Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/24366
Title: | From biomechanics to pathology: predicting axonal injury from patterns of strain after traumatic brain injury | Authors: | Donat, Cornelius K. Lopez, Maria Yanez Sastre, Magdalena Baxan, Nicoleta Goldfinger, Marc Seeamber, Reneira Muller, Franziska Davies, Polly Hellyer, Peter Siegkas, Petros Gentleman, Steve Sharp, David J. Ghajari, Mazdak |
Major Field of Science: | Natural Sciences | Field Category: | Medical Engineering | Keywords: | traumatic brain injury;diffusion tensor imaging;finite element modelling;quantitative histology | Issue Date: | 2021 | Source: | Brain, 2021, vol. 144, iss. 1 | Volume: | 144 | Issue: | 1 | Journal: | Brain | Abstract: | 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.</jats:p> | URI: | https://hdl.handle.net/20.500.14279/24366 | ISSN: | 14602156 | DOI: | 10.1093/brain/awaa336 | Rights: | The Author(s) | Type: | Article | Affiliation : | Imperial College London King's College London |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
awaa336.pdf | Open Access | 2.31 MB | Adobe PDF | View/Open |
CORE Recommender
SCOPUSTM
Citations
36
checked on Feb 1, 2024
WEB OF SCIENCETM
Citations
31
Last Week
0
0
Last month
3
3
checked on Oct 29, 2023
Page view(s)
228
Last Week
0
0
Last month
4
4
checked on Dec 3, 2024
Download(s)
168
checked on Dec 3, 2024
Google ScholarTM
Check
Altmetric
Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.