Susceptibility to arrhythmia in the infarcted heart depends on myofibroblast density

Computational modeling of cardiac electrophysiology and arrhythmogenesis: toward clinical translation

The complexity of cardiac electrophysiology, involving dynamic changes in numerous components across multiple spatial (from ion channel to organ) and temporal (from milliseconds to days) scales, makes an intuitive or empirical analysis of cardiac arrhythmogenesis challenging. Multiscale mechanistic computational models of cardiac electrophysiology provide precise control over individual parameters, and their reproducibility enables a thorough assessment of arrhythmia mechanisms. This review provides a comprehensive analysis of models of cardiac electrophysiology and arrhythmias, from the single cell to the organ level, and how they can be leveraged to better understand rhythm disorders in cardiac disease and to improve heart patient care. Key issues related to model development based on experimental data are discussed, and major families of human cardiomyocyte models and their applications are highlighted. An overview of organ-level computational modeling of cardiac electrophysiology and its clinical applications in personalized arrhythmia risk assessment and patient-specific therapy of atrial and ventricular arrhythmias is provided. The advancements presented here highlight how patient-specific computational models of the heart reconstructed from patient data have achieved success in predicting risk of sudden cardiac death and guiding optimal treatments of heart rhythm disorders. Finally, an outlook toward potential future advances, including the combination of mechanistic modeling and machine learning/artificial intelligence, is provided. As the field of cardiology is embarking on a journey toward precision medicine, personalized modeling of the heart is expected to become a key technology to guide pharmaceutical therapy, deployment of devices, and surgical interventions.

Functional significance of myocardial activity at 18F-FAPI PET/CT in hypertrophic cardiomyopathy identified by cardiac magnetic resonance feature-tracking strain analysis

PURPOSE

This study aimed to evaluate the functional significance of 18F-labeled fibroblast activation protein inhibitor (18F-FAPI) activity in hypertrophic cardiomyopathy (HCM) by comparison with cardiac magnetic resonance feature-tracking (CMR-FT) strain analysis.

METHODS

A total of 49 HCM patients were included in this study. Two independent control groups of healthy participants with a matched age and sex to the HCM patients were also enrolled. Left ventricular (LV) 18F-FAPI activity was analyzed for extent (FAPI%) and intensity (maximum target-to-background ratio, TBRmax). The CMR tissue characterization parameters of the LV included late gadolinium enhancement, native T1 value, and extracellular volume fraction. LV strain analysis was performed in radial, circumferential, and longitudinal peak strains (PS).

RESULTS

Intense LV myocardial 18F-FAPI uptake was observed in HCM patients, whereas no obvious uptake was detected in healthy participants (median TBRmax, 9.1 vs. 1.2, p < 0.001). The strain parameters of HCM patients, compared with healthy participants, were significantly impaired (mean radial PS, 23.5 vs. 36.0, mean circumferential PS, -14.5 vs. -20.0, and mean longitudinal PS, -9.9 vs. -16.0, all p < 0.001). At segmental levels, there was a moderate correlation between 18F-FAPI activity and strain parameters. The number of positive 18F-FAPI uptake segments (n = 653) was higher than that of hypertrophic segments (n = 190) and positive CMR tissue characterization segments (n = 525) (all p < 0.001). In segments with negative CMR tissue characterization findings, the strain capacity of positive 18F-FAPI uptake segments was lower than that of negative 18F-FAPI uptake segments (median radial PS, 30.5 vs. 36.1, p = 0.026 and median circumferential PS, -18.4 vs. -19.7, p = 0.041).

CONCLUSION

18F-FAPI imaging can partially reflect the potential strain reduction in HCM patients. 18F-FAPI imaging detects more involved myocardium than CMR tissue characterization techniques, and the additionally identified myocardium has impaired strain capacity.

The physics of heart rhythm disorders

The global burden caused by cardiovascular disease is substantial, with heart disease representing the most common cause of death around the world. There remains a need to develop better mechanistic models of cardiac function in order to combat this health concern. Heart rhythm disorders, or arrhythmias, are one particular type of disease which has been amenable to quantitative investigation. Here we review the application of quantitative methodologies to explore dynamical questions pertaining to arrhythmias. We begin by describing single-cell models of cardiac myocytes, from which two and three dimensional models can be constructed. Special focus is placed on results relating to pattern formation across these spatially-distributed systems, especially the formation of spiral waves of activation. Next, we discuss mechanisms which can lead to the initiation of arrhythmias, focusing on the dynamical state of spatially discordant alternans, and outline proposed mechanisms perpetuating arrhythmias such as fibrillation. We then review experimental and clinical results related to the spatio-temporal mapping of heart rhythm disorders. Finally, we describe treatment options for heart rhythm disorders and demonstrate how statistical physics tools can provide insights into the dynamics of heart rhythm disorders.

Mechanisms of arrhythmia termination during acute myocardial ischemia: Role of ephaptic coupling and complex geometry of border zone

Myocardial ischemia occurs when blood flow to the heart is reduced, preventing the heart muscle from receiving enough oxygen required for survival. Several anatomical and electrophysiological changes occur at the ischemic core (IC) and border zone (BZ) during myocardial ischemia, for example, gap junctional remodeling, changes in ionic channel kinetics and electrophysiologic changes in cell excitability, which promote the development of cardiac arrhythmia. Ephaptic coupling (EpC), which is an electrical field effect developed in the shared cleft space between adjacent cells, has been suggested to rescue the conduction when gap junctions are impaired, such as myocardial ischemia. In this manuscript, we explored the impact of EpC, electrophysiological and anatomical components of myocardial ischemia on reentry termination during non-ischemic and ischemic condition. Our results indicated that EpC and BZ with complex geometry have opposite effects on the reentry termination. In particular, the presence of homogeneous EpC terminates reentry, whereas BZ with complex geometry alone facilitates reentry by producing wave break-up and alternating conduction block. The reentry is terminated in the presence of homogeneous or heterogeneous EpC despite the presence of complex geometry of the BZ, independent of the location of BZ. The inhibition of reentry can be attributed to a current-to-load mismatch. Our results points to an antiarrhythmic role of EpC and a pro-arrhythmic role of BZ with complex geometry.

An Automata-Based Cardiac Electrophysiology Simulator to Assess Arrhythmia Inducibility

Personalized cardiac electrophysiology simulations have demonstrated great potential to study cardiac arrhythmias and help in therapy planning of radio-frequency ablation. Its application to analyze vulnerability to ventricular tachycardia and sudden cardiac death in infarcted patients has been recently explored. However, the detailed multi-scale biophysical simulations used in these studies are very demanding in terms of memory and computational resources, which prevents their clinical translation. In this work, we present a fast phenomenological system based on cellular automata (CA) to simulate personalized cardiac electrophysiology. The system is trained on biophysical simulations to reproduce cellular and tissue dynamics in healthy and pathological conditions, including action potential restitution, conduction velocity restitution and cell safety factor. We show that a full ventricular simulation can be performed in the order of seconds, emulate the results of a biophysical simulation and reproduce a patient’s ventricular tachycardia in a model that includes a heterogeneous scar region. The system could be used to study the risk of arrhythmia in infarcted patients for a large number of scenarios.

Mechanisms of ventricular arrhythmias elicited by coexistence of multiple electrophysiological remodeling in ischemia: A simulation study

Myocardial ischemia, injury and infarction (MI) are the three stages of acute coronary syndrome (ACS). In the past two decades, a great number of studies focused on myocardial ischemia and MI individually, and showed that the occurrence of reentrant arrhythmias is often associated with myocardial ischemia or MI. However, arrhythmogenic mechanisms in the tissue with various degrees of remodeling in the ischemic heart have not been fully understood. In this study, biophysical detailed single-cell models of ischemia 1a, 1b, and MI were developed to mimic the electrophysiological remodeling at different stages of ACS. 2D tissue models with different distributions of ischemia and MI areas were constructed to investigate the mechanisms of the initiation of reentrant waves during the progression of ischemia. Simulation results in 2D tissues showed that the vulnerable windows (VWs) in simultaneous presence of multiple ischemic conditions were associated with the dynamics of wave propagation in the tissues with each single pathological condition. In the tissue with multiple pathological conditions, reentrant waves were mainly induced by two different mechanisms: one is the heterogeneity along the excitation wavefront, especially the abrupt variation in conduction velocity (CV) across the border of ischemia 1b and MI, and the other is the decreased safe factor (SF) for conduction at the edge of the tissue in MI region which is attributed to the increased excitation threshold of MI region. Finally, the reentrant wave was observed in a 3D model with a scar reconstructed from MRI images of a MI patient. These comprehensive findings provide novel insights for understanding the arrhythmic risk during the progression of myocardial ischemia and highlight the importance of the multiple pathological stages in designing medical therapies for arrhythmias in ischemia.

Role of Scar and Border Zone Geometry on the Genesis and Maintenance of Re-Entrant Ventricular Tachycardia in Patients With Previous Myocardial Infarction

In patients with healed myocardial infarction, the left ventricular ejection fraction is characterized by low sensitivity and specificity in the prediction of future malignant arrhythmias. Thus, there is the need for new parameters in daily practice to perform arrhythmic risk stratification. The aim of this study is to identify some features of proarrhythmic geometric configurations of scars and border zones (BZ), by means of numerical simulations based on left ventricular models derived from post myocardial infarction patients. Two patients with similar clinical characteristics were included in this study. Both patients exhibited left ventricular scars characterized by subendo- and subepicardial BZ and a transmural BZ isthmus. The scar of patient #1 was significantly larger than that of patient #2, whereas the transmural BZ isthmus and the subdendo- and subepicardial BZs of patient #2 were thicker than those of patient #1. Patient #1 was positive at electrophysiologic testing, whereas patient #2 was negative. Based on the cardiac magnetic resonance (CMR) data, we developed a geometric model of the left ventricles of the two patients, taking into account the position, extent, and topological features of scars and BZ. The numerical simulations were based on the anisotropic monodomain model of electrocardiology. In the model of patient #1, sustained ventricular tachycardia (VT) was inducible by an S2 stimulus delivered at any of the six stimulation sites considered, while in the model of patient #2 we were not able to induce sustained VT. In the model of patient #1, making the subendo- and subepicardial BZs as thick as those of patient #2 did not affect the inducibility and maintenance of VT. On the other hand, in the model of patient #2, making the subendo- and subepicardial BZs as thin as those of patient #1 yielded sustained VT. In conclusion, the results show that the numerical simulations have an effective predictive capability in discriminating patients at high arrhythmic risk. The extent of the infarct scar and the presence of transmural BZ isthmuses and thin subendo- and subepicardial BZs promote sustained VT.

Machine Learning Identification of Pro-arrhythmic Structures in Cardiac Fibrosis

Cardiac fibrosis and other scarring of the heart, arising from conditions ranging from myocardial infarction to ageing, promotes dangerous arrhythmias by blocking the healthy propagation of cardiac excitation. Owing to the complexity of the dynamics of electrical signalling in the heart, however, the connection between different arrangements of blockage and various arrhythmic consequences remains poorly understood. Where a mechanism defies traditional understanding, machine learning can be invaluable for enabling accurate prediction of quantities of interest (measures of arrhythmic risk) in terms of predictor variables (such as the arrangement or pattern of obstructive scarring). In this study, we simulate the propagation of the action potential (AP) in tissue affected by fibrotic changes and hence detect sites that initiate re-entrant activation patterns. By separately considering multiple different stimulus regimes, we directly observe and quantify the sensitivity of re-entry formation to activation sequence in the fibrotic region. Then, by extracting the fibrotic structures around locations that both do and do not initiate re-entries, we use neural networks to determine to what extent re-entry initiation is predictable, and over what spatial scale conduction heterogeneities appear to act to produce this effect. We find that structural information within about 0.5 mm of a given point is sufficient to predict structures that initiate re-entry with more than 90% accuracy.

Addendum: Action potential propagation and block in a model of atrial tissue with myocyte-fibroblast coupling

The analytical theory of our earlier study (Mortensen et al., 2021, Math. Med. Biol., 38, 106-131) is extended to address the outstanding cases of fibroblast barrier distribution and myocyte strait distribution. In particular, closed-form approximations to the resting membrane potential and to the critical parameter values for propagation are derived for these two non-uniform fibroblast distributions and are in good agreement with numerical estimates.

Action potential propagation and block in a model of atrial tissue with myocyte-fibroblast coupling

The electrical coupling between myocytes and fibroblasts and the spacial distribution of fibroblasts within myocardial tissues are significant factors in triggering and sustaining cardiac arrhythmias, but their roles are poorly understood. This article describes both direct numerical simulations and an asymptotic theory of propagation and block of electrical excitation in a model of atrial tissue with myocyte-fibroblast coupling. In particular, three idealized fibroblast distributions are introduced: uniform distribution, fibroblast barrier and myocyte strait-all believed to be constituent blocks of realistic fibroblast distributions. Primary action potential biomarkers including conduction velocity, peak potential and triangulation index are estimated from direct simulations in all cases. Propagation block is found to occur at certain critical values of the parameters defining each idealized fibroblast distribution, and these critical values are accurately determined. An asymptotic theory proposed earlier is extended and applied to the case of a uniform fibroblast distribution. Biomarker values are obtained from hybrid analytical-numerical solutions of coupled fast-time and slow-time periodic boundary value problems and compare well to direct numerical simulations. The boundary of absolute refractoriness is determined solely by the fast-time problem and is found to depend on the values of the myocyte potential and on the slow inactivation variable of the sodium current ahead of the propagating pulse. In turn, these quantities are estimated from the slow-time problem using a regular perturbation expansion to find the steady state of the coupled myocyte-fibroblast kinetics. The asymptotic theory gives a simple analytical expression that captures with remarkable accuracy the block of propagation in the presence of fibroblasts.

Quantification of diffuse myocardial fibrosis using CMR extracellular volume fraction and serum biomarkers of collagen turnover with histologic quantification as standard of reference

PURPOSE

To compare the assessment of diffuse interstitial myocardial fibrosis in valvular diseases using cardiac magnetic resonance (CMR) extracellular volume fraction (ECV) quantification and serum biomarkers of collagen turnover using results of myocardial biopsy as standard of reference.

MATERIALS AND METHODS

This prospective monocentric study included consecutive patients before aortic valvular replacement. All patients underwent: i), 1.5T CMR with pre and post contrast T₁ mapping sequence and ECV computation; ii), serum quantification of matrix metalloproteinases (MMPs) and tissue inhibitor of metalloproteinases (TIMPs) and iii), myocardial biopsies were collected during surgery to assess collagen volume fraction (CVF). Patients with coronary artery disease were excluded. Correlation between native T1, ECV, CVF and serum biomarkers were assessed using Pearson correlation test. Agreement between basal anteroseptal ECV with global ECV was assessed using Bland-Altman test.

RESULTS

Twenty-one patients, 16 with aortic stenosis and 5 with aortic regurgitation were included. There were 12 men and 9 women with a mean age of 74.1±6.8 (SD) years (range: 32-84 years). Mean global ECV value was 26.7±2.7 (SD) % (range: 23.4-32.5%) and mean CVF value was 12.4±9.7% (range: 3.2-25.7%). ECV assessed at the basal anteroseptal segment correlated moderately with CVF (r=0.6; P=0.0026). There was a strong correlation and agreement between basal anteroseptal ECV and global ECV, (r=0.8; P<0.0001; bias 5.4±6.1%) but no correlation between global ECV and CVF (r=0.5; P=0.10). Global ECV poorly correlated with serum TIMP-1 (r=0.4; P=0.037) and MMP-2 (r=0.4; P=0.047). No correlation was found between serum biomarkers and basal anteroseptal- ECV or native T1.

CONCLUSION

In patients with severe aortic valvulopathy, diffuse myocardial fibrosis assessed by anterosepto-basal ECV correlates with histological myocardial fibrosis. Anteroseptobasal ECV strongly correlates with global ECV, which poorly correlates with TIMP-1 and MMP-2, serum biomarkers involved in the progression of heart failure.

Myocardial Fibrosis in a 3D Model: Effect of Texture on Wave Propagation

Non-linear electrical waves propagate through the heart and control cardiac contraction. Abnormal wave propagation causes various forms of the heart disease and can be lethal. One of the main causes of abnormality is a condition of cardiac fibrosis, which, from mathematical point of view, is the presence of multiple non-conducting obstacles for wave propagation. The fibrosis can have different texture which varies from diffuse (e.g., small randomly distributed obstacles), patchy (e.g., elongated interstitional stria), and focal (e.g., post-infarct scars) forms. Recently, Nezlobinsky et al. (2020) used 2D biophysical models to quantify the effects of elongation of obstacles (fibrosis texture) and showed that longitudinal and transversal propagation differently depends on the obstacle length resulting in anisotropy for wave propagation. In this paper, we extend these studies to 3D tissue models. We show that 3D consideration brings essential new effects; for the same obstacle length in 3D systems, anisotropy is about two times smaller compared to 2D, however, wave propagation is more stable with percolation threshold of about 60% (compared to 35% in 2D). The percolation threshold increases with the obstacle length for the longitudinal propagation, while it decreases for the transversal propagation. Further, in 3D, the dependency of velocity on the obstacle length for the transversal propagation disappears.

Variability in electrophysiological properties and conducting obstacles controls re-entry risk in heterogeneous ischaemic tissue

Ischaemia, in which inadequate blood supply compromises and eventually kills regions of cardiac tissue, can cause many types of arrhythmia, some life-threatening. A significant component of this is the effects of the resulting hypoxia, and concomitant hyperklaemia and acidosis, on the electrophysiological properties of myocytes. Clinical and experimental data have also shown that regions of structural heterogeneity (fibrosis, necrosis, fibro-fatty infiltration) can act as triggers for arrhythmias under acute ischaemic conditions. Mechanistic models have successfully captured these effects in silico. However, the relative significance of these separate facets of the condition, and how sensitive arrhythmic risk is to the extents of each, is far less explored. In this work, we use partitioned Gaussian process emulation and new metrics for source-sink mismatch that rely on simulations of bifurcating cardiac fibres to interrogate a model of heterogeneous ischaemic tissue. Re-entries were most sensitive to the level of hypoxia and the fraction of non-excitable tissue. In addition, our results reveal both protective and pro-arrhythmic effects of hyperklaemia, and present the levels of hyperklaemia, hypoxia and percentage of non-excitable tissue that pose the highest arrhythmic risks. This article is part of the theme issue 'Uncertainty quantification in cardiac and cardiovascular modelling and simulation'.

Sensitivity and Uncertainty Analysis of Two Human Atrial Cardiac Cell Models Using Gaussian Process Emulators

Biophysically detailed cardiac cell models reconstruct the action potential and calcium dynamics of cardiac myocytes. They aim to capture the biophysics of current flow through ion channels, pumps, and exchangers in the cell membrane, and are highly detailed. However, the relationship between model parameters and model outputs is difficult to establish because the models are both complex and non-linear. The consequences of uncertainty and variability in model parameters are therefore difficult to determine without undertaking large numbers of model evaluations. The aim of the present study was to demonstrate how sensitivity and uncertainty analysis using Gaussian process emulators can be used for a systematic and quantitive analysis of biophysically detailed cardiac cell models. We selected the Courtemanche and Maleckar models of the human atrial action potential for analysis because these models describe a similar set of currents, with different formulations. In our approach Gaussian processes emulate the main features of the action potential and calcium transient. The emulators were trained with a set of design data comprising samples from parameter space and corresponding model outputs, initially obtained from 300 model evaluations. Variance based sensitivity indices were calculated using the emulators, and first order and total effect indices were calculated for each combination of parameter and output. The differences between the first order and total effect indices indicated that the effect of interactions between parameters was small. A second set of emulators were then trained using a new set of design data with a subset of the model parameters with a sensitivity index of more than 0.1 (10%). This second stage analysis enabled comparison of mechanisms in the two models. The second stage sensitivity indices enabled the relationship between the L-type Ca 2+ current and the action potential plateau to be quantified in each model. Our quantitative analysis predicted that changes in maximum conductance of the ultra-rapid K + channel I Kur would have opposite effects on action potential duration in the two models, and this prediction was confirmed by additional simulations. This study has demonstrated that Gaussian process emulators are an effective tool for sensitivity and uncertainty analysis of biophysically detailed cardiac cell models.

Respiratory healthcare by design: Computational approaches bringing respiratory precision and personalised medicine closer to bedside

Precision medicine represents a potentially powerful means to alleviate the growing burden of chronic respiratory diseases. To realise its potential, however, we need a systems level understanding of how biological events (signalling pathways, cell-cell interactions, tissue mechanics) integrate across multiple spatial and temporal scales to give rise to pathology. This can be achieved most practically in silico: a paradigm that offers tight control over model parameters and rapid means of testing and generating mechanistic hypotheses. Patient-specific computational models that can enable identification of pathological mechanisms unique to patients' (omics, physiological, and anatomical) profiles and, therefore, personalised drug targets represent a major milestone in precision medicine. Current patient-based models in literature, especially medical devices, cardiac modelling, and respiratory medicine, rely mostly on (partial/ordinary) differential equations and have reached relatively advanced level of maturity. In respiratory medicine, patient-specific simulations mainly include subject scan-based lung mechanics models that can predict pulmonary function, but they treat the (sub)cellular processes as "black-boxes". A recent advance in simulating human airways at a cellular level to make clinical predictions raises the possibility of linking omics and cell level data/models with lung mechanics to understand respiratory pathology at a systems level. This is significant as this approach can be extended to understanding pathologies in other organs as well. Here, I will discuss ways in which computational models have already made contributions to personalised healthcare and how the paradigm can expedite clinical uptake of precision medicine strategies. I will mainly focus on an agent-based, asthmatic virtual patient that predicted the impact of multiple drug pharmacodynamics at the patient level, its potential to develop efficacious precision medicine strategies in respiratory medicine, and the regulatory and ethical challenges accompanying the mainstream application of such models.

Factors Promoting Conduction Slowing as Substrates for Block and Reentry in Infarcted Hearts

The development of effective and safe therapies for scar-related ventricular tachycardias requires a detailed understanding of the mechanisms underlying the conduction block that initiates electrical re-entries associated with these arrhythmias. Conduction block has been often associated with electrophysiological changes that prolong action potential duration (APD) within the border zone (BZ) of chronically infarcted hearts. However, experimental evidence suggests that remodeling processes promoting conduction slowing as opposed to APD prolongation mark the chronic phase. In this context, the substrate for the initial block at the mouth of an isthmus/diastolic channel leading to ventricular tachycardia is unclear. The goal of this study was to determine whether electrophysiological parameters associated with conduction slowing can cause block and re-entry in the BZ. In silico experiments were conducted on two-dimensional idealized infarct tissue as well as on a cohort of postinfarction porcine left ventricular models constructed from ex vivo magnetic resonance imaging scans. Functional conduction slowing in the BZ was modeled by reducing sodium current density, whereas structural conduction slowing was represented by decreasing tissue conductivity and including fibrosis. The arrhythmogenic potential of APD prolongation was also tested as a basis for comparison. Within all models, the combination of reduced sodium current with structural remodeling more often degenerated into re-entry and, if so, was more likely to be sustained for more cycles. Although re-entries were also detected in experiments with prolonged APD, they were often not sustained because of the subsequent block caused by long-lasting repolarization. Functional and structural conditions associated with slow conduction rather than APD prolongation form a potent substrate for arrhythmogenesis at the isthmus/BZ of chronically infarcted hearts. Reduced excitability led to block while slow conduction shortened the wavelength of propagation, facilitating the sustenance of re-entries. These findings provide important insights for models of patient-specific risk stratification and therapy planning.

Voltage during atrial fibrillation is superior to voltage during sinus rhythm in localizing areas of delayed enhancement on magnetic resonance imaging: An assessment of the posterior left atrium in patients with persistent atrial fibrillation

Background

Bipolar electrogram voltage during sinus rhythm (V_SR) has been used as a surrogate for atrial fibrosis in guiding catheter ablation of persistent atrial fibrillation (AF), but the fixed rate and wavefront characteristics present during sinus rhythm may not accurately reflect underlying functional vulnerabilities responsible for AF maintenance.

Objective

The purpose of this study was determine whether, given adequate temporal sampling, the spatial distribution of mean AF voltage (V_mAF) better correlates with delayed-enhancement magnetic resonance imaging (MRI-DE)–detected atrial fibrosis than V_SR.

Methods

AF was mapped (8 seconds) during index ablation for persistent AF (20 patients) using a 20-pole catheter (660 ± 28 points/map). After cardioversion, V_SR was mapped (557 ± 326 points/map). Electroanatomic and MRI-DE maps were co-registered in 14 patients.

Results

The time course of V_mAF was assessed from 1–40 AF cycles (∼8 seconds) at 1113 locations. V_mAF stabilized with sampling >4 seconds (mean voltage error 0.05 mV). Paired point analysis of V_mAF from segments acquired 30 seconds apart (3667 sites; 15 patients) showed strong correlation (r = 0.95; P <.001). Delayed enhancement (DE) was assessed across the posterior left atrial (LA) wall, occupying 33% ± 13%. V_mAF distributions were (median [IQR]) 0.21 [0.14–0.35] mV in DE vs 0.52 [0.34–0.77] mV in non-DE regions. V_SR distributions were 1.34 [0.65–2.48] mV in DE vs 2.37 [1.27–3.97] mV in non-DE. V_mAF threshold of 0.35 mV yielded sensitivity of 75% and specificity of 79% in detecting MRI-DE compared with 63% and 67%, respectively, for V_SR (1.8-mV threshold)_.

Conclusion

The correlation between low-voltage and posterior LA MRI-DE is significantly improved when acquired during AF vs sinus rhythm. With adequate sampling, mean AF voltage is a reproducible marker reflecting the functional response to the underlying persistent AF substrate.

Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia

In the chronic stage of myocardial infarction, a significant number of patients develop life-threatening ventricular tachycardias (VT) due to the arrhythmogenic nature of the remodeled myocardium. Radiofrequency ablation (RFA) is a common procedure to isolate reentry pathways across the infarct scar that are responsible for VT. Unfortunately, this strategy show relatively low success rates; up to 50% of patients experience recurrent VT after the procedure. In the last decade, intensive research in the field of computational cardiac electrophysiology (EP) has demonstrated the ability of three-dimensional (3D) cardiac computational models to perform in-silico EP studies. However, the personalization and modeling of certain key components remain challenging, particularly in the case of the infarct border zone (BZ). In this study, we used a clinical dataset from a patient with a history of infarct-related VT to build an image-based 3D ventricular model aimed at computational simulation of cardiac EP, including detailed patient-specific cardiac anatomy and infarct scar geometry. We modeled the BZ in eight different ways by combining the presence or absence of electrical remodeling with four different levels of image-based patchy fibrosis (0, 10, 20, and 30%). A 3D torso model was also constructed to compute the ECG. Patient-specific sinus activation patterns were simulated and validated against the patient's ECG. Subsequently, the pacing protocol used to induce reentrant VTs in the EP laboratory was reproduced in-silico. The clinical VT was induced with different versions of the model and from different pacing points, thus identifying the slow conducting channel responsible for such VT. Finally, the real patient's ECG recorded during VT episodes was used to validate our simulation results and to assess different strategies to model the BZ. Our study showed that reduced conduction velocities and heterogeneity in action potential duration in the BZ are the main factors in promoting reentrant activity. Either electrical remodeling or fibrosis in a degree of at least 30% in the BZ were required to initiate VT. Moreover, this proof-of-concept study confirms the feasibility of developing 3D computational models for cardiac EP able to reproduce cardiac activation in sinus rhythm and during VT, using exclusively non-invasive clinical data.

Concise Review: Reduction of Adverse Cardiac Scarring Facilitates Pluripotent Stem Cell-Based Therapy for Myocardial Infarction

Pluripotent stem cells (PSCs) are an attractive, reliable source for generating functional cardiomyocytes for regeneration of infarcted heart. However, inefficient cell engraftment into host tissue remains a notable challenge to therapeutic success due to mechanical damage or relatively inhospitable microenvironment. Evidence has shown that excessively formed scar tissues around cell delivery sites present as mechanical and biological barriers that inhibit migration and engraftment of implanted cells. In this review, we focus on the functional responses of stem cells and cardiomyocytes during the process of cardiac fibrosis and scar formation. Survival, migration, contraction, and coupling function of implanted cells may be affected by matrix remodeling, inflammatory factors, altered tissue stiffness, and presence of electroactive myofibroblasts in the fibrotic microenvironment. Although paracrine factors from implanted cells can improve cardiac fibrosis, the transient effect is insufficient for complete repair of an infarcted heart. Furthermore, investigation of interactions between implanted cells and fibroblasts including myofibroblasts helps the identification of new targets to optimize the host substrate environment for facilitating cell engraftment and functional integration. Several antifibrotic approaches, including the use of pharmacological agents, gene therapies, microRNAs, and modified biomaterials, can prevent progression of heart failure and have been developed as adjunct therapies for stem cell-based regeneration. Investigation and optimization of new biomaterials is also required to enhance cell engraftment of engineered cardiac tissue and move PSCs from a laboratory setting into translational medicine.

Dispersion of Recovery and Vulnerability to Re-entry in a Model of Human Atrial Tissue With Simulated Diffuse and Focal Patterns of Fibrosis

Fibrosis in atrial tissue can act as a substrate for persistent atrial fibrillation, and can be focal or diffuse. Regions of fibrosis are associated with slowed or blocked conduction, and several approaches have been used to model these effects. In this study a computational model of 2D atrial tissue was used to investigate how the spatial scale of regions of simulated fibrosis influenced the dispersion of action potential duration (APD) and vulnerability to re-entry in simulated normal human atrial tissue, and human tissue that has undergone remodeling as a result of persistent atrial fibrillation. Electrical activity was simulated in a 10 × 10 cm square 2D domain, with a spatially varying diffusion coefficient as described below. Cellular electrophysiology was represented by the Courtemanche model for human atrial cells, with the model parameters set for normal and remodeled cells. The effect of fibrosis was modeled with a smoothly varying diffusion coefficient, obtained from sampling a Gaussian random field (GRF) with length scales of between 1.25 and 10.0 mm. Twenty samples were drawn from each field, and used to allocate a value of diffusion coefficient between 0.05 and 0.2 mm²/ms. Dispersion of APD was assessed in each sample by pacing at a cycle length of 1,000 ms, followed by a premature beat with a coupling interval of 400 ms. Vulnerability to re-entry was assessed with an aggressive pacing protocol with pacing cycle lengths decreasing from 450 to 250 ms in 25 ms intervals for normal tissue and 300–150 ms for remodeled tissue. Simulated fibrosis at smaller spatial scales tended to lengthen APD, increase APD dispersion, and increase vulnerability to sustained re-entry relative to fibrosis at larger spatial scales. This study shows that when fibrosis is represented by smoothly varying tissue diffusion, the spatial scale of fibrosis has important effects on both dispersion of recovery and vulnerability to re-entry.

Quantitative systems models illuminate arrhythmia mechanisms in heart failure: Role of the Na+ -Ca2+ -Ca2+ /calmodulin-dependent protein kinase II-reactive oxygen species feedback

Quantitative systems modeling aims to integrate knowledge in different research areas with models describing biological mechanisms and dynamics to gain a better understanding of complex clinical syndromes. Heart failure (HF) is a chronic complex cardiac disease that results from structural or functional disorders impairing the ability of the ventricle to fill with or eject blood. Highly interactive and dynamic changes in mechanical, structural, neurohumoral, metabolic, and electrophysiological properties collectively predispose the failing heart to cardiac arrhythmias, which are responsible for about a half of HF deaths. Multiscale cardiac modeling and simulation integrate structural and functional data from HF experimental models and patients to improve our mechanistic understanding of this complex arrhythmia syndrome. In particular, they allow investigating how disease-induced remodeling alters the coupling of electrophysiology, Ca²⁺ and Na⁺ handling, contraction, and energetics that lead to rhythm derangements. The Ca²⁺ /calmodulin-dependent protein kinase II, which expression and activity are enhanced in HF, emerges as a critical hub that modulates the feedbacks between these various subsystems and promotes arrhythmogenesis. This article is categorized under: Physiology > Mammalian Physiology in Health and Disease Models of Systems Properties and Processes > Mechanistic Models Models of Systems Properties and Processes > Cellular Models Models of Systems Properties and Processes > Organ, Tissue, and Physiological Models.

Killing Many Birds With Two Stones: Hypoxia and Fibrosis Can Generate Ectopic Beats in a Human Ventricular Model

During cardiac diseases many types of anatomical and functional remodeling of cardiac tissue can occur. In this work, we focus on two conditions: hypoxia and fibrosis, which are part of complex pathological modifications that take place in many cardiac diseases (hypertrophic cardiomyopathy, hypertensive heart disease, and recurrent myocardial infarction) and respiratory diseases (obstructive pulmonary disease, obstructive sleep apnea, and cystic fibrosis). Using computational models of cardiac electrophysiology, we evaluate if the interplay between hypoxia and fibrosis is sufficient to trigger cardiac arrhythmia. We study the mechanisms behind the generation of ectopic beats, an arrhythmic trigger also known as premature ventricular contractions (PVCs), in regions with high hypoxia and fibrosis. First, we modify an electrophysiological model of myocytes of the human left ventricle to include the effects of hypoxia. Second, diffuse fibrosis is modeled by randomly replacing cardiac myocytes by non-excitable and non-conducting cells. The Monte Carlo method is used to evaluate the probability of a region to generate ectopic beats with respect to different levels of hypoxia and fibrosis. In addition, we evaluate the minimum size of three-dimensional slabs needed to sustain reentries for different stimulation protocols. The observed mechanism behind the initiation of ectopic beats is unidirectional block, giving rise to sustained micro-reentries inside the region with diffuse fibrosis and hypoxia. In summary, our results suggest that hypoxia and fibrosis are sufficient for the creation of a focal region in the heart that generates PVCs.

Modeling the Electrophysiological Properties of the Infarct Border Zone

Ventricular arrhythmias (VA) in patients with myocardial infarction (MI) are thought to be associated with structural and electrophysiological remodeling within the infarct border zone (BZ). Personalized computational models have been used to investigate the potential role of the infarct BZ in arrhythmogenesis, which still remains incompletely understood. Most recent models have relied on experimental data to assign BZ properties. However, experimental measurements vary significantly resulting in different computational representations of this region. Here, we review experimental data available in the literature to determine the most prominent properties of the infarct BZ. Computational models are then used to investigate the effect of different representations of the BZ on activation and repolarization properties, which may be associated with VA. Experimental data obtained from several animal species and patients with infarct show that BZ properties vary significantly depending on disease's stage, with the early disease stage dominated by ionic remodeling and the chronic stage by structural remodeling. In addition, our simulations show that ionic remodeling in the BZ leads to large repolarization gradients in the vicinity of the scar, which may have a significant impact on arrhythmia simulations, while structural remodeling plays a secondary role. We conclude that it is imperative to faithfully represent the properties of regions of infarction within computational models specific to the disease stage under investigation in order to conduct in silico mechanistic investigations.

Featured Article: TGF-β1 dominates extracellular matrix rigidity for inducing differentiation of human cardiac fibroblasts to myofibroblasts

Cardiac fibroblasts and their activated derivatives, myofibroblasts, play a critical role in wound healing after myocardial injury and often contribute to long-term pathological outcomes, such as excessive fibrosis. Thus, defining the microenvironmental factors that regulate the phenotype of cardiac fibroblasts and myofibroblasts could lead to new therapeutic strategies. Both chemical and biomechanical cues have previously been shown to induce myofibroblast differentiation in many organs and species. For example, transforming growth factor beta 1, a cytokine secreted by neutrophils, and rigid extracellular matrix environments have both been shown to promote differentiation. However, the relative contributions of transforming growth factor beta 1 and extracellular matrix rigidity, two hallmark cues in many pathological myocardial microenvironments, to the phenotype of human cardiac fibroblasts are unclear. We hypothesized that transforming growth factor beta 1 and rigid extracellular matrix environments would potentially have a synergistic effect on the differentiation of human cardiac fibroblasts to myofibroblasts. To test this, we seeded primary human adult cardiac fibroblasts onto coverslips coated with polydimethylsiloxane of various elastic moduli, introduced transforming growth factor beta 1, and longitudinally quantified cell phenotype by measuring expression of α-smooth muscle actin, the most robust indicator of myofibroblasts. Our data indicate that, although extracellular matrix rigidity influenced differentiation after one day of transforming growth factor beta 1 treatment, ultimately transforming growth factor beta 1 superseded extracellular matrix rigidity as the primary regulator of myofibroblast differentiation. We also measured expression of POSTN, FAP, and FSP1, proposed secondary indicators of fibroblast/myofibroblast phenotypes. Although these genes partially trended with α-smooth muscle actin expression, they were relatively inconsistent. Finally, we demonstrated that activated myofibroblasts incompletely revert to a fibroblast phenotype after they are re-plated onto new surfaces without transforming growth factor beta 1, suggesting differentiation is partially reversible. Our results provide new insights into how microenvironmental cues affect human cardiac fibroblast differentiation in the context of myocardial pathology, which is important for identifying effective therapeutic targets and dictating supporting cell phenotypes for engineered human cardiac disease models. Impact statement Heart disease is the leading cause of death worldwide. Many forms of heart disease are associated with fibrosis, which increases extracellular matrix (ECM) rigidity and compromises cardiac output. Fibrotic tissue is synthesized primarily by myofibroblasts differentiated from fibroblasts. Thus, defining the cues that regulate myofibroblast differentiation is important for understanding the mechanisms of fibrosis. However, previous studies have focused on non-human cardiac fibroblasts and have not tested combinations of chemical and mechanical cues. We tested the effects of TGF-β1, a cytokine secreted by immune cells after injury, and ECM rigidity on the differentiation of human cardiac fibroblasts to myofibroblasts. Our results indicate that differentiation is initially influenced by ECM rigidity, but is ultimately superseded by TGF-β1. This suggests that targeting TGF-β signaling pathways in cardiac fibroblasts may have therapeutic potential for attenuating fibrosis, even in rigid microenvironments. Additionally, our approach can be leveraged to engineer more precise multi-cellular human cardiac tissue models.

Personalized Imaging and Modeling Strategies for Arrhythmia Prevention and Therapy

The goal of this article is to review advances in computational modeling of the heart, with a focus on recent non-invasive clinical imaging- and simulation-based strategies aimed at improving the diagnosis and treatment of patients with arrhythmias and structural heart disease. Following a brief overview of the field of computational cardiology, we present recent applications of the personalized virtual-heart approach in predicting the optimal targets for infarct-related ventricular tachycardia and atrial fibrillation ablation, and in determining risk of sudden cardiac death in myocardial infarction patients. The hope is that with such models at the patient bedside, therapies could be improved, invasiveness of diagnostic procedures minimized, and health-care costs reduced.

Inverse estimation of cardiac activation times via gradient-based optimization

Computational modeling may provide a quantitative framework for integrating multiscale data to gain insight into mechanisms of heart disease, identify and test pharmacological and electrical therapy and interventions, and support clinical decisions. Patient-specific computational cardiac models can help guide such procedures, and cardiac inverse modeling is a promising alternative to adequately personalize these models. Indeed, full cardiac inverse modeling is currently becoming computationally feasible; however, fundamental work to assess the feasibility of emerging techniques is still needed. In this study, we use a partial differential equation-constrained optimal control approach to numerically investigate the identifiability of an initial activation sequence from synthetic (partial) observations of the extracellular potential using the bidomain approximation and 2D representations of cardiac tissue. Our results demonstrate that activation times and duration of several stimuli can be recovered even with high levels of noise, that it is sufficient to sample the observations at the electrocardiogram-relevant sampling frequency of 1 kHz, and that spatial resolutions that are coarser than the standard in electrophysiological simulations can be used. The optimization of activation times is still effective when synthetic data are generated with a different cell membrane kinetics model than optimized for. The findings thus indicate that the presented approach has potential for finding activation sequences from clinical data modalities, as an extension to existing cardiac imaging approaches.

Reentry via high-frequency pacing in a mathematical model for human-ventricular cardiac tissue with a localized fibrotic region

Localized heterogeneities, caused by the regional proliferation of fibroblasts, occur in mammalian hearts because of diseases like myocardial infarction. Such fibroblast clumps can become sources of pathological reentrant activities, e.g., spiral or scroll waves of electrical activation in cardiac tissue. The occurrence of reentry in cardiac tissue with heterogeneities, such as fibroblast clumps, can depend on the frequency at which the medium is paced. Therefore, it is important to study the reentry-initiating potential of such fibroblast clumps at different frequencies of pacing. We investigate the arrhythmogenic effects of fibroblast clumps at high- and low-frequency pacing. We find that reentrant waves are induced in the medium more prominently at high-frequency pacing than with low-frequency pacing. We also study the other factors that affect the potential of fibroblast clumps to induce reentry in cardiac tissue. In particular, we show that the ability of a fibroblast clump to induce reentry depends on the size of the clump, the distribution and percentage of fibroblasts in the clump, and the excitability of the medium. We study the process of reentry in two-dimensional and a three-dimensional mathematical models for cardiac tissue.

Modeling dynamics in diseased cardiac tissue: Impact of model choice

Cardiac arrhythmias have been traditionally simulated using continuous models that assume tissue homogeneity and use a relatively large spatial discretization. However, it is believed that the tissue fibrosis and collagen deposition, which occur on a micron-level, are critical factors in arrhythmogenesis in diseased tissues. Consequently, it remains unclear how well continuous models, which use averaged electrical properties, are able to accurately capture complex conduction behaviors such as re-entry in fibrotic tissues. The objective of this study was to compare re-entrant behavior in discrete microstructural models of fibrosis and in two types of equivalent continuous models, a homogenous continuous model and a hybrid continuous model with distinct heterogeneities. In the discrete model, increasing levels of tissue fibrosis lead to a substantial increase in the re-entrant cycle length which is inadequately reflected in the homogenous continuous models. These cycle length increases appear to be primarily due to increases in the tip path length and to altered restitution behavior, and suggest that it is critical to consider the discrete effects of fibrosis on conduction when studying arrhythmogenesis in fibrotic myocardium. Hybrid models are able to accurately capture some aspects of re-entry and, if carefully tuned, may provide a framework for simulating conduction in diseased tissues with both accuracy and efficiency.

An integrative appraisal of mechano-electric feedback mechanisms in the heart

Mechanically-induced alterations in cardiac electrophysiology are referred to as mechano-electric feedback (MEF), and play an important role in electrical regulation of cardiac performance. The influence of mechanical stress and strain on electrophysiology has been investigated at all levels, however the role of MEF in arrhythmia remains poorly understood. During the normal contraction of the heart, mechano-sensitive processes are an implicit component of cardiac activity. Under abnormal mechanical events, stretch-activated mechanisms may contribute to local or global changes in electrophysiology (EP). While such mechanisms have been hypothesised to be involved in mechanically-initiated arrhythmias, the details of these mechanisms and their importance remain elusive. We assess the theoretical role of stretch mechanisms using coupled models of cellular electrophysiology and sarcomere contraction dynamics. Using models of single ventricular myocytes, we first investigated the potential MEF contributions of stretch-activated currents (SAC), and stretch-induced myofilament calcium release, to test how strain and fibrosis may alter cellular electrophysiology. For all models investigated, SACs were alone not sufficient to create a pro-arrhythmic perturbation of the action potential with stretch. However, when combined with stretch-induced myofilament calcium release, the action potential could be shortened depending on the timing of the strain. This effect was highly model dependent, with a canine epicardial EP model being the most sensitive. These model results suggest that known mechanisms of mechano-electric coupling in cardiac myocyte may be sufficient to be pro-arrhythmic, but only in combination and under specific strain patterns.

High-frequency power within the QRS complex in ischemic cardiomyopathy patients with ventricular arrhythmias: Insights from a clinical study and computer simulation of cardiac fibrous tissue

BACKGROUND

The distribution of frequency power (DFP) within the QRS complex (QRS) is unclear. This study aimed to investigate the DFP within the QRS in ischemic cardiomyopathy (ICM) with lethal ventricular arrhythmias (L-VA). A computer simulation was performed to explore the mechanism of abnormal frequency power.

METHODS

The study included 31 ICM patients with and without L-VA (n = 10 and 21, respectively). We applied the continuous wavelet transform to measure the time-frequency power within the QRS. Integrated time-frequency power (ITFP) was measured within the frequency range of 5-300 Hz. The simulation model consisted of two-dimensional myocardial tissues intermingled with fibroblasts. We examined the relation between frequency power calculated from the simulated QRS and the fibroblast-to-myocyte ratio (r) of the model.

RESULTS

The frequency powers significantly increased from 180 to 300 Hz and from 5 to 15 Hz, and also decreased from 45 to 80 Hz in patients with ICM and L-VA compared with the normal individuals. They increased from 110 Hz to 250 Hz in ICM alone. In the simulation, the high-frequency power increased when the ratio (r) were 2.0-2.5. Functional reentry was initiated if the ratio (r) increased to 2.0.

CONCLUSIONS

Abnormal higher-frequency power (180-300 Hz) may provide arrhythmogenic signals in ICM with L-VA that may be associated with the fibrous tissue proliferation.

Cardiac Fibroblast Activation Post-Myocardial Infarction: Current Knowledge Gaps

In response to myocardial infarction (MI), the wound healing response of the left ventricle (LV) comprises overlapping inflammatory, proliferative, and maturation phases, and the cardiac fibroblast is a key cell type involved in each phase. It has recently been appreciated that, early post-MI, fibroblasts transform to a proinflammatory phenotype and secrete cytokines and chemokines as well as matrix metalloproteinases (MMPs). Later post-MI, fibroblasts are activated to anti-inflammatory and proreparative phenotypes and generate anti-inflammatory and proangiogenic factors and extracellular matrix (ECM) components that form the infarct scar. Additional studies are needed to systematically examine how fibroblast activation shifts over the timeframe of the MI response and how modulation at different activation stages could alter wound healing and LV remodeling in distinct ways. This review summarizes current fibroblast knowledge as the foundation for a discussion of existing knowledge gaps.

Reentry and Ectopic Pacemakers Emerge in a Three-Dimensional Model for a Slab of Cardiac Tissue with Diffuse Microfibrosis near the Percolation Threshold

Arrhythmias in cardiac tissue are generally associated with irregular electrical wave propagation in the heart. Cardiac tissue is formed by a discrete cell network, which is often heterogeneous. Recently, it was shown in simulations of two-dimensional (2D) discrete models of cardiac tissue that a wave crossing a fibrotic, heterogeneous region may produce reentry and transient or persistent ectopic activity provided the fraction of conducting connections is just above the percolation threshold. Here, we investigate the occurrence of these phenomena in three-dimensions by simulations of a discrete model representing a thin slab of cardiac tissue. This is motivated (i) by the necessity to study the relevance and properties of the percolation-related mechanism for the emergence of microreentries in three dimensions and (ii) by the fact that atrial tissue is quite thin in comparison with ventricular tissue. Here, we simplify the model by neglecting details of tissue anatomy, e. g. geometries of atria or ventricles and the anisotropy in the conductivity. Hence, our modeling study is confined to the investigation of the effect of the tissue thickness as well as to the comparison of the dynamics of electrical excitation in a 2D layer with the one in a 3D slab. Our results indicate a strong and non-trivial effect of the thickness even for thin tissue slabs on the probability of microreentries and ectopic beat generation. The strong correlation of the occurrence of microreentry with the percolation threshold reported earlier in 2D layers persists in 3D slabs. Finally, a qualitative agreement of 3D simulated electrograms in the fibrotic region with the experimentally observed complex fractional atrial electrograms (CFAE) as well as strong difference between simulated electrograms in 2D and 3D were found for the cases where reentry and ectopic activity were triggered by the micro-fibrotic region.

Nonlinear physics of electrical wave propagation in the heart: a review

The beating of the heart is a synchronized contraction of muscle cells (myocytes) that is triggered by a periodic sequence of electrical waves (action potentials) originating in the sino-atrial node and propagating over the atria and the ventricles. Cardiac arrhythmias like atrial and ventricular fibrillation (AF,VF) or ventricular tachycardia (VT) are caused by disruptions and instabilities of these electrical excitations, that lead to the emergence of rotating waves (VT) and turbulent wave patterns (AF,VF). Numerous simulation and experimental studies during the last 20 years have addressed these topics. In this review we focus on the nonlinear dynamics of wave propagation in the heart with an emphasis on the theory of pulses, spirals and scroll waves and their instabilities in excitable media with applications to cardiac modeling. After an introduction into electrophysiological models for action potential propagation, the modeling and analysis of spatiotemporal alternans, spiral and scroll meandering, spiral breakup and scroll wave instabilities like negative line tension and sproing are reviewed in depth and discussed with emphasis on their impact for cardiac arrhythmias.

Computational Representations of Myocardial Infarct Scars and Implications for Arrhythmogenesis

Image-based computational modeling is becoming an increasingly used clinical tool to provide insight into the mechanisms of reentrant arrhythmias. In the context of ischemic heart disease, faithful representation of the electrophysiological properties of the infarct region within models is essential, due to the scars known for arrhythmic properties. Here, we review the different computational representations of the infarcted region, summarizing the experimental measurements upon which they are based. We then focus on the two most common representations of the scar core (complete insulator or electrically passive tissue) and perform simulations of electrical propagation around idealized infarct geometries. Our simulations highlight significant differences in action potential duration and focal effective refractory period (ERP) around the scar, driven by differences in electrotonic loading, depending on the choice of scar representation. Finally, a novel mechanism for arrhythmia induction, following a focal ectopic beat, is demonstrated, which relies on localized gradients in ERP directly caused by the electrotonic sink effects of the neighboring passive scar.

Electrophysiology of Heart Failure Using a Rabbit Model: From the Failing Myocyte to Ventricular Fibrillation

Heart failure is a leading cause of death, yet its underlying electrophysiological (EP) mechanisms are not well understood. In this study, we use a multiscale approach to analyze a model of heart failure and connect its results to features of the electrocardiogram (ECG). The heart failure model is derived by modifying a previously validated electrophysiology model for a healthy rabbit heart. Specifically, in accordance with the heart failure literature, we modified the cell EP by changing both membrane currents and calcium handling. At the tissue level, we modeled the increased gap junction lateralization and lower conduction velocity due to downregulation of Connexin 43. At the biventricular level, we reduced the apex-to-base and transmural gradients of action potential duration (APD). The failing cell model was first validated by reproducing the longer action potential, slower and lower calcium transient, and earlier alternans characteristic of heart failure EP. Subsequently, we compared the electrical wave propagation in one dimensional cables of healthy and failing cells. The validated cell model was then used to simulate the EP of heart failure in an anatomically accurate biventricular rabbit model. As pacing cycle length decreases, both the normal and failing heart develop T-wave alternans, but only the failing heart shows QRS alternans (although moderate) at rapid pacing. Moreover, T-wave alternans is significantly more pronounced in the failing heart. At rapid pacing, APD maps show areas of conduction block in the failing heart. Finally, accelerated pacing initiated wave reentry and breakup in the failing heart. Further, the onset of VF was not observed with an upregulation of SERCA, a potential drug therapy, using the same protocol. The changes introduced at the cell and tissue level have increased the failing heart’s susceptibility to dynamic instabilities and arrhythmias under rapid pacing. However, the observed increase in arrhythmogenic potential is not due to a steepening of the restitution curve (not present in our model), but rather to a novel blocking mechanism.

Ventricular fibrillation (VF) is one of the leading causes of sudden death. During VF, the electrical wave of activation in the heart breaks up chaotically. Consequently, the heart is unable to contract synchronously and pump blood to the rest of the body. In our work we formulate and validate a model of heart failure (HF) that allows us to evaluate the arrhythmogenic potential of individual and combined electrophysiological changes. In diagnostic cardiology, the electrocardiogram (ECG) is one of the most commonly used tools for detecting abnormalities in the heart electrophysiology. One of our goals is to use our numerical model to link changes at the cellular and tissue level in a failing heart to a numerically computed ECG. This allows us to characterize the precursor to and the risk of VF. In order to understand the mechanisms underlying VF in HF, we design a test that simulates a HF patient performing physical exercise. We show that under fast heart rates with changes in pacing, HF patients are more prone to VF due to a new conduction blocking mechanism. In the long term, our mathematical model is suitable for investigating the effect of drug therapies in HF.

Computational rabbit models to investigate the initiation, perpetuation, and termination of ventricular arrhythmia

Current understanding of cardiac electrophysiology has been greatly aided by computational work performed using rabbit ventricular models. This article reviews the contributions of multiscale models of rabbit ventricles in understanding cardiac arrhythmia mechanisms. This review will provide an overview of multiscale modeling of the rabbit ventricles. It will then highlight works that provide insights into the role of the conduction system, complex geometric structures, and heterogeneous cellular electrophysiology in diseased and healthy rabbit hearts to the initiation and maintenance of ventricular arrhythmia. Finally, it will provide an overview on the contributions of rabbit ventricular modeling on understanding the mechanisms underlying shock-induced defibrillation.

Can heart function lost to disease be regenerated by therapeutic targeting of cardiac scar tissue?

Myocardial infarction results in scar tissue that cannot actively contribute to heart mechanical function and frequently causes lethal arrhythmias. The healing response after infarction involves inflammation, biochemical signaling, changes in cellular phenotype, activity, and organization, and alterations in electrical conduction due to variations in cell and tissue geometry and alterations in protein expression, organization, and function - particularly in membrane channels. The intensive research focus on regeneration of myocardial tissues has, as of yet, only met with modest success, with no near-term prospect of improving standard-of-care for patients with heart disease. An alternative concept for novel therapeutic approach is the rejuvenation of cardiac electrical and mechanical properties through the modification of scar tissue. Several peptide therapeutics, locally applied genetic therapies, or delivery of genetically modified cells have shown promise in improving the characteristics of the fibrous scar and post-myocardial infarction prognosis in experimental models. This review highlights several factors that contribute to arrhythmogenesis in scar formation and how these might be targeted to regenerate some of the electrical and mechanical function of the post-MI scar.

Computational modeling of cardiac fibroblasts and fibrosis

Altered fibroblast behavior can lead to pathologic changes in the heart such as arrhythmia, diastolic dysfunction, and systolic dysfunction. Computational models are increasingly used as a tool to identify potential mechanisms driving a phenotype or potential therapeutic targets against an unwanted phenotype. Here we review how computational models incorporating cardiac fibroblasts have clarified the role for these cells in electrical conduction and tissue remodeling in the heart. Models of fibroblast signaling networks have primarily focused on fibroblast cell lines or fibroblasts from other tissues rather than cardiac fibroblasts, specifically, but they are useful for understanding how fundamental signaling pathways control fibroblast phenotype. In the future, modeling cardiac fibroblast signaling, incorporating -omics and drug-interaction data into signaling network models, and utilizing multi-scale models will improve the ability of in silico studies to predict potential therapeutic targets against adverse cardiac fibroblast activity.

Myocyte-fibroblast communication in cardiac fibrosis and arrhythmias: Mechanisms and model systems

Development of cardiac fibrosis and arrhythmias is controlled by the activity of and communication between cardiomyocytes and fibroblasts in the heart. Myocyte-fibroblast interactions occur via both direct and indirect means including paracrine mediators, extracellular matrix interactions, electrical modulators, mechanical junctions, and membrane nanotubes. In the diseased heart, cardiomyocyte and fibroblast ratios and activity, and thus myocyte-fibroblast interactions, change and are thought to contribute to the course of disease including development of fibrosis and arrhythmogenic activity. Fibroblasts have a developing role in modulating cardiomyocyte electrical and hypertrophic activity, however gaps in knowledge regarding these interactions still exist. Research in this field has necessitated the development of unique approaches to isolate and control myocyte-fibroblast interactions. Numerous methods for 2D and 3D co-culture systems have been developed, while a growing part of this field is in the use of better tools for in vivo systems including cardiomyocyte and fibroblast specific Cre mouse lines for cell type specific genetic ablation. This review will focus on (i) mechanisms of myocyte-fibroblast communication and their effects on disease features such as cardiac fibrosis and arrhythmias as well as (ii) methods being used and currently developed in this field.

Precisely parameterized experimental and computational models of tissue organization

Patterns of cellular organization in diverse tissues frequently display a complex geometry and topology tightly related to the tissue function. Progressive disorganization of tissue morphology can lead to pathologic remodeling, necessitating the development of experimental and theoretical methods of analysis of the tolerance of normal tissue function to structural alterations. A systematic way to investigate the relationship of diverse cell organization to tissue function is to engineer two-dimensional cell monolayers replicating key aspects of the in vivo tissue architecture. However, it is still not clear how this can be accomplished on a tissue level scale in a parameterized fashion, allowing for a mathematically precise definition of the model tissue organization and properties down to a cellular scale with a parameter dependent gradual change in model tissue organization. Here, we describe and use a method of designing precisely parameterized, geometrically complex patterns that are then used to control cell alignment and communication of model tissues. We demonstrate direct application of this method to guiding the growth of cardiac cell cultures and developing mathematical models of cell function that correspond to the underlying experimental patterns. Several anisotropic patterned cultures spanning a broad range of multicellular organization, mimicking the cardiac tissue organization of different regions of the heart, were found to be similar to each other and to isotropic cell monolayers in terms of local cell-cell interactions, reflected in similar confluency, morphology and connexin-43 expression. However, in agreement with the model predictions, different anisotropic patterns of cell organization, paralleling in vivo alterations of cardiac tissue morphology, resulted in variable and novel functional responses with important implications for the initiation and maintenance of cardiac arrhythmias. We conclude that variations of tissue geometry and topology can dramatically affect cardiac tissue function even if the constituent cells are themselves similar, and that the proposed method can provide a general strategy to experimentally and computationally investigate when such variation can lead to impaired tissue function.