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Depressive Disorders: Etiology01:27

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Updated: Oct 20, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
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Depression over Time in Persons with Stroke: A Network Analysis Approach.

Sameer A Ashaie1,2, Jinyi Hung3, Carter J Funkhouser4,5

  • 1Center for Aphasia Research and Treatment, Shirley Ryan AbilityLab.

Journal of Affective Disorders Reports
|September 16, 2021
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Summary

Network analysis reveals that baseline connectivity of depressive symptoms predicts post-stroke depression persistence. Examining individual symptoms, not just sum-scores, is crucial for understanding depression course after stroke.

Keywords:
depressionemotionnetwork-analysisstroke

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Area of Science:

  • Neuroscience
  • Psychiatry
  • Public Health

Background:

  • Network analysis is a novel approach to understanding relationships among depressive symptoms.
  • This method has been underutilized in stroke populations.

Purpose of the Study:

  • To examine temporal changes in the network of depressive symptoms post-stroke.
  • To determine if baseline network characteristics predict depression persistence.

Main Methods:

  • Utilized network analysis on a dataset of 835 stroke survivors.
  • Assessed depressive symptoms at discharge, 3 months, and 12 months post-discharge.

Main Results:

  • The depressive symptom network was less connected at discharge compared to later time points.
  • Trouble focusing and feeling good as others were key predictable symptoms post-discharge.
  • Higher baseline network connectivity predicted persistent depression at 12 months.

Conclusions:

  • Baseline network connectivity is a significant predictor of post-stroke depression trajectory.
  • Highlights the importance of analyzing individual depressive symptoms over sum-scores.
  • Suggests network analysis can inform interventions for post-stroke depression.