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Human Brain Modeling with Its Anatomical Structure and Realistic Material Properties for Brain Injury Prediction

Overview of attention for article published in Annals of Biomedical Engineering, February 2018
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Title
Human Brain Modeling with Its Anatomical Structure and Realistic Material Properties for Brain Injury Prediction
Published in
Annals of Biomedical Engineering, February 2018
DOI 10.1007/s10439-018-1988-8
Pubmed ID
Authors

Noritoshi Atsumi, Yuko Nakahira, Eiichi Tanaka, Masami Iwamoto

Abstract

Impairments of executive brain function after traumatic brain injury (TBI) due to head impacts in traffic accidents need to be obviated. Finite element (FE) analyses with a human brain model facilitate understanding of the TBI mechanisms. However, conventional brain FE models do not suitably describe the anatomical structure in the deep brain, which is a critical region for executive brain function, and the material properties of brain parenchyma. In this study, for better TBI prediction, a novel brain FE model with anatomical structure in the deep brain was developed. The developed model comprises a constitutive model of brain parenchyma considering anisotropy and strain rate dependency. Validation was performed against postmortem human subject test data associated with brain deformation during head impact. Brain injury analyses were performed using head acceleration curves obtained from reconstruction analysis of rear-end collision with a human whole-body FE model. The difference in structure was found to affect the regions of strain concentration, while the difference in material model contributed to the peak strain value. The injury prediction result by the proposed model was consistent with the characteristics in the neuroimaging data of TBI patients due to traffic accidents.

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Geographical breakdown

Country Count As %
Unknown 62 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 13 21%
Student > Ph. D. Student 12 19%
Student > Master 9 15%
Other 3 5%
Professor > Associate Professor 3 5%
Other 7 11%
Unknown 15 24%
Readers by discipline Count As %
Engineering 30 48%
Agricultural and Biological Sciences 2 3%
Medicine and Dentistry 2 3%
Neuroscience 2 3%
Energy 1 2%
Other 1 2%
Unknown 24 39%