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

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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.