title: 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.
authors.affiliation: Departments of Anatomy and Neurobiology, Mechanical and Aerospace Engineering, Biomedical Engineering, and Physical Medicine and Rehabilitation, University of California, Irvine, USA. dreinken@uci.edu.
authors.forename: David J
authors.initials: DJ
authors.lastname: Reinkensmeyer
authors.affiliation: Department of Bioengineering, Imperial College of Science, Technology and Medicine, London, UK.
authors.forename: Etienne
authors.initials: E
authors.lastname: Burdet
authors.affiliation: Department Informatics, Bioengineering, Robotics and Systems Engineering, University of Genoa, Genoa, Italy.
authors.forename: Maura
authors.initials: M
authors.lastname: Casadio
authors.affiliation: Departments of Neurology and Neuroscience, John Hopkins University School of Medicine, Baltimore, MD, USA.
authors.forename: John W
authors.initials: JW
authors.lastname: Krakauer
authors.affiliation: Department of Rehabilitation Medicine, MOVE Research Institute Amsterdam, VU University Medical Center, Amsterdam, The Netherlands.|Reade, Centre for Rehabilitation and Rheumatology, Amsterdam, The Netherlands.|Department of Physical Therapy and Human Movement Sciences, Northwestern University, Chicago, IL, USA.
authors.forename: Gert
authors.initials: G
authors.lastname: Kwakkel
authors.affiliation: Department of Neurology, Program in Physical Therapy, Program in Occupational Therapy, Washington University School of Medicine, St Louis, MO, USA.
authors.forename: Catherine E
authors.initials: CE
authors.lastname: Lang
authors.affiliation: Department of Kinesiology, KU Leuven Movement Control & Neuroplasticity Research Group, Leuven, KU, Belgium.|Leuven Research Institute for Neuroscience & Disease (LIND), KU, Leuven, Belgium.
authors.forename: Stephan P
authors.initials: SP
authors.lastname: Swinnen
authors.affiliation: Sobell Department of Motor Neuroscience and UCLPartners Centre for Neurorehabilitation, UCL Institute of Neurology, Queen Square, London, UK.
authors.forename: Nick S
authors.initials: NS
authors.lastname: Ward
authors.affiliation: Division of Biokinesiology and Physical Therapy, University of Southern California, Los Angeles, USA.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Schweighofer
mesh_terms: D006801:Humans; D007858:Learning; D009422:Nervous System Diseases / Q000503:physiopathology* / Q000534:rehabilitation*; D000066530:Neurological Rehabilitation / Q000379:methods*; D009473:Neuronal Plasticity; D020127:Recovery of Function; D012371:Robotics; D000071939:Stroke Rehabilitation / Q000379:methods*
keywords: Computational modeling; Motor control; Motor learning; Neurorehabilitation; Plasticity; Stroke recovery