Novel Brain Signal Feedback Paradigm to Enhance Motor Learning After Stroke

Stroke (795,000/year in the US and 30 million existing stroke survivors in the world) damages brain neural structures that control coordinated upper limb movement. To most effectively target the brain damage, interventions should be directed so as to restore brain control serving coordination of peripheral neuromuscular function. Currently, there is a lack of a transformative intervention strategy, and only limited efficacy is seen in response to neural rehabilitation that is only peripherally-directed (limbs e.g.) or only directed at the brain. This study will employ a novel neural feedback approach with a closed-loop, real-time paradigm to engage and retrain existing brain function after stroke. Real-time functional magnetic resonance imaging (rtfMIR) provides neural feedback with the advantage of precisely identifying the location of brain activity for multiple cognitive and emotional tasks. However, the rtfMRI is costly and precludes motor learning that requires sitting and engaging the upper limb in complex motor tasks during imaging acquisition. In contrast, real-time functional near-infrared spectroscopy (rtfNIRS), although not as spatially precise as rtfMRI, offers a low-cost, portable solution to provide brain neural feedback during motor learning. This proposal will utilize both technologies in a hybrid, sequential motor learning protocol. Moreover, the study protocol will also simultaneously involve both central effective signals (through neural feedback) and peripheral affective signals by employing neutrally-triggered functional electrical stimulation (FES)-assisted coordination practice, which produces peripherally-induced affective signals from muscle and joint receptors. This novel combination intervention protocol will engage the central nervous system, motor effective pathway training along with induction of affective signal production (FES-assisted practice), all of which will be implemented within the framework of evidence-based motor learning principles.