Human Brain Modeling with Its Anatomical Structure and Realistic Material Properties for Brain Injury Prediction
- 264 Downloads
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.
KeywordsTraumatic brain injury Diffuse axonal injury Constitutive model Anisotropy Viscoelasticity Brain FE model Accident reconstruction analyses
We would like to thank Dr. J. Shinoda and Dr. Y. Asano (Chubu Medical Center for Prolonged Traumatic Brain Dysfunction, Kizawa Memorial Hospital, Minokamo-city, Gifu, Japan) for sharing their medical knowledge with us. We also thank Editage (www.editage.jp) for English language editing.
Conflict of interest
The authors do not have any conflict of interest to declare.
- 1.Asano, Y., J. Shinoda, A. Okumura, T. Aki, S. Takenaka, K. Miwa, M. Yamada, T. Ito, and K. Yokoyama. Utility of fractional anisotropy imaging analyzed by statistical parametric mapping for detecting minute brain lesions in chronic-stage patients who had mild or moderate traumatic brain injury. Neurol. Med. Chir. (Tokyo) 52:31–40, 2012.CrossRefGoogle Scholar
- 3.Atsumi, N., Y. Nakahira, M. Iwamoto, S. Hirabayashi, and E. Tanaka. Constitutive modeling of brain parenchyma taking account of strain rate dependency with anisotropy and application to brain injury analyses. SAE Tech. Paper 2016-01-1485, 2016.Google Scholar
- 8.Gehre, C., H. Gades, and P. Wernicke. Objective rating of signals using test and simulation responses. Proceedings of the 21st ESV Conference 09-0407, 2009.Google Scholar
- 9.Gehre, C. and S. Stahlschmidt. Assessment of dummy models by using objective rating methods. Proceedings of the 22nd ESV Conference 11-0216, 2011.Google Scholar
- 13.Giordano, C., and S. Kleiven. Development of an unbiased validation protocol to assess the biofidelity of finite element head models used in prediction of traumatic brain injury. Stapp Car Crash J. 60:336–471, 2016.Google Scholar
- 16.Holzapfel, G. A. Nonlinear solid mechanics: a continuum approach for engineering. Chichester: Wiley, pp. 282–294, 2000.Google Scholar
- 20.Iwamoto, M., Y. Nakahira, and D. Kato. Finite element analysis for investigating the effects of muscle activation on head-neck injury risks of drivers rear-ended by a car after an autonomous emergency braking. Proceedings of the 4th FAST-zero Conference, 20174698, 2017.Google Scholar
- 21.Ji, S., H. Ghadyani, R. P. Bolander, J. G. Beckwith, J. C. Ford, T. W. McAllister, L. A. Flashman, K. D. Paulsen, K. Ernstrom, S. Jain, R. Raman, L. Zhang, and R. M. Greenwald. Parametric comparisons of intracranial mechanical responses from three validated finite element models of the human head. Ann. Biomed. Eng. 42(1):11–24, 2014.CrossRefPubMedPubMedCentralGoogle Scholar
- 24.Krafft, M., A. Kulgren, and C. Tingvall. Crash pulse recorders in rear impacts—real life data. Proceedings of the 16th ESV Conference 98-S6-O-10, 1998.Google Scholar
- 33.Sahoo. D., C. Deck, and R. Willinger. Axonal strain as brain injury predictor based on real-world head trauma simulations. Proceedings of the IRCOBI Conference IRC-15-30, 2015.Google Scholar
- 34.Schunke, M., E. Schulte, and U. Schumacher. Prometheus LernAtlas der Anatomie Head/Neuroanatomy (Japanese Edition). Tokyo: Igaku-Shoin, pp. 292–315, 2009.Google Scholar
- 35.Shinoda, J., and Y. Asano. Cognitive, emotional and behavioral impairments following traumatic brain injury and the neuro-radiological diagnosis. Neurol Surg 39(2):115–127, 2011; (in Japanese).Google Scholar
- 38.The International Organization for Standardization (ISO). Road vehicles—anthropomorphic side impact dummy—lateral impact response requirements to assess the biofidelity of the dummy. ISO/TR 9790, December 1999.Google Scholar
- 39.The International Organization for Standardization (ISO). Road vehicles—objective rating metrics for dynamic systems. ISO/TR 16250, July 2013.Google Scholar
- 40.TURBO SQUID. Human Brain Ultimate. https://www.turbosquid.com/3d-models/human-brain-3d-model/624395.
- 44.Zhou, C., T. B. Khalil, and A. I. King. A new model comparing impact responses of the homogeneous and inhomogeneous human brain. Stapp Car Crash J. 39:121–137, 1995.Google Scholar