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运动模块在基于行走和原地任务中的可推广性——对全膝关节置换患者的基于分布的分析

Generalizability of motor modules across walking-based and in-place tasks - a distribution-based analysis on total knee replacement patients.

作者信息

Darvishi Mahziyar, Daroudi Sajjad, Tavasoli Shahabedin, Shafiezadeh Ali, Farahmand Farzam

机构信息

Mechanical Engineering Department, Sharif University of Technology, Tehran, Iran.

出版信息

Front Bioeng Biotechnol. 2025 Apr 7;13:1471582. doi: 10.3389/fbioe.2025.1471582. eCollection 2025.

Abstract

There are evidences that the nervous system produces motor tasks using a low-dimensional modular organization of muscle activations, known as motor modules. Previous studies have identified characteristic motor modules across similar tasks in healthy population. This study explored the generalizability of motor modules across two families of walking-based (level-walking, downhillwalking and stair-decent), in-place ascending (sit-to-stand, squat-to-stand), and in-place descending (stand-to-sit and stand-to-squat) motor tasks in a group of six individuals undergone total knee replacement (TKR) surgery. Motor modules were extracted from the EMG data of CAMS-Knee dataset using non-negative matrix factorization technique. A distribution-based approach, employing three levels of k-means clustering, was then applied to find the shared and task-specific modules, and assess their representability among the whole task-trial data. Results indicated a four- and a seven-subcluster arrangement of the shared and task-specific motor modules, depending upon the membership criteria. The first arrangement revealed motor modules which were shared across all tasks (min coverage index: 76%; modules' distinctness range: 7.08-8.91) and the latter among tasks of the same family mainly, although there remained some interfamily shared modules (min coverage index: 81%; modules' distinctness range: 7.17-9.89). It was concluded that there are shared motor modules across walking-based and in-place tasks in TKR individuals, with their generalizability and representability depending upon the analysis method. This finding highlights the importance of the analysis method in identifying the shared motor modules, as the main building blocks of motor control.

摘要

有证据表明,神经系统利用肌肉激活的低维模块化组织来产生运动任务,这种组织被称为运动模块。先前的研究已经在健康人群的相似任务中识别出了特征性的运动模块。本研究探讨了运动模块在一组接受全膝关节置换(TKR)手术的六名个体的两类运动任务中的通用性,这两类任务包括基于行走的(平路行走、下坡行走和下楼梯)、原地上升(从坐到站、从蹲到站)以及原地下降(从站到坐和从站到蹲)运动任务。使用非负矩阵分解技术从CAMS - Knee数据集的肌电图数据中提取运动模块。然后应用一种基于分布的方法,采用三级k均值聚类,来找到共享的和特定任务的模块,并评估它们在整个任务试验数据中的代表性。结果表明,根据隶属标准,共享的和特定任务的运动模块分别有四个和七个子聚类排列。第一种排列揭示了在所有任务中共享的运动模块(最小覆盖指数:76%;模块的离散度范围:7.08 - 8.91),而后者主要是在同一类任务中,尽管仍有一些跨类共享模块(最小覆盖指数:81%;模块的离散度范围:7.17 - 9.89)。研究得出结论,在接受TKR手术的个体中,基于行走的任务和原地任务之间存在共享的运动模块,其通用性和代表性取决于分析方法。这一发现突出了分析方法在识别作为运动控制主要组成部分的共享运动模块方面的重要性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/04f0/12009812/388099036b2b/fbioe-13-1471582-g001.jpg

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