Inferring Parameters of Pyramidal Neuron Excitability in Mouse Models of Alzheimer's Disease Using Biophysical Modeling and Deep Learning.

Alzheimer's disease (AD) is believed to occur when abnormal amounts of the proteins amyloid beta and tau aggregate in the brain, resulting in a progressive loss of neuronal function. Hippocampal neurons in transgenic mice with amyloidopathy or tauopathy exhibit altered intrinsic excitability properties. We used deep hybrid modeling (DeepHM), a recently developed parameter inference technique that combines deep learning with biophysical modeling, to map experimental data recorded from hippocampal CA1 neurons in transgenic AD mice and age-matched wildtype littermate controls to the parameter space of a conductance-based CA1 model.

Novel and flexible parameter estimation methods for data-consistent inversion in mechanistic modelling.

Predictions for physical systems often rely upon knowledge acquired from ensembles of entities, e.g. ensembles of cells in biological sciences.

Machine Learning Prediction of Cardiac Resynchronisation Therapy Response From Combination of Clinical and Model-Driven Data.

Up to 30-50% of chronic heart failure patients who underwent cardiac resynchronization therapy (CRT) do not respond to the treatment. Therefore, patient stratification for CRT and optimization of CRT device settings remain a challenge. The main goal of our study is to develop a predictive model of CRT outcome using a combination of clinical data recorded in patients before CRT and simulations of the response to biventricular (BiV) pacing in personalized computational models of the cardiac electrophysiology.

Generative adversarial networks for construction of virtual populations of mechanistic models: simulations to study Omecamtiv Mecarbil action.

Biophysical models are increasingly used to gain mechanistic insights by fitting and reproducing experimental and clinical data. The inherent variability in the recorded datasets, however, presents a key challenge. In this study, we present a novel approach, which integrates mechanistic modeling and machine learning to analyze in vitro cardiac mechanics data and solve the inverse problem of model parameter inference.

The 'Digital Twin' to enable the vision of precision cardiology.

Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the 'digital twin' of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions.

Model order reduction for left ventricular mechanics via congruency training.

Computational models of the cardiovascular system and specifically heart function are currently being investigated as analytic tools to assist medical practice and clinical trials. To achieve clinical utility, models should be able to assimilate the diagnostic multi-modality data available for each patient and generate consistent representations of the underlying cardiovascular physiology. While finite element models of the heart can naturally account for patient-specific anatomies reconstructed from medical images, optimizing the many other parameters driving simulated cardiac functions is challenging due to computational complexity.

Global Sensitivity Analysis of Ventricular Myocyte Model-Derived Metrics for Proarrhythmic Risk Assessment.

Multiscale computational models of the heart are being extensively investigated for improved assessment of drug-induced torsades de pointes (TdP) risk, a fatal side effect of many drugs. Model-derived metrics such as action potential duration and net charge carried by ionic currents () have been proposed as potential candidates for TdP risk stratification after being tested on small datasets. Unlike purely statistical approaches, model-derived metrics are thought to provide mechanism-based classification.

Gaussian Process Regressions for Inverse Problems and Parameter Searches in Models of Ventricular Mechanics.

Patient specific models of ventricular mechanics require the optimization of their many parameters under the uncertainties associated with imaging of cardiac function. We present a strategy to reduce the complexity of parametric searches for 3-D FE models of left ventricular contraction. The study employs automatic image segmentation and analysis of an image database to gain geometric features for several classes of patients.

Novel Two-Step Classifier for Torsades de Pointes Risk Stratification from Direct Features.

While pre-clinical Torsades de Pointes (TdP) risk classifiers had initially been based on drug-induced block of hERG potassium channels, it is now well established that improved risk prediction can be achieved by considering block of non-hERG ion channels. The current multi-channel TdP classifiers can be categorized into two classes. First, the classifiers that take as input the values of drug-induced block of ion channels (direct features).

Estimating the probabilities of rare arrhythmic events in multiscale computational models of cardiac cells and tissue.

Ectopic heartbeats can trigger reentrant arrhythmias, leading to ventricular fibrillation and sudden cardiac death. Such events have been attributed to perturbed Ca2+ handling in cardiac myocytes leading to spontaneous Ca2+ release and delayed afterdepolarizations (DADs). However, the ways in which perturbation of specific molecular mechanisms alters the probability of ectopic beats is not understood.

Verification of cardiac mechanics software: benchmark problems and solutions for testing active and passive material behaviour.

Models of cardiac mechanics are increasingly used to investigate cardiac physiology. These models are characterized by a high level of complexity, including the particular anisotropic material properties of biological tissue and the actively contracting material. A large number of independent simulation codes have been developed, but a consistent way of verifying the accuracy and replicability of simulations is lacking.

A high-resolution computational model of the deforming human heart.

Modeling of the heart ventricles is one of the most challenging tasks in soft tissue mechanics because cardiac tissue is a strongly anisotropic incompressible material with an active component of stress. In most current approaches with active force, the number of degrees of freedom (DOF) is limited by the direct method of solution of linear systems of equations. We develop a new approach for high-resolution heart models with large numbers of DOF by: (1) developing a hex-dominant finite element mixed formulation and (2) developing a Krylov subspace iterative method that is able to solve the system of linearized equations for saddle-point problems with active stress.

Optimizing cardiac resynchronization therapy to minimize ATP consumption heterogeneity throughout the left ventricle: a simulation analysis using a canine heart failure model.

Background: Cardiac resynchronization therapy (CRT) has been demonstrated to lead to restoration of oxygen consumption homogeneity throughout the left ventricle (LV), which is important for long-term reverse remodeling of the ventricles. However, research has focused exclusively on identifying the LV pacing sites that led to acute hemodynamic improvements. It remains unclear whether there exist LV pacing sites that could both improve the hemodynamics and result in ATP consumption homogeneity throughout the LV, thus maximizing both CRT short-term and long-term benefits.

Efficient preloading of the ventricles by a properly timed atrial contraction underlies stroke work improvement in the acute response to cardiac resynchronization therapy.

Background: The acute response to cardiac resynchronization therapy (CRT) has been shown to be due to 3 mechanisms: resynchronization of ventricular contraction, efficient preloading of the ventricles by a properly timed atrial contraction, and mitral regurgitation reduction. However, the contribution of each of the 3 mechanisms to the acute response to CRT, specifically stroke work improvement, has not been quantified.

Objective: To use a magnetic resonance image-based anatomically accurate 3-dimensional model of failing canine ventricular electromechanics to quantify the contribution of each of the 3 mechanisms to stroke work improvement and identify the predominant mechanisms.

Towards real-time simulation of cardiac electrophysiology in a human heart at high resolution.

We have developed the capability to rapidly simulate cardiac electrophysiological phenomena in a human heart discretised at a resolution comparable with the length of a cardiac myocyte. Previous scientific investigation has generally invoked simplified geometries or coarse-resolution hearts, with simulation duration limited to 10s of heartbeats. Using state-of-the-art high-performance computing techniques coupled with one of the most powerful computers available (the 20 PFlop/s IBM BlueGene/Q at Lawrence Livermore National Laboratory), high-resolution simulation of the human heart can now be carried out over 1200 times faster compared with published results in the field.

Effects of mechano-electric feedback on scroll wave stability in human ventricular fibrillation.

Recruitment of stretch-activated channels, one of the mechanisms of mechano-electric feedback, has been shown to influence the stability of scroll waves, the waves that underlie reentrant arrhythmias. However, a comprehensive study to examine the effects of recruitment of stretch-activated channels with different reversal potentials and conductances on scroll wave stability has not been undertaken; the mechanisms by which stretch-activated channel opening alters scroll wave stability are also not well understood. The goals of this study were to test the hypothesis that recruitment of stretch-activated channels affects scroll wave stability differently depending on stretch-activated channel reversal potential and channel conductance, and to uncover the relevant mechanisms underlying the observed behaviors.

Mechanisms Underlying Isovolumic Contraction and Ejection Peaks in Seismocardiogram Morphology.

A three-dimensional (3D) finite element electromechanical model of the heart is employed in simulations of seismocardiograms (SCGs). To simulate SCGs, a previously developed 3D model of ventricular contraction is extended by adding the mechanical interaction of the heart with the chest and internal organs. The proposed model reproduces the major peaks of seismocardiographic signals during the phases of the cardiac cycle.

Comparison of the effects of continuous and pulsatile left ventricular-assist devices on ventricular unloading using a cardiac electromechanics model.

Left ventricular-assist devices (LVADs) are used to supply blood to the body of patients with heart failure. Pressure unloading is greater for counter-pulsating LVADs than for continuous LVADs. However, several clinical trials have demonstrated that myocardial recovery is similar for both types of LVAD.

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.

Mapping of cardiac electrical activation with electromechanical wave imaging: an in silico-in vivo reciprocity study.

Background: Electromechanical wave imaging (EWI) is an entirely noninvasive, ultrasound-based imaging method capable of mapping the electromechanical activation sequence of the ventricles in vivo. Given the broad accessibility of ultrasound scanners in the clinic, the application of EWI could constitute a flexible surrogate for the 3-dimensional electrical activation.

Objective: The purpose of this report is to reproduce the electromechanical wave (EW) using an anatomically realistic electromechanical model, and establish the capability of EWI to map the electrical activation sequence in vivo when pacing from different locations.

Models of stretch-activated ventricular arrhythmias.

One of the most important components of mechanoelectric coupling is stretch-activated channels, sarcolemmal channels that open upon mechanical stimuli. Uncovering the mechanisms by which stretch-activated channels contribute to ventricular arrhythmogenesis under a variety of pathologic conditions is hampered by the lack of experimental methodologies that can record the 3-dimensional electromechanical activity simultaneously at high spatiotemporal resolution. Computer modeling provides such an opportunity.

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.

Comparative analysis of three different modalities for characterization of the seismocardiogram.

We introduce and compare three different modalities to study seismocardiogram (SCG) and its correlation with cardiac events. We used an accelerometer attached to the subject sternum to get a reference measure. Cardiac events were then approximately identified using echocardiography.

Mechanisms of mechanically induced spontaneous arrhythmias in acute regional ischemia.

Rationale: Although ventricular premature beats (VPBs) during acute regional ischemia have been linked to mechanical stretch of ischemic tissue, whether and how ischemia-induced mechanical dysfunction can induce VPBs and facilitate their degradation into reentrant arrhythmias has not been yet addressed.

Objective: This study used a novel multiscale electromechanical model of the rabbit ventricles to investigate the origin of and the substrate for spontaneous arrhythmias arising from ischemia-induced electrophysiological and mechanical changes.

Methods And Results: Two stages of ischemia were simulated.

Towards predictive modelling of the electrophysiology of the heart.

The simulation of cardiac electrical function is an example of a successful integrative multiscale modelling approach that is directly relevant to human disease. Today we stand at the threshold of a new era, in which anatomically detailed, tomographically reconstructed models are being developed that integrate from the ion channel to the electromechanical interactions in the intact heart. Such models hold high promise for interpretation of clinical and physiological measurements, for improving the basic understanding of the mechanisms of dysfunction in disease, such as arrhythmias, myocardial ischaemia and heart failure, and for the development and performance optimization of medical devices.