Abnormal Topology and Connectivity of Structural Covariance Network Related to Diagnosis and Phenotyping in Major Depressive Disorder

Motivation: Findings on brain network abnormalties in major depressive disorder (MDD) were mixed owing to small-scale and single-site designs. The diagnostic value of network topology and connectivity remain unclear. Goal(s): To identify robust structural network abnormalities in MDD and relevant clinical phenotypes and to discern the diagnostic value of network topology and connectivity. Approach: Group-level comparsion and individual-level machine learning classification was performed based on structural covariance network connectivity and topological metrics. Results: Different patterns of network topology and connectivity abnormalities were observed between first-episode drug-naive and recurrent patients with MDD. Topological metrics enabled more accurate classification performance on MDD diagnosis and phenotyping. Impact: Our findings advance the current understanding of network-level neurobiological mechanisms of MDD, providing a solid basis for future development of network topology-based diagnosis models.