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Anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subject variability

Computational models incorporating anisotropic features of brain tissue have become a valuable tool for studying the occurrence of traumatic brain injury. The tissue deformation in the direction of white matter tracts (axonal strain) was repeatedly shown to be an appropriate mechanical parameter to...

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Autores principales: Giordano, Chiara, Zappalà, Stefano, Kleiven, Svein
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5511602/
https://www.ncbi.nlm.nih.gov/pubmed/28233136
http://dx.doi.org/10.1007/s10237-017-0887-5
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author Giordano, Chiara
Zappalà, Stefano
Kleiven, Svein
author_facet Giordano, Chiara
Zappalà, Stefano
Kleiven, Svein
author_sort Giordano, Chiara
collection PubMed
description Computational models incorporating anisotropic features of brain tissue have become a valuable tool for studying the occurrence of traumatic brain injury. The tissue deformation in the direction of white matter tracts (axonal strain) was repeatedly shown to be an appropriate mechanical parameter to predict injury. However, when assessing the reliability of axonal strain to predict injury in a population, it is important to consider the predictor sensitivity to the biological inter-subject variability of the human brain. The present study investigated the axonal strain response of 485 white matter subject-specific anisotropic finite element models of the head subjected to the same loading conditions. It was observed that the biological variability affected the orientation of the preferential directions (coefficient of variation of 39.41% for the elevation angle—coefficient of variation of 29.31% for the azimuth angle) and the determination of the mechanical fiber alignment parameter in the model (gray matter volume 55.55–70.75%). The magnitude of the maximum axonal strain showed coefficients of variation of 11.91%. On the contrary, the localization of the maximum axonal strain was consistent: the peak of strain was typically located in a 2 cm(3) volume of the brain. For a sport concussive event, the predictor was capable of discerning between non-injurious and concussed populations in several areas of the brain. It was concluded that, despite its sensitivity to biological variability, axonal strain is an appropriate mechanical parameter to predict traumatic brain injury.
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spelling pubmed-55116022017-07-31 Anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subject variability Giordano, Chiara Zappalà, Stefano Kleiven, Svein Biomech Model Mechanobiol Original Paper Computational models incorporating anisotropic features of brain tissue have become a valuable tool for studying the occurrence of traumatic brain injury. The tissue deformation in the direction of white matter tracts (axonal strain) was repeatedly shown to be an appropriate mechanical parameter to predict injury. However, when assessing the reliability of axonal strain to predict injury in a population, it is important to consider the predictor sensitivity to the biological inter-subject variability of the human brain. The present study investigated the axonal strain response of 485 white matter subject-specific anisotropic finite element models of the head subjected to the same loading conditions. It was observed that the biological variability affected the orientation of the preferential directions (coefficient of variation of 39.41% for the elevation angle—coefficient of variation of 29.31% for the azimuth angle) and the determination of the mechanical fiber alignment parameter in the model (gray matter volume 55.55–70.75%). The magnitude of the maximum axonal strain showed coefficients of variation of 11.91%. On the contrary, the localization of the maximum axonal strain was consistent: the peak of strain was typically located in a 2 cm(3) volume of the brain. For a sport concussive event, the predictor was capable of discerning between non-injurious and concussed populations in several areas of the brain. It was concluded that, despite its sensitivity to biological variability, axonal strain is an appropriate mechanical parameter to predict traumatic brain injury. Springer Berlin Heidelberg 2017-02-23 2017 /pmc/articles/PMC5511602/ /pubmed/28233136 http://dx.doi.org/10.1007/s10237-017-0887-5 Text en © The Author(s) 2017 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.
spellingShingle Original Paper
Giordano, Chiara
Zappalà, Stefano
Kleiven, Svein
Anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subject variability
title Anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subject variability
title_full Anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subject variability
title_fullStr Anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subject variability
title_full_unstemmed Anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subject variability
title_short Anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subject variability
title_sort anisotropic finite element models for brain injury prediction: the sensitivity of axonal strain to white matter tract inter-subject variability
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5511602/
https://www.ncbi.nlm.nih.gov/pubmed/28233136
http://dx.doi.org/10.1007/s10237-017-0887-5
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