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Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
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Altered Static and Dynamic Functional Network Connectivity and Combined Machine Learning in Stroke.

Hao Liu1, Xin Huang2, Yu-Xin Yang1

  • 1School of Ophthalmology and Optometry, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, 330006, China.

Brain Topography
|January 9, 2025
PubMed
Summary
This summary is machine-generated.

This study reveals significant static and dynamic brain network alterations in stroke patients, impacting visual, cognitive, and motor functions. These findings aid in understanding stroke

Keywords:
Functional network connectivityIndependent component analysisResting-state networkRs-fMRIStroke

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Area of Science:

  • Neuroimaging
  • Neuroscience
  • Brain Network Analysis

Background:

  • Stroke causes brain damage, leading to structural and functional abnormalities.
  • Previous research shows motor and cognitive deficits post-stroke.
  • Limited studies explore static and dynamic functional network connectivity changes in stroke patients.

Purpose of the Study:

  • To investigate alterations in static and dynamic functional network connectivity in stroke patients.
  • To compare functional network connectivity between stroke patients and healthy controls (HCs).
  • To assess the potential of functional connectivity metrics for discriminating stroke patients from HCs.

Main Methods:

  • Resting-state functional magnetic resonance imaging (rs-fMRI) was used for 50 stroke patients and 50 HCs.
  • Independent Component Analysis (ICA) extracted resting-state networks (RSNs).
  • Static and dynamic functional network connectivity (dFNC) were analyzed, alongside temporal metrics. A Support Vector Machine (SVM) was used for classification.

Main Results:

  • Stroke patients showed reduced intra-network functional connectivity (FC) in specific brain regions (VAN, VN, DMN).
  • Significant differences in static functional network connectivity (FNC) were found between groups, including increased and decreased inter-network connections.
  • Stroke patients exhibited distinct dFNC patterns, spending more time in a specific state (state 4) compared to HCs.
  • SVM classification using FC and FNC achieved >70% accuracy and AUCs from 0.8284 to 0.9364.

Conclusions:

  • Stroke is associated with significant static and dynamic alterations in large-scale brain networks.
  • These network changes correlate with visual, cognitive, and motor functional deficits.
  • The findings provide insights into stroke's neural mechanisms and support diagnostic/therapeutic development.