Bridging stability and variability: Illuminating depression with static and dynamic resting-state functional...

Robust and clinically meaningful neural mechanisms of major depressive disorder (MDD) have yet to be identified. Although resting-state functional connectivity (rs-FC) from static and dynamic perspectives has provided valuable insights, findings across these approaches remain fragmented. This study sought to systematically investigate MDD-related abnormalities in static FC (sFC) and dynamic FC (dFC), and importantly, their relationship in MDD pathophysiology.

Connectome-based predictive modeling (CPM) was applied to resting-state fMRI data from 192 patients with MDD and 182 healthy controls to identify functional networks positively or negatively predicting depression severity (static neuromarkers). dFC states were derived using a sliding-window approach and K-means clustering. Group differences in temporal characteristics and summed dFC strength of CPM-derived networks were assessed, and multivariate linear regression was conducted to examine relationships between static and dynamic neuromarkers.

CPM identified a High-depression Network with enriched default mode network (DMN)-sensory FC; and a Low-depression Network with greater intra-network FC, particularly within the DMN and sensory networks. dFC analysis revealed four states. Patients with MDD showed a higher occurrence of a weakly-connected state (State 2) and exhibited summed within-state dFC strength that was significantly higher in the High-depression Network and lower in the Low-depression Network. Both within-state dFC and temporal characteristics significantly predicted summed sFC within CPM networks, together explaining 75%-82% of the variance in summed sFC strength.

These findings suggest that static neuromarkers in MDD may reflect both within-state dFC strength and temporal characteristics, bridging static and dynamic approaches and emphasizing that static neuromarkers may capture certain temporal dynamics.