A major problem in neurorehabilitation is the lack of objective outcomes to measure movement quality. Movement quality features, such as coordination and stability, are essential for everyday motor actions. These features allow reacting to continuously changing environment or to resist external perturbations. Neurological disorders affect movement quality, leading to functionally impaired movements. Recent findings suggest that the central nervous system organizes motor elements (eg, muscles, joints, fingers) into task-specific ensembles to stabilize motor tasks performance. A method to quantify this feature has been previously developed based on the uncontrolled manifold (UCM) hypothesis. UCM quantifies movement quality in a spatial-temporal domain using intertrial analysis of covariation between motor elements. In this point-of-view article, we first describe major obstacles (eg, the need for group analysis) that interfere with UCM application in clinical settings. Then, we propose a process of quantifying movement quality for a single individual with a novel use of bootstrapping simulations and UCM analysis. Finally, we reanalyze previously published data from individuals with neurological disorders performing a wide range of motor tasks, that is, multi-digit pressing and postural balance tasks. Our method allows one to assess motor quality impairments in a single individual and to detect clinically important motor behavior changes. Our solution may be incorporated into a clinical setting to assess sensorimotor impairments, evaluate the effects of specific neurological treatments, or track movement quality recovery over time. We also recommended the proposed solution to be used jointly with a typical statistical analysis of UCM parameters in cohort studies.

中文翻译：

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运动质量：一种基于神经科学原理的新型生物标志物

神经康复的一个主要问题是缺乏衡量运动质量的客观结果。运动质量特征，例如协调性和稳定性，对于日常运动动作至关重要。这些功能允许对不断变化的环境做出反应或抵抗外部扰动。神经系统疾病会影响运动质量，导致运动功能受损。最近的研究结果表明，中枢神经系统将运动元素（例如，肌肉、关节、手指）组织成特定任务的集合，以稳定运动任务的表现。之前已经基于不受控制的流形 (UCM) 假设开发了一种量化此特征的方法。UCM 使用运动元素之间的协变的试验间分析来量化时空域中的运动质量。在这篇观点性文章中，我们首先描述了在临床环境中干扰 UCM 应用的主要障碍（例如，需要进行组分析）。然后，我们提出了一个量化单个个体运动质量的过程，它采用了自举模拟和 UCM 分析的新用途。最后，我们重新分析了先前发布的来自执行各种运动任务（即多指按压和姿势平衡任务）的神经系统疾病患者的数据。我们的方法允许人们评估单个个体的运动质量损伤并检测临床上重要的运动行为变化。我们的解决方案可以纳入临床环境，以评估感觉运动障碍、评估特定神经治疗的效果或跟踪运动质量随时间的恢复情况。