Neurophysiological markers predicting recovery of standing in humans with chronic motor complete spinal cord injury

Abstract The appropriate selection of individual-specific spinal cord epidural stimulation (scES) parameters is crucial to re-enable independent standing with self-assistance for balance in individuals with chronic, motor complete spinal cord injury, which is a key achievement toward the recovery of...

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Autores principales:	Samineh Mesbah, Federica Gonnelli, Claudia A. Angeli, Ayman El-baz, Susan J. Harkema, Enrico Rejc
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2019
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/eef0f1100a2941d4b854ba791e833e78
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spelling	oai:doaj.org-article:eef0f1100a2941d4b854ba791e833e782021-12-02T15:09:56ZNeurophysiological markers predicting recovery of standing in humans with chronic motor complete spinal cord injury10.1038/s41598-019-50938-y2045-2322https://doaj.org/article/eef0f1100a2941d4b854ba791e833e782019-10-01T00:00:00Zhttps://doi.org/10.1038/s41598-019-50938-yhttps://doaj.org/toc/2045-2322Abstract The appropriate selection of individual-specific spinal cord epidural stimulation (scES) parameters is crucial to re-enable independent standing with self-assistance for balance in individuals with chronic, motor complete spinal cord injury, which is a key achievement toward the recovery of functional mobility. To date, there are no available algorithms that contribute to the selection of scES parameters for facilitating standing in this population. Here, we introduce a novel framework for EMG data processing that implements spectral analysis by continuous wavelet transform and machine learning methods for characterizing epidural stimulation-promoted EMG activity resulting in independent standing. Analysis of standing data collected from eleven motor complete research participants revealed that independent standing was promoted by EMG activity characterized by lower median frequency, lower variability of median frequency, lower variability of activation pattern, lower variability of instantaneous maximum power, and higher total power. Additionally, the high classification accuracy of assisted and independent standing allowed the development of a prediction algorithm that can provide feedback on the effectiveness of muscle-specific activation for standing promoted by the tested scES parameters. This framework can support researchers and clinicians during the process of selection of epidural stimulation parameters for standing motor rehabilitation.Samineh MesbahFederica GonnelliClaudia A. AngeliAyman El-bazSusan J. HarkemaEnrico RejcNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 9, Iss 1, Pp 1-18 (2019)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Samineh Mesbah Federica Gonnelli Claudia A. Angeli Ayman El-baz Susan J. Harkema Enrico Rejc Neurophysiological markers predicting recovery of standing in humans with chronic motor complete spinal cord injury
description	Abstract The appropriate selection of individual-specific spinal cord epidural stimulation (scES) parameters is crucial to re-enable independent standing with self-assistance for balance in individuals with chronic, motor complete spinal cord injury, which is a key achievement toward the recovery of functional mobility. To date, there are no available algorithms that contribute to the selection of scES parameters for facilitating standing in this population. Here, we introduce a novel framework for EMG data processing that implements spectral analysis by continuous wavelet transform and machine learning methods for characterizing epidural stimulation-promoted EMG activity resulting in independent standing. Analysis of standing data collected from eleven motor complete research participants revealed that independent standing was promoted by EMG activity characterized by lower median frequency, lower variability of median frequency, lower variability of activation pattern, lower variability of instantaneous maximum power, and higher total power. Additionally, the high classification accuracy of assisted and independent standing allowed the development of a prediction algorithm that can provide feedback on the effectiveness of muscle-specific activation for standing promoted by the tested scES parameters. This framework can support researchers and clinicians during the process of selection of epidural stimulation parameters for standing motor rehabilitation.
format	article
author	Samineh Mesbah Federica Gonnelli Claudia A. Angeli Ayman El-baz Susan J. Harkema Enrico Rejc
author_facet	Samineh Mesbah Federica Gonnelli Claudia A. Angeli Ayman El-baz Susan J. Harkema Enrico Rejc
author_sort	Samineh Mesbah
title	Neurophysiological markers predicting recovery of standing in humans with chronic motor complete spinal cord injury
title_short	Neurophysiological markers predicting recovery of standing in humans with chronic motor complete spinal cord injury
title_full	Neurophysiological markers predicting recovery of standing in humans with chronic motor complete spinal cord injury
title_fullStr	Neurophysiological markers predicting recovery of standing in humans with chronic motor complete spinal cord injury
title_full_unstemmed	Neurophysiological markers predicting recovery of standing in humans with chronic motor complete spinal cord injury
title_sort	neurophysiological markers predicting recovery of standing in humans with chronic motor complete spinal cord injury
publisher	Nature Portfolio
publishDate	2019
url	https://doaj.org/article/eef0f1100a2941d4b854ba791e833e78
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Neurophysiological markers predicting recovery of standing in humans with chronic motor complete spinal cord injury

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