Control of Arm Posture and Movement Followoing Stroke

描述（由申请人提供）：项目总结/摘要本项目分析了大脑中动脉闭塞导致的中风患者在伸手过程中的姿势和运动控制障碍。先前的研究表明，姿势和运动的控制可能都会受损，并对残疾有重要影响。我们最近的研究表明，正常的伸手需要控制姿势和运动，这两种功能由不同的神经机制指定，准确性取决于适应（学习）和两者之间的协调。根据我们的初步观察，我们假设中风降低了招募和放松平衡肌肉共同收缩的能力，这些肌肉共同收缩是克服与张力亢进相关的肢体姿势偏差和稳定手部对抗工作空间中不稳定负荷所必需的。我们进一步假设，中风后到达的主要障碍来自于肌肉共激活的比例和时间不当，从而限制了手臂轨迹和位置控制的独立性。 使用平面机器人手臂和新颖的EMG生物反馈方法，目的1提出表征中风相关的变化：1）通过拮抗肌的分级共激活在整个工作空间中保持姿势稳定性的能力; 2）不同水平共激活的潜伏期和发育时程，以及3）在存在机械扰动的情况下在肢体位置的调节期间使用本体感受反馈来抵消反射异常。 目的2检查姿势和运动控制整合中与中风相关的变化。使用平面机器人，我们将：1）探索姿势和运动任务之间的学习转移，评估两个控制器之间的耦合是否在中风后增加，以及2）确定轨迹规划是否适应中风后姿势调节的生物力学效应。 我们希望我们的研究结果将最终导致中风后手臂功能康复的新方法。我们预计，手臂功能的改善将促进应用机器人和生物反馈方法，首先分别训练共激活，姿势和运动控制，然后结合起来。

DESCRIPTION (provided by applicant): Project Summary/Abstract This project analyzes disordered control of posture and movement during reaching in patients with stroke resulting from middle cerebral artery occlusion. Prior studies have shown that control of posture and movement may both be impaired, and contribute importantly to disability. Our recent work has shown that normal reaching requires control of both posture and movement, that these two functions are specified by different neural mechanisms, and that accuracy is dependent on adaptation (learning) and coordination between the two. Based on our preliminary observations, we hypothesize that stroke degrades the ability to recruit and relax the balanced muscle co-contractions needed both to overcome limb postural bias associated with hypertonia and to stabilize the hand against unsteady loads across the workspace. We further hypothesize that major impairment in reaching post-stroke arises from improper scaling and timing of muscle coactivation, thus limiting the independence of arm trajectory and position control. Using a planar robot arm and novel EMG biofeedback methods, Aim 1 proposes to characterize the stroke- related changes in: 1) the ability to maintain postural stability throughout the workspace via graded coactivation of antagonist muscles; 2) the latency and developmental time course of different levels of coactivation, and 3) the use of proprioceptive feedback to counteract reflex abnormalities during regulation of limb position in the presence of mechanical perturbations. Aim 2 examines stroke-related changes in the integration of posture and movement control. Using the planar robot we will: 1) explore transfer of learning between posture and movement tasks, assessing whether coupling between the two controllers increases post-stroke, and 2) determine whether trajectory planning adapts post-stroke to account for the biomechanical effects of posture regulation. We expect our results will ultimately lead to new approaches for rehabilitating arm function post-stroke. We anticipate that improvement in arm function will be promoted by applying robotic and biofeedback methods first to train coactivation, posture and movement control separately, then in combination.