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      Deriving the Bidomain Model of Cardiac Electrophysiology From a Cell-Based Model; Properties and Comparisons

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          Abstract

          The bidomain model is considered to be the gold standard for numerical simulation of the electrophysiology of cardiac tissue. The model provides important insights into the conduction properties of the electrochemical wave traversing the cardiac muscle in every heartbeat. However, in normal resolution, the model represents the average over a large number of cardiomyocytes, and more accurate models based on representations of all individual cells have therefore been introduced in order to gain insight into the conduction properties close to the myocytes. The more accurate model considered here is referred to as the EMI model since both the extracellular space (E), the cell membrane (M) and the intracellular space (I) are explicitly represented in the model. Here, we show that the bidomain model can be derived from the cell-based EMI model and we thus reveal the close relation between the two models, and obtain an indication of the error introduced in the approximation. Also, we present numerical simulations comparing the results of the two models and thereby highlight both similarities and differences between the models. We observe that the deviations between the solutions of the models become larger for larger cell sizes. Furthermore, we observe that the bidomain model provides solutions that are very similar to the EMI model when conductive properties of the tissue are in the normal range, but large deviations are present when the resistance between cardiomyocytes is increased.

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          Verification of cardiac tissue electrophysiology simulators using an N-version benchmark.

          Ongoing developments in cardiac modelling have resulted, in particular, in the development of advanced and increasingly complex computational frameworks for simulating cardiac tissue electrophysiology. The goal of these simulations is often to represent the detailed physiology and pathologies of the heart using codes that exploit the computational potential of high-performance computing architectures. These developments have rapidly progressed the simulation capacity of cardiac virtual physiological human style models; however, they have also made it increasingly challenging to verify that a given code provides a faithful representation of the purported governing equations and corresponding solution techniques. This study provides the first cardiac tissue electrophysiology simulation benchmark to allow these codes to be verified. The benchmark was successfully evaluated on 11 simulation platforms to generate a consensus gold-standard converged solution. The benchmark definition in combination with the gold-standard solution can now be used to verify new simulation codes and numerical methods in the future.
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            Mechanisms of cardiac conduction: a history of revisions.

            Cardiac conduction is the process by which electrical excitation spreads through the heart, triggering individual myocytes to contract in synchrony. Defects in conduction disrupt synchronous activation and are associated with life-threatening arrhythmias in many pathologies. Therefore, it is scarcely surprising that this phenomenon continues to be the subject of active scientific inquiry. Here we provide a brief review of how the conceptual understanding of conduction has evolved over the last century and highlight recent, potentially paradigm-shifting developments.
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              Solvers for the cardiac bidomain equations.

              The bidomain equations are widely used for the simulation of electrical activity in cardiac tissue. They are especially important for accurately modeling extracellular stimulation, as evidenced by their prediction of virtual electrode polarization before experimental verification. However, solution of the equations is computationally expensive due to the fine spatial and temporal discretization needed. This limits the size and duration of the problem which can be modeled. Regardless of the specific form into which they are cast, the computational bottleneck becomes the repeated solution of a large, linear system. The purpose of this review is to give an overview of the equations and the methods by which they have been solved. Of particular note are recent developments in multigrid methods, which have proven to be the most efficient.
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                Author and article information

                Contributors
                Journal
                Front Physiol
                Front Physiol
                Front. Physiol.
                Frontiers in Physiology
                Frontiers Media S.A.
                1664-042X
                07 January 2022
                2021
                : 12
                : 811029
                Affiliations
                [1] 1Simula Research Laboratory , Oslo, Norway
                [2] 2Department of Informatics, University of Oslo , Oslo, Norway
                Author notes

                Edited by: André H. Erhardt, Weierstrass Institute for Applied Analysis and Stochastics (LG), Germany

                Reviewed by: Seth H. Weinberg, The Ohio State University, United States; Bradley John Roth, Oakland University, United States; Nagaiah Chamakuri, Indian Institute of Science Education and Research, India

                *Correspondence: Karoline Horgmo Jæger karolihj@ 123456simula.no

                This article was submitted to Biophysics, a section of the journal Frontiers in Physiology

                Article
                10.3389/fphys.2021.811029
                8782150
                35069265
                ab3b68a4-0d5f-449c-b8b9-67d9e1bee57d
                Copyright © 2022 Jæger and Tveito.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 08 November 2021
                : 13 December 2021
                Page count
                Figures: 6, Tables: 1, Equations: 74, References: 50, Pages: 13, Words: 7647
                Funding
                Funded by: Norges Forskningsråd, doi 10.13039/501100005416;
                Categories
                Physiology
                Original Research

                Anatomy & Physiology
                bidomain model,emi model,cell-based model,cardiac electrophysiology,cardiac conduction,cardiac tissue models,numerical simulation

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