Muscle Synergies: Implications for Clinical Evaluation and Rehabilitation of Movement.
Muscle Synergies: Implications for Clinical Evaluation and Rehabilitation of Movement.
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DOI：
10.1310/sci1701-16
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发表时间：
2011
影响因子：
2.9
通讯作者：
Ting LH
中科院分区：
文献类型：
--
作者：
Safavynia SA;Torres-Oviedo G;Ting LH
我们提出了一种称为肌肉协同分析的方法，它可以为临床医生提供深入了解肌肉活动的运动和功能结果的潜在神经策略。虽然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，大多数临床测试集中在行为和运动水平上评估运动输出。然而，改变的行为或运动结果可能是多种不同的神经异常与非常不同的肌肉协调模式的结果。由于肌肉活动反映了运动神经元的活动，并产生产生的力量，产生行为的结果，肌肉活动的分析可以提供一个更好的理解受损的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，难以解释，排除了其临床实用性。计算分析可用于从这些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假设为代表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，轻偏瘫中风患者表现出肌肉协同作用数量的差异，这可能反映了下行神经通路的中断，并与运动功能的缺陷相关。因此，肌肉协同分析可以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中如何变化。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信息。
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.