scholarly journals From Millimeters to Micrometers; Re-introducing Myocytes in Models of Cardiac Electrophysiology

2021 ◽  
Vol 12 ◽  
Author(s):  
Karoline Horgmo Jæger ◽  
Andrew G. Edwards ◽  
Wayne R. Giles ◽  
Aslak Tveito

Computational modeling has contributed significantly to present understanding of cardiac electrophysiology including cardiac conduction, excitation-contraction coupling, and the effects and side-effects of drugs. However, the accuracy of in silico analysis of electrochemical wave dynamics in cardiac tissue is limited by the homogenization procedure (spatial averaging) intrinsic to standard continuum models of conduction. Averaged models cannot resolve the intricate dynamics in the vicinity of individual cardiomyocytes simply because the myocytes are not present in these models. Here we demonstrate how recently developed mathematical models based on representing every myocyte can significantly increase the accuracy, and thus the utility of modeling electrophysiological function and dysfunction in collections of coupled cardiomyocytes. The present gold standard of numerical simulation for cardiac electrophysiology is based on the bidomain model. In the bidomain model, the extracellular (E) space, the cell membrane (M) and the intracellular (I) space are all assumed to be present everywhere in the tissue. Consequently, it is impossible to study biophysical processes taking place close to individual myocytes. The bidomain model represents the tissue by averaging over several hundred myocytes and this inherently limits the accuracy of the model. In our alternative approach both E, M, and I are represented in the model which is therefore referred to as the EMI model. The EMI model approach allows for detailed analysis of the biophysical processes going on in functionally important spaces very close to individual myocytes, although at the cost of significantly increased CPU-requirements.

2022 ◽  
Vol 12 ◽  
Author(s):  
Karoline Horgmo Jæger ◽  
Aslak Tveito

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.


2015 ◽  
Vol 1 (1) ◽  
pp. 413-417
Author(s):  
Eike M. Wülfers ◽  
Zhasur Zhamoliddinov ◽  
Olaf Dössel ◽  
Gunnar Seemann

AbstractUsing OpenCL, we developed a cross-platform software to compute electrical excitation conduction in cardiac tissue. OpenCL allowed the software to run parallelized and on different computing devices (e.g., CPUs and GPUs). We used the macroscopic mono-domain model for excitation conduction and an atrial myocyte model by Courtemanche et al. for ionic currents. On a CPU with 12 HyperThreading-enabled Intel Xeon 2.7 GHz cores, we achieved a speed-up of simulations by a factor of 1.6 against existing software that uses OpenMPI. On two high-end AMD FirePro D700 GPUs the OpenCL software ran 2.4 times faster than the OpenMPI implementation. The more nodes the discretized simulation domain contained, the higher speed-ups were achieved.


2018 ◽  
Vol 98 (5) ◽  
Author(s):  
Kharananda Sharma ◽  
Bradley J. Roth

2001 ◽  
Vol 280 (5) ◽  
pp. H2006-H2010 ◽  
Author(s):  
David J. Christini ◽  
Jeff Walden ◽  
Jay M. Edelberg

Dynamic regulation of biological systems requires real-time assessment of relevant physiological needs. Biosensors, which transduce biological actions or reactions into signals amenable to processing, are well suited for such monitoring. Typically, in vivo biosensors approximate physiological function via the measurement of surrogate signals. The alternative approach presented here would be to use biologically based biosensors for the direct measurement of physiological activity via functional integration of relevant governing inputs. We show that an implanted excitable-tissue biosensor (excitable cardiac tissue) can be used as a real-time, integrated bioprocessor to analyze the complex inputs regulating a dynamic physiological variable (heart rate). This approach offers the potential for long-term biologically tuned quantification of endogenous physiological function.


2020 ◽  
Vol 9 (4) ◽  
pp. 202-210
Author(s):  
Irum Kotadia ◽  
John Whitaker ◽  
Caroline Roney ◽  
Steven Niederer ◽  
Mark O’Neill ◽  
...  

Anisotropy is the property of directional dependence. In cardiac tissue, conduction velocity is anisotropic and its orientation is determined by myocyte direction. Cell shape and size, excitability, myocardial fibrosis, gap junction distribution and function are all considered to contribute to anisotropic conduction. In disease states, anisotropic conduction may be enhanced, and is implicated, in the genesis of pathological arrhythmias. The principal mechanism responsible for enhanced anisotropy in disease remains uncertain. Possible contributors include changes in cellular excitability, changes in gap junction distribution or function and cellular uncoupling through interstitial fibrosis. It has recently been demonstrated that myocyte orientation may be identified using diffusion tensor magnetic resonance imaging in explanted hearts, and multisite pacing protocols have been proposed to estimate myocyte orientation and anisotropic conduction in vivo. These tools have the potential to contribute to the understanding of the role of myocyte disarray and anisotropic conduction in arrhythmic states.


2019 ◽  
Author(s):  
Daniel E. Hurtado ◽  
Javiera Jilberto ◽  
Grigory Panasenko

AbstractGap junctions are key mediators of the intercellular communication in cardiac tissue, and their function is vital to sustain normal cardiac electrical activity. Conduction through gap junctions strongly depends on the hemichannel arrangement and transjunctional voltage, rendering the intercellular conductance highly non-Ohmic. Despite this marked non-linear behavior, current tissue-level models of cardiac conduction are rooted on the assumption that gap-junctions conductance is constant (Ohmic), which results in inaccurate predictions of electrical propagation, particularly in the low junctional-coupling regime observed under pathological conditions. In this work, we present a novel non-Ohmic multiscale (NOM) model of cardiac conduction that is suitable for tissue-level simulations. Using non-linear homogenization theory, we develop a conductivity model that seamlessly upscales the voltage-dependent conductance of gap junctions, without the need of explicitly modeling gap junctions. The NOM model allows for the simulation of electrical propagation in tissue-level cardiac domains that accurately resemble that of cell-based microscopic models for a wide range of junctional coupling scenarios, recovering key conduction features at a fraction of the computational complexity. A unique feature of the NOM model is the possibility of upscaling the response of non-symmetric gap-junction conductance distributions, which result in conduction velocities that strongly depend on the direction of propagation, thus allowing to model the normal and retrograde conduction observed in certain regions of the heart. We envision that the NOM model will enable organ-level simulations that are informed by sub- and inter-cellular mechanisms, delivering an accurate and predictive in-silico tool for understanding the heart function.Author summaryThe heart relies on the propagation of electrical impulses that are mediated gap junctions, whose conduction properties vary depending on the transjunctional voltage. Despite this non-linear feature, current mathematical models assume that cardiac tissue behaves like an Ohmic (linear) material, thus delivering inaccurate results when simulated in a computer. Here we present a novel mathematical multiscale model that explicitly includes the non-Ohmic response of gap junctions in its predictions. Our results show that the proposed model recovers important conduction features modulated by gap junctions at a fraction of the computational complexity. This contribution represents an important step towards constructing computer models of a whole heart that can predict organ-level behavior in reasonable computing times.


2021 ◽  
Vol 4 (4) ◽  
pp. 377-385
Author(s):  
Volodymyr M. Lucenko ◽  
Dmytro O. Progonov

Reliable protection of confidential data processed in critical information infrastructure elements of public institutions and private organizations is topical task today. Of particular interest are methods to prevent the leakage of confidential data by localizing informative (dangerous) signals that both carry an informative component, and have a signal level higher than predefined threshold. The increase in signal energy from personal computers is caused by increasing of its transistors switching speed. Modern passive shielding methods for secured computers, similar to the well-known program TEMPEST, require either costly and large shielding units or technological simplification by using of low-cost fragmentary shielding of computer’s individual elements. Therefore, localization of side electromagnetic radiation produced by personal computer is needed. The paper presents a cost-effective approach to reducing the level of computer’s electromagnetic radiation by passive method. The radiation are localized and measured by its estimation on personal computer’s elements, namely unshielded communication lines between video processor and a monitor, fragments of electric tracks on motherboards, etc. During experiments authors used ad-hoc miniature electric (ball antenna) and magnetic (Hall sensor) antennas connected to selective voltmeters. This approach significantly reduces the cost of equipment and measurements as well as requirements to analytics’ qualification for improving computer’s protection. Also, the alternative approach for computer protection is proposed. The approach is based on image content protection by distorting the image on the monitor instead of reducing electromagnetic radiation caused by signals from the monitor. The protection includes image scrambling using Arnold transform that randomly “shuffle” the lines in each frame.


2018 ◽  
Vol 28 (05) ◽  
pp. 979-1035 ◽  
Author(s):  
Annabelle Collin ◽  
Sébastien Imperiale

The aim of this paper is to provide a complete mathematical analysis of the periodic homogenization procedure that leads to the macroscopic bidomain model in cardiac electrophysiology. We consider space-dependent and tensorial electric conductivities as well as space-dependent physiological and phenomenological nonlinear ionic models. We provide the nondimensionalization of the bidomain equations and derive uniform estimates of the solutions. The homogenization procedure is done using 2-scale convergence theory which enables us to study the behavior of the nonlinear ionic models in the homogenization process.


Sign in / Sign up

Export Citation Format

Share Document