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      The effects of error-augmentation versus error-reduction paradigms in robotic therapy to enhance upper extremity performance and recovery post-stroke: a systematic review

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          Abstract

          Despite upper extremity function playing a crucial role in maintaining one’s independence in activities of daily living, upper extremity impairments remain one of the most prevalent post-stroke deficits. To enhance the upper extremity motor recovery and performance among stroke survivors, two training paradigms in the fields of robotics therapy involving modifying haptic feedback were proposed: the error-augmentation (EA) and error-reduction (ER) paradigms. There is a lack of consensus, however, as to which of the two paradigms yields superior training effects. This systematic review aimed to determine (i) whether EA is more effective than conventional repetitive practice; (ii) whether ER is more effective than conventional repetitive practice and; (iii) whether EA is more effective than ER in improving post-stroke upper extremity motor recovery and performance. The study search and selection process as well as the ratings of methodological quality of the articles were conducted by two authors separately, and the results were then compared and discussed among the two reviewers. Findings were analyzed and synthesized using the level of evidence. By August 1st 2017, 269 articles were found after searching 6 databases, and 13 were selected based on criteria such as sample size, type of participants recruited, type of interventions used, etc. Results suggest, with a moderate level of evidence, that EA is overall more effective than conventional repetitive practice (motor recovery and performance) and ER (motor performance only), while ER appears to be no more effective than conventional repetitive practice. However, intervention effects as measured using clinical outcomes were under most instance not ‘clinically meaningful’ and effect sizes were modest. While stronger evidence is required to further support the efficacy of error modification therapies, the influence of factors related to the delivery of the intervention (such as intensity, duration) and personal factors (such as stroke severity and time of stroke onset) deserves further investigations as well.

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          Principles of experience-dependent neural plasticity: implications for rehabilitation after brain damage.

          This paper reviews 10 principles of experience-dependent neural plasticity and considerations in applying them to the damaged brain. Neuroscience research using a variety of models of learning, neurological disease, and trauma are reviewed from the perspective of basic neuroscientists but in a manner intended to be useful for the development of more effective clinical rehabilitation interventions. Neural plasticity is believed to be the basis for both learning in the intact brain and relearning in the damaged brain that occurs through physical rehabilitation. Neuroscience research has made significant advances in understanding experience-dependent neural plasticity, and these findings are beginning to be integrated with research on the degenerative and regenerative effects of brain damage. The qualities and constraints of experience-dependent neural plasticity are likely to be of major relevance to rehabilitation efforts in humans with brain damage. However, some research topics need much more attention in order to enhance the translation of this area of neuroscience to clinical research and practice. The growing understanding of the nature of brain plasticity raises optimism that this knowledge can be capitalized upon to improve rehabilitation efforts and to optimize functional outcome.
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            Adaptive representation of dynamics during learning of a motor task.

            We investigated how the CNS learns to control movements in different dynamical conditions, and how this learned behavior is represented. In particular, we considered the task of making reaching movements in the presence of externally imposed forces from a mechanical environment. This environment was a force field produced by a robot manipulandum, and the subjects made reaching movements while holding the end-effector of this manipulandum. Since the force field significantly changed the dynamics of the task, subjects' initial movements in the force field were grossly distorted compared to their movements in free space. However, with practice, hand trajectories in the force field converged to a path very similar to that observed in free space. This indicated that for reaching movements, there was a kinematic plan independent of dynamical conditions. The recovery of performance within the changed mechanical environment is motor adaptation. In order to investigate the mechanism underlying this adaptation, we considered the response to the sudden removal of the field after a training phase. The resulting trajectories, named aftereffects, were approximately mirror images of those that were observed when the subjects were initially exposed to the field. This suggested that the motor controller was gradually composing a model of the force field, a model that the nervous system used to predict and compensate for the forces imposed by the environment. In order to explore the structure of the model, we investigated whether adaptation to a force field, as presented in a small region, led to aftereffects in other regions of the workspace. We found that indeed there were aftereffects in workspace regions where no exposure to the field had taken place; that is, there was transfer beyond the boundary of the training data. This observation rules out the hypothesis that the subject's model of the force field was constructed as a narrow association between visited states and experienced forces; that is, adaptation was not via composition of a look-up table. In contrast, subjects modeled the force field by a combination of computational elements whose output was broadly tuned across the motor state space. These elements formed a model that extrapolated to outside the training region in a coordinate system similar to that of the joints and muscles rather than end-point forces. This geometric property suggests that the elements of the adaptive process represent dynamics of a motor task in terms of the intrinsic coordinate system of the sensors and actuators.
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              What do motor "recovery" and "compensation" mean in patients following stroke?

              There is a lack of consistency among researchers and clinicians in the use of terminology that describes changes in motor ability following neurological injury. Specifically, the terms and definitions of motor compensation and motor recovery have been used in different ways, which is a potential barrier to interdisciplinary communication. This Point of View describes the problem and offers a solution in the form of definitions of compensation and recovery at the neuronal, motor performance, and functional levels within the framework of the International Classification of Functioning model.
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                Author and article information

                Contributors
                514-398-4500 , le.liu@mail.mcgill.ca
                Journal
                J Neuroeng Rehabil
                J Neuroeng Rehabil
                Journal of NeuroEngineering and Rehabilitation
                BioMed Central (London )
                1743-0003
                4 July 2018
                4 July 2018
                2018
                : 15
                : 65
                Affiliations
                [1 ]ISNI 0000 0004 1936 8649, GRID grid.14709.3b, School of Physical and Occupational Therapy, , McGill University, ; 3654 Promenade Sir-William-Osler, Montréal, Québec H3G 1Y5 Canada
                [2 ]Feil and Oberfeld Research Centre, Jewish Rehabilitation Hospital site of CRIR (CISSS Laval), Laval, Canada
                Author information
                http://orcid.org/0000-0002-3325-3181
                Article
                408
                10.1186/s12984-018-0408-5
                6033222
                29973250
                17f8a619-3e5b-4112-a2aa-b77b5a44b4c9
                © The Author(s). 2018

                Open AccessThis 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.

                History
                : 18 October 2017
                : 19 June 2018
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100000024, Canadian Institutes of Health Research;
                Award ID: MOP-77548
                Award Recipient :
                Categories
                Review
                Custom metadata
                © The Author(s) 2018

                Neurosciences
                cerebrovascular accident,upper extremity,robotics,error-augmentation,error-reduction,haptic guidance,evidence,pedro

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