The perivascular environment along the vertebral artery governs segment-specific structural and mechanical properties

Abstract

The vertebral arteries (VAs) are anatomically divided into four segments (V1–V4), which cumulatively transport blood flow through neck and ultimately form the posterior circulation of the brain. The vital physiological function of these conduit vessels depends on their geometry, composition and mechanical properties, all of which may vary among the defined arterial segments. Despite their significant role in blood circulation and susceptibility to injury, few studies have focused on characterizing the mechanical properties of VAs, and none have investigated the potential for segmental variation that could arise due to distinct perivascular environments. In this study, we compare the passive mechanical response of the central, juxtaposed arterial segments of porcine VAs (V2 and V3) via inflation-extension mechanical testing. Obtained experimental data and histological measures of arterial wall composition were used to adjust parameters of structure-motivated constitutive models that quantify the passive mechanical properties of each arterial segment and enable prediction of wall stress distributions under physiologic loads and boundary conditions. Our findings reveal significant segmental differences in the arterial wall geometry and structure. Nevertheless, similar wall stress distributions are predicted in these neighboring arterial segments if calculations account for their specific perivascular environments. These findings allow speculation that segmental differences in wall structure and geometry are a consequence of a previously introduced principle of optimal operation of arteries, which ensures effective bearing of physiological load and a favorable mechanical environment for mechanosensitive vascular smooth muscle cells.

Comparison of five viscoelastic models for estimating viscoelastic parameters using ultrasound shear wave elastography

“…Storage and loss moduli were then used to predict shear wave speed. Given the predefined shear wave speeds at different frequencies, the material parameters for the five viscoelastic models were identified by using the Levenberg-Marquardt nonlinear, least-square algorithm for minimizing the objective function which reflects the agreement between predefined and predicted shear wave speeds at different excitation frequencies, which yields unique estimates of the material parameters (Suh and Bai, 1998; Zhou et al, 2016). Residual error is the output of the objective function, normalΩ=∑n=1Nfalse(cnT−cnEfalse)2, where the superscript E and T refer to the experimentally measured and theoretically calculated values of shear wave speed, and subscript n indicates a particular experimental state.…”

Determination of Viscoelastic Properties of human Carotid Atherosclerotic Plaque by Inverse Boundary Value Analysis

“…A viscoelastic anisotropic (VA) model was developed based on the HGO model [14, 15] by incorporating a viscoelasticity formulation [16]. The second Piola-Kirchhoff stress tensor of VA model S m+1 at time t m+1 is given by [17, 18]: Sm+1=(Svol∞+Sg∞++Sf∞+∑α=1nQα)m+1 where “∞” expresses the equilibrium condition when the time approaches infinity.…”

A comparative study of cohesive zone models for predicting delamination fracture behaviors of arterial wall

“…The parameter values for the HGO and CZM models were taken from references [4,10,22]. The value of the interfacial stiffness K was assigned as the same one from reference [14].…”

“…In the current study, the peeling path is obtained from the experimental observations. The parameter values for the HGO and CZM models were taken from references [7,8,18] , as shown in Table 1. The value of the interfacial stiffness K was assigned as the same one from reference [12] .…”