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Adams WP, Raisch TB, Zhao Y, Davalos R, Barrett S, King DR, Bain CB, Colucci-Chang K, Blair GA, Hanlon A, Lozano A, Veeraraghavan R, Wan X, Deschenes I, Smyth JW, Hoeker GS, Gourdie RG, Poelzing S. Extracellular Perinexal Separation Is a Principal Determinant of Cardiac Conduction. Circ Res 2023; 133:658-673. [PMID: 37681314 PMCID: PMC10561697 DOI: 10.1161/circresaha.123.322567] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 08/29/2023] [Indexed: 09/09/2023]
Abstract
BACKGROUND Cardiac conduction is understood to occur through gap junctions. Recent evidence supports ephaptic coupling as another mechanism of electrical communication in the heart. Conduction via gap junctions predicts a direct relationship between conduction velocity (CV) and bulk extracellular resistance. By contrast, ephaptic theory is premised on the existence of a biphasic relationship between CV and the volume of specialized extracellular clefts within intercalated discs such as the perinexus. Our objective was to determine the relationship between ventricular CV and structural changes to micro- and nanoscale extracellular spaces. METHODS Conduction and Cx43 (connexin43) protein expression were quantified from optically mapped guinea pig whole-heart preparations perfused with the osmotic agents albumin, mannitol, dextran 70 kDa, or dextran 2 MDa. Peak sodium current was quantified in isolated guinea pig ventricular myocytes. Extracellular resistance was quantified by impedance spectroscopy. Intercellular communication was assessed in a heterologous expression system with fluorescence recovery after photobleaching. Perinexal width was quantified from transmission electron micrographs. RESULTS CV primarily in the transverse direction of propagation was significantly reduced by mannitol and increased by albumin and both dextrans. The combination of albumin and dextran 70 kDa decreased CV relative to albumin alone. Extracellular resistance was reduced by mannitol, unchanged by albumin, and increased by both dextrans. Cx43 expression and conductance and peak sodium currents were not significantly altered by the osmotic agents. In response to osmotic agents, perinexal width, in order of narrowest to widest, was albumin with dextran 70 kDa; albumin or dextran 2 MDa; dextran 70 kDa or no osmotic agent, and mannitol. When compared in the same order, CV was biphasically related to perinexal width. CONCLUSIONS Cardiac conduction does not correlate with extracellular resistance but is biphasically related to perinexal separation, providing evidence that the relationship between CV and extracellular volume is determined by ephaptic mechanisms under conditions of normal gap junctional coupling.
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Affiliation(s)
- William P. Adams
- Center for Vascular and Heart Research at Fralin Biomedical Research Institute at VTC
- Translational Biology, Medicine and Health Program at Virginia Tech
| | - Tristan B. Raisch
- Center for Vascular and Heart Research at Fralin Biomedical Research Institute at VTC
- Translational Biology, Medicine and Health Program at Virginia Tech
| | - Yajun Zhao
- School of Biomedical Engineering and Sciences, Virginia Tech
| | - Rafael Davalos
- School of Biomedical Engineering and Sciences, Virginia Tech
| | | | - D. Ryan King
- Center for Vascular and Heart Research at Fralin Biomedical Research Institute at VTC
- Translational Biology, Medicine and Health Program at Virginia Tech
| | - Chandra B. Bain
- Center for Vascular and Heart Research at Fralin Biomedical Research Institute at VTC
| | - Katrina Colucci-Chang
- Center for Vascular and Heart Research at Fralin Biomedical Research Institute at VTC
- School of Biomedical Engineering and Sciences, Virginia Tech
| | - Grace A. Blair
- Center for Vascular and Heart Research at Fralin Biomedical Research Institute at VTC
- Translational Biology, Medicine and Health Program at Virginia Tech
| | - Alexandra Hanlon
- Virginia Tech Center for Biostatistics and Health Data Science, Roanoke, Virginia
| | - Alicia Lozano
- Virginia Tech Center for Biostatistics and Health Data Science, Roanoke, Virginia
| | - Rengasayee Veeraraghavan
- Department of Biomedical Engineering, College of Engineering, The Ohio State University
- The Frick Center for Heart Failure and Arrhythmia, Dorothy M. Davis Heart and Lung Research Institute, College of Medicine, The Ohio State University Wexner Medical Center
| | - Xiaoping Wan
- The Frick Center for Heart Failure and Arrhythmia, Dorothy M. Davis Heart and Lung Research Institute, College of Medicine, The Ohio State University Wexner Medical Center
| | - Isabelle Deschenes
- The Frick Center for Heart Failure and Arrhythmia, Dorothy M. Davis Heart and Lung Research Institute, College of Medicine, The Ohio State University Wexner Medical Center
| | - James W. Smyth
- Center for Vascular and Heart Research at Fralin Biomedical Research Institute at VTC
- Department of Biological Sciences, College of Science, Virginia Tech
- Department of Basic Science Education, Virginia Tech Carilion School of Medicine, Roanoke, Virginia
| | - Gregory S. Hoeker
- Center for Vascular and Heart Research at Fralin Biomedical Research Institute at VTC
| | - Robert G. Gourdie
- Center for Vascular and Heart Research at Fralin Biomedical Research Institute at VTC
- School of Biomedical Engineering and Sciences, Virginia Tech
- Department of Basic Science Education, Virginia Tech Carilion School of Medicine, Roanoke, Virginia
| | - Steven Poelzing
- Center for Vascular and Heart Research at Fralin Biomedical Research Institute at VTC
- Translational Biology, Medicine and Health Program at Virginia Tech
- School of Biomedical Engineering and Sciences, Virginia Tech
- Department of Basic Science Education, Virginia Tech Carilion School of Medicine, Roanoke, Virginia
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2
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Kamalakkanna A, Johnston PR, Johnston BM. Improving the accuracy of retrieved cardiac electrical conductivities. THE ANZIAM JOURNAL 2022; 63:C154-C167. [PMID: 37193264 PMCID: PMC10183297 DOI: 10.21914/anziamj.v63.17148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/18/2023]
Abstract
Accurate values for the six cardiac conductivities of the bidomain model are crucial for meaningful electrophysiological simulations of cardiac tissue and are yet to be achieved. A two-stage optimisation process is used to retrieve the cardiac conductivities from cardiac potentials measured on a multi-electrode array-the first stage simultaneously fits all six conductivities, and the second stage fits a subset of the conductivities (intracellular conductivities), while holding the remainder of the conductivities (extracellular conductivities) constant. Previous studies have shown that the intracellular conductivities are retrieved to a lesser degree of accuracy than extracellular conductivities. This study tests the proposition that there exists a relationship between the extracellular and intracellular conductivities during the second stage of the optimisation that affects the accuracy of the retrieved intracellular conductivities. A measure to quantify this relationship is developed using polynomial chaos. The results show that a significant relationship does exist, and thus any errors in the extracellular conductivities are magnified in the retrieved intracellular conductivities. Thus, it is suggested that future protocols for retrieving conductivities incorporate the uncertainty in the extracellular conductivities.
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Affiliation(s)
- A Kamalakkanna
- School of Environment and Science, Griffith University, Nathan, QLD 4111, Australia
| | - P R Johnston
- School of Environment and Science, Griffith University, Nathan, QLD 4111, Australia
| | - B M Johnston
- School of Environment and Science, Griffith University, Nathan, QLD 4111, Australia
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3
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Johnston BM, Johnston PR. Which bidomain conductivity is the most important for modelling heart and torso surface potentials during ischaemia? Comput Biol Med 2021; 137:104830. [PMID: 34534792 DOI: 10.1016/j.compbiomed.2021.104830] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2021] [Revised: 08/29/2021] [Accepted: 08/31/2021] [Indexed: 10/20/2022]
Abstract
Mathematical simulations using the bidomain model, which represents cardiac tissue as consisting of an intracellular and an extracellular space, are a key approach that can be used to improve understanding of heart conditions such as ischaemia. However, key inputs to these models, such as the bidomain conductivity values, are not known with any certainty. Since efforts are underway to measure these values, it would be useful to be able to quantify the effect on model outputs of uncertainty in these inputs, and also to determine, if possible, which are the most important values to focus on in experimental studies. Our previous work has systematically studied the sensitivity of heart surface potentials to the bidomain conductivity values, and this was performed using a half-ellipsoidal model of the left ventricle. This study uses a bi-ventricular heart in a torso model and this time looks at the sensitivity of the torso surface potentials, as well as the heart surface potentials, to various conductivity values (blood, torso and the six bidomain conductivities). We found that both epicardial and torso potentials are the most sensitive to the intracellular longitudinal (along the cardiac fibres) conductivity (gil) with more minor sensitivity to the torso conductivity, and that changes in gil have a significant effect on the surface potential distributions on both the torso and the heart.
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Affiliation(s)
- Barbara M Johnston
- School of Environment and Science, Griffith University, Nathan, Queensland, 4111, Australia.
| | - Peter R Johnston
- School of Environment and Science, Griffith University, Nathan, Queensland, 4111, Australia
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4
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A modified approach to determine the six cardiac bidomain conductivities. Comput Biol Med 2021; 135:104549. [PMID: 34171640 PMCID: PMC10183296 DOI: 10.1016/j.compbiomed.2021.104549] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2021] [Revised: 05/31/2021] [Accepted: 06/01/2021] [Indexed: 11/23/2022]
Abstract
Accurate values for the six cardiac bidomain conductivities are crucial for meaningful computational studies of conduction in cardiac tissue, and are yet to be determined by experimental means. Although previous studies have proposed an approach using a multi-electrode array to measure potentials, from which the conductivities can be determined, it has been found that the conductivities cannot be retrieved consistently when the noise in the potentials varies. This paper presents a protocol, which not only has been shown to retrieve the conductivities to a reasonable accuracy, but does so under the presence of a more appropriate additive Gaussian noise model, while using fewer computational resources. Through repetitions of the protocol, a comparison of two pre-fabricated 128 electrode arrays, one array with a square arrangement of electrodes and the other with a rectangular arrangement, was made against a 75-electrode array proposed in previous studies. Results indicated that the two pre-fabricated arrays were generally more capable of obtaining the cardiac conductivities to a higher degree of accuracy than the 75-electrode array. The 128-electrode rectangular array was orientated such that the length of the array first ran along the direction of the fibres, then was reorientated such that the length of the array ran perpendicular to the direction of the fibres. The 128-electrode rectangular array, when orientated in this manner, was more capable of retrieving the conductivities than the remainder of the arrays tested, and thus we suggest this arrangement be used during experimental trials.
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5
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Pagani S, Manzoni A. Enabling forward uncertainty quantification and sensitivity analysis in cardiac electrophysiology by reduced order modeling and machine learning. INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING 2021; 37:e3450. [PMID: 33599106 PMCID: PMC8244126 DOI: 10.1002/cnm.3450] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Revised: 02/05/2021] [Accepted: 02/07/2021] [Indexed: 06/12/2023]
Abstract
We present a new, computationally efficient framework to perform forward uncertainty quantification (UQ) in cardiac electrophysiology. We consider the monodomain model to describe the electrical activity in the cardiac tissue, coupled with the Aliev-Panfilov model to characterize the ionic activity through the cell membrane. We address a complete forward UQ pipeline, including both: (i) a variance-based global sensitivity analysis for the selection of the most relevant input parameters, and (ii) a way to perform uncertainty propagation to investigate the impact of intra-subject variability on outputs of interest depending on the cardiac potential. Both tasks exploit stochastic sampling techniques, thus implying overwhelming computational costs because of the huge amount of queries to the high-fidelity, full-order computational model obtained by approximating the coupled monodomain/Aliev-Panfilov system through the finite element method. To mitigate this computational burden, we replace the full-order model with computationally inexpensive projection-based reduced-order models (ROMs) aimed at reducing the state-space dimensionality. Resulting approximation errors on the outputs of interest are finally taken into account through artificial neural network (ANN)-based models, enhancing the accuracy of the whole UQ pipeline. Numerical results show that the proposed physics-based ROMs outperform regression-based emulators relying on ANNs built with the same amount of training data, in terms of both numerical accuracy and overall computational efficiency.
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Affiliation(s)
- Stefano Pagani
- MOX, Dipartimento di MatematicaPolitecnico di MilanoMilanItaly
| | - Andrea Manzoni
- MOX, Dipartimento di MatematicaPolitecnico di MilanoMilanItaly
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6
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Approaches for determining cardiac bidomain conductivity values: progress and challenges. Med Biol Eng Comput 2020; 58:2919-2935. [PMID: 33089458 DOI: 10.1007/s11517-020-02272-z] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2020] [Accepted: 09/17/2020] [Indexed: 10/23/2022]
Abstract
Modelling the electrical activity of the heart is an important tool for understanding electrical function in various diseases and conduction disorders. Clearly, for model results to be useful, it is necessary to have accurate inputs for the models, in particular the commonly used bidomain model. However, there are only three sets of four experimentally determined conductivity values for cardiac ventricular tissue and these are inconsistent, were measured around 40 years ago, often produce different results in simulations and do not fully represent the three-dimensional anisotropic nature of cardiac tissue. Despite efforts in the intervening years, difficulties associated with making the measurements and also determining the conductivities from the experimental data have not yet been overcome. In this review, we summarise what is known about the conductivity values, as well as progress to date in meeting the challenges associated with both the mathematical modelling and the experimental techniques. Graphical abstract Epicardial potential distributions, arising from a subendocardial ischaemic region, modelled using conductivity data from the indicated studies.
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7
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Fresca S, Manzoni A, Dedè L, Quarteroni A. Deep learning-based reduced order models in cardiac electrophysiology. PLoS One 2020; 15:e0239416. [PMID: 33002014 PMCID: PMC7529269 DOI: 10.1371/journal.pone.0239416] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 09/06/2020] [Indexed: 01/06/2023] Open
Abstract
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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Affiliation(s)
- Stefania Fresca
- MOX - Dipartimento di Matematica, Politecnico di Milano, Milano, Italy
- * E-mail:
| | - Andrea Manzoni
- MOX - Dipartimento di Matematica, Politecnico di Milano, Milano, Italy
| | - Luca Dedè
- MOX - Dipartimento di Matematica, Politecnico di Milano, Milano, Italy
| | - Alfio Quarteroni
- MOX - Dipartimento di Matematica, Politecnico di Milano, Milano, Italy
- Mathematics Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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8
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Coveney S, Clayton RH. Sensitivity and Uncertainty Analysis of Two Human Atrial Cardiac Cell Models Using Gaussian Process Emulators. Front Physiol 2020; 11:364. [PMID: 32390867 PMCID: PMC7191317 DOI: 10.3389/fphys.2020.00364] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2019] [Accepted: 03/30/2020] [Indexed: 12/20/2022] Open
Abstract
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.
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Affiliation(s)
| | - Richard H. Clayton
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
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9
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Corrado C, Razeghi O, Roney C, Coveney S, Williams S, Sim I, O'Neill M, Wilkinson R, Oakley J, Clayton RH, Niederer S. Quantifying atrial anatomy uncertainty from clinical data and its impact on electro-physiology simulation predictions. Med Image Anal 2020; 61:101626. [PMID: 32000114 DOI: 10.1016/j.media.2019.101626] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2019] [Revised: 11/05/2019] [Accepted: 12/05/2019] [Indexed: 12/11/2022]
Abstract
Patient-specific computational models of structure and function are increasingly being used to diagnose disease and predict how a patient will respond to therapy. Models of anatomy are often derived after segmentation of clinical images or from mapping systems which are affected by image artefacts, resolution and contrast. Quantifying the impact of uncertain anatomy on model predictions is important, as models are increasingly used in clinical practice where decisions need to be made regardless of image quality. We use a Bayesian probabilistic approach to estimate the anatomy and to quantify the uncertainty about the shape of the left atrium derived from Cardiac Magnetic Resonance images. We show that we can quantify uncertain shape, encode uncertainty about the left atrial shape due to imaging artefacts, and quantify the effect of uncertain shape on simulations of left atrial activation times.
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Affiliation(s)
- Cesare Corrado
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom.
| | - Orod Razeghi
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Caroline Roney
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Sam Coveney
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Steven Williams
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Iain Sim
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Mark O'Neill
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
| | - Richard Wilkinson
- School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
| | - Jeremy Oakley
- School of Mathematics and Statistics, University of Sheffield, Sheffield, United Kingdom
| | - Richard H Clayton
- Insigneo Institute for in-silico Medicine and Department of Computer Science, University of Sheffield, Sheffield, United Kingdom
| | - Steven Niederer
- Division of Imaging Sciences & Biomedical Engineering, King's College London, London SE17EH, United Kingdom
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10
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Johnston BM, Johnston PR. Differences between models of partial thickness and subendocardial ischaemia in terms of sensitivity analyses of ST-segment epicardial potential distributions. Math Biosci 2019; 318:108273. [PMID: 31647934 DOI: 10.1016/j.mbs.2019.108273] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2019] [Revised: 10/15/2019] [Accepted: 10/15/2019] [Indexed: 01/24/2023]
Abstract
Mathematical modelling is a useful technique to help elucidate the connection between non-transmural ischaemia and ST elevation and depression of the ECG. Generally, models represent non-transmural ischaemia using an ischaemic zone that extends from the endocardium partway to the epicardium. However, recent experimental work has suggested that ischaemia typically arises within the heart wall. This work examines the effect of modelling cardiac ischaemia in the left ventricle using two different models: subendocardial ischaemia and partial thickness ischaemia, representing the first and second scenarios, respectively. We found that it is possible, only in the model of subendocardial ischaemia, to see a single minimum on the epicardial surface above the ischaemic region, and this only occurs for low ischaemic thicknesses. This may help to explain the rarity of ST depression that is located over the ischaemic region. It was also found that, in both models, the epicardial potential distribution is most sensitive to the proximity of the ischaemic region to the epicardium, rather than to the thickness of the ischaemic region. Since proximity does not indicate the thickness of the ischaemic region, this suggests a reason why it may be difficult to determine the degree of ischaemia using the ST segment of the ECG.
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Affiliation(s)
- Barbara M Johnston
- School of Environment and Science, and Queensland Micro- and Nanotechnology Centre, Griffith University, Nathan, Queensland 4111, Australia.
| | - Peter R Johnston
- School of Environment and Science, and Queensland Micro- and Nanotechnology Centre, Griffith University, Nathan, Queensland 4111, Australia
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11
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Lopez-Perez A, Sebastian R, Izquierdo M, Ruiz R, Bishop M, Ferrero JM. Personalized Cardiac Computational Models: From Clinical Data to Simulation of Infarct-Related Ventricular Tachycardia. Front Physiol 2019; 10:580. [PMID: 31156460 PMCID: PMC6531915 DOI: 10.3389/fphys.2019.00580] [Citation(s) in RCA: 41] [Impact Index Per Article: 8.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2018] [Accepted: 04/25/2019] [Indexed: 12/20/2022] Open
Abstract
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.
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Affiliation(s)
- Alejandro Lopez-Perez
- Center for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
| | - Rafael Sebastian
- Computational Multiscale Simulation Lab (CoMMLab), Universitat de València, Valencia, Spain
| | - M Izquierdo
- INCLIVA Health Research Institute, Valencia, Spain.,Arrhythmia Unit, Cardiology Department, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Ricardo Ruiz
- INCLIVA Health Research Institute, Valencia, Spain.,Arrhythmia Unit, Cardiology Department, Hospital Clínico Universitario de Valencia, Valencia, Spain
| | - Martin Bishop
- Division of Imaging Sciences & Biomedical Engineering, Department of Biomedical Engineering, King's College London, London, United Kingdom
| | - Jose M Ferrero
- Center for Research and Innovation in Bioengineering (Ci2B), Universitat Politècnica de València, Valencia, Spain
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Abstract
The treatment of individual patients in cardiology practice increasingly relies on advanced imaging, genetic screening and devices. As the amount of imaging and other diagnostic data increases, paralleled by the greater capacity to personalize treatment, the difficulty of using the full array of measurements of a patient to determine an optimal treatment seems also to be paradoxically increasing. Computational models are progressively addressing this issue by providing a common framework for integrating multiple data sets from individual patients. These models, which are based on physiology and physics rather than on population statistics, enable computational simulations to reveal diagnostic information that would have otherwise remained concealed and to predict treatment outcomes for individual patients. The inherent need for patient-specific models in cardiology is clear and is driving the rapid development of tools and techniques for creating personalized methods to guide pharmaceutical therapy, deployment of devices and surgical interventions.
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Affiliation(s)
- Steven A Niederer
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK.
| | - Joost Lumens
- Department of Biomedical Engineering, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University Medical Center, Maastricht, Netherlands
- IHU LIRYC, Electrophysiology and Heart Modeling Institute, Fondation Bordeaux Université, Pessac, France
| | - Natalia A Trayanova
- Department of Biomedical Engineering and the Institute for Computational Medicine, Johns Hopkins University, Baltimore, MD, USA
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13
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Johnston BM, Johnston PR. Sensitivity analysis of ST-segment epicardial potentials arising from changes in ischaemic region conductivities in early and late stage ischaemia. Comput Biol Med 2018; 102:288-299. [DOI: 10.1016/j.compbiomed.2018.06.005] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Revised: 06/07/2018] [Accepted: 06/07/2018] [Indexed: 11/30/2022]
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14
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Lawson BA, Burrage K, Burrage P, Drovandi CC, Bueno-Orovio A. Slow Recovery of Excitability Increases Ventricular Fibrillation Risk as Identified by Emulation. Front Physiol 2018; 9:1114. [PMID: 30210355 PMCID: PMC6121112 DOI: 10.3389/fphys.2018.01114] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2018] [Accepted: 07/25/2018] [Indexed: 12/28/2022] Open
Abstract
Purpose: Rotor stability and meandering are key mechanisms determining and sustaining cardiac fibrillation, with important implications for anti-arrhythmic drug development. However, little is yet known on how rotor dynamics are modulated by variability in cellular electrophysiology, particularly on kinetic properties of ion channel recovery. Methods: We propose a novel emulation approach, based on Gaussian process regression augmented with machine learning, for data enrichment, automatic detection, classification, and analysis of re-entrant biomarkers in cardiac tissue. More than 5,000 monodomain simulations of long-lasting arrhythmic episodes with Fenton-Karma ionic dynamics, further enriched by emulation to 80 million electrophysiological scenarios, were conducted to investigate the role of variability in ion channel densities and kinetics in modulating rotor-driven arrhythmic behavior. Results: Our methods predicted the class of excitation behavior with classification accuracy up to 96%, and emulation effectively predicted frequency, stability, and spatial biomarkers of functional re-entry. We demonstrate that the excitation wavelength interpretation of re-entrant behavior hides critical information about rotor persistence and devolution into fibrillation. In particular, whereas action potential duration directly modulates rotor frequency and meandering, critical windows of excitability are identified as the main determinants of breakup. Further novel electrophysiological insights of particular relevance for ventricular arrhythmias arise from our multivariate analysis, including the role of incomplete activation of slow inward currents in mediating tissue rate-dependence and dispersion of repolarization, and the emergence of slow recovery of excitability as a significant promoter of this mechanism of dispersion and increased arrhythmic risk. Conclusions: Our results mechanistically explain pro-arrhythmic effects of class Ic anti-arrhythmics in the ventricles despite their established role in the pharmacological management of atrial fibrillation. This is mediated by their slow recovery of excitability mode of action, promoting incomplete activation of slow inward currents and therefore increased dispersion of repolarization, given the larger influence of these currents in modulating the action potential in the ventricles compared to the atria. These results exemplify the potential of emulation techniques in elucidating novel mechanisms of arrhythmia and further application to cardiac electrophysiology.
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Affiliation(s)
- Brodie A Lawson
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Kevin Burrage
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia.,Department of Computer Science, University of Oxford, Oxford, United Kingdom
| | - Pamela Burrage
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
| | - Christopher C Drovandi
- ARC Centre of Excellence for Mathematical and Statistical Frontiers, School of Mathematical Sciences, Queensland University of Technology, Brisbane, QLD, Australia
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Dhamala J, Arevalo HJ, Sapp J, Horácek BM, Wu KC, Trayanova NA, Wang L. Quantifying the uncertainty in model parameters using Gaussian process-based Markov chain Monte Carlo in cardiac electrophysiology. Med Image Anal 2018; 48:43-57. [PMID: 29843078 DOI: 10.1016/j.media.2018.05.007] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2017] [Revised: 03/17/2018] [Accepted: 05/14/2018] [Indexed: 02/02/2023]
Abstract
Model personalization requires the estimation of patient-specific tissue properties in the form of model parameters from indirect and sparse measurement data. Moreover, a low-dimensional representation of the parameter space is needed, which often has a limited ability to reveal the underlying tissue heterogeneity. As a result, significant uncertainty can be associated with the estimated values of the model parameters which, if left unquantified, will lead to unknown variability in model outputs that will hinder their reliable clinical adoption. Probabilistic estimation of model parameters, however, remains an unresolved challenge. Direct Markov Chain Monte Carlo (MCMC) sampling of the posterior distribution function (pdf) of the parameters is infeasible because it involves repeated evaluations of the computationally expensive simulation model. To accelerate this inference, one popular approach is to construct a computationally efficient surrogate and sample from this approximation. However, by sampling from an approximation, efficiency is gained at the expense of sampling accuracy. In this paper, we address this issue by integrating surrogate modeling of the posterior pdf into accelerating the Metropolis-Hastings (MH) sampling of the exact posterior pdf. It is achieved by two main components: (1) construction of a Gaussian process (GP) surrogate of the exact posterior pdf by actively selecting training points that allow for a good global approximation accuracy with a focus on the regions of high posterior probability; and (2) use of the GP surrogate to improve the proposal distribution in MH sampling, in order to improve the acceptance rate. The presented framework is evaluated in its estimation of the local tissue excitability of a cardiac electrophysiological model in both synthetic data experiments and real data experiments. In addition, the obtained posterior distributions of model parameters are interpreted in relation to the factors contributing to parameter uncertainty, including different low-dimensional representations of the parameter space, parameter non-identifiability, and parameter correlations.
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Affiliation(s)
- Jwala Dhamala
- Rochester Institute of Technology, Rochester, NY, USA. http://www.jwaladhamala.com
| | | | - John Sapp
- Dalhousie University, Halifax, Canada
| | | | | | | | - Linwei Wang
- Rochester Institute of Technology, Rochester, NY, USA
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