Forward Euler Stability of the Bidomain Model of Cardiac Tissue

Mechano-electrical coupling as framework for understanding functional remodeling during LBBB and CRT

It is not understood why, after onset of left bundle-branch block (LBBB), acute worsening of cardiac function is followed by a further gradual deterioration of function, whereas most adverse cardiac events lead to compensatory adaptations. We investigated whether mechano-electrical coupling (MEC) can explain long-term remodeling with LBBB and cardiac resynchronization therapy (CRT). To this purpose, we used an integrative modeling approach relating local ventricular electrophysiology, calcium handling, and excitation-contraction coupling to global cardiovascular mechanics and hemodynamics. Each ventricular wall was composed of multiple mechanically and electrically coupled myocardial segments. MEC was incorporated by allowing adaptation of L-type Ca2+ current aiming at minimal dispersion of local external work, an approach that we previously applied to replicate T-wave memory in a synchronous heart after a period of asynchronous activation. LBBB instantaneously decreased left-ventricular stroke work and increased end-diastolic volume. During sustained LBBB, MEC reduced intraventricular dispersion of mechanical workload and repolarization. However, MEC-induced reduction in contractility in late-activated regions was larger than the contractility increase in early-activated regions, resulting in further decrease of stroke work and increase of end-diastolic volume. Upon the start of CRT, stroke work increased despite a wider dispersion of mechanical workload. During sustained CRT, MEC-induced reduction in dispersion of workload and repolarization coincided with a further reduction in end-diastolic volume. In conclusion, MEC may represent a useful framework for better understanding the long-term changes in cardiac electrophysiology and contraction following LBBB as well as CRT.

Mechano-electrical feedback explains T-wave morphology and optimizes cardiac pump function: insight from a multi-scale model

In the ECG, T- and R-wave are concordant during normal sinus rhythm (SR), but discordant after a period of ventricular pacing (VP). Experiments showed that the latter phenomenon, called T-wave memory, is mediated by a mechanical stimulus. By means of a mathematical model, we investigated the hypothesis that slow acting mechano-electrical feedback (MEF) explains T-wave memory. In our model, electromechanical behavior of the left ventricle (LV) was simulated using a series of mechanically and electrically coupled segments. Each segment comprised ionic membrane currents, calcium handling, and excitation-contraction coupling. MEF was incorporated by locally adjusting conductivity of L-type calcium current (g(CaL)) to local external work. In our set-up, g(CaL) could vary up to 25%, 50%, 100% or unlimited amount around its default value. Four consecutive simulations were performed: normal SR (with MEF), acute VP, sustained VP (with MEF), and acutely restored SR. MEF led to T-wave concordance in normal SR and to discordant T-waves acutely after restoring SR. Simulated ECGs with a maximum of 25-50% adaptation closely resembled those during T-wave memory experiments in vivo and also provided the best compromise between optimal systolic and diastolic function. In conclusion, these simulation results indicate that slow acting MEF in the LV can explain a) the relatively small differences in systolic shortening and mechanical work during SR, b) the small dispersion in repolarization time, c) the concordant T-wave during SR, and d) T-wave memory. The physiological distribution in electrophysiological properties, reflected by the concordant T-wave, may serve to optimize cardiac pump function.

In-depth analysis and evaluation of diffusive glioma models

Glioma is one of the most aggressive types of brain tumor. Several mathematical models have been developed during the past two decades, toward simulating the mechanisms that govern the development of glioma. The most common models use the diffusion-reaction equation (DRE) for simulating the spatiotemporal variation of tumor cell concentration. Nevertheless, despite the applications presented, there has been little work on studying the details of the mathematical solution and implementation of the 3-D diffusion model and presenting a qualitative analysis of the algorithmic results. This paper presents a complete mathematical framework on the solution of the DRE using different numerical schemes. This framework takes into account all characteristics of the latest models, such as brain tissue heterogeneity, anisotropic tumor cell migration, chemotherapy, and resection modeling. The different numerical schemes presented have been evaluated based upon the degree to which the DRE exact solution is approximated. Experiments have been conducted both on real datasets and a test case for which there is a known algebraic expression of the solution. Thus, it is possible to calculate the accuracy of the different models.

A complete mathematical study of a 3D model of heterogeneous and anisotropic glioma evolution

Glioma is the most aggressive type of brain cancer. Several mathematical models have been developed towards identifying the mechanism of tumor growth. The most successful models have used variations of the diffusion-reaction equation, with the recent ones taking into account brain tissue heterogeneity and anisotropy. However, to the best of our knowledge, there hasn't been any work studying in detail the mathematical solution and implementation of the 3D diffusion model, addressing related heterogeneity and anisotropy issues. To this end, this paper introduces a complete mathematical framework on how to derive the solution of the equation using different numerical approximation of finite differences. It indicates how different proliferation rate schemes can be incorporated in this solution and presents a comparative study of different numerical approaches.

Numerical solution of the bidomain equations

Knowledge of cardiac electrophysiology is efficiently formulated in terms of mathematical models. However, most of these models are very complex and thus defeat direct mathematical reasoning founded on classical and analytical considerations. This is particularly so for the celebrated bidomain model that was developed almost 40 years ago for the concurrent analysis of extra- and intracellular electrical activity. Numerical simulations based on this model represent an indispensable tool for studying electrophysiology. However, complex mathematical models, steep gradients in the solutions and complicated geometries lead to extremely challenging computational problems. The greatest achievement in scientific computing over the past 50 years has been to enable the solving of linear systems of algebraic equations that arise from discretizations of partial differential equations in an optimal manner, i.e. such that the central processing unit (CPU) effort increases linearly with the number of computational nodes. Over the past decade, such optimal methods have been introduced in the simulation of electrophysiology. This development, together with the development of affordable parallel computers, has enabled the solution of the bidomain model combined with accurate cellular models, on geometries resembling a human heart. However, in spite of recent progress, the full potential of modern computational methods has yet to be exploited for the solution of the bidomain model. This paper reviews the development of numerical methods for solving the bidomain model. However, the field is huge and we thus restrict our focus to developments that have been made since the year 2000.