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Properties of cardiac conduction in a cell-based computational model.

Authors :
Jæger, Karoline Horgmo
Edwards, Andrew G.
McCulloch, Andrew
Tveito, Aslak
Source :
PLoS Computational Biology. 5/31/2019, Vol. 15 Issue 5, p1-35. 35p. 2 Color Photographs, 3 Diagrams, 1 Chart, 12 Graphs.
Publication Year :
2019

Abstract

The conduction of electrical signals through cardiac tissue is essential for maintaining the function of the heart, and conduction abnormalities are known to potentially lead to life-threatening arrhythmias. The properties of cardiac conduction have therefore been the topic of intense study for decades, but a number of questions related to the mechanisms of conduction still remain unresolved. In this paper, we demonstrate how the so-called EMI model may be used to study some of these open questions. In the EMI model, the extracellular space, the cell membrane, the intracellular space and the cell connections are all represented as separate parts of the computational domain, and the model therefore allows for study of local properties that are hard to represent in the classical homogenized bidomain or monodomain models commonly used to study cardiac conduction. We conclude that a non-uniform sodium channel distribution increases the conduction velocity and decreases the time delays over gap junctions of reduced coupling in the EMI model simulations. We also present a theoretical optimal cell length with respect to conduction velocity and consider the possibility of ephaptic coupling (i.e. cell-to-cell coupling through the extracellular potential) acting as an alternative or supporting mechanism to gap junction coupling. We conclude that for a non-uniform distribution of sodium channels and a sufficiently small intercellular distance, ephaptic coupling can influence the dynamics of the sodium channels and potentially provide cell-to-cell coupling when the gap junction connection is absent. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
1553734X
Volume :
15
Issue :
5
Database :
Academic Search Index
Journal :
PLoS Computational Biology
Publication Type :
Academic Journal
Accession number :
136757485
Full Text :
https://doi.org/10.1371/journal.pcbi.1007042