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Myoelectric interface training enables targeted reduction in abnormal muscle co-activation

BACKGROUND: Abnormal patterns of muscle co-activation contribute to impaired movement after stroke. Previously, we developed a myoelectric computer interface (MyoCI) training paradigm to improve stroke-induced arm motor impairment by reducing the abnormal co-activation of arm muscle pairs. However,...

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Autores principales: Seo, Gang, Kishta, Ameen, Mugler, Emily, Slutzky, Marc W., Roh, Jinsook
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9250207/
https://www.ncbi.nlm.nih.gov/pubmed/35778757
http://dx.doi.org/10.1186/s12984-022-01045-z
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author Seo, Gang
Kishta, Ameen
Mugler, Emily
Slutzky, Marc W.
Roh, Jinsook
author_facet Seo, Gang
Kishta, Ameen
Mugler, Emily
Slutzky, Marc W.
Roh, Jinsook
author_sort Seo, Gang
collection PubMed
description BACKGROUND: Abnormal patterns of muscle co-activation contribute to impaired movement after stroke. Previously, we developed a myoelectric computer interface (MyoCI) training paradigm to improve stroke-induced arm motor impairment by reducing the abnormal co-activation of arm muscle pairs. However, it is unclear to what extent the paradigm induced changes in the overall intermuscular coordination in the arm, as opposed to changing just the muscles trained with the MyoCI. This study examined the intermuscular coordination patterns of thirty-two stroke survivors who participated in 6 weeks of MyoCI training. METHODS: We used non-negative matrix factorization to identify the arm muscle synergies (coordinated patterns of muscle activity) during a reaching task before and after the training. We examined the extent to which synergies changed as the training reduced motor impairment. In addition, we introduced a new synergy analysis metric, disparity index (DI), to capture the changes in the individual muscle weights within a synergy. RESULTS: There was no consistent pattern of change in the number of synergies across the subjects after the training. The composition of muscle synergies, calculated using a traditional synergy similarity metric, also did not change after the training. However, the disparity of muscle weights within synergies increased after the training in the participants who responded to MyoCI training—that is, the specific muscles that the MyoCI was targeting became less correlated within a synergy. This trend was not observed in participants who did not respond to the training. CONCLUSIONS: These findings suggest that MyoCI training reduced arm impairment by decoupling only the muscles trained while leaving other muscles relatively unaffected. This suggests that, even after injury, the nervous system is capable of motor learning on a highly fractionated level. It also suggests that MyoCI training can do what it was designed to do—enable stroke survivors to reduce abnormal co-activation in targeted muscles. Trial registration This study was registered at ClinicalTrials.gov (NCT03579992, Registered 09 July 2018—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03579992?term=NCT03579992&draw=2&rank=1)
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spelling pubmed-92502072022-07-03 Myoelectric interface training enables targeted reduction in abnormal muscle co-activation Seo, Gang Kishta, Ameen Mugler, Emily Slutzky, Marc W. Roh, Jinsook J Neuroeng Rehabil Research BACKGROUND: Abnormal patterns of muscle co-activation contribute to impaired movement after stroke. Previously, we developed a myoelectric computer interface (MyoCI) training paradigm to improve stroke-induced arm motor impairment by reducing the abnormal co-activation of arm muscle pairs. However, it is unclear to what extent the paradigm induced changes in the overall intermuscular coordination in the arm, as opposed to changing just the muscles trained with the MyoCI. This study examined the intermuscular coordination patterns of thirty-two stroke survivors who participated in 6 weeks of MyoCI training. METHODS: We used non-negative matrix factorization to identify the arm muscle synergies (coordinated patterns of muscle activity) during a reaching task before and after the training. We examined the extent to which synergies changed as the training reduced motor impairment. In addition, we introduced a new synergy analysis metric, disparity index (DI), to capture the changes in the individual muscle weights within a synergy. RESULTS: There was no consistent pattern of change in the number of synergies across the subjects after the training. The composition of muscle synergies, calculated using a traditional synergy similarity metric, also did not change after the training. However, the disparity of muscle weights within synergies increased after the training in the participants who responded to MyoCI training—that is, the specific muscles that the MyoCI was targeting became less correlated within a synergy. This trend was not observed in participants who did not respond to the training. CONCLUSIONS: These findings suggest that MyoCI training reduced arm impairment by decoupling only the muscles trained while leaving other muscles relatively unaffected. This suggests that, even after injury, the nervous system is capable of motor learning on a highly fractionated level. It also suggests that MyoCI training can do what it was designed to do—enable stroke survivors to reduce abnormal co-activation in targeted muscles. Trial registration This study was registered at ClinicalTrials.gov (NCT03579992, Registered 09 July 2018—Retrospectively registered, https://clinicaltrials.gov/ct2/show/NCT03579992?term=NCT03579992&draw=2&rank=1) BioMed Central 2022-07-01 /pmc/articles/PMC9250207/ /pubmed/35778757 http://dx.doi.org/10.1186/s12984-022-01045-z Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Seo, Gang
Kishta, Ameen
Mugler, Emily
Slutzky, Marc W.
Roh, Jinsook
Myoelectric interface training enables targeted reduction in abnormal muscle co-activation
title Myoelectric interface training enables targeted reduction in abnormal muscle co-activation
title_full Myoelectric interface training enables targeted reduction in abnormal muscle co-activation
title_fullStr Myoelectric interface training enables targeted reduction in abnormal muscle co-activation
title_full_unstemmed Myoelectric interface training enables targeted reduction in abnormal muscle co-activation
title_short Myoelectric interface training enables targeted reduction in abnormal muscle co-activation
title_sort myoelectric interface training enables targeted reduction in abnormal muscle co-activation
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9250207/
https://www.ncbi.nlm.nih.gov/pubmed/35778757
http://dx.doi.org/10.1186/s12984-022-01045-z
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