longitudinal changes within the MDD group following ECT. Linear mixed-effects models, correlation analysis, and machine learning were employed to further investigate the relationship between network topological properties and clinical symptoms. At baseline, adolescents with MDD exhibited reduced global normalized clustering and decreased nodal centrality across networks, with abnormalities predominantly in the limbic and sensorimotor systems. Following ECT, group differences in global network metrics were no longer significant, and topological abnormalities in networks related to emotional regulation and cognitive control were ameliorated. However, persistent topological disruptions in the sensorimotor and visual networks remained. Compare to topological metrics, machine learning models leveraging raw structural connectivity matrices demonstrated superior predictive performance for clinical efficacy. This study offers novel insights into the disorder's neurobiological mechanisms and the therapeutic effects of ECT.