Simulation Methods and Validation Criteria for Modeling Cardiac Ventricular Electrophysiology

Krishnamoorthi, S.; Perotti, Luigi E.; Borgstrom, N P; Ajijola, Olujimi A.; Frid, Anna Alexandra; Ponnaluri, Aditya V. S.; Weiss, James N.; Qu, Zhilin; Klug, William S.; Ennis, Daniel B.; Garfinkel, Alan

doi:10.1371/journal.pone.0114494

Cited by 51 publications

(69 citation statements)

References 48 publications

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“…Its dysfunction is associated with diseases such as arrhythmias and with sudden cardiac death (Mann et al, 2015). Yet, because of the inherent difficulty of imaging Purkinje networks in vivo, their inclusion in electrophysiological models remains challenging (Krishnamoorthi et al, 2014). Here we proposed an algorithm to incorporate the Purkinje network in the left and right ventricles of a human heart model (Baillargeon et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

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Generating Purkinje networks in the human heart

Costabal

Hurtado

Kuhl

2016

Journal of Biomechanics

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The Purkinje network is an integral part of the excitation system in the human heart. Yet, to date, there is no in vivo imaging technique to accurately reconstruct its geometry and structure. Computational modeling of the Purkinje network is increasingly recognized as an alternative strategy to visualize, simulate, and understand the role of the Purkinje system. However, most computational models either have to be generated manually, or fail to smoothly cover the irregular surfaces inside the left and right ventricles. Here we present a new algorithm to reliably create robust Purkinje networks within the human heart. We made the source code of this algorithm freely available online. Using Monte Carlo simulations, we demonstrate that the fractal tree algorithm with our new projection method generates denser and more compact Purkinje networks than previous approaches on irregular surfaces. Under similar conditions, our algorithm generates a network with 1219 ± 61 branches, three times more than a conventional algorithm with 419 ± 107 branches. With a coverage of 11 ± 3 mm, the surface density of our new Purkije network is twice as dense as the conventional network with 22 ± 7 mm. To demonstrate the importance of a dense Purkinje network in cardiac electrophysiology, we simulated three cases of excitation: with our new Purkinje network, with left-sided Purkinje network, and without Purkinje network. Simulations with our new Purkinje network predicted more realistic activation sequences and activation times than simulations without. Six-lead electrocardiograms of the three case studies agreed with the clinical electrocardiograms under physiological conditions, under pathological conditions of right bundle branch block, and under pathological conditions of trifascicular block. Taken together, our results underpin the importance of the Purkinje network in realistic human heart simulations. Human heart modeling has the potential to support the design of personalized strategies for single- or bi-ventricular pacing, radiofrequency ablation, and cardiac defibrillation with the common goal to restore a normal heart rhythm.

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Section: Discussionmentioning

confidence: 99%

“…The inherent complexity of the Purkinje network makes its manual generation complicated and time consuming (Krishnamoorthi et al, 2014). Recently, algorithms based on fractal trees (Lindenmayer, 1968) have emerged has a tool for automatic network generation.…”

Section: Introductionmentioning

confidence: 99%

Generating Purkinje networks in the human heart

Costabal

Hurtado

Kuhl

2016

Journal of Biomechanics

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“…In order to simulate the electromechanical coupling, we first solve the monodomain equation of electrophysiology [30] using the Mahajan et al [25] cell model. To begin the activation sequence, we apply a stimulus current to 145 nodes at the apex of the ventricle and then measure the time required to reach 0 mV in every element in the model (Fig.…”

Section: Numerical Examplesmentioning

confidence: 99%

A viscoactive constitutive modeling framework with variational updates for the myocardium

Ponnaluri

Perotti

Ennis

et al. 2017

Computer Methods in Applied Mechanics and Engineering

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We present a constitutive modeling framework for contractile cardiac mechanics by formulating a single variational principle from which incremental stress–strain relations and kinetic rate equations for active contraction and relaxation can all be derived. The variational framework seamlessly incorporates the hyperelastic behavior of the relaxed and contracted tissue along with the rate – and length – dependent generation of contractile force. We describe a three-element, Hill-type model that unifies the active tension and active deformation approaches. As in the latter approach, we multiplicatively decompose the total deformation gradient into active and elastic parts, with the active deformation parametrizing the contractile Hill element. We adopt as internal variables the fiber, cross-fiber, and sheet normal stretch ratios. The kinetics of these internal variables are modeled via definition of a kinetic potential function derived from experimental force–velocity relations. Additionally, we account for dissipation during tissue deformation by adding a Newtonian viscous potential. To model the force activation, the kinetic equations are coupled with the calcium transient obtained from a cardiomyocyte electrophysiology model. We first analyze our model at the material point level using stress and strain versus time curves for different viscosity values. Subsequently, we couple our constitutive framework with the finite element method (FEM) and study the deformation of three-dimensional tissue slabs with varying cardiac myocyte orientation. Finally, we simulate the contraction and relaxation of an ellipsoidal left ventricular model and record common kinematic measures, such as ejection fraction, and myocardial tissue volume changes.

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“…Cardiac models have been created to simulate every scale of function from the whole heart down to protein (drug molecular target) function [27][28][29] . The heart is intrinsically regulated across scales with tissue scale electrical properties, including anisotropic conductivity and wave curvature altering cellular scale electrophysiology and tissue properties, with shape and boundary conditions of the heart all featuring.…”

Section: A C C E P T E D Accepted Manuscriptmentioning

confidence: 99%

The opportunities and challenges for biophysical modelling of beneficial and adverse drug actions on the heart

Niederer

Oliveira

Curtis

2017

Current Opinion in Systems Biology

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Highlights-Biophysical models have reached a level of sophistication whereby they are able to simulate outcomes using the principal proteins that determine acute electrophysiological drug responses -Multi-scale simulations allow the effects, characterised at the protein scale, of drugs to be interpreted in the context of the whole heart. -Despite their inbuilt assumptions and inherent limitations biophysical models provide a rational framework for integrating disparate pharmacological measurements. AbstractPharmacology is characterised by linking compound molecular properties to cellular and organ scale therapeutic and toxic outcomes. Biophysical modelling allow data from these disparate sources to be integrated and interpreted based on known physiology and physical constraints of the biological systems of interest. Here we describe the recent use of biophysical models to simulate therapeutic and adverse drug effects on the heart and how this provides a new framework for data integration and identifying drug mechanisms.

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Simulation Methods and Validation Criteria for Modeling Cardiac Ventricular Electrophysiology

Cited by 51 publications

References 48 publications

Generating Purkinje networks in the human heart

Generating Purkinje networks in the human heart

A viscoactive constitutive modeling framework with variational updates for the myocardium

The opportunities and challenges for biophysical modelling of beneficial and adverse drug actions on the heart

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