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

Ann Biomed Eng

Human Science Research-Domain, Toyota Central R&D Labs., Inc., 41-1, Yokomichi, Nagakute-city, Aichi, 480-1192, Japan.

Published: May 2018

AI Article Synopsis

  • Traumatic brain injuries (TBI) from traffic accidents can impair executive brain function, highlighting the need for better predictive models of these injuries.
  • Traditional finite element (FE) brain models fail to accurately depict the deep brain's anatomy and the brain's material properties, which are vital for understanding TBIs.
  • This study introduces a novel FE model that incorporates deep brain structure and advanced material properties, which has been validated through human test data and shows promising predictive capabilities aligned with neuroimaging findings from TBI patients.

Article 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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Source
http://dx.doi.org/10.1007/s10439-018-1988-8DOI Listing

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