EFFECTIVENESS OF ROBOT-ASSISTED HAND MOVEMENT TRAINING AFTER STROKE

项目摘要/摘要 该项目广泛的、长期的、科学的目标是确定行为和神经因素 确定卒中后机械手运动训练的效果。实现以下目标的重要社会影响 这一目标将是设计更有效的机器人康复练习技术，这将使 中风患者可以增加他们的运动恢复，而不是目前的方法。在我们的 在之前的资助中，我们发现手指本体感觉受损的参与者(用一种新的机器人进行量化 协议)并没有从机器人手指训练中获得功能上的好处。收益减少也与以下因素相关 躯体感觉系统的损伤和异常激活。我们的工作假设是手指 本体感觉完整性是机器人辅助的一种途径，因为它允许这种辅助刺激 类Hebbian学习机制。在这个“希伯来假说”的基础上，我们增加了另外两个假说： 通过对本体感觉受损的人进行有针对性的训练来提高本体感觉能力将增强 后续机器人手指训练的有效性和本体感觉完整性调节自发自我 正规训练之外的训练。我们将用一种新型的机器人在一系列实验中检验这些假设 手指训练装置(“手指”)，帮助参与者做出不同的握法来演奏音乐剧中的音符 类似于吉他英雄的电脑游戏。我们将招募手部运动缺陷至少六个的参与者 中风后几个月，随机选择手指本体感觉完好的参与者参加目标1，以及 那些手指本体感觉受损的人参与目标2。目标1是识别Hebbian的大小 受益于机器人辅助的运动训练。机器人辅助增强了本体感觉输入，但它通常 它还能增加奖励，因为它能增加任务的成功率。我们将确定 增强了本体感觉输入，超出了由于增强奖励而带来的好处。目标2是确定 哪些手指本体感觉可以通过有针对性的机器人本体感觉训练来改善，从而 增强对后续机器人手指运动训练的反应。目标3是确定数学模型 预测目标1和目标2中参与者所经历的不同训练形式的反应。 输入将是基线行为和人口学测量，损伤与感觉和运动结构重叠 (通过解剖核磁共振)，以及激活感觉-运动网络(通过功能磁共振和脑电)。我们的目标是进一步提供 洞察机器人训练反应的高方差，并确定谁对本体感觉反应最好 训练。目标4是确定本体感觉受损在日常生活中减少自发使用手中的作用 使用一种新型的可穿戴传感器，因为在机器人治疗过程之外使用手可能会产生强大的 培训效果。对这些假设的测试将提供如何操纵独特资源的洞察力 每个患者使用基于机器人的运动训练所保持的感觉运动可塑性。

Project Summary/Abstract The broad, long-term, scientific objective of this project is to identify the behavioral and neural factors that determine the efficacy of robotic hand movement training after stroke. An important societal impact of achieving this objective will be the design of more effective robotic rehabilitation exercise technology, which will allow people with a stroke to increase their movement recovery beyond that possible with current approaches. In our previous grant, we found that participants with impaired finger proprioception (quantified robotically with a novel protocol) did not achieve a functional benefit from robotic finger training. Reduced benefit also correlated with injury to and abnormal activation of the somatosensory system. Our working hypothesis is that finger proprioceptive integrity is a gateway for robotic assistance because it allows such assistance to stimulate a Hebbian-like learning mechanism. Building on this “Hebbian Hypothesis”, we add two other hypotheses: improving proprioceptive capacity with targeted training for those with impaired proprioception will enhance the effectiveness of subsequent robotic finger training, and proprioceptive integrity modulates spontaneous self- training outside of formal training. We will test these hypotheses in a series of experiments with a novel robotic finger training device (“FINGER”) that assists participants in making different grips to play notes in a musical computer game similar to Guitar Hero. We will recruit participants with hand movement deficits at least six months after stroke and randomize the participants with intact finger proprioception to participate in Aim 1, and those with impaired finger proprioception to participate in Aim 2. Aim 1 is to identify the magnitude of the Hebbian benefit from robot-assisted movement training. Robotic assistance enhances proprioceptive input, but it typically also enhances reward, because it increases task success. We will determine the magnitude of the benefit of the enhanced proprioceptive input, beyond the benefit due to enhanced reward. Aim 2 is to determine the extent to which finger proprioception can be improved through targeted robotic proprioceptive training, and thereby enhance response to subsequent robotic finger movement training. Aim 3 is to identify mathematical models that predict the response to the different forms of training experienced by the participants in Aim 1 and 2. The model inputs will be baseline behavioral and demographic measures, lesion overlap with sensory and motor structures (via anatomical MRI), and activation of sensory-motor networks (via fMRI and EEG). We aim to provide further insight into the high variance in robotic training response, and determine who responds best to proprioceptive training. Aim 4 is to identify the role of impaired proprioception in decreasing spontaneous hand use in daily life using a novel wearable sensor, because hand use outside of robotic therapy sessions likely exerts a powerful training effect. Testing of these hypotheses will provide insight into how to manipulate the unique resources for sensory motor plasticity that each patient retains using robot-based movement training.