Modeling the different sections of the cardiac conduction system to obtain realistic electrocardiograms

Fibroblast Density is a Risk Factor for Drug-induced Arrhythmias

A recent study by Kawatou et al . has shown that the local heterogeneity of ion channel conductance is a critical substrate for focal or reentrant arrhythmias. However, the role of fibroblasts with repolarization heterogeneity in the initiation and maintenance of arrhythmias remains unknown. In this study, we investigated how diffuse fibrosis contributes to the formation of focal and reentrant arrhythmias under drug-induced heterogeneity using physiologically detailed mathematical models of the human heart. To simulate drug-induced heterogeneity, we varied the maximum conductance of transmembrane potassium and calcium currents, leading to heterogeneity in action potential duration (APD). Then, we assessed the effects of different fibrosis densities (FD) on the occurrence of premature ventricular complexes (PVCs). Fibroblasts were randomly and evenly inserted into the tissue, and various FD levels ranging from 0 to 35% were examined. We found a biphasic relationship between FD and drug-induced PVCs. Within a certain range of FD, FD positively correlated with PVC susceptibility. However, excessively high fibrosis levels were associated with reduced susceptibility to PVCs. In addition, the self-sustainability of arrhythmias exhibited a positive correlation with FD. This study demonstrates the interplay between the diffuse fibrosis and the drug-induced heterogeneity of APD in the genesis of ventricular arrhythmias.

Author summary

Sudden cardiac death remains a leading cause of death worldwide. Understanding the mechanisms underlying arrhythmia and its precursors is critical for the development of effective therapies and drugs. Repolarization heterogeneity plays a crucial role in both the initiation and maintenance of arrhythmias. Fibroblasts constitute a vital component of cardiac structure, originating from the remodeling of ventricular wall cells or the transformation of injured myocardial cells. Fibroblasts are known to couple with and alter the electrical properties of myocardial cells. However, our understanding of the role of fibroblasts in the development of arrhythmia remains limited. In this study, we employed a physiologically detailed mathematical model of cardiac tissue to investigate the roles of drug-induced heterogeneity and diffuse fibrosis in the initiation and maintenance of arrhythmias. We used 2D and 3D computational models to simulate various levels of drug-induced heterogeneity conditions with normal to pathological levels of fibroblast density (FD). We found that within a certain range of FD, fibroblasts promote PVCs under drug-induced heterogeneity. However, if FD exceeds 30%, the occurrence of PVCs decreases (biphasic relationship). On the other hand, the self-sustainability of VF (ventricular fibrillation) consistently increases with FD. This study implies that fibroblasts in cardiac tissue may play different roles in the initiation and maintenance of arrhythmia.

Generating Purkinje networks in the human heart

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±3mm, the surface density of our new Purkije network is twice as dense as the conventional network with 22±7mm. 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.

Three-dimensional cardiac computational modelling: methods, features and applications

The combination of computational models and biophysical simulations can help to interpret an array of experimental data and contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmias. For this reason, three-dimensional (3D) cardiac computational modelling is currently a rising field of research. The advance of medical imaging technology over the last decades has allowed the evolution from generic to patient-specific 3D cardiac models that faithfully represent the anatomy and different cardiac features of a given alive subject. Here we analyse sixty representative 3D cardiac computational models developed and published during the last fifty years, describing their information sources, features, development methods and online availability. This paper also reviews the necessary components to build a 3D computational model of the heart aimed at biophysical simulation, paying especial attention to cardiac electrophysiology (EP), and the existing approaches to incorporate those components. We assess the challenges associated to the different steps of the building process, from the processing of raw clinical or biological data to the final application, including image segmentation, inclusion of substructures and meshing among others. We briefly outline the personalisation approaches that are currently available in 3D cardiac computational modelling. Finally, we present examples of several specific applications, mainly related to cardiac EP simulation and model-based image analysis, showing the potential usefulness of 3D cardiac computational modelling into clinical environments as a tool to aid in the prevention, diagnosis and treatment of cardiac diseases.