Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling

Abstract Neuromusculoskeletal (NMS) models can aid in studying the impacts of the nervous and musculoskeletal systems on one another. These computational models facilitate studies investigating mechanisms and treatment of musculoskeletal and neurodegenerative conditions. In this study, we present a...

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Autores principales:	Victoria L. Volk, Landon D. Hamilton, Donald R. Hume, Kevin B. Shelburne, Clare K. Fitzpatrick
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9d004cc22eb74a80994e523040408e25
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spelling	oai:doaj.org-article:9d004cc22eb74a80994e523040408e252021-11-28T12:16:55ZIntegration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling10.1038/s41598-021-02298-92045-2322https://doaj.org/article/9d004cc22eb74a80994e523040408e252021-11-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-02298-9https://doaj.org/toc/2045-2322Abstract Neuromusculoskeletal (NMS) models can aid in studying the impacts of the nervous and musculoskeletal systems on one another. These computational models facilitate studies investigating mechanisms and treatment of musculoskeletal and neurodegenerative conditions. In this study, we present a predictive NMS model that uses an embedded neural architecture within a finite element (FE) framework to simulate muscle activation. A previously developed neuromuscular model of a motor neuron was embedded into a simple FE musculoskeletal model. Input stimulation profiles from literature were simulated in the FE NMS model to verify effective integration of the software platforms. Motor unit recruitment and rate coding capabilities of the model were evaluated. The integrated model reproduced previously published output muscle forces with an average error of 0.0435 N. The integrated model effectively demonstrated motor unit recruitment and rate coding in the physiological range based upon motor unit discharge rates and muscle force output. The combined capability of a predictive NMS model within a FE framework can aid in improving our understanding of how the nervous and musculoskeletal systems work together. While this study focused on a simple FE application, the framework presented here easily accommodates increased complexity in the neuromuscular model, the FE simulation, or both.Victoria L. VolkLandon D. HamiltonDonald R. HumeKevin B. ShelburneClare K. FitzpatrickNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Victoria L. Volk Landon D. Hamilton Donald R. Hume Kevin B. Shelburne Clare K. Fitzpatrick Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling
description	Abstract Neuromusculoskeletal (NMS) models can aid in studying the impacts of the nervous and musculoskeletal systems on one another. These computational models facilitate studies investigating mechanisms and treatment of musculoskeletal and neurodegenerative conditions. In this study, we present a predictive NMS model that uses an embedded neural architecture within a finite element (FE) framework to simulate muscle activation. A previously developed neuromuscular model of a motor neuron was embedded into a simple FE musculoskeletal model. Input stimulation profiles from literature were simulated in the FE NMS model to verify effective integration of the software platforms. Motor unit recruitment and rate coding capabilities of the model were evaluated. The integrated model reproduced previously published output muscle forces with an average error of 0.0435 N. The integrated model effectively demonstrated motor unit recruitment and rate coding in the physiological range based upon motor unit discharge rates and muscle force output. The combined capability of a predictive NMS model within a FE framework can aid in improving our understanding of how the nervous and musculoskeletal systems work together. While this study focused on a simple FE application, the framework presented here easily accommodates increased complexity in the neuromuscular model, the FE simulation, or both.
format	article
author	Victoria L. Volk Landon D. Hamilton Donald R. Hume Kevin B. Shelburne Clare K. Fitzpatrick
author_facet	Victoria L. Volk Landon D. Hamilton Donald R. Hume Kevin B. Shelburne Clare K. Fitzpatrick
author_sort	Victoria L. Volk
title	Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling
title_short	Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling
title_full	Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling
title_fullStr	Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling
title_full_unstemmed	Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling
title_sort	integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/9d004cc22eb74a80994e523040408e25
work_keys_str_mv	AT victorialvolk integrationofneuralarchitecturewithinafiniteelementframeworkforimprovedneuromusculoskeletalmodeling AT landondhamilton integrationofneuralarchitecturewithinafiniteelementframeworkforimprovedneuromusculoskeletalmodeling AT donaldrhume integrationofneuralarchitecturewithinafiniteelementframeworkforimprovedneuromusculoskeletalmodeling AT kevinbshelburne integrationofneuralarchitecturewithinafiniteelementframeworkforimprovedneuromusculoskeletalmodeling AT clarekfitzpatrick integrationofneuralarchitecturewithinafiniteelementframeworkforimprovedneuromusculoskeletalmodeling
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Integration of neural architecture within a finite element framework for improved neuromusculoskeletal modeling

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