CT imaging-based prediction and stratification of motor and cognitive behavior after stroke for targeted game-based robot therapy: Diversity Supplement

项目摘要 随着中风成为世界范围内导致残疾的主要原因，有必要改善目前的康复护理。 此外，护理负担很大，许多发展中国家没有资源。 对中风患者的治疗是必要的。以可负担性为重点，母项目旨在开发和 将触觉机器人集成到基于社区的环境中，以治疗认知和运动残留症患者 中风造成的损伤。这些1自由度的触觉机器人通过新颖的末端执行器施加扭矩 能够支持和促进伸展和抓住上肢运动恢复的组件。父辈 格兰特的目标是计划利用单个和分组的触觉机器人以及严肃的游戏来动态调整和 为出现运动和认知障碍谱系的患者提供个性化治疗。一共有四个 基于患者功能行为的建议治疗组：低认知/低运动，中等 认知/低运动，低认知/中等运动，中等认知和中等运动。父辈 格兰特主要专注于量化患者功能行为的变化，而不是将这些变化联系起来 到可能支持运动和认知的神经基质。 这一多样性补充将侧重于培养有才华的博士前学生开发小说 能够支持机器人系统基于社区的应用的算法。以社区为基础的 在康复环境和疗养院，使用脑成像帮助靶点的机会往往有限 治疗。成像平台通常很昂贵，因此临床医生仅依靠功能行为来 确定治疗方向。计算机断层扫描(CT或CAT)使医生能够看到大脑内部 而且通常是一种非常实惠的成像方法，可以深入了解中风后的大脑状态。 我们的目标是利用这项技术创建预测算法，以支持我们的个性化 基于病变参数对运动和认知功能的潜在影响的机器人辅助治疗。

Project Summary With stroke being the leading cause of disability worldwide, there is a need to improve current rehabilitative care. Furthermore, the burden of care is significant, and many developing countries do not have the resources necessary to treat people with stroke. With a focus on affordability, the parent project aims to develop and integrate a haptic robot into a community-based environment to treat patients with residual cognitive and motor impairments due to stroke. These 1 degree-of-freedom haptic robots apply torques via novel end-effector assemblies that can support and facilitate reaching and grasping motor recovery in the upper limb. The parent grant aims to plans to leverage single and grouped haptic robots and serious gaming to dynamically adjust and personalize therapy for patients presenting along motor and cognitive impairment spectrums. There are four proposed treatment groups based on patient functional behavior: low cognitive/low motor, moderate cognitive/low motor, low cognitive/moderate motor, and moderate cognitive and moderate motor. The parent grant focuses primarily on quantifying changes in patient functional behavior without connecting these changes to neural substrates that may underpin motor and cognition. This diversity supplement will focus on training a talented pre-doctoral student to develop novel algorithms that can support the community-based application of the robot systems. In the community-based rehabilitation setting and nursing homes, there is often limited chances to use brain imaging to help target treatment. Imaging platforms are typically expensive and therefore clinicians rely on only functional behavior to determine treatment direction. A computed tomography (CT or CAT) scan allows doctors to see inside the brain and is often an extremely affordable imaging method that can provide insight into the brain state after stroke. The goal is to leverage this technology to create predictive algorithms that can support personalization of our robot-assisted treatment based on the potential impact of lesion parameters on motor and cognitive function.