Research Vision

One in four individuals will experience a stroke in their lifetime, and over half of survivors are left with lasting motor disability that results in major economic and societal losses. Currently, rehabilitation for these individuals is applied in a one-size fits all manner that has less than a 50% response rate. Inspired by the stunning success of precision medicine, my research aims to improve patient outcomes by developing precision rehabilitation tailored to patients’ underlying sensorimotor deficits.

 

A major barrier to precision rehabilitation is that current assessments are too coarse to identify underlying sensory or motor deficits causing impairment. Consequently, the factors that impact responsiveness to therapy are poorly understood. While damage to the motor system has been heavily explored, somatosensory damage—which occurs in over half of patients—is a key, overlooked factor. Following stroke, recovery is an exploratory trial-and-error process to identify alternate neural circuits to recover motor function. Mechanistically, this process requires feedback; if somatosensory damage impairs feedback mechanisms, the ability to identify alternate pathways and relearn lost motor skills is unguided, resulting in variable, sub-optimal outcomes.

 

In my research, I have developed robotic tasks that can independently assess and train somatosensory and motor function, that can be integrated into gamified training paradigms to provide feedback for reinforcement based learning. I further pair these tasks with neuroimaging (EEG, fMRI) to map specific somatosensory and motor learning processes in the brain, to inform how focal damage from stroke impacts recovery.  My research aims to translate these techniques to create clinic-ready technologies that can expand our knowledge of underlying factors impeding recovery, and dually develop rehabilitation methods to address them.