Modeling of human intervertebral disc annulus fibrosus with complex multi-fiber networks.

Collagen fibers within the annulus fibrosus (AF) lamellae are unidirectionally aligned with alternating orientations between adjacent layers. AF constitutive models often combine two adjacent lamellae into a single equivalent layer containing two fiber networks with a crisscross pattern. Additionally, AF models overlook the inter-lamellar matrix (ILM) as well as elastic fiber networks in between lamellae. We developed a nonhomogenous micromechanical model as well as two coarser homogenous hyperelastic and microplane models of the human AF, and compared their performances against measurements (tissue level uniaxial and biaxial tests as well as whole disc experiments) and seven published hyperelastic models. The micromechanical model had a realistic non-homogenous distribution of collagen fiber networks within each lamella and elastic fiber network in the ILM. For small matrix linear moduli (<0.2 MPa), the ILM showed substantial anisotropy (>10%) due to the elastic fiber network. However, at moduli >0.2 MPa, the effects of the elastic fiber network on differences in stress-strain responses at different directions disappeared (<10%). Variations in sample geometry and boundary conditions (due to uncertainty) markedly affected stress-strain responses of the tissue in uniaxial and biaxial tests (up to 16 times). In tissue level tests, therefore, simulations should represent testing conditions (e.g., boundary conditions, specimen geometry, preloads) as closely as possible. Stress/strain fields estimated from the single equivalent layer approach (conventional method) yielded different results from those predicted by the anatomically more accurate apparoach (i.e., layerwise). In addition, in a disc under a compressive force (symmetric loading), asymmetric stress-strain distributions were computed when using a layerwise simulation. Although all developed and selected published AF models predicted gross compression-displacement responses of the whole disc within the range of measured data, some showed excessively stiff or compliant responses under tissue-level uniaxial/biaxial tests. This study emphasizes, when constructing and validating constitutive models of AF, the importance of the proper simulation of individual lamellae as distinct layers, and testing parameters (sample geometric dimensions/loading/boundary conditions).

Homogenization of heterogeneous brain tissue under quasi-static loading: a visco-hyperelastic model of a 3D RVE.

Researches, in the recent years, reveal the utmost importance of brain tissue assessment regarding its mechanical properties, especially for automatic robotic tools, surgical robots and helmet producing. For this reason, experimental and computational investigation of the brain behavior under different conditions seems crucial. However, experiments do not normally show the distribution of stress and injury in microscopic scale, and due to various factors are costly. Development of micromechanical methods, which could predict the brain behavior more appropriately, could highly be helpful in reducing these costs. This study presents computational analysis of heterogeneous part of the brain tissue under quasi-static loading. Heterogeneity is created by irregular distribution of neurons in a representative volume element (RVE). Considering time-dependent behavior of the tissue, a visco-hyperelastic constitutive model is developed to predict the RVE behavior more realistically. The RVE is studied in different loads and load rates; 1, 2, 3, 10 and 15% strain load are applied at 0.03 and 0.2 s on the RVE as tensile and shear loads. Due to complexity in geometry, self-consistent approximation method is employed to increase the volume fraction of neurons and analyze RVE behavior in various NVFs. The results show increasing the load rate leads to a raise in the maximum stress that indicates the tissue is more vulnerable at higher rates. Moreover, stiffness of the tissue is enhanced in higher NVFs. Additionally, it is found that axons undergo higher stresses; hence, they are more sensitive in accidents which lead to axonal death and would cause TBI and DAI.