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.
School of Life Science and Technology, Center for Information in Medicine, University of Electronic Science and Technology of China, Chengdu, 611731, China.
Med Biol Eng Comput. 2023 Aug;61(8):2139-2148. doi: 10.1007/s11517-023-02865-4. Epub 2023 Jun 20.
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.
目前，神经科学领域的研究主要集中在分析与中枢神经系统内运动相关的脑电图（EEG）活动。然而，目前缺乏研究来探讨个体进行长期力量训练对大脑静息状态的影响。因此，研究上肢握力与静息态 EEG 网络之间的相关性至关重要。在这项研究中，我们使用现有的数据集，通过相干性分析构建静息态 EEG 网络。建立了一个多元线性回归模型，以检查个体的脑网络特性与其在握持任务中的最大自主收缩（MVC）之间的相关性。该模型用于预测个体的 MVC。β和γ频段的结果表明，RSN 连接性与 MVC 之间存在显著相关性（p < 0.05），特别是在左半球额顶和额枕连接。两个频段的 RSN 特性与 MVC 均呈显著相关，相关系数大于 0.60（p < 0.01）。此外，预测的 MVC 与实际 MVC 呈正相关，相关系数为 0.70，均方根误差为 5.67（p < 0.01）。研究结果表明，静息态 EEG 网络与上肢握力密切相关，通过静息态脑网络可以间接反映个体的肌肉力量。
