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Micromechanics of brain white matter tissue: a fiber-reinforced hyperelastic model using embedded element technique
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Micromechanics of brain white matter tissue: a fiber-reinforced hyperelastic model using embedded element technique

Yousefsani, S. A

Micromechanics of brain white matter tissue: a fiber-reinforced hyperelastic model using embedded element technique

Yousefsani, S. A ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1016/j.jmbbm.2018.02.002
  3. Publisher: Elsevier Ltd , 2018
  4. Abstract:
  5. A transverse-plane hyperelastic micromechanical model of brain white matter tissue was developed using the embedded element technique (EET). The model consisted of a histology-informed probabilistic distribution of axonal fibers embedded within an extracellular matrix, both described using the generalized Ogden hyperelastic material model. A correcting method, based on the strain energy density function, was formulated to resolve the stiffness redundancy problem of the EET in large deformation regime. The model was then used to predict the homogenized tissue behavior and the associated localized responses of the axonal fibers under quasi-static, transverse, large deformations. Results indicated that with a sufficiently large representative volume element (RVE) and fine mesh, the statistically randomized microstructure implemented in the RVE exhibits directional independency in transverse plane, and the model predictions for the overall and local tissue responses, characterized by the normalized strain energy density and Cauchy and von Mises stresses, are independent from the modeling parameters. Comparison of the responses of the probabilistic model with that of a simple uniform RVE revealed that only the first one is capable of representing the localized behavior of the tissue constituents. The validity test of the model predictions for the corona radiata against experimental data from the literature indicated a very close agreement. In comparison with the conventional direct meshing method, the model provided almost the same results after correcting the stiffness redundancy, however, with much less computational cost and facilitated geometrical modeling, meshing, and boundary conditions imposing. It was concluded that the EET can be used effectively for detailed probabilistic micromechanical modeling of the white matter in order to provide more accurate predictions for the axonal responses, which are of great importance when simulating the brain trauma or tumor growth. © 2018 Elsevier Ltd
  6. Keywords:
  7. Embedded element technique ; Large deformation ; Stiffness redundancy ; Composite micromechanics ; Deformation ; Elasticity ; Forecasting ; Probability distributions ; Redundancy ; Stiffness ; Strain energy ; Axonal distribution ; Embedded element ; Hyperelastic material models ; Localized response ; Probabilistic micromechanical modeling ; Probabilistic RVE ; Representative volume element (RVE) ; Strain energy density functions ; Tissue
  8. Source: Journal of the Mechanical Behavior of Biomedical Materials ; Volume 80 , April , 2018 , Pages 194-202 ; 17516161 (ISSN)
  9. URL: https://www.sciencedirect.com/science/article/pii/S1751616118300742