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使用基于模块的分析框架研究中风后个体行走过程中的肌肉协调性:文献综述与综合分析

Using a Module-Based Analysis Framework for Investigating Muscle Coordination during Walking in Individuals Poststroke: A Literature Review and Synthesis.

作者信息

Seamon Bryant A, Neptune Richard R, Kautz Steven A

机构信息

Ralph H. Johnson VA Medical Center, Charleston, SC, USA.

Department of Health Sciences and Research, College of Health Professions, Medical University of South Carolina, Charleston, SC, USA.

出版信息

Appl Bionics Biomech. 2018 Jun 3;2018:3795754. doi: 10.1155/2018/3795754. eCollection 2018.

DOI:10.1155/2018/3795754
PMID:29967653
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6008620/
Abstract

Factorization methods quantitatively group electromyographic signals from several muscles during dynamic tasks into multiple modules where each module consists of muscles that are coactive during the movement. Module-based analyses may provide an analytical framework for testing theories of poststroke motor control recovery based on one's ability to move independently from mass flexion-extension muscle group coactivation. Such a framework may be useful for understanding the causality between underlying neural impairments, biomechanical function, and walking performance in individuals poststroke. Our aim is to synthesize current evidence regarding the relationships between modules, gait mechanics, and rehabilitation in individuals poststroke. We synthesized eleven studies that performed module-based analyses during walking tasks for individuals poststroke. Modules were primarily identified by nonnegative matrix factorization, and fewer modules correlated with poor walking performance on biomechanical and clinical measures. Fewer modules indicated reduced ability to control individual muscle timing during paretic leg stance. There was evidence that rehabilitation can lead to the use of more and/or better-timed modules. While future work will need to establish the ability of modules to identify impairment mechanisms, they appear to offer a promising analytical approach for evaluating motor control.

摘要

分解方法在动态任务中将来自几块肌肉的肌电信号定量分组为多个模块,每个模块由运动过程中共同激活的肌肉组成。基于模块的分析可能为基于个体从大量屈伸肌群共同激活中独立运动的能力来测试中风后运动控制恢复理论提供一个分析框架。这样一个框架可能有助于理解中风个体潜在神经损伤、生物力学功能和步行表现之间的因果关系。我们的目的是综合关于中风个体中模块、步态力学和康复之间关系的现有证据。我们综合了11项针对中风个体在步行任务中进行基于模块分析的研究。模块主要通过非负矩阵分解来识别,较少的模块与生物力学和临床测量中较差的步行表现相关。较少的模块表明在患侧腿站立期间控制单个肌肉时机的能力下降。有证据表明康复可以导致使用更多和/或时机更好的模块。虽然未来的工作需要确定模块识别损伤机制的能力,但它们似乎为评估运动控制提供了一种有前景的分析方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6beb/6008620/2bc0ccdca260/ABB2018-3795754.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6beb/6008620/de49950ab770/ABB2018-3795754.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6beb/6008620/457e15103022/ABB2018-3795754.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6beb/6008620/b0d38c11ce69/ABB2018-3795754.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6beb/6008620/2bc0ccdca260/ABB2018-3795754.004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6beb/6008620/de49950ab770/ABB2018-3795754.001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6beb/6008620/457e15103022/ABB2018-3795754.002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6beb/6008620/b0d38c11ce69/ABB2018-3795754.003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/6beb/6008620/2bc0ccdca260/ABB2018-3795754.004.jpg

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本文引用的文献

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J Biomech. 2017 Oct 3;63:98-103. doi: 10.1016/j.jbiomech.2017.08.006. Epub 2017 Aug 20.
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Do coursework summative assessments predict clinical performance? A systematic review.
中风幸存者在伴有节律性听觉提示的行走过程中,时间不对称性与肌肉协同作用之间的关联。
Arch Rehabil Res Clin Transl. 2022 Feb 24;4(2):100187. doi: 10.1016/j.arrct.2022.100187. eCollection 2022 Jun.
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The Prognostic Utility of Electroencephalography in Stroke Recovery: A Systematic Review and Meta-Analysis.脑电图在脑卒中康复中的预后价值:系统评价和荟萃分析。
Neurorehabil Neural Repair. 2022 Apr;36(4-5):255-268. doi: 10.1177/15459683221078294. Epub 2022 Mar 20.
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Merged swing-muscle synergies and their relation to walking characteristics in subacute post-stroke patients: An observational study.合并的摆动肌协同作用及其与亚急性脑卒中患者步行特征的关系:一项观察性研究。
PLoS One. 2022 Feb 4;17(2):e0263613. doi: 10.1371/journal.pone.0263613. eCollection 2022.
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