Stroke is the leading cause of motor disabilities and places high pressure on healthcare infrastructures due to the imbalance between the need for serving an aging society and available neurorehabilitation resources. Thus, there has been a surge in the production of novel rehabilitative technologies for accelerating recovery. Despite the successful development of such devices, lack of objective standards besides clinical investigations using subjective measures have made controversial recommendations regarding several devices, including robots. To address the need for effective rehabilitative technologies, this one year NSF/FDA Scholar-in-Residence project is focused on the design, implementation, and evaluation of a novel, objective, and robust algorithmic biomarker of recovery, named Delta CorticoMuscular Information-based Connectivity (D-CMiC). The proposed algorithm quantifies the connectivity between the central nervous system (CNS) and the peripheral nervous system (PNS) by designing and implementing a protocol for simultaneously measuring electrical activity from the brain and an ankle muscle on the affected side of recovering post-stroke patients. Project outcomes will produce a scientific vision regarding the neurophysiology of recovery and expedite availability of more effective rehabilitation devices to patients for a range of neurological disorders beyond stroke (such as Parkinson’s disease, Essential Tremor and Ataxia). For educational impact, the project will generate a unique transdisciplinary educational environment by conducting workshops regarding emerging Brain-Computer Interface (BCI) technologies in medicine and undergraduate team projects for human-machine interfacing, with a focus on promoting STEM activities within underrepresented groups.
The goal of this project is to design, implement, and evaluate a robust algorithmic biomarker of stroke recovery, which quantifies the spectrotemporal neurophysiological connectivity between the CNS (using electroencephalography (EEG)) and PNS (using high-density surface electromyography (HD-sEMG)). The project is motivated by the lack of objective standards and direct neurophysiological metrics for quantifying the “true†efficacy of rehabilitation in terms of the translation between central and peripheral nervous systems to control functional movements. The predictive capability, precision, and efficiency of the developed D-CMiC metric will be analyzed by collecting data from recovering stroke patients and healthy subjects. Unique D-CMiC features include: (1) accurately and objectively tracking corticomuscular functional connectivity in the Delta/low frequency band, which will be robust to stochastic high-frequency artifacts while encoding the 3 temporal phases of motor control: preparation, execution, relaxation; (2) computationally modeling of corticomuscular connectivity through the fusion of spectrotemporal similarity measures, which will reward persistent true-positive CNS-PNS connections while diminishing false-positives; and (3) building the basis for the first medical device development tool (MDDT) for the systematic, objective, and transparent evaluation of pre-market rehabilitation devices, aligned with the FDA’s mission.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
