Personalized Computer Simulation of Diastolic Function in Heart Failure

The search for a parameter representing left ventricular relaxation from non-invasive and invasive diagnostic tools has been extensive, since heart failure (HF) with preserved ejection fraction (HF-pEF) is a global health problem. We explore here the feasibility using patient-specific cardiac computer modeling to capture diastolic parameters in patients suffering from different degrees of systolic HF. Fifty eight patients with idiopathic dilated cardiomyopathy have undergone thorough clinical evaluation, including cardiac magnetic resonance imaging (MRI), heart catheterization, echocardiography, and cardiac biomarker assessment. A previously-introduced framework for creating multi-scale patient-specific cardiac models has been applied on all these patients. Novel parameters, such as global stiffness factor and maximum left ventricular active stress, representing cardiac active and passive tissue properties have been computed for all patients. Invasive pressure measurements from heart catheterization were then used to evaluate ventricular relaxation using the time constant of isovolumic relaxation Tau (s). Parameters from heart catheterization and the multi-scale model have been evaluated and compared to patient clinical presentation. The model parameter global stiffness factor, representing diastolic passive tissue properties, is correlated signif-icantly across the patient population with s. This study shows that multi-modal cardiac models can successfully capture diastolic (dys) function, a prerequisite for future clinical trials on HF-pEF.     

Multiple sclerosis: getting personal with induced pluripotent stem cells

Human induced pluripotent stem (iPS) cells can be derived from lineage-restricted cells and represent an important tool to develop novel patient-specific cell therapies and research models for inherited and acquired diseases. Recently, patient-derived iPS cells, containing donor genetic background, have offered a breakthrough approach to study human genetics of neurodegenerative diseases. By offering an unlimited source of patient-specific disease-relevant cells, iPS cells hold great promise for understanding disease mechanisms, identifying molecular targets and developing phenotypic screens for drug discovery. This review will discuss the potential impact of using iPS cell-derived models in multiple sclerosis (MS) research and highlight some of the current challenges and prospective for generating novel therapeutic treatments for MS patients.     

Effect of Global Cardiac Ischemia on Human Ventricular Fibrillation: Insights from a Multi-scale Mechanistic Model of the Human Heart

Acute regional ischemia in the heart can lead to cardiac arrhythmias such as ventricular fibrillation (VF), which in turn compromise cardiac output and result in secondary global cardiac ischemia. The secondary ischemia may influence the underlying arrhythmia mechanism. A recent clinical study documents the effect of global cardiac ischaemia on the mechanisms of VF. During 150 seconds of global ischemia the dominant frequency of activation decreased, while after reperfusion it increased rapidly. At the same time the complexity of epicardial excitation, measured as the number of epicardical phase singularity points, remained approximately constant during ischemia. Here we perform numerical studies based on these clinical data and propose explanations for the observed dynamics of the period and complexity of activation patterns. In particular, we study the effects on ischemia in pseudo-1D and 2D cardiac tissue models as well as in an anatomically accurate model of human heart ventricles. We demonstrate that the fall of dominant frequency in VF during secondary ischemia can be explained by an increase in extracellular potassium, while the increase during reperfusion is consistent with washout of potassium and continued activation of the ATP-dependent potassium channels. We also suggest that memory effects are responsible for the observed complexity dynamics. In addition, we present unpublished clinical results of individual patient recordings and propose a way of estimating extracellular potassium and activation of ATP-dependent potassium channels from these measurements.     

Production of de novo cardiomyocytes: human pluripotent stem cell differentiation and direct reprogramming

Cardiovascular disease is a leading cause of death worldwide. The limited capability of heart tissue to regenerate has prompted methodological developments for creating de novo cardiomyocytes, both in vitro and in vivo. Beyond uses in cell replacement therapy, patient-specific cardiomyocytes may find applications in drug testing, drug discovery, and disease modeling. Recently, approaches for generating cardiomyocytes have expanded to encompass three major sources of starting cells: human pluripotent stem cells (hPSCs), adult heart-derived cardiac progenitor cells (CPCs), and reprogrammed fibroblasts. We discuss state-of-the-art methods for generating de novo cardiomyocytes from hPSCs and reprogrammed fibroblasts, highlighting potential applications and future challenges.     

Three-Dimensional Computational Modeling of an Extra-Descending Aortic Assist Device Using Fluid-Structure Interaction

BackgroundThe incidence of heart failure is anticipated to rise by 2030, resulting in more than 8 million adults with this condition in US. Despite the advancement in pharmacological and surgical treatments, some patients progress to severe forms of cardiac dysfunction requiring cardiac transplantation as a last-resort treatment. Cardiac assist devices play an essential role in the recovery of normal cardiac performance through reversible remodeling or in assisting the weak organ to prolong survival rate. However, these devices need to be monitored carefully, as prolonged use may lead to physiological maladaptation and further cardiac complications. The optimization of such devices has done through the development and use of numerical simulations that allow the analysis of in-vivo hemodynamic patterns of blood flow. This study aims to investigate the performance of a model of extra-aortic assist device surrounding the descending aorta through three-dimensional patient-specific modeling.MethodsA three-dimensional model of the aorta was constructed from patient-specific cardiac CT images of a 60-year-old male diagnosed with left ventricular failure at the Tehran Heart Center (THC). Numerical simulation was conducted for two complete cardiac cycles using fluid-structure interaction (FSI) analysis under the assumption that the balloon and the aortic vessel behave as linear elastic materials, and that blood is a Newtonian and incompressible fluid.ResultsThe numerical simulation demonstrated a high correlation between the FSI analysis and clinical data of the patient-specific anatomical and physiological conditions. Blood velocity, pressure, deformation, and strain contours were simulated and analyzed through three-dimensional modeling. Compared to the unassisted aorta, the device provided an increase in blood flow displacement of an additional 15 ml of blood in the descending aorta, brachiocephalic, carotid, and subclavian arteries. The maximum von Mises stress distribution across the aortic vessel was higher than the stress imposed on the system in the unassisted heart, with values of 3.3 MPa and 0.28 MPa, respectively. Numerical investigation of structural responses revealed that no remarkable force was exerted on the aortic valve by the device at the descending aorta.ConclusionWe present the numerical investigation of a counterpulsation device around the descending aorta that has not previously been tested on human or animal models. While this extra-aortic balloon pump (EABP) did not show a significant improvement in coronary perfusion, there is room for improvement in further studies to optimize the geometry of the balloon. Additional investigations are required to determine the efficacy of this device and its safety before in-vivo experimental studies are pursued. This simulation has clinical relevance when choosing an appropriate cardiac assist device to address patient-specific physiological and pathological conditions.     

Estimation of tissue contractility from cardiac cine-MRI using a biomechanical heart model

The objective of this paper is to propose and assess an estimation procedure—based on data assimilation principles—well suited to obtain some regional values of key biophysical parameters in a beating heart model, using actual Cine-MR images. The motivation is twofold: (1) to provide an automatic tool for personalizing the characteristics of a cardiac model in order to achieve predictivity in patient-specific modeling and (2) to obtain some useful information for diagnosis purposes in the estimated quantities themselves. In order to assess the global methodology, we specifically devised an animal experiment in which a controlled infarct was produced and data acquired before and after infarction, with an estimation of regional tissue contractility—a key parameter directly affected by the pathology—performed for every measured stage. After performing a preliminary assessment of our proposed methodology using synthetic data, we then demonstrate a full-scale application by first estimating contractility values associated with 6 regions based on the AHA subdivision, before running a more detailed estimation using the actual AHA segments. The estimation results are assessed by comparison with the medical knowledge of the specific infarct, and with late enhancement MR images. We discuss their accuracy at the various subdivision levels, in the light of the inherent modeling limitations and of the intrinsic information contents featured in the data.     

Fast Simulation of Mechanical Heterogeneity in the Electrically Asynchronous Heart Using the MultiPatch Module

Cardiac electrical asynchrony occurs as a result of cardiac pacing or conduction disorders such as left bundle-branch block (LBBB). Electrically asynchronous activation causes myocardial contraction heterogeneity that can be detrimental for cardiac function. Computational models provide a tool for understanding pathological consequences of dyssynchronous contraction. Simulations of mechanical dyssynchrony within the heart are typically performed using the finite element method, whose computational intensity may present an obstacle to clinical deployment of patient-specific models. We present an alternative based on the CircAdapt lumped-parameter model of the heart and circulatory system, called the MultiPatch module. Cardiac walls are subdivided into an arbitrary number of patches of homogeneous tissue. Tissue properties and activation time can differ between patches. All patches within a wall share a common wall tension and curvature. Consequently, spatial location within the wall is not required to calculate deformation in a patch. We test the hypothesis that activation time is more important than tissue location for determining mechanical deformation in asynchronous hearts. We perform simulations representing an experimental study of myocardial deformation induced by ventricular pacing, and a patient with LBBB and heart failure using endocardial recordings of electrical activation, wall volumes, and end-diastolic volumes. Direct comparison between simulated and experimental strain patterns shows both qualitative and quantitative agreement between model fibre strain and experimental circumferential strain in terms of shortening and rebound stretch during ejection. Local myofibre strain in the patient simulation shows qualitative agreement with circumferential strain patterns observed in the patient using tagged MRI. We conclude that the MultiPatch module produces realistic regional deformation patterns in the asynchronous heart and that activation time is more important than tissue location within a wall for determining myocardial deformation. The CircAdapt model is therefore capable of fast and realistic simulations of dyssynchronous myocardial deformation embedded within the closed-loop cardiovascular system.     

A framework for biomechanics simulations using four-chamber cardiac models

Computational cardiac models have been extensively used to study different cardiac biomechanics; specifically, finite-element analysis has been one of the tools used to study the internal stresses and strains in the cardiac wall during the cardiac cycle. Cubic-Hermite finite element meshes have been used for simulating cardiac biomechanics due to their convergence characteristics and their ability to capture smooth geometries compactly–fewer elements are needed to build the cardiac geometry–compared to linear tetrahedral meshes. Such meshes have previously been used only with simple ventricular geometries with non-physiological boundary conditions due to challenges associated with creating cubic-Hermite meshes of the complex heart geometry. However, it is critical to accurately capture the different geometric characteristics of the heart and apply physiologically equivalent boundary conditions to replicate the in vivo heart motion. In this work, we created a four-chamber cardiac model utilizing cubic-Hermite elements and simulated a full cardiac cycle by coupling the 3D finite element model with a lumped circulation model. The myocardial fiber-orientations were interpolated within the mesh using the Log-Euclidean method to overcome the singularity associated with interpolation of orthogonal matrices. Physiologically equivalent rigid body constraints were applied to the nodes along the valve plane and the accuracy of the resulting simulations were validated using open source clinical data. We then simulated a complete cardiac cycle of a healthy heart and a heart with acute myocardial infarction. We compared the pumping functionality of the heart for both cases by calculating the ventricular work. We observed a 20% reduction in acute work done by the heart immediately after myocardial infarction. The myocardial wall displacements obtained from the four-chamber model are comparable to actual patient data, without requiring complicated non-physiological boundary conditions usually required in truncated ventricular heart models.     

In vivo estimation of elastic heterogeneity in an infarcted human heart

In myocardial infarction, muscle tissue of the heart is damaged as a result of ceased or severely impaired blood flow. Survivors have an increased risk of further complications, possibly leading to heart failure. Material properties play an important role in determining post-infarction outcome. Due to spatial variation in scarring, material properties can be expected to vary throughout the tissue of a heart after an infarction. In this study we propose a data assimilation technique that can efficiently estimate heterogeneous elastic material properties in a personalized model of cardiac mechanics. The proposed data assimilation is tested on a clinical dataset consisting of regional left ventricular strains and in vivo pressures during atrial systole from a human with a myocardial infarction. Good matches to regional strains are obtained, and simulated equi-biaxial tests are carried out to demonstrate regional heterogeneities in stress–strain relationships. A synthetic data test shows a good match of estimated versus ground truth material parameter fields in the presence of no to low levels of noise. This study is the first to apply adjoint-based data assimilation to the important problem of estimating cardiac elastic heterogeneities in 3-D from medical images.     

Model systems for the discovery and development of antiarrhythmic drugs

Cardiovascular diseases are the leading cause of mortality worldwide and about 25% of cardiovascular deaths are due to disturbances in cardiac rhythm or “arrhythmias”. Arrhythmias were traditionally treated with antiarrhythmic drugs, but increasing awareness of the risks of presently available antiarrhythmic agents has greatly limited their usefulness. Most common treatment algorithms still involve small molecule drugs, and antiarrhythmic agents with improved efficacy and safety are sorely needed. This paper reviews the model systems that are available for discovery and development of new antiarrhythmic drugs. We begin with a presentation of screening methods used to identify specific channel-interacting agents, with a particular emphasis on high-throughput screens. Traditional manual electrophysiological methods, automated electrophysiology, fluorescent dye methods, flux assays and radioligand binding assays are reviewed. We then discuss a variety of relevant arrhythmia models. Two models are widely used in testing for arrhythmogenic actions related to excess action potential prolongation, an important potential adverse effect of chemical entities affecting cardiac rhythm: the methoxamine-sensitized rabbit and the dog with chronic atrioventricular block. We then go on to review models used to assess potential antiarrhythmic actions. For ventricular arrhythmias, chemical induction methods, cardiac or neural electrical stimulation, ischaemic heart models and models of cardiac channelopathies can be used to identify effective antiarrhythmic agents. For atrial arrhythmias, potentially useful models include vagally-maintained atrial fibrillation, acute asphyxia with atrial burst-pacing, sterile pericarditis, Y-shaped atria surgical incisions, chronic atrial dilation models, atrial electrical remodelling due to sustained atrial tachycardia, heart failure-related atrial remodelling, and acute atrial ischaemia. It is hoped that the new technologies now available and the recently-developed models for arrhythmia-response assessment will permit the introduction of newer and more effective antiarrhythmic therapies in the near future.     

心力衰竭的物理诊断现状

心力衰竭是各种心脏疾病发展的终末阶段,及时准确的确诊心力衰竭、辨别其病情变化可进一步指导治疗及缩短住院时间,并为心力衰竭的预后评价提供参考依据。目前,确诊心力衰竭主要基于患者的临床症状、体征及各种影像学表现、生化指标等做出综合评价,而物理诊断是诊断心力衰竭不可或缺的依据。心脏彩超已应用于临床多年,可从多个层面评估左室充盈压,是初步评价心脏功能的便捷措施。核心脏病学可评估心脏交感神经支配的区域及激活模式,预测心力衰竭的发病率和死亡率。心脏磁共振可用于进行危险分层及确定是否适合手术/介入治疗。此外,心脏CT及心导管术也是用于评价心功能的常见物理检查方法。本文主要总结了目前各种诊断心力衰竭的物理诊断方法的最新进展,以进一步指导临床实践。     

Patient-specific Modeling of the Heart: Estimation of Ventricular Fiber Orientations

Patient-specific simulations of heart (dys)function aimed at personalizing cardiac therapy are hampered by the absence of in vivo imaging technology for clinically acquiring myocardial fiber orientations. The objective of this project was to develop a methodology to estimate cardiac fiber orientations from in vivo images of patient heart geometries. An accurate representation of ventricular geometry and fiber orientations was reconstructed, respectively, from high-resolution ex vivo structural magnetic resonance (MR) and diffusion tensor (DT) MR images of a normal human heart, referred to as the atlas. Ventricular geometry of a patient heart was extracted, via semiautomatic segmentation, from an in vivo computed tomography (CT) image. Using image transformation algorithms, the atlas ventricular geometry was deformed to match that of the patient. Finally, the deformation field was applied to the atlas fiber orientations to obtain an estimate of patient fiber orientations. The accuracy of the fiber estimates was assessed using six normal and three failing canine hearts. The mean absolute difference between inclination angles of acquired and estimated fiber orientations was 15.4 °. Computational simulations of ventricular activation maps and pseudo-ECGs in sinus rhythm and ventricular tachycardia indicated that there are no significant differences between estimated and acquired fiber orientations at a clinically observable level.The new insights obtained from the project will pave the way for the development of patient-specific models of the heart that can aid physicians in personalized diagnosis and decisions regarding electrophysiological interventions.     

An integrated approach to patient-specific predictive modeling for single ventricle heart palliation

In patients with congenital heart disease and a single ventricle (SV), ventricular support of the circulation is inadequate, and staged palliative surgery (usually 3 stages) is needed for treatment. In the various palliative surgical stages individual differences in the circulation are important and patient-specific surgical planning is ideal. In this study, an integrated approach between clinicians and engineers has been developed, based on patient-specific multi-scale models, and is here applied to predict stage 2 surgical outcomes. This approach involves four distinct steps: (1) collection of pre-operative clinical data from a patient presenting for SV palliation, (2) construction of the pre-operative model, (3) creation of feasible virtual surgical options which couple a three-dimensional model of the surgical anatomy with a lumped parameter model (LPM) of the remainder of the circulation and (4) performance of post-operative simulations to aid clinical decision making. The pre-operative model is described, agreeing well with clinical flow tracings and mean pressures. Two surgical options (bi-directional Glenn and hemi-Fontan operations) are virtually performed and coupled to the pre-operative LPM, with the hemodynamics of both options reported. Results are validated against postoperative clinical data. Ultimately, this work represents the first patient-specific predictive modeling of stage 2 palliation using virtual surgery and closed-loop multi-scale modeling.     

Electromechanical models of the ventricles

Computational modeling has traditionally played an important role in dissecting the mechanisms for cardiac dysfunction. Ventricular electromechanical models, likely the most sophisticated virtual organs to date, integrate detailed information across the spatial scales of cardiac electrophysiology and mechanics and are capable of capturing the emergent behavior and the interaction between electrical activation and mechanical contraction of the heart. The goal of this review is to provide an overview of the latest advancements in multiscale electromechanical modeling of the ventricles. We first detail the general framework of multiscale ventricular electromechanical modeling and describe the state of the art in computational techniques and experimental validation approaches. The powerful utility of ventricular electromechanical models in providing a better understanding of cardiac function is then demonstrated by reviewing the latest insights obtained by these models, focusing primarily on the mechanisms by which mechanoelectric coupling contributes to ventricular arrythmogenesis, the relationship between electrical activation and mechanical contraction in the normal heart, and the mechanisms of mechanical dyssynchrony and resynchronization in the failing heart. Computational modeling of cardiac electromechanics will continue to complement basic science research and clinical cardiology and holds promise to become an important clinical tool aiding the diagnosis and treatment of cardiac disease.     

Precision medicine in human heart modeling

Precision medicine is a new frontier in healthcare that uses scientific methods to customize medical treatment to the individual genes, anatomy, physiology, and lifestyle of each person. In cardiovascular health, precision medicine has emerged as a promising paradigm to enable cost-effective solutions that improve quality of life and reduce mortality rates. However, the exact role in precision medicine for human heart modeling has not yet been fully explored. Here, we discuss the challenges and opportunities for personalized human heart simulations, from diagnosis to device design, treatment planning, and prognosis. With a view toward personalization, we map out the history of anatomic, physical, and constitutive human heart models throughout the past three decades. We illustrate recent human heart modeling in electrophysiology, cardiac mechanics, and fluid dynamics and highlight clinically relevant applications of these models for drug development, pacing lead failure, heart failure, ventricular assist devices, edge-to-edge repair, and annuloplasty. With a view toward translational medicine, we provide a clinical perspective on virtual imaging trials and a regulatory perspective on medical device innovation. We show that precision medicine in human heart modeling does not necessarily require a fully personalized, high-resolution whole heart model with an entire personalized medical history. Instead, we advocate for creating personalized models out of population-based libraries with geometric, biological, physical, and clinical information by morphing between clinical data and medical histories from cohorts of patients using machine learning. We anticipate that this perspective will shape the path toward introducing human heart simulations into precision medicine with the ultimate goals to facilitate clinical decision making, guide treatment planning, and accelerate device design.

     

Generating fibre orientation maps in human heart models using Poisson interpolation

Smoothly varying muscle fibre orientations in the heart are critical to its electrical and mechanical function. From detailed histological studies and diffusion tensor imaging, we now know that fibre orientations in humans vary gradually from approximately ? 70° in the outer wall to +80° in the inner wall. However, the creation of fibre orientation maps for computational analyses remains one of the most challenging problems in cardiac electrophysiology and cardiac mechanics. Here, we show that Poisson interpolation generates smoothly varying vector fields that satisfy a set of user-defined constraints in arbitrary domains. Specifically, we enforce the Poisson interpolation in the weak sense using a standard linear finite element algorithm for scalar-valued second-order boundary value problems and introduce the feature to be interpolated as a global unknown. User-defined constraints are then simply enforced in the strong sense as Dirichlet boundary conditions. We demonstrate that the proposed concept is capable of generating smoothly varying fibre orientations, quickly, efficiently and robustly, both in a generic bi-ventricular model and in a patient-specific human heart. Sensitivity analyses demonstrate that the underlying algorithm is conceptually able to handle both arbitrarily and uniformly distributed user-defined constraints; however, the quality of the interpolation is best for uniformly distributed constraints. We anticipate our algorithm to be immediately transformative to experimental and clinical settings, in which it will allow us to quickly and reliably create smooth interpolations of arbitrary fields from data-sets, which are sparse but uniformly distributed.     

Conditional Gene Targeting of Connexin43: Exploring the Consequences of Gap Junction Remodeling in the Heart

Abnormalities in cardiac gap junction expression have been postulated to contribute to arrhythmias and ventricular dysfunction. We investigated the role of cardiac gap junctions by generating a heart-specific conditional knock-out (CKO) of connexin43 (Cx43), the major cardiac gap junction protein. While the Cx43 CKO mice have normal heart structure and contractile function, they die suddenly from spontaneous ventricular arrhythmias. Because abnormalities in gap junction expression in the diseased heart can be focal, we also generated chimeric mice formed from Cx43-null embryonic stem (ES) cells and wildtype recipient blastocysts. Heterogeneous Cx43 expression in the chimeric mice resulted in conduction defects and depressed contractile function. These novel genetic murine models of Cx43 loss of function in the adult mouse heart define gap junctional abnormalities as a key molecular feature of the arrhythmogenic substrate and an important factor in heart dysfunction.     

Models of cardiac electromechanics based on individual hearts imaging data Image-based electromechanical models of the heart

Current multi-scale computational models of ventricular electromechanics describe the full process of cardiac contraction on both the micro- and macro- scales including: the depolarization of cardiac cells, the release of calcium from intracellular stores, tension generation by cardiac myofilaments, and mechanical contraction of the whole heart. Such models are used to reveal basic mechanisms of cardiac contraction as well as the mechanisms of cardiac dysfunction in disease conditions. In this paper, we present a methodology to construct finite element electromechanical models of ventricular contraction with anatomically accurate ventricular geometry based on magnetic resonance and diffusion tensor magnetic resonance imaging of the heart. The electromechanical model couples detailed representations of the cardiac cell membrane, cardiac myofilament dynamics, electrical impulse propagation, ventricular contraction, and circulation to simulate the electrical and mechanical activity of the ventricles. The utility of the model is demonstrated in an example simulation of contraction during sinus rhythm using a model of the normal canine ventricles.     

Combining Smaller Patch,RV Remodeling and Tissue Regeneration in Pulmonary Valve Replacement Surgery Design May Lead to Better Post-Surgery RV Cardiac Function for Patients with Tetralogy of Fallot

Patients with repaired Tetralogy of Fallot (ToF), a congenital heart defect which includes a ventricular septal defect and severe right ventricular outflow obstruction, account for the majority of cases with late onset right ventricle (RV) failure. The current surgical approach, which includes pulmonary valve replacement/insertion (PVR), has yielded mixed results. A computational parametric study using 7 patient-specific RV/LV models based on cardiac magnetic resonance (CMR) data as "virtual surgery" was performed to investigate the impact of patch size, RV remodeling and tissue regeneration in PVR surgery design on RV cardiac functions. Two patch sizes, three degrees of scar trimming (RV volume shrinkages: 9%, 17%, 25%) and hypothetical use of regenerated myocardium as replacement of patch and scar were considered in these models. Our preliminary results indicate that each of the three techniques (smaller patch, RV remodeling, and myocardium regeneration) had modest improvement on post-PVR RV ejection fraction (from 1.76%-4% over the conventional PVR procedure) and combination of all three techniques had the best performance (a 4.74% improvement in ejection fraction over the conventional PVR, for the patient studied). Changes in RV stress, strain and curvatures were also observed. However, their linkages to RV ejection fraction were less clear. Further investigations are required to confirm our findings.     

Conditional gene targeting of connexin43: exploring the consequences of gap junction remodeling in the heart

Abnormalities in cardiac gap junction expression have been postulated to contribute to arrhythmias and ventricular dysfunction. We investigated the role of cardiac gap junctions by generating a heart-specific conditional knock-out (CKO) of connexin43 (Cx43), the major cardiac gap junction protein. While the Cx43 CKO mice have normal heart structure and contractile function, they die suddenly from spontaneous ventricular arrhythmias. Because abnormalities in gap junction expression in the diseased heart can be focal, we also generated chimeric mice formed from Cx43-null embryonic stem (ES) cells and wildtype recipient blastocysts. Heterogeneous Cx43 expression in the chimeric mice resulted in conduction defects and depressed contractile function. These novel genetic murine models of Cx43 loss of function in the adult mouse heart define gap junctional abnormalities as a key molecular feature of the arrhythmogenic substrate and an important factor in heart dysfunction.