Department of Neurological Rehabilitation, Suzhou Municipal Hospital, The Affiliated Suzhou Hospital of Nanjing Medical University, Gusu School of Nanjing Medical University, Suzhou, 215000, China.
Laboratory of Rehabilitation Engineering, Intelligent Medical Engineering Research Center, School of Control Science and Engineering, Shandong University, Jinan, 250061, China.
J Neuroeng Rehabil. 2024 Oct 24;21(1):190. doi: 10.1186/s12984-024-01488-6.
A given movement requires precise coordination of multiple muscles under the control of center nervous system. However, detailed knowledge about the changing characteristics of neuromuscular control for multi-muscle coordination in post-stroke hemiplegic patients during standing is still lacking. This study aimed to investigate the hemiplegia-linked neuromuscular dysfunction during standing from the perspective of multi-muscle dynamical coordination by utilizing a novel network approach - weighted recurrence network (WRN).
Ten male hemiplegic patients with first-ever stroke and 10 age-matched healthy male adults were instructed to stand on a platform quietly for 30 s with eyes opened and eyes closed, respectively. The WRN was constructed based on the surface electromyography signals of 16 muscles from trunk, hips, thighs and calves. Relevant topological parameters, including clustering coefficient (C) and average shortest path length (L), were extracted to evaluate the dynamical coordination of multiple muscles. A measure of node centrality in network theory, degree of centrality (DC), was innovatively introduced to assess the contribution of single muscle in the multi-muscle dynamical coordination. The standing-related assessment metric, center of pressure (COP), was provided by the platform directly.
Results showed that the post-stroke hemiplegic patients stood with remarkably higher similarity of muscle activation and more coupled intermuscular dynamics, characterized by higher C and lower L than the healthy subjects (p < 0.05). The DC values and rankings of back, hip and calf muscles on the affected side were significantly decreased, whereas those on the unaffected side were significantly increased in hemiplegia group compared with the healthy group (p < 0.05). Without visual feedback, subjects exhibited enhanced muscle coordination and increased muscle involvement (p < 0.05). A decrease in C and an increase in L of WRN were observed with decreased COP areas (p < 0.05).
These findings revealed that stroke-induced hemiplegia could significantly influence the neuromuscular control, which was manifested as more coupled intermuscular dynamics, abnormal deactivation of muscles on affected side and compensation of muscles on unaffected side from the perspective of multi-muscle coordination. Enhanced multi-muscle dynamical coordination was strongly associated with impaired postural control. This study provides a novel analytical tool for evaluation of neuromuscular dysfunction and specification of responsible muscles for impaired postural control in stroke-induced hemiplegic patients, and could be potentially applied in clinical practice.
给定的运动需要在中枢神经系统的控制下精确协调多个肌肉。然而，关于中风后偏瘫患者站立时多肌肉协调的神经肌肉控制的变化特征，我们仍然知之甚少。本研究旨在利用一种新的网络方法 - 加权递归网络（WRN），从多肌肉动力学协调的角度研究偏瘫患者的神经肌肉功能障碍。
10 名首次中风的偏瘫男性患者和 10 名年龄匹配的健康男性成年人分别在睁眼和闭眼的情况下在平台上安静站立 30 秒。WRN 是基于躯干、臀部、大腿和小腿 16 块肌肉的表面肌电图信号构建的。提取相关拓扑参数，包括聚类系数（C）和平均最短路径长度（L），以评估多肌肉的动力学协调。创新性地引入网络理论中的节点中心度度量 - 中心度（DC），评估单个肌肉在多肌肉动力学协调中的贡献。平台直接提供站立相关评估指标 - 重心（COP）。
结果表明，与健康受试者相比，中风后偏瘫患者站立时肌肉激活的相似性明显更高，肌肉间的相互作用更为耦合，表现为 C 值较高，L 值较低（p < 0.05）。偏瘫组患侧的背、髋和小腿肌肉的 DC 值和排名明显降低，而健侧的 DC 值和排名明显升高（p < 0.05）。在没有视觉反馈的情况下，患者表现出增强的肌肉协调性和更多的肌肉参与（p < 0.05）。随着 COP 区域的减小，WRN 的 C 值减小，L 值增大（p < 0.05）。
这些发现表明，中风引起的偏瘫会显著影响神经肌肉控制，这表现为多肌肉协调的肌肉间相互作用更为耦合、患侧肌肉的异常去激活和健侧肌肉的代偿。增强的多肌肉动力学协调与姿势控制受损密切相关。本研究为评估中风后偏瘫患者的神经肌肉功能障碍和确定导致姿势控制受损的责任肌肉提供了一种新的分析工具，并可能在临床实践中得到应用。
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