Barroso Filipe O, Torricelli Diego, Molina-Rueda Francisco, Alguacil-Diego Isabel M, Cano-de-la-Cuerda Roberto, Santos Cristina, Moreno Juan C, Miangolarra-Page Juan C, Pons José L
Department of Physiology, Feinberg School of Medicine - Northwestern University, Chicago, IL, United States; Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council (CSIC), Madrid, Spain; Centre ALGORITMI, University of Minho, Azurém, Guimarães, Portugal.
Neural Rehabilitation Group, Cajal Institute, Spanish National Research Council (CSIC), Madrid, Spain.
J Biomech. 2017 Oct 3;63:98-103. doi: 10.1016/j.jbiomech.2017.08.006. Epub 2017 Aug 20.
The understanding of biomechanical deficits and impaired neural control of gait after stroke is crucial to prescribe effective customized treatments aimed at improving walking function. Instrumented gait analysis has been increasingly integrated into the clinical practice to enhance precision and inter-rater reliability for the assessment of pathological gait. On the other hand, the analysis of muscle synergies has gained relevance as a novel tool to describe the neural control of walking. Since muscle synergies and gait analysis capture different but equally important aspects of walking, we hypothesized that their combination can improve the current clinical tools for the assessment of walking performance. To test this hypothesis, we performed a complete bilateral, lower limb biomechanical and muscle synergies analysis on nine poststroke hemiparetic patients during overground walking. Using stepwise multiple regression, we identified a number of kinematic, kinetic, spatiotemporal and synergy-related features from the paretic and non-paretic side that, combined together, allow to predict impaired walking function better than the Fugl-Meyer Assessment score. These variables were time of peak knee flexion, VAF values, duration of stance phase, peak of paretic propulsion and range of hip flexion. Since these five variables describe important biomechanical and neural control features underlying walking deficits poststroke, they may be feasible to drive customized rehabilitation therapies aimed to improve walking function. This paper demonstrates the feasibility of combining biomechanical and neural-related measures to assess locomotion performance in neurologically injured individuals.
了解中风后步态的生物力学缺陷和神经控制受损对于制定旨在改善步行功能的有效个性化治疗至关重要。仪器化步态分析已越来越多地融入临床实践,以提高病理性步态评估的精度和评分者间的可靠性。另一方面,肌肉协同作用分析作为描述步行神经控制的一种新工具已变得越来越重要。由于肌肉协同作用和步态分析捕捉了步行中不同但同样重要的方面,我们假设它们的结合可以改进当前用于评估步行表现的临床工具。为了验证这一假设,我们对9名中风后偏瘫患者在地面行走过程中进行了完整的双侧下肢生物力学和肌肉协同作用分析。使用逐步多元回归,我们从患侧和非患侧识别了一些运动学、动力学、时空和协同相关特征,这些特征结合在一起,比Fugl-Meyer评估评分能更好地预测步行功能受损情况。这些变量包括屈膝峰值时间、VAF值、站立相持续时间、患侧推进峰值和髋关节屈曲范围。由于这五个变量描述了中风后步行缺陷背后重要的生物力学和神经控制特征,它们可能有助于推动旨在改善步行功能的个性化康复治疗。本文证明了结合生物力学和神经相关测量来评估神经损伤个体运动表现的可行性。