Pierella Camilla, Pellegrino Laura, Muller Margit, Inglese Matilde, Solaro Claudio, Coscia Martina, Casadio Maura
Department of Neurosciences, Rehabilitation, Ophthalmology, Genetics, and Maternal and Children's Sciences (DINOGMI), University of Genoa, Genoa, Italy.
Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genoa, Genoa, Italy.
Front Neurorobot. 2022 Jul 11;16:920118. doi: 10.3389/fnbot.2022.920118. eCollection 2022.
Multiple sclerosis (MS) is an autoimmune and neurodegenerative disease resulting in motor impairments associated with muscle weakness and lack of movement coordination. The goal of this work was to quantify upper limb motor deficits in asymptomatic MS subjects with a robot-based assessment including performance and muscle synergies analysis. A total of 7 subjects (MS: 3 M-4 F; 42 ± 10 years) with clinically definite MS according to McDonald criteria, but with no clinical disability, and 7 age- and sex-matched subjects without a history of neurological disorders participated in the study. All subjects controlled a cursor on the computer screen by moving their hand or applying forces in 8 coplanar directions at their self-selected speed. They grasped the handle of a robotic planar manipulandum that generated four different environments: null, assistive or resistive forces, and rigid constraint. Simultaneously, the activity of 15 upper body muscles was recorded. Asymptomatic MS subjects generated less smooth and less accurate cursor trajectories than control subjects in controlling a force profile, while the end-point error was significantly different also in the other environments. The EMG analysis revealed different muscle activation patterns in MS subjects when exerting isometric forces or when moving in presence of external forces generated by a robot. While the two populations had the same number and similar structure of muscle synergies, they had different activation profiles. These results suggested that a task requiring to control forces against a rigid environment allows better than movement tasks to detect early sensory-motor signs related to the onset of symptoms of multiple sclerosis and to differentiate between stages of the disease.
多发性硬化症(MS)是一种自身免疫性神经退行性疾病,会导致与肌肉无力和运动协调能力缺失相关的运动障碍。这项研究的目的是通过基于机器人的评估,包括对运动表现和肌肉协同作用的分析,来量化无症状MS患者的上肢运动缺陷。共有7名根据麦克唐纳标准临床确诊为MS但无临床残疾的受试者(3名男性和4名女性;年龄42±10岁)以及7名年龄和性别匹配且无神经疾病病史的受试者参与了这项研究。所有受试者通过移动手部或在8个共面方向上以自己选择的速度施加力来控制电脑屏幕上的光标。他们握住一个机器人平面操作器的手柄,该操作器会产生四种不同的环境:零力、助力或阻力,以及刚性约束。同时,记录15块上身肌肉的活动情况。在控制力分布时,无症状MS受试者产生的光标轨迹不如对照组受试者平滑和准确,而在其他环境中终点误差也有显著差异。肌电图分析显示,MS受试者在施加等长力或在机器人产生的外力作用下移动时,肌肉激活模式不同。虽然两组受试者的肌肉协同作用数量相同且结构相似,但它们的激活模式不同。这些结果表明,一项需要在刚性环境中控制力的任务比运动任务更能检测出与多发性硬化症症状发作相关的早期感觉运动体征,并区分疾病的不同阶段。