Effect of myocyte-fibroblast coupling on the onset of pathological dynamics in a model of ventricular tissue

Managing lethal cardiac arrhythmias is one of the biggest challenges in modern cardiology, and hence it is very important to understand the factors underlying such arrhythmias. While early afterdepolarizations (EAD) of cardiac cells is known to be one such arrhythmogenic factor, the mechanisms underlying the emergence of tissue level arrhythmias from cellular level EADs is not fully understood. Another known arrhythmogenic condition is fibrosis of cardiac tissue that occurs both due to aging and in many types of heart diseases. In this paper we describe the results of a systematic insilico study, using the TNNP model of human cardiac cells and MacCannell model for (myo) fibroblasts, on the possible effects of diffuse fibrosis on arrhythmias occurring via EADs. We find that depending on the resting potential of fibroblasts (VFR), M-F coupling can either increase or decrease the region of parameters showing EADs. Fibrosis increases the probability of occurrence of arrhythmias after a single focal stimulation and this effect increases with the strength of the M-F coupling. While in our simulations, arrhythmias occur due to fibrosis induced ectopic activity, we do not observe any specific fibrotic pattern that promotes the occurrence of these ectopic sources

Mechanistic Inquiry into the Role of Tissue Remodeling in Fibrotic Lesions in Human Atrial Fibrillation

AbstractAtrial fibrillation (AF), the most common arrhythmia in humans, is initiated when triggered activity from the pulmonary veins propagates into atrial tissue and degrades into reentrant activity. Although experimental and clinical findings show a correlation between atrial fibrosis and AF, the causal relationship between the two remains elusive. This study used an array of 3D computational models with different representations of fibrosis based on a patient-specific atrial geometry with accurate fibrotic distribution to determine the mechanisms by which fibrosis underlies the degradation of a pulmonary vein ectopic beat into AF. Fibrotic lesions in models were represented with combinations of: gap junction remodeling; collagen deposition; and myofibroblast proliferation with electrotonic or paracrine effects on neighboring myocytes. The study found that the occurrence of gap junction remodeling and the subsequent conduction slowing in the fibrotic lesions was a necessary but not sufficient condition for AF development, whereas myofibroblast proliferation and the subsequent electrophysiological effect on neighboring myocytes within the fibrotic lesions was the sufficient condition necessary for reentry formation. Collagen did not alter the arrhythmogenic outcome resulting from the other fibrosis components. Reentrant circuits formed throughout the noncontiguous fibrotic lesions, without anchoring to a specific fibrotic lesion

A Multiscale In Silico Study to Characterize the Atrial Electrical Activity of Patients With Atrial Fibrillation : A Translational Study to Guide Ablation Therapy

The atrial substrate undergoes electrical and structural remodeling during atrial fibrillation. Detailed multiscale models were used to study the effect of structural remodeling induced at the cellular and tissue levels. Simulated electrograms were used to train a machine-learning algorithm to characterize the substrate. Also, wave propagation direction was tracked from unannotated electrograms. In conclusion, in silico experiments provide insight into electrograms\u27 information of the substrate

A Multiscale in Silico Study to Characterize the Atrial Electrical Activity of Patients With Atrial Fibrillation. A Translational Study to Guide Ablation Therapy

[ES] La fibrilación auricular es la arritmia cardíaca más común. Durante la fibrilación auricular, el sustrato auricular sufre una serie de cambios o remodelados a nivel eléctrico y estructural. La remodelación eléctrica se caracteriza por la alteración de una serie de canales iónicos, lo que cambia la morfología del potential de transmembrana conocido como potencial de acción. La remodelación estructural es un proceso complejo que involucra la interacción de varios procesos de señalización, interacción celular y cambios en la matriz extracelular. Durante la remodelación estructural, los fibroblastos que abundan en el tejido cardíaco, comienzan a diferenciarse en miofibroblastos que son los encargados de mantener la estructura de la matriz extracelular depositando colágeno. Además, la señalización paracrina de los miofibroblastos afecta a los canales iónicos de los miocitos circundantes. Se utilizaron modelos computacionales muy detallados a diferentes escalas para estudiar la remodelación estructural inducida a nivel celular y tisular. Se realizó una adaptación de un modelo de fibroblastos humanos a nivel celular para reproducir la electrofisiología de los miofibroblastos durante la fibrilación auricular. Además, se evaluó la exploración de la interacción del calcio en la electrofisiología de los miofibroblastos ajustando el canal de calcio a los datos experimentales. A nivel tisular, se estudió la infiltración de miofibroblastos para cuantificar el aumento de vulnerabilidad a una arritmia cardíaca. Los miofibroblastos cambian la dinámica de la reentrada. Una baja densidad de miofibroblastos permite la propagación a través del área fibrótica y crea puntos de salida de actividad focal y roturas de ondas dentro de esta área. Además, las composiciones de fibrosis juegan un papel clave en la alteración del patrón de propagación. La alteración del patrón de propagación afecta a los electrogramas recogidos en la superficie del tejido. La morfología del electrograma se alteró dependiendo de la disposición y composición del tejido fibrótico. Se combinaron modelos detallados de tejido cardíaco con modelos realistas de los catéteres de mapeo disponibles comercialmente para comprender las señales registradas clínicamente. Se generó un modelo de ruido a partir de señales clínicas para reproducir los artefactos de señal en el modelo. Se utilizaron electrogramas de modelos de dos dominios altamente detallados para entrenar un algoritmo de aprendizaje automático para caracterizar el sustrato fibrótico auricular. Las características que cuantifican la complejidad de las señales fueron extraídas para identificar la densidad fibrótica y la transmuralidad fibrótica. Posteriormente, se generaron mapas de fibrosis utilizando el registro del paciente como prueba de concepto. El mapa de fibrosis proporciona información sobre el sustrato fibrótico sin utilizar un valor único de corte de 0,5 milivoltios. Además, utilizando la medición del flujo de información como la entropía de transferencia combinada con gráficos dirigidos, en este estudio, se siguió la dirección de propagación del frente de onda. La transferencia de entropía con gráficos dirigidos proporciona información crucial durante la electrofisiología para comprender la dinámica de propagación de ondas durante la fibrilación auricular. En conclusión, esta tesis presenta un estudio in silico multiescala que proporciona información sobre los mediadores celulares responsables de la remodelación de la matriz extracelular y su electrofisiología. Además, proporciona una configuración realista para crear datos in silico que pueden ser usados para aplicaciones clínicas y servir de soporte al tratamiento de ablación.[CA] La fibril·lació auricular és l'arrítmia cardíaca més freqüent, en la qual el substrat auricular patix una sèrie de remodelacions elèctriques i estructurals. La remodelació de tipus elèctric es caracteritza per l'alteració d'un conjunt de canals iònics que modifica la morfologia del voltatge transmembrana, conegut com a potencial d'acció. La remodelació estructural és un fenomen complex que implica la relació entre diversos processos de senyalització, interaccions cel·lulars i canvis en la matriu extracel·lular. Durant la remodelació estructural, els abundants fibroblasts presents en el teixit cardíac comencen a diferenciar-se en miofibroblasts, els quals s'encarreguen de mantenir l'estructura de la matriu extracel·lular dipositant-hi col·lagen. A més, la senyalització paracrina dels miofibroblasts amb els miòcits circumdants també afectarà els canals iònics. Es van utilitzar models computacionals molt detallats a diferents escales per estudiar la remodelació estructural induïda a nivell tissular i cel·lular. Es va fer una adaptació a nivell cel·lular d'un model de fibroblasts humans per reproduir-hi l'electrofisiologia dels miofibroblasts durant la fibril·lació auricular. A més, l'exploració de la interacció del calci amb l'electrofisiologia dels miofibroblasts va ser avaluada mitjançant l'adequació del canal de calci a les dades experimentals. A nivell tissular es va estudiar la infiltració de miofibroblasts per tal de quantificar l'augment de vulnerabilitat que això conferia per patir una arrítmia cardíaca. Els miofibroblasts canvien la dinàmica de la reentrada, i presentar-ne una baixa densitat permet la propagació a través de la zona fibròtica, tot creant punts de sortida d'activitat focal i trencaments d'ones dins d'aquesta àrea. A més, les composicions de fibrosi tenen un paper clau en l'alteració del patró de propagació, afectant els electrogrames recollits en la superfície del teixit. La morfologia dels electrogrames es va veure alterada en funció de la disposició i la composició del teixit fibròtic. Per comprendre els senyals clínicament registrats es van combinar models detallats de teixits cardíacs amb models realistes dels catèters de cartografia disponibles comercialment. Es va generar un model de soroll a partir de senyals clínics per reproduir-hi els artefactes de senyal. Es van utilitzar electrogrames de models de bidominis molt detallats per entrenar un algoritme d'aprenentatge automàtic destinat a caracteritzar el substrat fibròtic auricular. Les característiques que quantifiquen la complexitat dels senyals van ser extretes per identificar la densitat i transmuralitat fibròtica. Posteriorment, es van generar mapes de fibrosi mitjançant la gravació del pacient com a prova de concepte. El mapa de fibrosi proporciona informació sobre el substrat fibròtic sense utilitzar un sol valor de tensió de tall de 0,5 mV. A més, utilitzant la mesura del flux d'informació com l'entropia de transferència combinada amb gràfics dirigits, en aquest estudi es va fer un seguiment de la direcció de propagació de l'ona. L'entropia de transferència amb gràfics dirigits proporciona informació crucial durant l'electrofisiologia per entendre la dinàmica de propagació d'ones durant la fibril·lació auricular. En conclusió, aquesta tesi presenta un estudi multi-escala in silico que proporciona informació sobre els mediadors cel·lulars responsables de la remodelació de la matriu extracel·lular i la seva electrofisiologia. A més, proporciona una configuració realista per crear dades in silico que es poden traduir a aplicacions clíniques que puguen donar suport al tractament de l'ablació.[EN] Atrial fibrillation is the most common cardiac arrhythmia. During atrial fibrillation, the atrial substrate undergoes a series of electrical and structural remodeling. The electrical remodeling is characterized by the alteration of specific ionic channels, which changes the morphology of the transmembrane voltage known as action potential. Structural remodeling is a complex process involving the interaction of several signalling pathways, cellular interaction, and changes in the extracellular matrix. During structural remodeling, fibroblasts, abundant in the cardiac tissue, start to differentiate into myofibroblasts, which are responsible for maintaining the extracellular matrix structure by depositing collagen. Additionally, myofibroblasts paracrine signalling with surrounding myocytes will also affect ionic channels. Highly detailed computational models at different scales were used to study the effect of structural remodeling induced at the cellular and tissue levels.At the cellular level, a human fibroblast model was adapted to reproduce the myofibroblast electrophsyiology during atrial fibrillation. Additionally, the calcium handling in myofibroblast electrophysiology was assessed by fitting calcium ion channel to experimental data. At the tissue level, myofibroblasts infiltration was studied to quantify the increase of vulnerability to cardiac arrhythmia. Myofibroblasts alter the dynamics of reentry. A low density of myofibroblasts allows the propagation through the fibrotic area and creates focal activity exit points and wave breaks inside this area. Moreover, fibrosis composition plays a key role in the alteration of the propagation pattern. The alteration of the propagation pattern affects the electrograms computed at the surface of the tissue. Electrogram morphology was altered depending on the arrangement and composition of the fibrotic tissue. Detailed cardiac tissue models were combined with realistic models of the commercially available mapping catheters to understand the clinically recorded signals. A noise model from clinical signals was generated to reproduce the signal artifacts in the model. Electrograms from highly detailed bidomain models were used to train a machine learning algorithm to characterize the atrial fibrotic substrate. Features that quantify the complexity of the signals were extracted to identify fibrotic density and fibrotic transmurality. Subsequently, fibrosis maps were generated using patient recordings as a proof of concept. Fibrosis map provides information about the fibrotic substrate without using a single cut-off voltage value of 0.5 mV. Furthermore, in this study, using information theory measurements such as transfer entropy combined with directed graphs, the wave propagation direction was tracked. Transfer entropy with directed graphs provides crucial information during electrophysiology to understand wave propagation dynamics during atrial fibrillation. In conclusion, this thesis presents a multiscale in silico study atrial fibrillation mechanisms providing insight into the cellular mediators responsible for the extracellular matrix remodeling and its electrophysiology. Additionally, it provides a realistic setup to create in silico data that can be translated to clinical applications that could support ablation treatment.Sánchez Arciniegas, JP. (2021). A Multiscale in Silico Study to Characterize the Atrial Electrical Activity of Patients With Atrial Fibrillation. A Translational Study to Guide Ablation Therapy [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/171456TESI

Atrial Fibrosis Hampers Non-invasive Localization of Atrial Ectopic Foci From Multi-Electrode Signals: A 3D Simulation Study

[EN] Introduction: Focal atrial tachycardia is commonly treated by radio frequency ablation with an acceptable long-term success. Although the location of ectopic foci tends to appear in specific hot-spots, they can be located virtually in any atrial region. Multi-electrode surface ECG systems allow acquiring dense body surface potential maps (BSPM) for non-invasive therapy planning of cardiac arrhythmia. However, the activation of the atria could be affected by fibrosis and therefore biomarkers based on BSPM need to take these effects into account. We aim to analyze the effect of fibrosis on a BSPM derived index, and its potential application to predict the location of ectopic foci in the atria. Methodology: We have developed a 3D atrial model that includes 5 distributions of patchy fibrosis in the left atrium at 5 different stages. Each stage corresponds to a different amount of fibrosis that ranges from 2 to 40%. The 25 resulting 3D models were used for simulation of Focal Atrial Tachycardia (FAT), triggered from 19 different locations described in clinical studies. BSPM were obtained for all simulations, and the body surface potential integral maps (BSPiM) were calculated to describe atrial activations. A machine learning (ML) pipeline using a supervised learning model and support vector machine was developed to learn the BSPM patterns of each of the 475 activation sequences and relate them to the origin of the FAT source. Results: Activation maps for stages with more than 15% of fibrosis were greatly affected, producing conduction blocks and delays in propagation. BSPiMs did not always cluster into non-overlapped groups since BSPiMs were highly altered by the conduction blocks. From stage 3 (15% fibrosis) the BSPiMs showed differences for ectopic beats placed around the area of the pulmonary veins. Classification results were mostly above 84% for all the configurations studied when a large enough number of electrodes were used to map the torso. However, the presence of fibrosis increases the area of the ectopic focus location and therefore decreases the utility for the electrophysiologist. Conclusions: The results indicate that the proposed ML pipeline is a promising methodology for non-invasive ectopic foci localization from BSPM signal even when fibrosis is present.This work was partially supported by Ministerio de Economia y Competitividad and Fondo Europeo de Desarrollo Regional (FEDER) DPI2015-69125-R and TIN2014-59932-JIN (MINECO/FEDER, UE).Godoy, EJ.; Lozano, M.; García-Fernández, I.; Ferrer-Albero, A.; Macleod, R.; Saiz, J.; Sebastián, R. (2018). Atrial Fibrosis Hampers Non-invasive Localization of Atrial Ectopic Foci From Multi-Electrode Signals: A 3D Simulation Study. Frontiers in Physiology. 9:1-18. https://doi.org/10.3389/fphys.2018.00404S1189Boyle, P. M., Zahid, S., & Trayanova, N. A. (2016). Towards personalized computational modelling of the fibrotic substrate for atrial arrhythmia. EP Europace, 18(suppl_4), iv136-iv145. doi:10.1093/europace/euw358Courtemanche, M., Ramirez, R. J., & Nattel, S. (1998). Ionic mechanisms underlying human atrial action potential properties: insights from a mathematical model. American Journal of Physiology-Heart and Circulatory Physiology, 275(1), H301-H321. doi:10.1152/ajpheart.1998.275.1.h301Daccarett, M., Badger, T. J., Akoum, N., Burgon, N. S., Mahnkopf, C., Vergara, G., … Marrouche, N. F. (2011). Association of Left Atrial Fibrosis Detected by Delayed-Enhancement Magnetic Resonance Imaging and the Risk of Stroke in Patients With Atrial Fibrillation. Journal of the American College of Cardiology, 57(7), 831-838. doi:10.1016/j.jacc.2010.09.049Dössel, O., Krueger, M. W., Weber, F. M., Wilhelms, M., & Seemann, G. (2012). Computational modeling of the human atrial anatomy and electrophysiology. 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P., Haqqani, H., Stevenson, I. H., Vohra, J. K., Morton, J. B., … Kalman, J. M. (2005). Focal Atrial Tachycardia From the Ostium of the Coronary Sinus. Journal of the American College of Cardiology, 45(9), 1488-1493. doi:10.1016/j.jacc.2005.01.042Kistler, P. M., Roberts-Thomson, K. C., Haqqani, H. M., Fynn, S. P., Singarayar, S., Vohra, J. K., … Kalman, J. M. (2006). P-Wave Morphology in Focal Atrial Tachycardia. Journal of the American College of Cardiology, 48(5), 1010-1017. doi:10.1016/j.jacc.2006.03.058Kistler, P. M., Sanders, P., Fynn, S. P., Stevenson, I. H., Hussin, A., Vohra, J. K., … Kalman, J. M. (2003). Electrophysiological and Electrocardiographic Characteristics of Focal Atrial Tachycardia Originating From the Pulmonary Veins. Circulation, 108(16), 1968-1975. doi:10.1161/01.cir.0000095269.36984.75Kistler, P. M., Sanders, P., Hussin, A., Morton, J. B., Vohra, J. K., Sparks, P. B., & Kalman, J. M. (2003). Focal atrial tachycardia arising from the mitral annulus. Journal of the American College of Cardiology, 41(12), 2212-2219. doi:10.1016/s0735-1097(03)00484-4Andrew MacCannell, K., Bazzazi, H., Chilton, L., Shibukawa, Y., Clark, R. B., & Giles, W. R. (2007). A Mathematical Model of Electrotonic Interactions between Ventricular Myocytes and Fibroblasts. Biophysical Journal, 92(11), 4121-4132. doi:10.1529/biophysj.106.101410MacLeod, R. S., Kholmovski, E., DiBella, E. V. R., Oakes, R. S., Blauer, J. E., Fish, E., … Marrouche, N. F. (2008). Integration of MRI in evaluation and ablation of atrial fibrillation. 2008 Computers in Cardiology. doi:10.1109/cic.2008.4748981Maleckar, M. M., Greenstein, J. L., Giles, W. R., & Trayanova, N. A. (2009). Electrotonic Coupling between Human Atrial Myocytes and Fibroblasts Alters Myocyte Excitability and Repolarization. Biophysical Journal, 97(8), 2179-2190. doi:10.1016/j.bpj.2009.07.054Morgan, R., Colman, M. A., Chubb, H., Seemann, G., & Aslanidi, O. V. (2016). 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Detection and Quantification of Left Atrial Structural Remodeling With Delayed-Enhancement Magnetic Resonance Imaging in Patients With Atrial Fibrillation. Circulation, 119(13), 1758-1767. doi:10.1161/circulationaha.108.811877Ramanathan, C., Jia, P., Ghanem, R., Calvetti, D., & Rudy, Y. (2003). Noninvasive Electrocardiographic Imaging (ECGI): Application of the Generalized Minimal Residual (GMRes) Method. Annals of Biomedical Engineering, 31(8), 981-994. doi:10.1114/1.1588655Santangeli, P., & Marchlinski, F. E. (2017). Techniques for the provocation, localization, and ablation of non–pulmonary vein triggers for atrial fibrillation. Heart Rhythm, 14(7), 1087-1096. doi:10.1016/j.hrthm.2017.02.030Santangeli, P., Zado, E. S., Hutchinson, M. D., Riley, M. P., Lin, D., Frankel, D. S., … Marchlinski, F. E. (2016). Prevalence and distribution of focal triggers in persistent and long-standing persistent atrial fibrillation. 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(2016). Percolation as a mechanism to explain atrial fractionated electrograms and reentry in a fibrosis model based on imaging data. Heart Rhythm, 13(7), 1536-1543. doi:10.1016/j.hrthm.2016.03.019Ward, J. H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58(301), 236-244. doi:10.1080/01621459.1963.10500845Weber, F. M., Keller, D. U. J., Bauer, S., Seemann, G., Lorenz, C., & Dössel, O. (2011). Predicting Tissue Conductivity Influences on Body Surface Potentials—An Efficient Approach Based on Principal Component Analysis. IEEE Transactions on Biomedical Engineering, 58(2), 265-273. doi:10.1109/tbme.2010.2090151Zhao, J., Kharche, S., Hansen, B., Csepe, T., Wang, Y., Stiles, M., & Fedorov, V. (2015). Optimization of Catheter Ablation of Atrial Fibrillation: Insights Gained from Clinically-Derived Computer Models. International Journal of Molecular Sciences, 16(12), 10834-10854. doi:10.3390/ijms16051083

Circ Res

Atrial fibrillation (AF) is the most common sustained arrhythmia in humans. The mechanisms that govern AF initiation and persistence are highly complex, of dynamic nature, and involve interactions across multiple temporal and spatial scales in the atria. This article aims to review the mathematical modeling and computer simulation approaches to understanding AF mechanisms and aiding in its management. Various atrial modeling approaches are presented, with descriptions of the methodological basis and advancements in both lower-dimensional and realistic geometry models. A review of the most significant mechanistic insights made by atrial simulations is provided. The article showcases the contributions that atrial modeling and simulation have made not only to our understanding of the pathophysiology of atrial arrhythmias, but also to the development of AF management approaches. A summary of the future developments envisioned for the field of atrial simulation and modeling is also presented. The review contends that computational models of the atria assembled with data from clinical imaging modalities that incorporate electrophysiological and structural remodeling could become a first line of screening for new AF therapies and approaches, new diagnostic developments, and new methods for arrhythmia prevention.DP1 HL123271/HL/NHLBI NIH HHS/United StatesDP1HL123271/DP/NCCDPHP CDC HHS/United States2015-04-25T00:00:00Z24763468PMC4043630vault:242

A Review of Healthy and Fibrotic Myocardium Microstructure Modeling and Corresponding Intracardiac Electrograms

Computational simulations of cardiac electrophysiology provide detailed information on the depolarization phenomena at different spatial and temporal scales. With the development of new hardware and software, in silico experiments have gained more importance in cardiac electrophysiology research. For plane waves in healthy tissue, in vivo and in silico electrograms at the surface of the tissue demonstrate symmetric morphology and high peak-to-peak amplitude. Simulations provided insight into the factors that alter the morphology and amplitude of the electrograms. The situation is more complex in remodeled tissue with fibrotic infiltrations. Clinically, different changes including fractionation of the signal, extended duration and reduced amplitude have been described. In silico, numerous approaches have been proposed to represent the pathological changes on different spatial and functional scales. Different modeling approaches can reproduce distinct subsets of the clinically observed electrogram phenomena. This review provides an overview of how different modeling approaches to incorporate fibrotic and structural remodeling affect the electrogram and highlights open challenges to be addressed in future research

A Computational Based Approach for Non-invasive Localization of Atrial ectopic foci

Las arritmias auriculares son las arritmias cardı́acas más comunes, afectan a seis millones de personas en Europa e imponen una enorme carga sanitaria en la sociedad. Las nuevas tecnologı́as médicas están ayudando a los electrofisiólogos a adaptar el tratamiento a cada paciente de diferentes maneras. Por ejemplo, la resonancia magnética (MRI) permite evaluar la distribución espacial de la fibrosis auricular; los mapas electroanatómicos (EAM) permiten obtener una caracterización eléctrica de los tejidos en tiempo real; Las imágenes electrocardiográficas (ECGI) permiten estudiar la actividad eléctrica cardı́aca de forma no invasiva; y la ablación por radiofrecuencia (RFA), permite eliminar el tejido patológico en el corazón que desencadena o mantiene una arritmia. A pesar del acceso a tecnologı́as avanzadas y de la existencia de guı́as clı́nicas bien desarrolladas para el tratamiento de las arritmias auriculares, las tasas de éxito del tratamiento a largo plazo siguen siendo bajas, debido a la complejidad de la enfermedad. Por lo tanto, existe una necesidad imperiosa de mejorar los resultados clı́nicos en beneficio de los pacientes y el sistema de salud. Se podrı́an emplear modelos biofı́sicos detallados de las aurı́culas y el torso para integrar todos los datos del paciente en un solo modelo 3D de referencia capaz de reproducir los complejos patrones de activación eléctrica observados en experimentos y la clı́nica. Sin embargo, existen algunas limitaciones relacionadas con la dificultad de construir tales modelos para cada paciente o realizar un número considerable de simulaciones para planificar la terapia óptima de RFA. Teniendo en cuenta todas esas limitaciones, proponemos utilizar modelos biofı́sicos detallados y simulaciones como una herramienta para entrenar sistemas de aprendizaje automático, para lo cual dispondrı́amos de todos los datos y variables del problema, que serı́an imposibles de obtener en un entorno clı́nico real. Por lo tanto, podemos realizar cientos de simulaciones electrofisiológicas, considerando una variedad de escenarios y patologı́as comunes, y entrenar un sistema que deberı́a ser capaz de reconocerlos a partir de un conjunto limitado de datos no invasivos del paciente, como un electrocardiograma (ECG), o mapa de potencial de superficie corporal (BSPM).Abstract Atrial arrhythmias are the most common cardiac arrhythmia, affecting six million people in Europe and imposing a huge healthcare bur- den on society. New technologies are helping electrophysiologists to tailor the treatment to each patient in different ways. For instance, magnetic resonance imaging (MRI) allows to assess the spatial distribution of atrial fibrosis; electro-anatomical maps (EAM) permit to obtain an electrical char- acterization of tissue in real-time; electrocardiographic imaging (ECGI) al- lows to study cardiac electrical activity non-invasively; and radiofrequency ablation (RFA), allows to eliminate pathological tissue in the heart that is triggering or sustaining an arrhythmia. Despite the access to advanced technologies and well-developed clinical guidelines for the management of atrial arrhythmia, long-term treatment success rates remain low, due to the complexity of the disease. Therefore, there is a compelling need to improve clinical outcomes for the benefit of patients and the healthcare system. Detailed biophysical models of the atria and torso could be employed to integrate all the patient data into a single reference 3D model able to re- produce the complex electrical activation patterns observed in experiments and clinics. However, there are some limitations related to the difficulty of building such models for each patient, or performing a substantial number of simulations to plan the optimal RFA therapy. Considering all those lim- itations, we propose to use detailed biophysical models and simulations as a tool to train machine learning systems, for which we have all the ground- truth data which would be impossible to obtain in a real clinical setting. Therefore, we can perform hundreds of electrophysiology simulations, con- sidering a variety of common scenarios and pathologies, and train a system that should be able to recognize them from a limited set of non-invasive pa- tient data, such as an electrocardiogram (ECG), or a body surface potential map (BSPM)

Patchy fibrosis promotes trigger–substrate interactions that both generate and maintain atrial fibrillation

Fibrosis has been mechanistically linked to arrhythmogenesis in multiple cardiovascular conditions, including atrial fibrillation (AF). Previous studies have demonstrated that fibrosis can create functional barriers to conduction which may promote excitation wavebreak and the generation of re-entry, while also acting to pin re-entrant excitation in stable rotors during AF. However, few studies have investigated the role of fibrosis in the generation of AF triggers in detail. We apply our in-house computational framework to study the impact of fibrosis on the generation of AF triggers and trigger–substrate interactions in two- and three-dimensional atrial tissue models. Our models include a reduced and efficient description of stochastic, spontaneous cellular triggers as well as a simple model of heterogeneous intercellular coupling. Our results demonstrate that fibrosis promotes the emergence of focal excitations, primarily through reducing the electrotonic load on individual fibre strands. This enables excitation to robustly initiate within these single strands before spreading to neighbouring strands and inducing a full tissue focal excitation. Enhanced conduction block can allow trigger–substrate interactions that result in the emergence of complex, re-entrant excitation patterns. This study provides new insight into the mechanisms by which fibrosis promotes the triggers and substrate necessary to induce and sustain arrhythmia