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Altered Salience-Default Mode Network Dynamics in Subclinical Depression: A Preclustering-Based Co-Activation Pattern

Bo Zhang1,2,3, Zhinan Yu1,2,3, Feifan Yan1,2,3

  • 1Tianjin International Joint Research Center for Neural Engineering, Academy of Medical Engineering and Translational Medicine, Medical School, Tianjin University, Tianjin, China.

CNS Neuroscience & Therapeutics
|February 4, 2026
PubMed
Summary

Altered brain network dynamics, specifically between the salience network (SN) and default mode network (DMN), are key indicators of subclinical depression (SD). These changes in network coordination show potential for accurate diagnostic markers.

Keywords:
default mode networkfunctional magnetic resonance imagingsalience networksubclinical depression

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Clinical Psychology

Background:

  • Subclinical depression (SD) is associated with aberrant brain activity and connectivity in major functional networks.
  • The dynamic interplay between the default mode network (DMN), frontoparietal network (FPN), and salience network (SN) in SD is not well understood.
  • Understanding network dynamics is crucial for elucidating the neuropathology of SD.

Purpose of the Study:

  • To investigate the dynamic coordination patterns among core brain networks in individuals with subclinical depression (SD).
  • To explore the potential of these dynamic network features for the clinical diagnosis of SD using machine learning.

Main Methods:

  • Resting-state functional magnetic resonance imaging (fMRI) data were acquired from 26 individuals with SD and 33 healthy controls (HCs).
  • A novel preclustering-based co-activation pattern method was employed to analyze dynamic network coordination.
  • Machine learning algorithms were utilized to assess the diagnostic utility of observed network dynamics.

Main Results:

  • Individuals with SD showed reduced dwell time in the salience network (SN) and increased SN-to-DMN transition frequency, correlating with depressive severity.
  • An ensemble learning model utilizing SN-DMN dynamic features achieved 96.44% accuracy in differentiating SD from HCs.
  • Dynamic functional connectivity alterations between SN and DMN are significant in subclinical depression.

Conclusions:

  • Altered SN-DMN dynamic coordination may serve as a potential neuroimaging marker for subclinical depression (SD).
  • Findings support a neurocognitive model where disrupted SN-DMN dynamics contribute to attentional biases and rumination in SD.
  • Dynamic network analysis offers promising avenues for early detection and understanding of SD.