From biomechanics to pathology: predicting axonal injury from patterns of strain after traumatic brain injury.


Journal

Brain : a journal of neurology
ISSN: 1460-2156
Titre abrégé: Brain
Pays: England
ID NLM: 0372537

Informations de publication

Date de publication:
12 02 2021
Historique:
received: 13 01 2020
revised: 01 09 2020
accepted: 14 09 2020
pubmed: 18 1 2021
medline: 21 4 2021
entrez: 17 1 2021
Statut: ppublish

Résumé

The relationship between biomechanical forces and neuropathology is key to understanding traumatic brain injury. White matter tracts are damaged by high shear forces during impact, resulting in axonal injury, a key determinant of long-term clinical outcomes. However, the relationship between biomechanical forces and patterns of white matter injuries, associated with persistent diffusion MRI abnormalities, is poorly understood. This limits the ability to predict the severity of head injuries and the design of appropriate protection. Our previously developed human finite element model of head injury predicted the location of post-traumatic neurodegeneration. A similar rat model now allows us to experimentally test whether strain patterns calculated by the model predicts in vivo MRI and histology changes. Using a controlled cortical impact, mild and moderate injuries (1 and 2 mm) were performed. Focal and axonal injuries were quantified with volumetric and diffusion 9.4 T MRI at 2 weeks post injury. Detailed analysis of the corpus callosum was conducted using multi-shell diffusion MRI and histopathology. Microglia and astrocyte density, including process parameters, along with white matter structural integrity and neurofilament expression were determined by quantitative immunohistochemistry. Linear mixed effects regression analyses for strain and strain rate with the employed outcome measures were used to ascertain how well immediate biomechanics could explain MRI and histology changes. The spatial pattern of mechanical strain and strain rate in the injured cortex shows good agreement with the probability maps of focal lesions derived from volumetric MRI. Diffusion metrics showed abnormalities in the corpus callosum, indicating white matter changes in the segments subjected to high strain, as predicted by the model. The same segments also exhibited a severity-dependent increase in glia cell density, white matter thinning and reduced neurofilament expression. Linear mixed effects regression analyses showed that mechanical strain and strain rate were significant predictors of in vivo MRI and histology changes. Specifically, strain and strain rate respectively explained 33% and 28% of the reduction in fractional anisotropy, 51% and 29% of the change in neurofilament expression and 51% and 30% of microglia density changes. The work provides evidence that strain and strain rate in the first milliseconds after injury are important factors in determining patterns of glial and axonal injury and serve as experimental validators of our computational model of traumatic brain injury. Our results provide support for the use of this model in understanding the relationship of biomechanics and neuropathology and can guide the development of head protection systems, such as airbags and helmets.

Identifiants

pubmed: 33454735
pii: 6102820
doi: 10.1093/brain/awaa336
pmc: PMC7990483
doi:

Types de publication

Journal Article Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

70-91

Subventions

Organisme : Department of Health
ID : II-LB-0715-20006
Pays : United Kingdom
Organisme : Department of Health
ID : NIHR-RP-011-048
Pays : United Kingdom
Organisme : Wellcome Trust
Pays : United Kingdom

Informations de copyright

© The Author(s) (2021). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For permissions, please email: journals.permissions@oup.com.

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Auteurs

Cornelius K Donat (CK)

Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.
Royal British Legion Centre for Blast Injury Studies, Imperial College London, London, UK.

Maria Yanez Lopez (M)

Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.
Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.

Magdalena Sastre (M)

Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.

Nicoleta Baxan (N)

Biological Imaging Centre, Central Biomedical Services, Imperial College London, London, UK.

Marc Goldfinger (M)

Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.

Reneira Seeamber (R)

Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.

Franziska Müller (F)

Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.

Polly Davies (P)

Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.

Peter Hellyer (P)

Centre for Neuroimaging Sciences, King's College London, London, UK.

Petros Siegkas (P)

Design Engineering, Imperial College London, UK.

Steve Gentleman (S)

Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.

David J Sharp (DJ)

Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, UK.
Royal British Legion Centre for Blast Injury Studies, Imperial College London, London, UK.
UK Dementia Research Institute, Care Research and Technology Centre; Imperial College London, London, UK.

Mazdak Ghajari (M)

Royal British Legion Centre for Blast Injury Studies, Imperial College London, London, UK.
Design Engineering, Imperial College London, UK.

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Classifications MeSH