Safavynia Seyed A, Torres-Oviedo Gelsy, Ting Lena H
Neuroscience Program, Emory University, Atlanta, Georgia.
Top Spinal Cord Inj Rehabil. 2011 Summer;17(1):16-24. doi: 10.1310/sci1701-16.
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.
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。