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Related Experiment Video

Updated: May 23, 2026

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
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Functional brain network analysis in patients with upper-limb spasticity after stroke.

Fangwen Gao1, Man He2, Xubo Hou3

  • 1Department of Biomedical Engineering, Chengde Medical University, Chengde, Hebei, China.

Frontiers in Human Neuroscience
|December 31, 2025
PubMed
Summary

Stroke-related upper limb spasticity impairs motor function. Brain network analysis reveals reduced functional connectivity and altered network efficiency in patients, suggesting it

Keywords:
brain networkselectroencephalogramfunctional connectivitygraph theroystrokeupper-limb spasticity

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Science

Background:

  • Stroke is a leading cause of death and disability, often resulting in upper-limb spasticity that severely impacts quality of life.
  • Current clinical assessments for spasticity have limitations in objectivity, and the underlying neurophysiological mechanisms require further investigation.
  • Brain network analysis presents a novel approach to understanding the neural basis of spasticity.

Purpose of the Study:

  • To investigate the neurophysiological mechanisms of upper-limb spasticity following stroke using functional brain network analysis.
  • To explore the relationship between brain network characteristics and spasticity severity.
  • To identify potential objective biomarkers for spasticity assessment and personalized rehabilitation.

Main Methods:

  • Multi-channel electroencephalography (EEG) signals were recorded from eight stroke patients with upper-limb spasticity (MAS grades 1-2) and eight healthy controls during specific upper limb movements.
  • Functional brain networks were constructed using the weighted phase delay index.
  • Graph theory metrics, including node degree, global efficiency, local efficiency, clustering coefficient, and small-world properties, were analyzed.

Main Results:

  • Patients with spasticity exhibited significantly lower functional connectivity than healthy controls, particularly in alpha and beta frequency bands, with reduced cross-regional synchrony in frontal, central, and temporal regions.
  • Graph theory analysis revealed significantly reduced global efficiency, local efficiency, and clustering coefficient in spasticity patients compared to controls.
  • Node degree analysis indicated abnormal compensatory activation in temporal and parietal regions in patients, contrasting with higher central and frontal node degrees in healthy individuals.

Conclusions:

  • Post-stroke upper-limb spasticity is associated with disruptions in cortical network dynamics, not solely a peripheral issue.
  • Impaired local and global network integration, along with inefficient compensatory mechanisms, characterize spasticity.
  • Brain network metrics, such as global and local efficiency, show potential as objective biomarkers for quantifying spasticity and guiding precision rehabilitation strategies.