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Subject‐specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models

Musculoskeletal modelling is an important platform on which to study the biomechanics of morphological structures in vertebrates and is widely used in clinical, zoological and palaeontological fields. The popularity of this approach stems from the potential to non‐invasively quantify biologically im...

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Autores principales: Charles, James P., Grant, Barbara, D’Août, Kristiaan, Bates, Karl T.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7542200/
https://www.ncbi.nlm.nih.gov/pubmed/32598483
http://dx.doi.org/10.1111/joa.13261
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author Charles, James P.
Grant, Barbara
D’Août, Kristiaan
Bates, Karl T.
author_facet Charles, James P.
Grant, Barbara
D’Août, Kristiaan
Bates, Karl T.
author_sort Charles, James P.
collection PubMed
description Musculoskeletal modelling is an important platform on which to study the biomechanics of morphological structures in vertebrates and is widely used in clinical, zoological and palaeontological fields. The popularity of this approach stems from the potential to non‐invasively quantify biologically important but difficult‐to‐measure functional parameters. However, while it is known that model predictions are highly sensitive to input values, it is standard practice to build models by combining musculoskeletal data from different sources resulting in ‘generic’ models for a given species. At present, there are little quantitative data on how merging disparate anatomical data in models impacts the accuracy of these functional predictions. This issue is addressed herein by quantifying the accuracy of both subject‐specific human limb models containing individualised muscle force‐generating properties and models built using generic properties from both elderly and young individuals, relative to experimental muscle torques obtained from an isokinetic dynamometer. The results show that subject‐specific models predict isokinetic muscle torques to a greater degree of accuracy than generic models at the ankle (root‐mean‐squared error – 7.9% vs. 49.3% in elderly anatomy‐based models), knee (13.2% vs. 57.3%) and hip (21.9% vs. 32.8%). These results have important implications for the choice of musculoskeletal properties in future modelling studies, and the relatively high level of accuracy achieved in the subject‐specific models suggests that such models can potentially address questions about inter‐subject variations of muscle functions. However, despite relatively high levels of overall accuracy, models built using averaged generic muscle architecture data from young, healthy individuals may lack the resolution and accuracy required to study such differences between individuals, at least in certain circumstances. The results do not wholly discourage the continued use of averaged generic data in musculoskeletal modelling studies but do emphasise the need for to maximise the accuracy of input values if studying intra‐species form–function relationships in the musculoskeletal system.
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spelling pubmed-75422002020-10-16 Subject‐specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models Charles, James P. Grant, Barbara D’Août, Kristiaan Bates, Karl T. J Anat Original Papers Musculoskeletal modelling is an important platform on which to study the biomechanics of morphological structures in vertebrates and is widely used in clinical, zoological and palaeontological fields. The popularity of this approach stems from the potential to non‐invasively quantify biologically important but difficult‐to‐measure functional parameters. However, while it is known that model predictions are highly sensitive to input values, it is standard practice to build models by combining musculoskeletal data from different sources resulting in ‘generic’ models for a given species. At present, there are little quantitative data on how merging disparate anatomical data in models impacts the accuracy of these functional predictions. This issue is addressed herein by quantifying the accuracy of both subject‐specific human limb models containing individualised muscle force‐generating properties and models built using generic properties from both elderly and young individuals, relative to experimental muscle torques obtained from an isokinetic dynamometer. The results show that subject‐specific models predict isokinetic muscle torques to a greater degree of accuracy than generic models at the ankle (root‐mean‐squared error – 7.9% vs. 49.3% in elderly anatomy‐based models), knee (13.2% vs. 57.3%) and hip (21.9% vs. 32.8%). These results have important implications for the choice of musculoskeletal properties in future modelling studies, and the relatively high level of accuracy achieved in the subject‐specific models suggests that such models can potentially address questions about inter‐subject variations of muscle functions. However, despite relatively high levels of overall accuracy, models built using averaged generic muscle architecture data from young, healthy individuals may lack the resolution and accuracy required to study such differences between individuals, at least in certain circumstances. The results do not wholly discourage the continued use of averaged generic data in musculoskeletal modelling studies but do emphasise the need for to maximise the accuracy of input values if studying intra‐species form–function relationships in the musculoskeletal system. John Wiley and Sons Inc. 2020-06-29 2020-11 /pmc/articles/PMC7542200/ /pubmed/32598483 http://dx.doi.org/10.1111/joa.13261 Text en © 2020 The Authors. Journal of Anatomy published by John Wiley & Sons Ltd on behalf of Anatomical Society This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Original Papers
Charles, James P.
Grant, Barbara
D’Août, Kristiaan
Bates, Karl T.
Subject‐specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models
title Subject‐specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models
title_full Subject‐specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models
title_fullStr Subject‐specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models
title_full_unstemmed Subject‐specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models
title_short Subject‐specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models
title_sort subject‐specific muscle properties from diffusion tensor imaging significantly improve the accuracy of musculoskeletal models
topic Original Papers
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7542200/
https://www.ncbi.nlm.nih.gov/pubmed/32598483
http://dx.doi.org/10.1111/joa.13261
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