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

Area of Science:

• Neuroscience
• Psychiatry
• Medical Imaging

Background:

• Major depression disorder (MDD) lacks identified neural mechanisms.
• Resting-state functional connectivity (rs-FC) offers insights but findings are fragmented.
• This study investigates MDD-related static and dynamic FC abnormalities and their relationship.

Purpose of the Study:

• To systematically investigate MDD-related abnormalities in static FC (sFC) and dynamic FC (dFC).
• To explore the relationship between sFC and dFC in MDD pathophysiology.
• To identify functional networks predicting depression severity using connectome-based predictive modeling (CPM).

Main Methods:

• Applied CPM to resting-state fMRI data from 192 MDD patients and 182 controls.
• Derived dFC states using sliding-window and K-means clustering.
• Assessed group differences in dFC temporal characteristics and strength, and used regression to link static and dynamic markers.

Main Results:

• Identified a High-depression Network (enriched DMN-sensory FC) and a Low-depression Network (intra-network FC).
• MDD patients showed higher occurrence of a weakly-connected state (State 2).
• Within-state dFC and temporal characteristics predicted summed sFC, explaining 75%-82% of variance.

Conclusions:

• Static neuromarkers in MDD may reflect within-state dFC strength and temporal characteristics.
• This bridges static and dynamic FC approaches in MDD research.
• Static neuromarkers can capture certain temporal dynamics of brain activity in MDD.