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Depressive Disorders: MDD and Dysthymia01:27

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Related Experiment Video

Updated: Apr 18, 2026

Dynamic Inter-subject Functional Connectivity Reveals Moment-to-Moment Brain Network Configurations Driven by Continuous or Communication Paradigms
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Bridging stability and variability: Illuminating depression with static and dynamic resting-state functional

Xiaoling Xie1, Linlin Fan2, Hang Yu1

  • 1Department of Psychology, Sun Yat-sen University, Guangzhou, Guangdong, China.

Journal of Affective Disorders
|April 16, 2026
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Summary

This study reveals that static brain connectivity markers in major depression disorder (MDD) may capture dynamic functional connectivity patterns. These findings link static and dynamic brain activity in MDD pathophysiology.

Keywords:
Connectome-based predictive modelingDynamic functional connectivityFunctional magnetic resonance imagingMajor depressive disorderResting-state functional connectivity

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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.