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动态疲劳过程中小腿主动肌与拮抗肌的小波相干分析

Analysis of Wavelet Coherence in Calf Agonist-Antagonist Muscles during Dynamic Fatigue.

作者信息

Ni Xindi, Ieong Loi, Xiang Mai, Liu Ye

机构信息

School of Sport Science, Beijing Sport University, Beijing 100084, China.

出版信息

Life (Basel). 2024 Sep 9;14(9):1137. doi: 10.3390/life14091137.

Abstract

Dynamic muscle fatigue during repetitive movements can lead to changes in communication between the central nervous system and peripheral muscles. This study investigated these changes by examining electromyogram (EMG) characteristics from agonist and antagonist muscles during a fatiguing task. Twenty-two healthy male university students (age: 22.92 ± 2.19 years) performed heel raises until fatigue. EMG signals from lateral gastrocnemius (GL) and tibialis anterior (TA) muscles were processed using synchrosqueezed wavelet transform (SST). Root mean square (RMS), mean frequency (MF), power across frequency ranges, wavelet coherence, and co-activation ratio were computed. During the initial 80% of the task, RMS and EMG power increased for both muscles, while MF declined. In the final 20%, GL parameters stabilized, but TA showed significant decreases. Beta and gamma intermuscular coherence increased upon reaching 60% of the task. Alpha coherence and co-activation ratio remained constant. Results suggest that the central nervous system adopts a differentiated control strategy for agonist and antagonist muscles during fatigue progression. Initially, a coordinated "common drive" mechanism enhances both muscle groups' activity. Later, despite continued increases in muscle activity, neural-muscular coupling remains stable. This asynchronous, differentiated control mechanism enhances our understanding of neuromuscular adaptations during fatigue, potentially contributing to the development of more targeted fatigue assessment and management strategies.

摘要

重复性运动过程中的动态肌肉疲劳会导致中枢神经系统与外周肌肉之间的通讯发生变化。本研究通过在疲劳任务期间检查主动肌和拮抗肌的肌电图(EMG)特征来探究这些变化。22名健康男性大学生(年龄:22.92±2.19岁)进行提踵运动直至疲劳。使用同步挤压小波变换(SST)处理来自腓骨外侧肌(GL)和胫骨前肌(TA)的EMG信号。计算均方根(RMS)、平均频率(MF)、跨频率范围的功率、小波相干性和共激活率。在任务的最初80%期间,两块肌肉的RMS和EMG功率均增加,而MF下降。在最后的20%期间,GL参数稳定,但TA显示出显著下降。在达到任务的60%时,β和γ肌间相干性增加。α相干性和共激活率保持不变。结果表明,在疲劳进展过程中,中枢神经系统对主动肌和拮抗肌采用了差异化的控制策略。最初,一种协调的“共同驱动”机制增强了两组肌肉的活动。后来,尽管肌肉活动持续增加,但神经肌肉耦合保持稳定。这种异步、差异化的控制机制增进了我们对疲劳期间神经肌肉适应的理解,可能有助于开发更具针对性的疲劳评估和管理策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e6cb/11433323/1339dc78ba1b/life-14-01137-g001.jpg

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