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

In this study, coherence analysis was utilized to construct resting-state EEG networks using the available datasets. A multiple linear regression model was established to examine the correlation between the brain network properties of individuals and their maximum voluntary contraction (MVC) during gripping tasks. The model was used to predict individual MVC. The beta and gamma frequency bands showed significant correlation between RSN connectivity and MVC (p <  0.05), particularly in left hemisphere frontoparietal and fronto-occipital connectivity. RSN properties were consistently correlated with MVC in both bands, with correlation coefficients greater than 0.60 (p <  0.01). Additionally, predicted MVC positively correlated with actual MVC, with a coefficient of 0.70 and root mean square error of 5.67 (p <  0.01). The results show that the resting-state EEG network is closely related to upper body grip strength, which can indirectly reflect an individual’s muscle strength through the resting brain network.Graphical abstract
Source: Medical and Biological Engineering and Computing - Category: Biomedical Engineering Source Type: research