Detailed Electromechanical Model of Ventricular Wedge

We developed a three-dimensional computational model for describing electro-mechanical behavior of wedge-shaped preparation extracted from the left ventricular wall including excitation wave propagation, high-resolution geometry and fiber orientation. The cardiac tissue is simulated by an incompressible hyperplastic material. We used non-linear partial differential equations describing the deformation of the cardiac tissue, and a detailed 'Ekaterinburg-Oxford' (EO) cellular model of the electrical and mechanical activity of the cardiomyocytes in the tissue. Electro-mechanical coupling in the model accounts for mechano-electric feedbacks both in the cells and in the tissue. Numerical experiments with the model of the wedge preparation formed of initially identical cardiomyocytes revealed that electrical and mechanical interaction between the cells, as well as intracellular mechanoelectric feedbacks caused pronounced nonuniformity of their behavior. © 2018 Creative Commons Attribution.Russian Foundation for Basic Research, RFBR: 18-31-00416Russian Academy of Sciences, RAS: АААА-А18- 118020590030-1This work was carried out within the framework of the IIF UrB RAS themes (Nos. AAAA-A18-118020590031-8) and was supported by RFBR (18-31-00416), the Program of the Presidium RAS #27 (project АААА-А18- 118020590030-1) and Act 211 Government of the Russian Federation, contract № 02.A03.21.0006

Unsupervised deep network for image texture transformation: Improving the quality of cross-correlation analysis and mechanical vortex visualisation during cardiac fibrillation

Visualisation of cardiac fibrillation plays a very considerable role in cardiophysiological study and clinical applications. One of the ways to obtain the image of these phenomena is the registration of mechanical displacement fields reflecting the track from electrical activity. In this work, we read these fields using cross-correlation analysis from the video of open pig's epicardium at the start of fibrillation recorded with electrocardiogram. However, the quality of obtained displacement fields remains low due to the weak pixels heterogeneity of the frames. It disables to see more clearly such interesting phenomena as mechanical vortexes that underline the mechanical dysfunction of fibrillation. The applying of chemical or mechanical markers to solve this problem can affect the course of natural processes and falsify the results. Therefore, we developed a novel scheme of an unsupervised deep neural network that is based on the state-of-art positional coding technology for a multilayer perceptron. This network enables to generate a couple of frames with a more heterogeneous pixel texture, that is more suitable for cross-correlation analysis methods, from two consecutive frames. The novel network scheme was tested on synthetic pairs of images with different texture heterogeneity and frequency of displacement fields and also it was compared with different filters on our cardiac tissue image dataset. The testing showed that the displacement fields obtained with our method are closer to the ground truth than those which were computed only with the cross-correlation analysis in low contrast images case where filtering is impossible. Moreover, our model showed the best results comparing with the one of the popular filter CLAHE on our dataset. As a result, using our approach, it was possible to register more clearly a mechanical vortex on the epicardium at the start of fibrillation continuously for several milliseconds for the first time. © 2023 The Author(s)Ministry of Education and Science of the Russian Federation, Minobrnauka; Ural Federal University, UrFUThe research funding from the Ministry of Science and Higher Education of the Russian Federation (Ural Federal University project within the Priority-2030 Program) is gratefully acknowledged

In silico analysis of the contribution of cardiomyocyte-fibroblast electromechanical interaction to the arrhythmia

Although fibroblasts are about 5–10 times smaller than cardiomyocytes, their number in the ventricle is about twice that of cardiomyocytes. The high density of fibroblasts in myocardial tissue leads to a noticeable effect of their electromechanical interaction with cardiomyocytes on the electrical and mechanical functions of the latter. Our work focuses on the analysis of the mechanisms of spontaneous electrical and mechanical activity of the fibroblast-coupled cardiomyocyte during its calcium overload, which occurs in a variety of pathologies, including acute ischemia. For this study, we developed a mathematical model of the electromechanical interaction between cardiomyocyte and fibroblasts and used it to simulate the impact of overloading cardiomyocytes. In contrast to modeling only the electrical interaction between cardiomyocyte and fibroblasts, the following new features emerge in simulations with the model that accounts for both electrical and mechanical coupling and mechano-electrical feedback loops in the interacting cells. First, the activity of mechanosensitive ion channels in the coupled fibroblasts depolarizes their resting potential. Second, this additional depolarization increases the resting potential of the coupled myocyte, thus augmenting its susceptibility to triggered activity. The triggered activity associated with the cardiomyocyte calcium overload manifests itself in the model either as early afterdepolarizations or as extrasystoles, i.e., extra action potentials and extra contractions. Analysis of the model simulations showed that mechanics contribute significantly to the proarrhythmic effects in the cardiomyocyte overloaded with calcium and coupled with fibroblasts, and that mechano-electrical feedback loops in both the cardiomyocyte and fibroblasts play a key role in this phenomenon. Copyright © 2023 Kursanov, Balakina-Vikulova, Solovyova, Panfilov and Katsnelson.Russian Science Foundation, RSF: 21-14-00226The study was supported by the Russian Science Foundation grant No. 21-14-00226

Functional heterogeneity arising due to electrical and mechanical interactions between cardial myocytes in mathematical model of homogeneous myocardial fiber

We developed a mathematical model describing heart muscle strand as a one-dimensional continuous medium of cardiomyocytes, through which electrical excitation propagates and excites the cells for contraction. Intracellular excitation-contraction coupling is presented by means of our earlier published model describing mechanical function of the cardiomyocyte evoked by action potential development and calcium activation of cross-bridge formation. The whole strand model simulates also mechanical interaction between the cardiomyocytes in the tissue and accounts for both intracellular and intercellular electro-mechanical coupling and mechano-electric feedback mechanisms. Numerical experiments with the strand formed of initially identical cardiomyocytes revealed that electrical and mechanical interaction between the cells, as well as intracellular mechano-electric feedbacks caused pronounced nonuniformity of their behavior. Model analysis suggests that cooperative mechanisms of myofilament calcium activation play the key role in dynamic adjustment of electrical and mechanical activity of the interacting cardiomyocytes in the tissue. © 2015

The importance of mechanical conditions in the testing of excitation abnormalities in a population of electro-mechanical models of human ventricular cardiomyocytes

Background: Populations of in silico electrophysiological models of human cardiomyocytes represent natural variability in cell activity and are thoroughly calibrated and validated using experimental data from the human heart. The models have been shown to predict the effects of drugs and their pro-arrhythmic risks. However, excitation and contraction are known to be tightly coupled in the myocardium, with mechanical loads and stretching affecting both mechanics and excitation through mechanisms of mechano-calcium-electrical feedback. However, these couplings are not currently a focus of populations of cell models. Aim: We investigated the role of cardiomyocyte mechanical activity under different mechanical conditions in the generation, calibration, and validation of a population of electro-mechanical models of human cardiomyocytes. Methods: To generate a population, we assumed 11 input parameters of ionic currents and calcium dynamics in our recently developed TP + M model as varying within a wide range. A History matching algorithm was used to generate a non-implausible parameter space by calibrating the action potential and calcium transient biomarkers against experimental data and rejecting models with excitation abnormalities. The population was further calibrated using experimental data on human myocardial force characteristics and mechanical tests involving variations in preload and afterload. Models that passed the mechanical tests were validated with additional experimental data, including the effects of drugs with high or low pro-arrhythmic risk. Results: More than 10% of the models calibrated on electrophysiological data failed mechanical tests and were rejected from the population due to excitation abnormalities at reduced preload or afterload for cell contraction. The final population of accepted models yielded action potential, calcium transient, and force/shortening outputs consistent with experimental data. In agreement with experimental and clinical data, the models demonstrated a high frequency of excitation abnormalities in simulations of Dofetilide action on the ionic currents, in contrast to Verapamil. However, Verapamil showed a high frequency of failed contractions at high concentrations. Conclusion: Our results highlight the importance of considering mechanoelectric coupling in silico cardiomyocyte models. Mechanical tests allow a more thorough assessment of the effects of interventions on cardiac function, including drug testing. Copyright © 2023 Dokuchaev, Kursanov, Balakina-Vikulova, Katsnelson and Solovyova.Russian Science Foundation, RSF: 19-14-00134This work was supported by Russian Science Foundation grant No. 19-14-00134