Department of Motor Sciences and Wellness, University of Naples "Parthenope", Naples, Italy.
Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France.
Ann N Y Acad Sci. 2022 Oct;1516(1):247-261. doi: 10.1111/nyas.14860. Epub 2022 Jul 15.
Human voluntary movement stems from the coordinated activations in space and time of many musculoskeletal segments. However, the current methodological approaches to study human movement are still limited to the evaluation of the synergies among a few body elements. Network science can be a useful approach to describe movement as a whole and to extract features that are relevant to understanding both its complex physiology and the pathophysiology of movement disorders. Here, we propose to represent human movement as a network (that we named the kinectome), where nodes represent body points, and edges are defined as the correlations of the accelerations between each pair of them. We applied this framework to healthy individuals and patients with Parkinson's disease, observing that the patients' kinectomes display less symmetrical patterns as compared to healthy controls. Furthermore, we used the kinectomes to successfully identify both healthy and diseased subjects using short gait recordings. Finally, we highlighted topological features that predict the individual clinical impairment in patients. Our results define a novel approach to study human movement. While deceptively simple, this approach is well-grounded, and represents a powerful tool that may be applied to a wide spectrum of frameworks.
人类的随意运动源于许多骨骼肌肉节段在空间和时间上的协调激活。然而，目前研究人类运动的方法学方法仍然仅限于评估几个身体元素之间的协同作用。网络科学可以作为一种有用的方法来描述整体运动，并提取与理解其复杂生理学和运动障碍的病理生理学相关的特征。在这里，我们提出将人类运动表示为一个网络（我们称之为运动组构），其中节点代表身体点，边缘则定义为它们之间每对加速度的相关性。我们将这个框架应用于健康个体和帕金森病患者，观察到与健康对照组相比，患者的运动组构显示出较少的对称模式。此外，我们使用运动组构成功地识别了使用短步态记录的健康和患病受试者。最后，我们强调了预测患者个体临床障碍的拓扑特征。我们的研究结果定义了一种研究人类运动的新方法。虽然表面上看起来很简单，但这种方法是有充分依据的，并且代表了一种强大的工具，可以应用于广泛的框架。
