Santuz Alessandro, Brüll Leon, Ekizos Antonis, Schroll Arno, Eckardt Nils, Kibele Armin, Schwenk Michael, Arampatzis Adamantios
Department of Training and Movement Sciences, Humboldt-Universität zu Berlin, 10115 Berlin, Germany; Berlin School of Movement Science, Humboldt-Universität zu Berlin, 10115 Berlin, Germany; Atlantic Mobility Action Project, Brain Repair Centre, Department of Medical Neuroscience, Dalhousie University, Halifax, Nova Scotia B3H 4R2, Canada.
Department of Training and Movement Sciences, Humboldt-Universität zu Berlin, 10115 Berlin, Germany; Berlin School of Movement Science, Humboldt-Universität zu Berlin, 10115 Berlin, Germany; Network Aging Research, Heidelberg University, 69117 Heidelberg, Germany.
iScience. 2020 Jan 24;23(1):100796. doi: 10.1016/j.isci.2019.100796. Epub 2019 Dec 24.
Is the control of movement less stable when we walk or run in challenging settings? Intuitively, one might answer that it is, given that challenging locomotion externally (e.g., rough terrain) or internally (e.g., age-related impairments) makes our movements more unstable. Here, we investigated how young and old humans synergistically activate muscles during locomotion when different perturbation levels are introduced. Of these control signals, called muscle synergies, we analyzed the local stability and the complexity (or irregularity) over time. Surprisingly, we found that perturbations force the central nervous system to produce muscle activation patterns that are less unstable and less complex. These outcomes show that robust locomotion control in challenging settings is achieved by producing less complex control signals that are more stable over time, whereas easier tasks allow for more unstable and irregular control.
当我们在具有挑战性的环境中行走或奔跑时，运动控制是否会变得不那么稳定？直观地说，人们可能会回答是这样，因为外部具有挑战性的运动（例如，崎岖地形）或内部因素（例如，与年龄相关的功能障碍）会使我们的动作更加不稳定。在这里，我们研究了年轻人和老年人在引入不同扰动水平的运动过程中如何协同激活肌肉。对于这些被称为肌肉协同作用的控制信号，我们分析了其随时间变化的局部稳定性和复杂性（或不规则性）。令人惊讶的是，我们发现扰动会迫使中枢神经系统产生不太不稳定且不太复杂的肌肉激活模式。这些结果表明，在具有挑战性的环境中，通过产生随着时间推移更稳定且不太复杂的控制信号来实现稳健的运动控制，而较简单的任务则允许更不稳定和不规则的控制。
