A tissue-level anisotropic criterion for brain injury based on microstructural axonal deformation

• Published in: Journal of the mechanical behavior of biomedical materials Vol. 5; no. 1; pp. 41 - 52
• Main Authors: Cloots, R.J.H., van Dommelen, J.A.W., Geers, M.G.D.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 2012
• Subjects: Anisotropy; Axons; Axons - pathology; Biomechanical Phenomena; Brain; Brain injury criterion; Cellular; Computer simulation; Criteria; Diffuse axonal injury (DAI); Diffuse Axonal Injury - pathology; Finite element method; Injuries; Mathematical models; Microstructure; Models, Biological; Neurons; Stress, Mechanical; Traumatic brain injury (TBI); Diffuse axonal injury (DAI); Finite element method; Axons; Traumatic brain injury (TBI); Brain injury criterion; Neurons
• ISSN: 1751-6161; 1878-0180
• DOI: 10.1016/j.jmbbm.2011.09.012

Different length scales from micrometers to several decimeters play an important role in diffuse axonal injury. The kinematics at the head level result in local impairments at the cellular level. Finite element methods can be used for predicting brain injury caused by a mechanical loading of the head. Because of its oriented microstructure, the sensitivity of brain tissue to a mechanical load can be expected to be orientation dependent. However, the criteria for injury that are currently used at the tissue level in finite element head models are isotropic and therefore do not consider this orientation dependence, which might inhibit a reliable assessment of injury. In this study, an anisotropic brain injury criterion is developed that is able to describe the effects of the oriented microstructure based on micromechanical simulations. The effects of both the main axonal direction and of local deviations from this direction are accounted for. With the anisotropic criterion for brain injury, computational head models will be able to account for aspects of diffuse axonal injury at the cellular level and can therefore more reliably predict injury.