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Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke

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The correlation between upper body grip strength and resting-state EEG network.

Xiabing Zhang^1,2, Bin Lu^1,2, Chunli Chen^1,2

¹The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, No. 2006, Xiyuan Ave, West Hi-Tech Zone, Chengdu, 611731, Sichuan, China.

Medical & Biological Engineering & Computing

|June 20, 2023

Summary

Resting-state electroencephalogram (EEG) networks correlate with upper body grip strength. This finding suggests that brain network analysis can indirectly indicate an individual's muscle strength, opening new avenues for neuroscience research.

Keywords:

Grip strength Maximum voluntary contraction Resting-state EEG network

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Area of Science:

Neuroscience
Brain Network Analysis
Human Physiology

Background:

Current neuroscience research emphasizes movement-related electroencephalogram (EEG) activity.
Limited studies explore the effects of strength training on resting-state brain networks.
Investigating the link between grip strength and resting-state EEG is crucial.

Purpose of the Study:

To examine the correlation between upper body grip strength and resting-state EEG networks.
To determine if resting brain network properties can predict muscle strength.
To explore the relationship between maximum voluntary contraction (MVC) and brain connectivity.

Main Methods:

Utilized coherence analysis to construct resting-state EEG networks.
Employed a multiple linear regression model to assess correlations.
Analyzed beta and gamma frequency bands for significant relationships.

Main Results:

Significant correlations found between resting-state network (RSN) connectivity and MVC in beta and gamma bands (p < 0.05).
Left hemisphere frontoparietal and fronto-occipital connectivity showed notable associations.
RSN properties strongly correlated with MVC (correlation coefficient > 0.60, p < 0.01).
Predicted MVC positively correlated with actual MVC (coefficient = 0.70, p < 0.01).

Conclusions:

Resting-state EEG networks are closely associated with upper body grip strength.
Brain network properties can serve as an indirect indicator of muscle strength.
Findings highlight the potential of EEG network analysis in assessing physical capacity.