Computational neurorehabilitation: modeling plasticity and learning to predict recovery

Abstract

Despite progress in using computational approaches to inform medicine and neuroscience in the last 30 years, there have been few attempts to model the mechanisms underlying sensorimotor rehabilitation. We argue that a fundamental understanding of neurologic recovery, and as a result accurate predictions at the individual level, will be facilitated by developing computational models of the salient neural processes, including plasticity and learning systems of the brain, and integrating them into a context specific to rehabilitation. Here, we therefore discuss Computational Neurorehabilitation, a newly emerging field aimed at modeling plasticity and motor learning to understand and improve movement recovery of individuals with neurologic impairment. We first explain how the emergence of robotics and wearable sensors for rehabilitation is providing data that make development and testing of such models increasingly feasible. We then review key aspects of plasticity and motor learning that such models will incorporate. We proceed by discussing how computational neurorehabilitation models relate to the current benchmark in rehabilitation modeling – regression-based, prognostic modeling. We then critically discuss the first computational neurorehabilitation models, which have primarily focused on modeling rehabilitation of the upper extremity after stroke, and show how even simple models have produced novel ideas for future investigation. Finally, we conclude with key directions for future research, anticipating that soon we will see the emergence of mechanistic models of motor recovery that are informed by clinical imaging results and driven by the actual movement content of rehabilitation therapy as well as wearable sensor-based records of daily activity.

The Efficiency, Efficacy, and Retention of Motor Training in Chronic Stroke

“…The intervention was based on the Accelerated Skill Acquisition Program (ASAP) 14,29 . In brief, ASAP includes 1) elements of purposeful and skilled movement execution, including challenging and progressive practice, 2) support for patients' control or autonomy by choices of specific tasks to be practiced, 3) collaborative problem solving to identify and address movement needs, 4) and encouragement of self-direction in extending practice to community contexts.…”

“…Rehabilitation in the chronic phase after stroke is based on the premise that motor learning determines activity-dependent sensorimotor recovery [1][2][3][4] . Extensive research in motor skill learning 5 has shown that: 1) increasing the amount of training increases efficacy ("practice makes perfect"), 2) increasing the amount and duration of training decrease efficiency ("the power law of practice" 6 ), and 3) increasing the amount of training improves retention following training.…”

Importancia del proceso de evaluación fisioterapéutica en neurorehabilitación

“…La capacidad de los robots para aplicar perturbaciones discretas los hacen ideales para cuantificar el comportamiento reflexivo y en particular, explorar respuestas de latencia larga más complejas que no pueden ser obtenidas, por ejemplo, con un golpe de martillo. Según Reinkensmeyer et al (2016) en el marco de los modelos de neurorehabilitación computacional estos modelos predicen los resultados funcionales del paciente al relacionar las representaciones computacionales de la plasticidad y el aprendizaje con la actividad sensoriomotora lograda en la neurorehabilitación y/o durante la vida diaria.…”

Brain-machine Interface in Robot-assisted Neurorehabilitation for Patients with Stroke and Upper Extremity Weakness – the Therapeutic Turning Point

“…These basic principles of neural plasticity can be more quantified prospectively and then systematic comprehensive approach based on the quantified mechanisms shall be possible [60,61].…”