Motor control-based assessment of therapy effects in individuals post-stroke: implications for prediction of response and subject-specific modifications

Producing a coordinated motion such as walking is, at its root, the result of healthy communication pathways between the central nervous system and the musculoskeletal system. The central nervous system produces an electrical signal responsible for the excitation of a muscle, and the musculoskeletal system contains the necessary equipment for producing a movement-driving force to achieve a desired motion. Motor control refers to the ability an individual has to produce a desired motion, and the complexity of motor control is a mathematical concept stemming from how the electrical signals from the central nervous system translate to muscle activations. Exercising a high-level complexity of motor control is critical to producing a smooth motion. However, the occurrence of a sudden, detrimental neurological event like a stroke damages these connecting pathways between these two systems, and the result is a motion that is uncoordinated and energy-inefficient due to diminished motor control complexity.

Stroke is a leading cause of disability with nearly 800,000 stroke victims each year in the U.S. alone, amounting to an estimated cost of $45.5B. Impaired mobility following a stroke is a widespread effect, with more than half of survivors over the age of 65 affected in this way, and up to 80% of survivors at some point experiencing hemiparesis during post-stroke recovery. As such, given the importance of independent mobility for quality of life, improving gait mechanics and mobility of stroke survivors has been the goal of rehabilitation efforts for decades.

In this work, we mold together the forefronts of statistics and computational physics-based modeling to obtain insight and information about post-stroke hemiparetic gait mechanics and what drives them that would otherwise be unavailable. We expand upon previous work to quantify motor control complexity as it relates to the health of the neuromuscular system and analyze the effect of a specific therapy on motor control of individuals post-stroke. Secondly, we aim to develop a predictive model to conclude whether an individual will respond to the therapy based on kinematic and dynamic features from pre-therapy recordings. Lastly, we will determine how to individually tailor this therapy in order to achieve maximum improvement in motor control complexity in order to improve gait mechanics in individuals post-stroke.