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

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Abstract

Current research in the field of neuroscience primarily focuses on the analysis of electroencephalogram (EEG) activities associated with movement within the central nervous system. However, there is a dearth of studies investigating the impact of prolonged individual strength training on the resting state of the brain. Therefore, it is crucial to examine the correlation between upper body grip strength and resting-state EEG networks. 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.

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Funding

This work was supported by the STI 2030—Major Projects (#2022ZD0208900, #2022ZD0211400, #2022ZD0208500), the National Natural Science Foundation of China (#62103085, #U19A2082), the Key R&D projects of Science & Technology Department of Sichuan Province (#23ZDYF0961), and the Scientific Research Foundation of Sichuan Provincial People's Hospital (#2021LY21).

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Authors and Affiliations

  1. The 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

    Xiabing Zhang, Bin Lu, Chunli Chen, Lei Yang, Wanjun Chen, Dezhong Yao, Fali Li & Peng Xu

  2. School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Xiabing Zhang, Bin Lu, Chunli Chen, Lei Yang, Wanjun Chen, Dezhong Yao, Fali Li & Peng Xu

  3. Research Unit of NeuroInformation, Chinese Academy of Medical Sciences, Chengdu, 611731, China

    Dezhong Yao, Fali Li & Peng Xu

  4. School of Electrical Engineering, Zhengzhou University, Zhengzhou, 450001, China

    Dezhong Yao

  5. Department of Rehabilitation, Southwest Hospital, Army Medical University, Chongqing, 400038, China

    Jingming Hou

  6. Robotics Research Center, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Jing Qiu

  7. Radiation Oncology Key Laboratory of Sichuan Province, Chengdu, 610041, China

    Peng Xu

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  1. Xiabing Zhang
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Zhang, X., Lu, B., Chen, C. et al. The correlation between upper body grip strength and resting-state EEG network. Med Biol Eng Comput 61, 2139–2148 (2023). https://doi.org/10.1007/s11517-023-02865-4

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