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本文引用的文献

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Split-belt treadmill training poststroke: a case study.脑卒中后分带跑步机训练:案例研究。
J Neurol Phys Ther. 2010 Dec;34(4):202-7. doi: 10.1097/NPT.0b013e3181fd5eab.
2
Sequential activation of motor cortical neurons contributes to intralimb coordination during reaching in the cat by modulating muscle synergies.运动皮层神经元的顺序激活通过调节肌肉协同作用有助于猫在伸展过程中的肢体内协调。
J Neurophysiol. 2011 Jan;105(1):388-409. doi: 10.1152/jn.00469.2010. Epub 2010 Nov 10.
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Encoding of motor skill in the corticomuscular system of musicians.音乐家皮质肌肉系统中的运动技能编码。
Curr Biol. 2010 Oct 26;20(20):1869-74. doi: 10.1016/j.cub.2010.09.045. Epub 2010 Oct 14.
4
Subject-specific muscle synergies in human balance control are consistent across different biomechanical contexts.人体平衡控制中的特定于主题的肌肉协同作用在不同的生物力学环境中是一致的。
J Neurophysiol. 2010 Jun;103(6):3084-98. doi: 10.1152/jn.00960.2009. Epub 2010 Apr 14.
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Thinking about walking: effects of conscious correction versus distraction on locomotor adaptation.思考行走:意识纠正与分心对运动适应的影响。
J Neurophysiol. 2010 Apr;103(4):1954-62. doi: 10.1152/jn.00832.2009. Epub 2010 Feb 10.
6
Merging of healthy motor modules predicts reduced locomotor performance and muscle coordination complexity post-stroke.健康运动模块的融合预示着中风后运动表现和肌肉协调性降低。
J Neurophysiol. 2010 Feb;103(2):844-57. doi: 10.1152/jn.00825.2009. Epub 2009 Dec 9.
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Training induces changes in white-matter architecture.训练可引起白质结构的变化。
Nat Neurosci. 2009 Nov;12(11):1370-1. doi: 10.1038/nn.2412. Epub 2009 Oct 11.
8
Reduction of common motoneuronal drive on the affected side during walking in hemiplegic stroke patients.偏瘫性中风患者行走时患侧共同运动神经元驱动的降低。
Clin Neurophysiol. 2008 Dec;119(12):2813-8. doi: 10.1016/j.clinph.2008.07.283. Epub 2008 Oct 10.
9
Modulation of phasic and tonic muscle synergies with reaching direction and speed.随着伸展方向和速度对相位性和紧张性肌肉协同作用的调节。
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10
Neuromechanics of muscle synergies for posture and movement.用于姿势和运动的肌肉协同作用的神经力学
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肌肉协同作用:对运动临床评估与康复的意义

Muscle Synergies: Implications for Clinical Evaluation and Rehabilitation of Movement.

作者信息

Safavynia Seyed A, Torres-Oviedo Gelsy, Ting Lena H

机构信息

Neuroscience Program, Emory University, Atlanta, Georgia.

出版信息

Top Spinal Cord Inj Rehabil. 2011 Summer;17(1):16-24. doi: 10.1310/sci1701-16.

DOI:10.1310/sci1701-16
PMID:21796239
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3143193/
Abstract

We present a method called muscle synergy analysis, which can offer clinicians insight into both underlying neural strategies for movement and functional outcomes of muscle activity. Although neural dysfunction is central to many motor deficits, neural activity during movements is not directly measurable. Consequently, the majority of clinical tests focus on evaluating motor outputs at the behavioral and kinematic levels. However, altered behavioral or kinematic outcomes could be the result of multiple distinct neural abnormalities with very different muscle coordination patterns. Because muscle activity reflects motoneuron activity and generates the forces that produce behavioral outcomes, an analysis of muscle activity may provide a better understanding of the functional neural deficits in the impaired nervous system. Unfortunately electromyographic datasets can be large, highly variable, and difficult to interpret, precluding their clinical utility. Computational analyses can be used to extract muscle synergies from such datasets, revealing underlying patterns that may reflect different levels of neural function. These muscle synergies are hypothesized to represent motor modules recruited by the nervous system to flexibly perform biomechanical subtasks necessary for movement. For example, hemiparetic stroke patients exhibit differences in the number of muscle synergies, which may reflect disruptions in descending neural pathways and are correlated to deficits in motor function. Muscle synergy analysis may thus offer the clinician a better view of the neural structure underlying motor behaviors and how they change in motor deficits and rehabilitation. Such information could inform diagnostic tools and evidence-based interventions specifically targeted to a patient's deficits.

摘要

我们提出了一种名为肌肉协同分析的方法,该方法能够为临床医生提供有关运动的潜在神经策略和肌肉活动功能结果的见解。尽管神经功能障碍是许多运动缺陷的核心,但运动过程中的神经活动无法直接测量。因此,大多数临床测试都集中在行为和运动学水平上评估运动输出。然而,行为或运动学结果的改变可能是多种不同神经异常以及截然不同的肌肉协调模式导致的。由于肌肉活动反映运动神经元活动并产生导致行为结果的力量,因此对肌肉活动的分析可能有助于更好地理解受损神经系统中的功能性神经缺陷。不幸的是,肌电图数据集可能很大、高度可变且难以解释,这限制了它们的临床应用。计算分析可用于从此类数据集中提取肌肉协同作用,揭示可能反映不同神经功能水平的潜在模式。这些肌肉协同作用被认为代表了神经系统招募的运动模块,以灵活地执行运动所需的生物力学子任务。例如,偏瘫中风患者在肌肉协同作用的数量上存在差异,这可能反映了下行神经通路的中断,并与运动功能缺陷相关。因此,肌肉协同分析可能会让临床医生更好地了解运动行为背后的神经结构以及它们在运动缺陷和康复过程中是如何变化的。这些信息可以为专门针对患者缺陷的诊断工具和循证干预提供依据。