Resting-state Brain Network Analysis And Prediction: An Eeg Study On Exercise-induced Muscle Fatigue

PURPOSE: Exercise-induced muscle fatigue is a complex physiological phenomenon involving the central and peripheral nervous systems, and the tolerance of fatigue has a large variability among subjects. The present study has been designed to find an effective way for predicting muscle fatigue and probing the neural mechanism of fatigue by a resting-state EEG network. METHODS: In this study, thirteen elite athletes (female) were enrolled to take part in an elbow flexion and extension task. Five-minute before- and after-fatigue-exercise resting-state EEG and fatiguing task electromyography (EMG) data were recorded. Based on the graph theory, we constructed the resting-state EEG network, and compared the network differences before- and after-task. To further validate, the correlation between the before-fatigue resting-state EEG network properties and the EMG-related fatigue indexes Mean power frequency(MPF) during exercise was profiled. Finally, a prediction model based on the before-fatigue resting-state EEG network property, including Clustering coefficient (CC), local efficiency (Le), global efficiency (Ge), and characteristic path length (CPL), was established to predict the EMG related fatigue indexes after fatigue. RESULTS: Firstly, the results of this study demonstrated a significant relationship between the resting-state brain network and muscle fatigue during exercise(P < 0.05). Secondly, we also validate that a significant relationship between resting-state brain network properties and MPF (P < 0.05). Finally, the study proved that the before-fatigue resting-state EEG network single property could predict the fatigue tolerance for individual subject accurately (CC: r = 0.651, P < 0.05; CPL: r = 0.592, P < 0.05; Ge: r = 0.603, P < 0.05; Le: r = 0.654, P < 0.05). CONCLUSION: In all, the resting-state brain network may provide indicators for the monitoring and prediction of muscle fatigue in athletes, in the meantime, provide biological markers for recognition and regulation of muscle fatigue based on BCI.