Reduced brain modularity may underlie accelerated disease progression in first-episode, drug-naïve depression

Depression presents considerable heterogeneity in its clinical course, yet reliable biomarkers for predicting individual trajectories remain elusive. Brain modularity, a fundamental topological property of structural networks, reflects the balance between functional segregation and integration. This study investigates the prognostic significance of brain modularity in depression progression and its association with white matter alterations.

In this longitudinal study, 142 first-episode, medication-naïve patients with depression underwent diffusion MRI-based structural network analysis. Based on baseline modularity values, participants were stratified into high- and low-modularity groups. Key white matter network metrics-including rich-club connections, global efficiency, and nodal efficiency-were assessed. Depression severity was measured using the Hamilton Depression Rating Scale (HDRS). Logistic regression and receiver operating characteristic (ROC) analyses were employed to evaluate the prognostic utility of brain modularity in predicting symptom progression.

At baseline, patients with lower modularity exhibited disrupted network organization. Longitudinally, these individuals showed a steeper decline in rich-club connections, global efficiency, and left hippocampal nodal efficiency, alongside significantly greater HDRS worsening. Baseline modularity was inversely correlated with the rate of depression progression, with logistic regression confirming its predictive value. ROC analysis demonstrated robust classification performance.

Reduced brain modularity predisposes individuals to accelerated white matter network alterations and worsening depressive symptoms. These findings highlight brain modularity as a potential biomarker for identifying individuals at heightened risk of depression progression, offering a novel target for early intervention.