Annals of biomedical engineering
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Many human brain finite element (FE) models lack mesoscopic (~ 1 mm) white matter structures, which may limit their capability in predicting TBI and assessing tissue-based injury metrics such as axonal strain. This study investigated an embedded method to explicitly incorporate white matter axonal fibers into an existing 50th percentile male brain model. The white matter was decomposed into myelinated axon tracts and an isotropic ground substance that had similar material properties to gray matter. ⋯ Finally, the new axon-based model was extensively validated for brain-skull relative deformation under various loading conditions (n = 17) and showed good biofidelity compared to other brain models. Through these analyses, we demonstrated the applicability of this method for incorporating axonal fiber tracts into an existing FE brain model. The axon-based model will be a useful tool for understanding the mechanisms of TBI, evaluating tissue-based injury metrics, and developing injury mitigation systems.
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We examine the influence of shear anisotropy of brain tissue on the potential for mild traumatic brain injury. First we develop a new constitutive description for the white matter in the brain that can capture the anisotropic behavior of the white matter in both tension and shear. The material parameters for the models are determined using a set of three experiments already published in the literature. ⋯ This computational model is two-dimensional and is used to simulate a previously published injury-causing event in the National Hockey League, using axonal strain as criterion to assess the level of diffuse axonal injury. It is demonstrated that the inclusion of shear anisotropy affects both the nature and the extent of predicted injury. Further, the locations of the predicted injury are more consistent with observations in the literature.
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The purpose of this paper is to propose and develop a large strain embedded finite element formulation that can be used to explicitly model axonal fiber bundle tractography from diffusion tensor imaging of the brain. Once incorporated, the fibers offer the capability to monitor tract-level strains that give insight into the biomechanics of brain injury. We show that one commercial software has a volume and mass redundancy issue when including embedded axonal fiber and that a newly developed algorithm is able to correct this discrepancy. We provide a validation analysis for stress and energy to demonstrate the method.