From Millimeters to Micrometers; Re-introducing Myocytes in Models of Cardiac Electrophysiology
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ABSTRACT:
SUBMITTER: Jæger K
PROVIDER: S-EPMC8578869 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The traditional cardiac model-building paradigm involves constructing a composite model using data collected from many cells. Equations are derived for each relevant cellular component (e.g., ion channel, exchanger) independently. After the equations for all components are combined to form the composite model, a subset of parameters is tuned, often arbitrarily and by hand, until the model output matches a target objective, such as an action potential. Unfortunately, such models often fail to accurately simulate behavior that is dynamically dissimilar (e.g., arrhythmia) to the simple target objective to which the model was fit. In this study, we develop a new approach in which data are collected via a series of complex electrophysiology protocols from single cardiac myocytes and then used to tune model parameters via a parallel fitting method known as a genetic algorithm (GA). The dynamical complexity of the electrophysiological data, which can only be fit by an automated method such as a GA, leads to more accurately parameterized models that can simulate rich cardiac dynamics. The feasibility of the method is first validated computationally, after which it is used to develop models of isolated guinea pig ventricular myocytes that simulate the electrophysiological dynamics significantly better than does a standard guinea pig model. In addition to improving model fidelity generally, this approach can be used to generate a cell-specific model. By so doing, the approach may be useful in applications ranging from studying the implications of cell-to-cell variability to the prediction of intersubject differences in response to pharmacological treatment.
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Project description:Predicting the electrical behavior of the heart, from the cellular scale to the tissue level, relies on the numerical approximation of coupled nonlinear dynamical systems. These systems describe the cardiac action potential, that is the polarization/depolarization cycle occurring at every heart beat that models the time evolution of the electrical potential across the cell membrane, as well as a set of ionic variables. Multiple solutions of these systems, corresponding to different model inputs, are required to evaluate outputs of clinical interest, such as activation maps and action potential duration. More importantly, these models feature coherent structures that propagate over time, such as wavefronts. These systems can hardly be reduced to lower dimensional problems by conventional reduced order models (ROMs) such as, e.g., the reduced basis method. This is primarily due to the low regularity of the solution manifold (with respect to the problem parameters), as well as to the nonlinear nature of the input-output maps that we intend to reconstruct numerically. To overcome this difficulty, in this paper we propose a new, nonlinear approach relying on deep learning (DL) algorithms-such as deep feedforward neural networks and convolutional autoencoders-to obtain accurate and efficient ROMs, whose dimensionality matches the number of system parameters. We show that the proposed DL-ROM framework can efficiently provide solutions to parametrized electrophysiology problems, thus enabling multi-scenario analysis in pathological cases. We investigate four challenging test cases in cardiac electrophysiology, thus demonstrating that DL-ROM outperforms classical projection-based ROMs.
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Project description:AimsCardiac optical mapping is the gold standard for measuring complex electrophysiology in ex vivo heart preparations. However, new methods for optical mapping in vivo have been elusive. We aimed at developing and validating an experimental method for performing in vivo cardiac optical mapping in pig models.Methods and resultsFirst, we characterized ex vivo the excitation-ratiometric properties during pacing and ventricular fibrillation (VF) of two near-infrared voltage-sensitive dyes (di-4-ANBDQBS/di-4-ANEQ(F)PTEA) optimized for imaging blood-perfused tissue (n = 7). Then, optical-fibre recordings in Langendorff-perfused hearts demonstrated that ratiometry permits the recording of optical action potentials (APs) with minimal motion artefacts during contraction (n = 7). Ratiometric optical mapping ex vivo also showed that optical AP duration (APD) and conduction velocity (CV) measurements can be accurately obtained to test drug effects. Secondly, we developed a percutaneous dye-loading protocol in vivo to perform high-resolution ratiometric optical mapping of VF dynamics (motion minimal) using a high-speed camera system positioned above the epicardial surface of the exposed heart (n = 11). During pacing (motion substantial) we recorded ratiometric optical signals and activation via a 2D fibre array in contact with the epicardial surface (n = 7). Optical APs in vivo under general anaesthesia showed significantly faster CV [120 (63-138) cm/s vs. 51 (41-64) cm/s; P = 0.032] and a statistical trend to longer APD90 [242 (217-254) ms vs. 192 (182-233) ms; P = 0.095] compared with ex vivo measurements in the contracting heart. The average rate of signal-to-noise ratio (SNR) decay of di-4-ANEQ(F)PTEA in vivo was 0.0671 ± 0.0090 min-1. However, reloading with di-4-ANEQ(F)PTEA fully recovered the initial SNR. Finally, toxicity studies (n = 12) showed that coronary dye injection did not generate systemic nor cardiac damage, although di-4-ANBDQBS injection induced transient hypotension, which was not observed with di-4-ANEQ(F)PTEA.ConclusionsIn vivo optical mapping using voltage ratiometry of near-infrared dyes enables high-resolution cardiac electrophysiology in translational pig models.
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Project description:BackgroundQuestionnaires are used routinely in clinical research to measure health status and quality of life. Questionnaire measurements are traditionally formally assessed by indices of reliability (the degree of measurement error) and validity (the extent to which the questionnaire measures what it is supposed to measure). Neither of these indices assesses the degree to which the questionnaire is able to discriminate between individuals, an important aspect of measurement. This paper introduces and extends an existing index of a questionnaire's ability to distinguish between individuals, that is, the questionnaire's discrimination.MethodsFerguson (1949) 1 derived an index of test discrimination, coefficient delta, for psychometric tests with dichotomous (correct/incorrect) items. In this paper a general form of the formula, deltaG, is derived for the more general class of questionnaires allowing for several response choices. The calculation and characteristics of deltaG are then demonstrated using questionnaire data (GHQ-12) from 2003-2004 British Household Panel Survey (N = 14761). Coefficients for reliability (alpha) and discrimination (deltaG) are computed for two commonly-used GHQ-12 coding methods: dichotomous coding and four-point Likert-type coding.ResultsBoth scoring methods were reliable (alpha > 0.88). However, deltaG was substantially lower (0.73) for the dichotomous coding of the GHQ-12 than for the Likert-type method (deltaG = 0.96), indicating that the dichotomous coding, although reliable, failed to discriminate between individuals.ConclusionCoefficient deltaG was shown to have decisive utility in distinguishing between the cross-sectional discrimination of two equally reliable scoring methods. Ferguson's delta has been neglected in discussions of questionnaire design and performance, perhaps because it has not been implemented in software and was restricted to questionnaires with dichotomous items, which are rare in health care research. It is suggested that the more general formula introduced here is reported as deltaG, to avoid the implication that items are dichotomously coded.
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Project description:We present an applied approach to optimal experimental design and estimability analysis for mechanistic models of cardiac electrophysiology, by extending and improving on existing computational and graphical methods. These models are 'multi-scale' in the sense that the modeled phenomena occur over multiple spatio-temporal scales (e.g., single cell vs. whole heart). As a consequence, empirical observations of multi-scale phenomena often require multiple distinct experimental procedures. We discuss the use of conventional optimal design criteria (e.g., D-optimality) in combining experimental observations across multiple scales and multiple experimental modalities. In addition, we present an improved 'sensitivity plot' - a graphical assessment of parameter estimability - that overcomes a well-known limitation in this context. These techniques are demonstrated using a working Hodgkin-Huxley cell model and three simulated experimental procedures: single cell stimulation, action potential propagation, and voltage clamp. In light of these assessments, we discuss two model modifications that improve parameter estimability, and show that the choice of optimality criterion has a profound effect on the contribution of each experiment.
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