Visco-hyperelastic characterization of human brain white matter micro-level constituents in different strain rates.

In this study, we propose a computational characterization technique for obtaining the material properties of axons and extracellular matrix (ECM) in human brain white matter. To account for the dynamic behavior of the brain tissue, data from time-dependent relaxation tests of human brain white matter in different strain rates are extracted and formulated by a visco-hyperelastic constitutive model consisting of the Ogden hyperelastic model and the Prony series expansion. Through micromechanical finite element simulation, a derivative-free optimization framework designed to minimize the difference between the numerical and experimental data is used to identify the material properties of the axons and ECM. The Prony series expansion parameters of axons and ECM are found to be highly affected by the Prony series expansion coefficients of the brain white matter. The optimal parameters of axons and ECM are verified through micromechanical simulation by comparing the averaged numerical response with that of the experimental data. Moreover, the initial shear modulus and the reduced shear modulus of the axons are found for different strain rates of 0.0001, 0.01, and 1 s-1. Consequently, first- and second-order regressions are used to find relations for the prediction of the shear modulus at the intermediate strain rates. Graphical Abstract The applied procedure for characterization of brain white matter micro-level constituents. The macro-level experimental data in different strain rates are used in the context of simulation-based optimization to obtain the properties of axons and extracellular matrix material.

A poro-hyper-viscoelastic rate-dependent constitutive modeling for the analysis of brain tissues.

In this paper, the dynamic behavior of bovine brain tissue, measured from in-vitro unconfined compression tests, is examined and represented through a viscoelastic biphasic model. The experiments have been carried out under three compression speeds of 10, 100, and 1000 mm/s. The results exhibited significant rate-dependent behavior. The brain tissue is modeled as a biphasic continuum consisting of a compressible solid matrix, fully saturated with an incompressible interstitial fluid. The governing equations based on conservation of mass and momentum are used to describe the solid-fluid interactions. An inverse scheme is employed in which a finite element model runs iteratively to optimize constitutive constants. The obtained material parameters of the proposed biphasic model show relatively good agreement (R2 ≥ 0.96) with the experimental tissue mechanical responses at different rates. The model can successfully capture the key aspects of the rate-dependency for both solid and fluid phases under large strain deformation. This poro-hyper viscoelastic model can effectively estimate the global and local rate-dependent tissue deformations, the spatial variations in pore spaces, hydrostatic pressure as well as fluid diffusion through the tissue.

Rate-dependent constitutive modeling of brain tissue.

In this paper, the dynamic behavior of bovine brain tissue, measured from a set of in vitro experiments, is investigated and represented through a nonlinear viscoelastic constitutive model. The brain samples were tested by employing unconfined compression tests at three different deformation rates of 10, 100, and 1000 mm/s. The tissue exhibited a significant rate-dependent behavior with different compression speeds. Based on the parallel rheological framework approach, a nonlinear viscoelastic model that captures the key aspects of the rate dependency in large-strain behavior was introduced. The proposed model was numerically calibrated to the tissue test data from three different deformation rates. The determined material parameters provided an excellent constitutive representation of tissue response in comparison with the test results. The obtained material parameters were employed in finite element simulations of tissue under compression loadings and successfully verified by the experimental results, thus demonstrating the computational compatibility of the proposed material model. The results of this paper provide groundwork in developing a characterization framework for large-strain and rate-dependent behavior of brain tissue at moderate to high strain rates which is of the highest importance in biomechanical analysis of the traumatic brain injury.