Directed Information Flow Analysis Reveals Muscle Fatigue-Related Changes in Muscle Networks and Corticomuscular Coupling

Abstract

As a common neurophysiological phenomenon, voluntary muscle fatigue is accompanied by changes in both the central nervous system and peripheral muscles. Considering the effectiveness of the muscle network and the functional corticomuscular coupling (FCMC) in analyzing motor function, muscle fatigue can be analyzed by quantitating the intermuscular coupling and corticomuscular coupling. However, existing coherence-based research on muscle fatigue are limited by the inability of the coherence algorithm to identify the coupling direction, which cannot further reveal the underlying neural mechanism of muscle fatigue. To address this problem, we applied the time-delayed maximal information coefficient (TDMIC) method to quantitate the directional informational interaction in the muscle network and FCMC during a right-hand stabilized grip task. Eight healthy subjects were recruited to the present study. For the muscle networks, the beta-band information flow increased significantly due to muscle fatigue, and the information flow between the synergist muscles were stronger than that between the synergist and antagonist muscles. The information flow in the muscle network mainly flows to flexor digitorum superficialis (FDS), flexor carpi ulnar (FCU), and brachioradialis (BR). For the FCMC, muscle fatigue caused a significant decrease in the beta- and gamma-band bidirectional information flow. Further analysis revealed that the beta-band information flow was significantly stronger in the descending direction [electroencephalogram (EEG) to surface electromyography (sEMG)] than that in the ascending direction (sEMG to EEG) during pre-fatigue tasks. After muscle fatigue, the beta-band information flow in the ascending direction was significantly stronger than that in the descending direction. The present study demonstrates the influence of muscle fatigue on information flow in muscle networks and FCMC. We proposes that beta-band intermuscular and corticomuscular informational interaction plays an adjusting role in autonomous movement completion under muscle fatigue. Directed information flow analysis can be used as an effective method to explore the neural mechanism of muscle fatigue on the macroscopic scale.

Exploring Central-Peripheral Nervous System Interaction Through Multimodal Biosignals: A Systematic Review

“…In a few of the cases, right-handed people are preferred [18], [21], [22], [25], [39]- [41], [44], [45], [50], [51], [53]- [57]. Subject's consent is also taken in some literature [21], [22], [24], [26]- [35], [37]- [43], [45]- [47], [52], [54], [56], [58]- [62]. Meanwhile, there is no information about the consent in other remaining studies.…”

“…Mental stress is imposed by arithmetic task [27], [38], [42], [58], Stroop task [53], control task [55], video clips [62], images [51], [54], gamming [24], and film clips [76]. Physical stress is induced by cold pressor task [70], [71], rope skipping [25], fatiguing exercise [22], [56], handgrip task [44], [75]. Cognition is evaluated using go/no go task [45], surgical task [74], audio stimuli [29], images [67], [69], visual stimuli [41], film clips [77], reading comic strips [73], audio visual stimuli [43], gamming [57], and deceptive task [28].…”

“…Small no. of participants (<10) are there in [35], [39], [50], [51], [55], [56], [60], [73], [75], [78]. Large no.…”

“…From EEG, the following time-frequency domain features are extracted namely wavelet packet energy [46], absolute value of logarithm, and power of discrete wavelet transform (DWT) from slow alpha, alpha, beta, and gamma band [18], average wavelet energy in the beta and gamma bands [56], Relative Wavelet Entropy [78]. From EMG cumulated and average wavelet energy in sEMG during pre-and post-muscle fatigue is extracted [56].…”

Grand challenges in physical neuroergonomics

“…Although changes in motor unit recruitment mediated by central mechanisms can be indirectly reflected in EMG signals, it is difficult to analyze the neural mechanisms behind muscle state at a systemic level using the single EMG-based method. Rather than focusing on isolated muscles or cortical areas, functional networks should be constructed at both the muscle and cerebral levels (Liang et al, 2021 ). It will relay how the central nervous system coordinates the activity of multiple muscles to achieve a variety of behavioral goals, and addresses the interaction among the involved sub-systems (muscles, joints, nervous system), instead of analyzing the response of each sub-system separately.…”