Reinkensmeyer, DJ;
Burdet, E;
Casadio, M;
Krakauer, JW;
Kwakkel, G;
Lang, CE;
Swinnen, SP;
... Schweighofer, N; + view all
(2016)
Computational neurorehabilitation: modeling plasticity and learning to predict recovery.
Journal of NeuroEngineering and Rehabilitation
, 13
, Article 42. 10.1186/s12984-016-0148-3.
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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.
| Type: | Article |
|---|---|
| Title: | Computational neurorehabilitation: modeling plasticity and learning to predict recovery |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1186/s12984-016-0148-3 |
| Publisher version: | https://doi.org/10.1186/s12984-016-0148-3 |
| Language: | English |
| Additional information: | Copyright © 2016 Reinkensmeyer et al. Open Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
| Keywords: | Neurorehabilitation, Computational modeling, Motor control, Plasticity, Motor learning, Stroke recovery |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Clinical and Movement Neurosciences |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1489910 |
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