Three-dimensional cardiac computational modelling: methods, features and applications

[EN] The combination of computational models and biophysical simulations can help to interpret an array of experimental data and contribute to the understanding, diagnosis and treatment of complex diseases such as cardiac arrhythmias. For this reason, three-dimensional (3D) cardiac computational modelling is currently a rising field of research. The advance of medical imaging technology over the last decades has allowed the evolution from generic to patient-specific 3D cardiac models that faithfully represent the anatomy and different cardiac features of a given alive subject. Here we analyse sixty representative 3D cardiac computational models developed and published during the last fifty years, describing their information sources, features, development methods and online availability. This paper also reviews the necessary components to build a 3D computational model of the heart aimed at biophysical simulation, paying especial attention to cardiac electrophysiology (EP), and the existing approaches to incorporate those components. We assess the challenges associated to the different steps of the building process, from the processing of raw clinical or biological data to the final application, including image segmentation, inclusion of substructures and meshing among others. We briefly outline the personalisation approaches that are currently available in 3D cardiac computational modelling. Finally, we present examples of several specific applications, mainly related to cardiac EP simulation and model-based image analysis, showing the potential usefulness of 3D cardiac computational modelling into clinical environments as a tool to aid in the prevention, diagnosis and treatment of cardiac diseases.This work was partially supported by the "VI Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica" from the Ministerio de Economia y Competitividad of Spain (TIN2012-37546-C03-01 and TIN2011-28067) and the European Commission (European Regional Development Funds - ERDF - FEDER) and by "eTorso project" (GVA/2013-001404) from the Generalitat Valenciana (Spain). ALP is financially supported by the program "Ayudas para contratos predoctorales para la formacion de doctores" from the Ministerio de Economia y Competitividad of Spain (BES-2013-064089).López Pérez, AD.; Sebastián Aguilar, R.; Ferrero De Loma-Osorio, JM. (2015). Three-dimensional cardiac computational modelling: methods, features and applications. BioMedical Engineering OnLine. 14(35):1-31. https://doi.org/10.1186/s12938-015-0033-5S1311435Koushanpour E, Collings W: Validation and dynamic applications of an ellipsoid model of the left ventricle. J Appl Physiol 1966, 21: 1655–61.Ghista D, Sandler H: An analytic elastic-viscoelastic model for the shape and the forces in the left ventricle. J Biomech 1969, 2: 35–47.Janz RF, Grimm AF: Finite-Element Model for the Mechanical Behavior of the Left Ventricle: prediction of deformation in the potassium-arrested rat heart. Circ Res 1972, 30: 244–52.Van den Broek JHJM, Van den Broek MHLM: Application of an ellipsoidal heart model in studying left ventricular contractions. J Biomech 1980, 13: 493–503.Colli Franzone P, Guerri L, Pennacchio M, Taccardi B: Spread of excitation in 3-D models of the anisotropic cardiac tissue. II. Effects of fiber architecture and ventricular geometry. Math Biosci 1998, 147: 131–71.Kerckhoffs RCP, Bovendeerd PHM, Kotte JCS, Prinzen FW, Smits K, Arts T: Homogeneity of cardiac contraction despite physiological asynchrony of depolarization: a model study. Ann Biomed Eng 2003, 31: 536–47.Sermesant M, Moireau P, Camara O, Sainte-Marie J, Andriantsimiavona R, Cimrman R, et al.: Cardiac function estimation from MRI using a heart model and data assimilation: advances and difficulties. Med Image Anal 2006, 10: 642–56.Okajima M, Fujino T, Kobayashi T, Yamada K: Computer simulation of the propagation process in excitation of the ventricles. Circ Res 1968, 23: 203–11.Horan LG, Hand RC, Johnson JC, Sridharan MR, Rankin TB, Flowers NC: A theoretical examination of ventricular repolarization and the secondary T wave. Circ Res 1978, 42: 750–7.Miller WT, Geselowitz DB: Simulation studies of the electrocardiogram. I. The normal heart. Circ Res 1978, 43: 301–15.Vetter FJ, McCulloch AD: Three-dimensional analysis of regional cardiac function: a model of rabbit ventricular anatomy. Prog Biophys Mol Biol 1998, 69: 157–83.Nielsen PMF, LeGrice IJ, Smaill BH, Hunter PJ: Mathematical model of geometry and fibrous structure of the heart. Am J Physiol Heart Circ Physiol 1991, 260: H1365–78.Stevens C, Remme E, LeGrice I, Hunter P: Ventricular mechanics in diastole: material parameter sensitivity. J Biomech 2003, 36: 737–48.Aoki M, Okamoto Y, Musha T, Harumi KI: Three-dimensional simulation of the ventricular depolarization and repolarization processes and body surface potentials: normal heart and bundle branch block. IEEE Trans Biomed Eng 1987, 34: 454–62.Thakor NV, Eisenman LN: Three-dimensional computer model of the heart: fibrillation induced by extrastimulation. Comput Biomed Res 1989, 22: 532–45.Freudenberg J, Schiemann T, Tiede U, Höhne KH: Simulation of cardiac excitation patterns in a three-dimensional anatomical heart atlas. Comput Biol Med 2000, 30: 191–205.Trunk P, Mocnik J, Trobec R, Gersak B: 3D heart model for computer simulations in cardiac surgery. Comput Biol Med 2007, 37: 1398–403.Siregar P, Sinteff JP, Julen N, Le Beux P: An interactive 3D anisotropic cellular automata model of the heart. Comput Biomed Res 1998, 31: 323–47.Harrild DM, Henriquez CS: A computer model of normal conduction in the human atria. Circ Res 2000, 87: e25–36.Bodin ON, Kuz’min AV: Synthesis of a realistic model of the surface of the heart. Biomed Eng (NY) 2006, 40: 280–3.Ruiz-Villa CA, Tobón C, Rodríguez JF, Ferrero JM, Hornero F, Saíz J: Influence of atrial dilatation in the generation of re-entries caused by ectopic activity in the left atrium. Comput Cardiol 2009, 36: 457–60.Blanc O, Virag N, Vesin JM, Kappenberger L: A computer model of human atria with reasonable computation load and realistic anatomical properties. IEEE Trans Biomed Eng 2001, 48: 1229–37.Zemlin CW, Herzel H, Ho SY, Panfilov AV: A realistic and efficient model of excitation propagation in the human atria. In Comput Simul Exp Assess Card Electrophysiol. Edited by: Virag N, Kappenberger L, Blanc O. Futura Publishing Company, Inc, Arkmonk, New York; 2001:29–34.Seemann G, Höper C, Sachse FB, Dössel O, Holden AV, Zhang H: Heterogeneous three-dimensional anatomical and electrophysiological model of human atria. Philos Trans R Soc A Math Phys Eng Sci 2006, 364: 1465–81.Zhao J, Butters TD, Zhang H, LeGrice IJ, Sands GB, Smaill BH: Image-based model of atrial anatomy and electrical activation: a computational platform for investigating atrial arrhythmia. IEEE Trans Med Imaging 2013, 32: 18–27.Creswell LL, Wyers SG, Pirolo JS, Perman WH, Vannier MW, Pasque MK: Mathematical modeling of the heart using magnetic resonance imaging. IEEE Trans Med Imaging 1992, 11: 581–9.Lorange M, Gulrajani RM: A computer heart model incorporating anisotropic propagation: I. Model construction and simulation of normal activation. J Electrocardiol 1993, 26: 245–61.Winslow RL, Scollan DF, Holmes A, Yung CK, Zhang J, Jafri MS: Electrophysiological modeling of cardiac ventricular function: from cell to organ. Annu Rev Biomed Eng 2000, 2: 119–55.Virag N, Jacquemet V, Henriquez CS, Zozor S, Blanc O, Vesin JM, et al.: Study of atrial arrhythmias in a computer model based on magnetic resonance images of human atria. Chaos 2002, 12: 754–63.Helm PA, Tseng HJ, Younes L, McVeigh ER, Winslow RL: Ex vivo 3D diffusion tensor imaging and quantification of cardiac laminar structure. Magn Reson Med 2005, 54: 850–9.Arevalo HJ, Helm PA, Trayanova NA: Development of a model of the infarcted canine heart that predicts arrhythmia generation from specific cardiac geometry and scar distribution. Comput Cardiol 2008, 35: 497–500.Plotkowiak M, Rodriguez B, Plank G, Schneider JE, Gavaghan D, Kohl P, et al.: High performance computer simulations of cardiac electrical function based on high resolution MRI datasets. In Int Conf Comput Sci 2008, LNCS 5101. Springer–Verlag, Berlin Heidelberg; 2008:571–80.Heidenreich EA, Ferrero JM, Doblaré M, Rodríguez JF: Adaptive macro finite elements for the numerical solution of monodomain equations in cardiac electrophysiology. Ann Biomed Eng 2010, 38: 2331–45.Gurev V, Lee T, Constantino J, Arevalo H, Trayanova NA: Models of cardiac electromechanics based on individual hearts imaging data: Image-based electromechanical models of the heart. Biomech Model Mechanobiol 2011, 10: 295–306.Deng D, Jiao P, Ye X, Xia L: An image-based model of the whole human heart with detailed anatomical structure and fiber orientation. Comput Math Methods Med 2012, 2012: 16.Aslanidi OV, Nikolaidou T, Zhao J, Smaill BH, Gilbert SH, Holden AV, et al.: Application of micro-computed tomography with iodine staining to cardiac imaging, segmentation, and computational model development. IEEE Trans Med Imaging 2013, 32: 8–17.Haddad R, Clarysse P, Orkisz M, Croisille P, Revel D, Magnin IE: A realistic anthropomorphic numerical model of the beating heart. In Funct Imaging Model Heart 2005, LNCS 3504. Springer–Verlag, Berlin Heidelberg; 2005:384–93.Appleton B, Wei Q, Liu N, Xia L, Crozier S, Liu F, et al.: An electrical heart model incorporating real geometry and motion. In 27th Annu Int Conf Eng Med Biol Soc (IEEE-EMBS 2005). IEEE, Shanghai, China; 2006:345–8.Niederer S, Rhode K, Razavi R, Smith N: The importance of model parameters and boundary conditions in whole organ models of cardiac contraction. In Funct Imaging Model Heart 2009, LNCS 5528. Springer–Verlag, Berlin Heidelberg; 2009:348–56.Yang G, Toumoulin C, Coatrieux JL, Shu H, Luo L, Boulmier D: A 3D static heart model from a MSCT data set. In 27th Annu Int Conf IEEE Eng Med Biol Soc (IEEE-EMBS 2005). IEEE, Shangai, China; 2006:5499–502.Romero D, Sebastian R, Bijnens BH, Zimmerman V, Boyle PM, Vigmond EJ, et al.: Effects of the purkinje system and cardiac geometry on biventricular pacing: a model study. Ann Biomed Eng 2010, 38: 1388–98.Lorenzo-Valdés M, Sanchez-Ortiz GI, Mohiaddin R, Rueckert D: Atlas-based segmentation and tracking of 3D cardiac MR images using non-rigid registration. In Med Image Comput Comput Assist Interv 2002, LNCS 2488. Springer–Verlag, Berlin Heidelberg; 2002:642–50.Ordas S, Oubel E, Sebastian R, Frangi AF: Computational anatomy atlas of the heart. In 5th Int Symp Image Signal Process Anal (ISPA 2007). IEEE, Istanbul, Turkey; 2007:338–42.Burton RAB, Plank G, Schneider JE, Grau V, Ahammer H, Keeling SL, et al.: Three-dimensional models of individual cardiac histoanatomy: tools and challenges. Ann N Y Acad Sci 2006, 1080: 301–19.Plank G, Burton RAB, Hales P, Bishop M, Mansoori T, Bernabeu MO, et al.: Generation of histo-anatomically representative models of the individual heart: tools and application. Philos Trans R Soc A Math Phys Eng Sci 2009, 367: 2257–92.Bishop MJ, Plank G, Burton RAB, Schneider JE, Gavaghan DJ, Grau V, et al.: Development of an anatomically detailed MRI-derived rabbit ventricular model and assessment of its impact on simulations of electrophysiological function. Am J Physiol - Heart Circ Physiol 2010, 298: H699–718.Ecabert O, Peters J, Schramm H, Lorenz C, von Berg J, Walker MJ, et al.: Automatic model-based segmentation of the heart in CT images. IEEE Trans Med Imaging 2008, 27: 1189–201.Ecabert O, Peters J, Walker MJ, Ivanc T, Lorenz C, von Berg J, et al.: Segmentation of the heart and great vessels in CT images using a model-based adaptation framework. Med Image Anal 2011, 15: 863–76.Schulte RF, Sands GB, Sachse FB, Dössel O, Pullan AJ: Creation of a human heart model and its customisation using ultrasound images. Biomed Tech Eng 2001, 46: 26–8.Wenk JF, Zhang Z, Cheng G, Malhotra D, Acevedo-Bolton G, Burger M, et al.: First finite element model of the left ventricle with mitral valve: insights into ischemic mitral regurgitation. Ann Thorac Surg 2010, 89: 1546–53.Frangi AF, Rueckert D, Schnabel JA, Niessen WJ: Automatic construction of multiple-object three-dimensional statistical shape models: application to cardiac modeling. IEEE Trans Med Imaging 2002, 21: 1151–66.Hoogendoorn C, Duchateau N, Sánchez-Quintana D, Whitmarsh T, Sukno FM, De Craene M, et al.: A high-resolution atlas and statistical model of the human heart from multislice CT. IEEE Trans Med Imaging 2013, 32: 28–44.Vadakkumpadan F, Rantner LJ, Tice B, Boyle P, Prassl AJ, Vigmond E, et al.: Image-based models of cardiac structure with applications in arrhythmia and defibrillation studies. J Electrocardiol 2009, 42: 157.Perperidis D, Mohiaddin R, Rueckert D: Construction of a 4D statistical atlas of the cardiac anatomy and its use in classification. In Med Image Comput Comput Interv 2005, LNCS 3750. Springer–Verlag, Berlin Heidelberg; 2005:402–10.Lötjönen J, Kivistö S, Koikkalainen J, Smutek D, Lauerma K: Statistical shape model of atria, ventricles and epicardium from short- and long-axis MR images. Med Image Anal 2004, 8: 371–86.Lorenz C, von Berg J: A comprehensive shape model of the heart. Med Image Anal 2006, 10: 657–70.Mansoori T, Plank G, Burton R, Schneider J, Khol P, Gavaghan D, et al.: An iterative method for registration of high-resolution cardiac histoanatomical and MRI images. In 4th IEEE Int Symp Biomed Imaging: From Nano to Macro (ISBI 2007). IEEE, Arlington, VA (USA); 2007:572–5.Gibb M, Burton RAB, Bollensdorff C, Afonso C, Mansoori T, Schotten U, et al.: Resolving the three-dimensional histology of the heart. In Comput Methods Syst Biol - Lect Notes Comput Sci 7605. Springer, Berlin Heidelberg; 2012:2–16.Burton RAB, Lee P, Casero R, Garny A, Siedlecka U, Schneider JE, et al.: Three-dimensional histology: tools and application to quantitative assessment of cell-type distribution in rabbit heart. Europace 2014,16(Suppl 4):iv86–95.Niederer SA, Shetty AK, Plank G, Bostock J, Razavi R, Smith NP, et al.: Biophysical modeling to simulate the response to multisite left ventricular stimulation using a quadripolar pacing lead. Pacing Clin Electrophysiol 2012, 35: 204–14.Weese J, Groth A, Nickisch H, Barschdorf H, Weber FM, Velut J, et al.: Generating anatomical models of the heart and the aorta from medical images for personalized physiological simulations. Med Biol Eng Comput 2013, 51: 1209–19.Gibb M, Bishop M, Burton R, Kohl P, Grau V, Plank G, et al.: The role of blood vessels in rabbit propagation dynamics and cardiac arrhythmias. In Funct Imaging Model Heart - FIMH 2009, LNCS 5528. Springer, Berlin Heidelberg; 2009:268–76.Prassl AJ, Kickinger F, Ahammer H, Grau V, Schneider JE, Hofer E, et al.: Automatically generated, anatomically accurate meshes for cardiac electrophysiology problems. IEEE Trans Biomed Eng 2009, 56: 1318–30.Dux-Santoy L, Sebastian R, Felix-Rodriguez J, Ferrero JM, Saiz J: Interaction of specialized cardiac conduction system with antiarrhythmic drugs: a simulation study. IEEE Trans Biomed Eng 2011, 58: 3475–8.Lamata P, Niederer S, Nordsletten D, Barber DC, Roy I, Hose DR, et al.: An accurate, fast and robust method to generate patient-specific cubic Hermite meshes. Med Image Anal 2011, 15: 801–13.Pathmanathan P, Cooper J, Fletcher A, Mirams G, Murray P, Osborne J, et al.: A computational study of discrete mechanical tissue models. Phys Biol 2009, 6: 036001.Niederer SA, Kerfoot E, Benson AP, Bernabeu MO, Bernus O, Bradley C, et al.: Verification of cardiac tissue electrophysiology simulators using an N-version benchmark. Philos Trans R Soc A Math Phys Eng Sci 2011, 369: 4331–51.Ten Tusscher KHWJ, Panfilov AV: Cell model for efficient simulation of wave propagation in human ventricular tissue under normal and pathological conditions. Phys Med Biol 2006, 51: 6141–56.LeGrice I, Smaill B, Chai L, Edgar S, Gavin J, Hunter P: Laminar structure of the heart: ventricular myocyte arrangement and connective tissue architecture in the dog. Am J Physiol Heart Circ Physiol 1995, 269: H571–82.Anderson RH, Smerup M, Sanchez-Quintana D, Loukas M, Lunkenheimer PP: The three-dimensional arrangement of the myocytes in the ventricular walls. Clin Anat 2009, 22: 64–76.Clerc L: Directional differences of impulse spread in trabecular muscle from mammalian heart. J Physiol 1976, 255: 335–46.Streeter DD Jr, Spotnitz HM, Patel DP, Ross J Jr, Sonnenblick EH: Fiber orientation in the canine left ventricle during diastole and systole. Circ Res 1969, 24: 339–47.Scollan D, Holmes A, Winslow R, Forder J: Histological validation of myocardial microstructure obtained from diffusion tensor magnetic resonance imaging. Am J Physiol Heart Circ Physiol 1998, 275: H2308–18.Hsu EW, Muzikant AL, Matulevicius SA, Penland RC, Henriquez CS: Magnetic resonance myocardial fiber-orientation mapping with direct histological correlation. Am J Physiol Heart Circ Physiol 1998, 274: H1627–34.Holmes AA, Scollan DF, Winslow RL: Direct histological validation of diffusion tensor MRI in formaldehyde-fixed myocardium. Magn Reson Med 2000, 44: 157–61.Sermesant M, Forest C, Pennec X, Delingette H, Ayache N: Deformable biomechanical models: application to 4D cardiac image analysis. Med Image Anal 2003, 7: 475–88.Peyrat JM, Sermesant M, Pennec X, Delingette H, Xu C, McVeigh ER, et al.: A computational framework for the statistical analysis of cardiac diffusion tensors: application to a small database of canine hearts. IEEE Trans Med Imaging 2007, 26: 1500–14.Toussaint N, Sermesant M, Stoeck CT, Kozerke S, Batchelor PG: In vivo human 3D cardiac fibre architecture: reconstruction using curvilinear interpolation of diffusion tensor images. Med Image Comput Comput Assist Interv 2010,13(Pt 1):418–25.Toussaint N, Stoeck CT, Schaeffter T, Kozerke S, Sermesant M, Batchelor PG: In vivo human cardiac fibre architecture estimation using shape-based diffusion tensor processing. Med Image Anal 2013, 17: 1243–55.Bishop MJ, Hales P, Plank G, Gavaghan DJ, Scheider J, Grau V: Comparison of rule-based and DTMRI-derived fibre architecture in a whole rat ventricular computational model. In Funct Imaging Model Heart 2009, LNCS 5528. Springer–Verlag, Berlin Heidelberg; 2009:87–96.Bayer JD, Blake RC, Plank G, Trayanova NA: A novel rule-based algorithm for assigning myocardial fiber orientation to computational heart models. Ann Biomed Eng 2012, 40: 2243–54.Dobrzynski H, Anderson RH, Atkinson A, Borbas Z, D’Souza A, Fraser JF, et al.: Structure, function and clinical relevance of the cardiac conduction system, including the atrioventricular ring and outflow tract tissues. Pharmacol Ther 2013, 139: 260–88.Tranum-Jensen J, Wilde AA, Vermeulen JT, Janse MJ: Morphology of electrophysiologically identified junctions between Purkinje fibers and ventricular muscle in rabbit and pig hearts. Circ Res 1991, 69: 429–37.Boyle PM, Deo M, Plank G, Vigmond EJ: Purkinje-mediated effects in the response of quiescent ventricles to defibrillation shocks. Ann Biomed Eng 2010, 38: 456–68.Behradfar E, Nygren A, Vigmond EJ: The role of Purkinje-myocardial coupling during ventricular arrhythmia: a modeling study. PLoS One 2014., 9: Article ID e88000DiFrancesco D, Noble D: A model of cardiac electrical activity incorporating ionic pumps and concentration changes. Philos Trans R Soc B Biol Sci 1985, 307: 353–98.Stewart P, Aslanidi OV, Noble D, Noble PJ, Boyett MR, Zhang H: Mathematical models of the electrical action potential of Purkinje fibre cells. Philos Trans R Soc A Math Phys Eng Sci 2009, 367: 2225–55.Li P, Rudy Y: A model of canine purkinje cell electrophysiology and Ca(2+) cycling: rate dependence, triggered activity, and comparison to ventricular myocytes. Circ Res 2011, 109: 71–9.Chinchapatnam P, Rhode KS, Ginks M, Mansi T, Peyrat JM, Lambiase P, et al.: Estimation of volumetric myocardial apparent conductivity from endocardial electro-anatomical mapping. In 31st Annu Int Conf IEEE Eng Med Biol Soc (EMBC 2009). IEEE, Minneapolis, MN (USA); 2009:2907–10.Durrer D, Van Dam RT, Freud GE, Janse MJ, Meijler FL, Arzbaecher RC: Total excitation of the isolated human heart. Circulation 1970, 41: 899–912.Pollard AE, Barr RC: Computer simulations of activation in an anatomically based model of the human ventricular conduction system. IEEE Trans Biomed Eng 1991, 38: 982–96.Abboud S, Berenfeld O, Sadeh D: Simulation of high-resolution QRS complex using a ventricular model with a fractal conduction system. Effects of ischemia on high-frequency QRS potentials. Circ Res 1991, 68: 1751–60.Sebastian R, Zimmerman V, Romero D, Sanchez-Quintana D, Frangi AF: Characterization and modeling of the peripheral cardiac conduction system. IEEE Trans Med Imaging 2013, 32: 45–55.Bordas R, Gillow K, Lou Q, Efimov IR, Gavaghan D, Kohl P, et al.: Rabbit-specific ventricular model of cardiac electrophysiological function including specialized conduction system. Prog Biophys Mol Biol 2011, 107: 90–100.Stephenson RS, Boyett MR, Hart G, Nikolaidou T, Cai X, Corno AF, et al.: Contrast enhanced micro-computed tomography resolves the 3-dimensional morphology of the cardiac conduction system in mammalian hearts. PLoS One 2012., 7: Article ID e35299Berenfeld O, Jalife J: Purkinje-Muscle reentry as a mechanism of polymorphic ventricular arrhythmias in a 3-dimensional model of the ventricles. Circ Res 1998, 82: 1063–77.Azzouzi A, Coudière Y, Turpault R, Zemzemi N: A mathematical model of the Purkinje-muscle junctions. Math Biosci Eng MBE 2011, 8: 915–30.Dux-Santoy L, Sebastian R, Rodriguez JF, Ferrero JM: Modeling the different sections of the cardiac conduction system to obtain realistic electrocardiograms. In 35th Annu Int Conf IEEE Eng Med Biol Soc (EMBC 2013). IEEE, Osaka, Japan; 2013:6846–9.Cardenes R, Sebastian R, Berruezo A, Camara O: Inverse

Mechanistic investigation of Ca2+ alternans in human heart failure and its modulation by fibroblasts

[EN] Heart failure (HF) is characterized, among other factors, by a progressive loss of contractile function and by the formation of an arrhythmogenic substrate, both aspects partially related to intracellular Ca2+ cycling disorders. In failing hearts both electrophysiological and structural remodeling, including fibroblast proliferation, contribute to changes in Ca2+ handling which promote the appearance of Ca2+ alternans (Ca-alt). Ca-alt in turn give rise to repolarization alternans, which promote dispersion of repolarization and contribute to reentrant activity. The computational analysis of the incidence of Ca2+ and/or repolarization alternans under HF conditions in the presence of fibroblasts could provide a better understanding of the mechanisms leading to HF arrhythmias and contractile function disorders. Methods and findings The goal of the present study was to investigate in silico the mechanisms leading to the formation of Ca-alt in failing human ventricular myocytes and tissues with disperse fibroblast distributions. The contribution of ionic currents variability to alternans formation at the cellular level was analyzed and the results show that in normal ventricular tissue, altered Ca2+ dynamics lead to Ca-alt, which precede APD alternans and can be aggravated by the presence of fibroblasts. Electrophysiological remodeling of failing tissue alone is sufficient to develop alternans. The incidence of alternans is reduced when fibroblasts are present in failing tissue due to significantly depressed Ca2+ transients. The analysis of the underlying ionic mechanisms suggests that Ca-alt are driven by Ca2+-handling protein and Ca2+ cycling dysfunctions in the junctional sarcoplasmic reticulum and that their contribution to alternans occurrence depends on the cardiac remodeling conditions and on myocyte-fibroblast interactions. Conclusion It can thus be concluded that fibroblasts modulate the formation of Ca-alt in human ventricular tissue subjected to heart failure-related electrophysiological remodeling. Pharmacological therapies should thus consider the extent of both the electrophysiological and structural remodeling present in the failing heart.This work was partially supported by the Plan Estatal de Investigación Científica y Técnica y de Innovación 2013 2016" from the Ministerio de Economía, Industria y Competitividad of Spain and Fondo Europeo de Desarrollo Regional (FEDER) DPI2016-75799-R (AEI/FEDER, UE), and by the Programa de Ayudas de Investigación y Desarrollo (PAID-01-17) from the Universitat Politècnica de València. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Mora-Fenoll, MT.; Gomez, JF.; Morley, G.; Ferrero De Loma-Osorio, JM.; Trenor Gomis, BA. (2019). Mechanistic investigation of Ca2+ alternans in human heart failure and its modulation by fibroblasts. PLoS ONE. 14(6):1-19. https://doi.org/10.1371/journal.pone.0217993S119146Glukhov, A. V., Fedorov, V. V., Kalish, P. W., Ravikumar, V. K., Lou, Q., Janks, D., … Efimov, I. R. (2012). Conduction Remodeling in Human End-Stage Nonischemic Left Ventricular Cardiomyopathy. Circulation, 125(15), 1835-1847. doi:10.1161/circulationaha.111.047274Lou, Q., Fedorov, V. V., Glukhov, A. V., Moazami, N., Fast, V. G., & Efimov, I. R. (2011). Transmural Heterogeneity and Remodeling of Ventricular Excitation-Contraction Coupling in Human Heart Failure. Circulation, 123(17), 1881-1890. doi:10.1161/circulationaha.110.989707Gomez, J. F., Cardona, K., & Trenor, B. (2015). Lessons learned from multi-scale modeling of the failing heart. Journal of Molecular and Cellular Cardiology, 89, 146-159. doi:10.1016/j.yjmcc.2015.10.016Kohl, P., & Gourdie, R. G. (2014). Fibroblast–myocyte electrotonic coupling: Does it occur in native cardiac tissue? Journal of Molecular and Cellular Cardiology, 70, 37-46. doi:10.1016/j.yjmcc.2013.12.024Gaudesius, G., Miragoli, M., Thomas, S. P., & Rohr, S. (2003). Coupling of Cardiac Electrical Activity Over Extended Distances by Fibroblasts of Cardiac Origin. Circulation Research, 93(5), 421-428. doi:10.1161/01.res.0000089258.40661.0cKohl, P., Camelliti, P., Burton, F. L., & Smith, G. L. (2005). Electrical coupling of fibroblasts and myocytes: relevance for cardiac propagation. Journal of Electrocardiology, 38(4), 45-50. doi:10.1016/j.jelectrocard.2005.06.096Camelliti, P., Green, C. R., LeGrice, I., & Kohl, P. (2004). Fibroblast Network in Rabbit Sinoatrial Node. Circulation Research, 94(6), 828-835. doi:10.1161/01.res.0000122382.19400.14Rook, M. B., van Ginneken, A. C., de Jonge, B., el Aoumari, A., Gros, D., & Jongsma, H. J. (1992). Differences in gap junction channels between cardiac myocytes, fibroblasts, and heterologous pairs. American Journal of Physiology-Cell Physiology, 263(5), C959-C977. doi:10.1152/ajpcell.1992.263.5.c959Mahoney, V. M., Mezzano, V., Mirams, G. R., Maass, K., Li, Z., Cerrone, M., … Morley, G. E. (2016). Connexin43 contributes to electrotonic conduction across scar tissue in the intact heart. Scientific Reports, 6(1). doi:10.1038/srep26744Quinn, T. A., Camelliti, P., Rog-Zielinska, E. A., Siedlecka, U., Poggioli, T., O’Toole, E. T., … Kohl, P. (2016). Electrotonic coupling of excitable and nonexcitable cells in the heart revealed by optogenetics. Proceedings of the National Academy of Sciences, 113(51), 14852-14857. doi:10.1073/pnas.1611184114Rubart, M., Tao, W., Lu, X.-L., Conway, S. J., Reuter, S. P., Lin, S.-F., & Soonpaa, M. H. (2017). Electrical coupling between ventricular myocytes and myofibroblasts in the infarcted mouse heart. Cardiovascular Research, 114(3), 389-400. doi:10.1093/cvr/cvx163Miragoli, M., Gaudesius, G., & Rohr, S. (2006). Electrotonic Modulation of Cardiac Impulse Conduction by Myofibroblasts. Circulation Research, 98(6), 801-810. doi:10.1161/01.res.0000214537.44195.a3Jacquemet, V., & Henriquez, C. S. (2008). Loading effect of fibroblast-myocyte coupling on resting potential, impulse propagation, and repolarization: insights from a microstructure model. American Journal of Physiology-Heart and Circulatory Physiology, 294(5), H2040-H2052. doi:10.1152/ajpheart.01298.2007Li, Y., Asfour, H., & Bursac, N. (2017). Age-dependent functional crosstalk between cardiac fibroblasts and cardiomyocytes in a 3D engineered cardiac tissue. Acta Biomaterialia, 55, 120-130. doi:10.1016/j.actbio.2017.04.027Zlochiver, S., Muñoz, V., Vikstrom, K. L., Taffet, S. M., Berenfeld, O., & Jalife, J. (2008). Electrotonic Myofibroblast-to-Myocyte Coupling Increases Propensity to Reentrant Arrhythmias in Two-Dimensional Cardiac Monolayers. Biophysical Journal, 95(9), 4469-4480. doi:10.1529/biophysj.108.136473Nguyen, T. P., Xie, Y., Garfinkel, A., Qu, Z., & Weiss, J. N. (2011). Arrhythmogenic consequences of myofibroblast–myocyte coupling. Cardiovascular Research, 93(2), 242-251. doi:10.1093/cvr/cvr292Greisas, A., & Zlochiver, S. (2016). The Multi-Domain Fibroblast/Myocyte Coupling in the Cardiac Tissue: A Theoretical Study. Cardiovascular Engineering and Technology, 7(3), 290-304. doi:10.1007/s13239-016-0266-xSridhar, S., Vandersickel, N., & Panfilov, A. V. (2017). Effect of myocyte-fibroblast coupling on the onset of pathological dynamics in a model of ventricular tissue. Scientific Reports, 7(1). doi:10.1038/srep40985Gomez, J. F., Cardona, K., Martinez, L., Saiz, J., & Trenor, B. (2014). Electrophysiological and Structural Remodeling in Heart Failure Modulate Arrhythmogenesis. 2D Simulation Study. PLoS ONE, 9(7), e103273. doi:10.1371/journal.pone.0103273KODAMA, M., KATO, K., HIRONO, S., OKURA, Y., HANAWA, H., YOSHIDA, T., … AIZAWA, Y. (2004). Linkage Between Mechanical and Electrical Alternans in Patients with Chronic Heart Failure. Journal of Cardiovascular Electrophysiology, 15(3), 295-299. doi:10.1046/j.1540-8167.2004.03016.xRosenbaum, D. S., Jackson, L. E., Smith, J. M., Garan, H., Ruskin, J. N., & Cohen, R. J. (1994). Electrical Alternans and Vulnerability to Ventricular Arrhythmias. New England Journal of Medicine, 330(4), 235-241. doi:10.1056/nejm199401273300402Jordan, P. N., & Christini, D. J. (2006). Action Potential Morphology Influences Intracellular Calcium Handling Stability and the Occurrence of Alternans. Biophysical Journal, 90(2), 672-680. doi:10.1529/biophysj.105.071340Cherry, E. M. (2017). Distinguishing mechanisms for alternans in cardiac cells using constant-diastolic-interval pacing. Chaos: An Interdisciplinary Journal of Nonlinear Science, 27(9), 093902. doi:10.1063/1.4999354Groenendaal, W., Ortega, F. A., Krogh-Madsen, T., & Christini, D. J. (2014). Voltage and Calcium Dynamics Both Underlie Cellular Alternans in Cardiac Myocytes. Biophysical Journal, 106(10), 2222-2232. doi:10.1016/j.bpj.2014.03.048Nolasco, J. B., & Dahlen, R. W. (1968). A graphic method for the study of alternation in cardiac action potentials. Journal of Applied Physiology, 25(2), 191-196. doi:10.1152/jappl.1968.25.2.191Picht, E., DeSantiago, J., Blatter, L. A., & Bers, D. M. (2006). Cardiac Alternans Do Not Rely on Diastolic Sarcoplasmic Reticulum Calcium Content Fluctuations. Circulation Research, 99(7), 740-748. doi:10.1161/01.res.0000244002.88813.91Díaz, M. E., O’Neill, S. C., & Eisner, D. A. (2004). Sarcoplasmic Reticulum Calcium Content Fluctuation Is the Key to Cardiac Alternans. Circulation Research, 94(5), 650-656. doi:10.1161/01.res.0000119923.64774.72Zhou, X., Bueno-Orovio, A., Orini, M., Hanson, B., Hayward, M., Taggart, P., … Rodriguez, B. (2016). In Vivo and In Silico Investigation Into Mechanisms of Frequency Dependence of Repolarization Alternans in Human Ventricular Cardiomyocytes. Circulation Research, 118(2), 266-278. doi:10.1161/circresaha.115.307836Xie, L.-H., Sato, D., Garfinkel, A., Qu, Z., & Weiss, J. N. (2008). Intracellular Ca Alternans: Coordinated Regulation by Sarcoplasmic Reticulum Release, Uptake, and Leak. Biophysical Journal, 95(6), 3100-3110. doi:10.1529/biophysj.108.130955Cutler, M. J., Wan, X., Laurita, K. R., Hajjar, R. J., & Rosenbaum, D. S. (2009). Targeted SERCA2a Gene Expression Identifies Molecular Mechanism and Therapeutic Target for Arrhythmogenic Cardiac Alternans. Circulation: Arrhythmia and Electrophysiology, 2(6), 686-694. doi:10.1161/circep.109.863118Kanaporis, G., & Blatter, L. A. (2015). The Mechanisms of Calcium Cycling and Action Potential Dynamics in Cardiac Alternans. Circulation Research, 116(5), 846-856. doi:10.1161/circresaha.116.305404Pastore, J. M., Girouard, S. D., Laurita, K. R., Akar, F. G., & Rosenbaum, D. S. (1999). Mechanism Linking T-Wave Alternans to the Genesis of Cardiac Fibrillation. Circulation, 99(10), 1385-1394. doi:10.1161/01.cir.99.10.1385O’Hara, T., Virág, L., Varró, A., & Rudy, Y. (2011). Simulation of the Undiseased Human Cardiac Ventricular Action Potential: Model Formulation and Experimental Validation. PLoS Computational Biology, 7(5), e1002061. doi:10.1371/journal.pcbi.1002061Mora, M. T., Ferrero, J. M., Romero, L., & Trenor, B. (2017). Sensitivity analysis revealing the effect of modulating ionic mechanisms on calcium dynamics in simulated human heart failure. PLOS ONE, 12(11), e0187739. doi:10.1371/journal.pone.0187739Andrew 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.106.101410Spach, M. S., Heidlage, J. F., Dolber, P. C., & Barr, R. C. (2000). Electrophysiological Effects of Remodeling Cardiac Gap Junctions and Cell Size. Circulation Research, 86(3), 302-311. doi:10.1161/01.res.86.3.302Kieval, R. S., Spear, J. F., & Moore, E. N. (1992). Gap junctional conductance in ventricular myocyte pairs isolated from postischemic rabbit myocardium. Circulation Research, 71(1), 127-136. doi:10.1161/01.res.71.1.127Gomez, J. F., Cardona, K., Romero, L., Ferrero, J. M., & Trenor, B. (2014). Electrophysiological and Structural Remodeling in Heart Failure Modulate Arrhythmogenesis. 1D Simulation Study. PLoS ONE, 9(9), e106602. doi:10.1371/journal.pone.0106602Taggart, P., Sutton, P. M., Opthof, T., Coronel, R., Trimlett, R., Pugsley, W., & Kallis, P. (2000). Inhomogeneous Transmural Conduction During Early Ischaemia in Patients with Coronary Artery Disease. Journal of Molecular and Cellular Cardiology, 32(4), 621-630. doi:10.1006/jmcc.2000.1105Heidenreich E. Algoritmos para ecuaciones de reacción difusión aplicados a electrofisiología. Ph.D. Thesis. Universidad de Zaragoza. 2009. https://institutoi4.net/wp-content/uploads/2017/08/TesisEAH.pdfHeidenreich, E. A., Ferrero, J. M., Doblaré, M., & Rodríguez, J. F. (2010). Adaptive Macro Finite Elements for the Numerical Solution of Monodomain Equations in Cardiac Electrophysiology. Annals of Biomedical Engineering, 38(7), 2331-2345. doi:10.1007/s10439-010-9997-2Xie, Y., Garfinkel, A., Weiss, J. N., & Qu, Z. (2009). Cardiac alternans induced by fibroblast-myocyte coupling: mechanistic insights from computational models. American Journal of Physiology-Heart and Circulatory Physiology, 297(2), H775-H784. doi:10.1152/ajpheart.00341.2009Luo, C. H., & Rudy, Y. (1991). A model of the ventricular cardiac action potential. Depolarization, repolarization, and their interaction. Circulation Research, 68(6), 1501-1526. doi:10.1161/01.res.68.6.1501Pruvot, E. J., Katra, R. P., Rosenbaum, D. S., & Laurita, K. R. (2004). Role of Calcium Cycling Versus Restitution in the Mechanism of Repolarization Alternans. Circulation Research, 94(8), 1083-1090. doi:10.1161/01.res.0000125629.72053.95Kanaporis, G., & Blatter, L. A. (2017). Membrane potential determines calcium alternans through modulation of SR Ca 2+ load and L-type Ca 2+ current. Journal of Molecular and Cellular Cardiology, 105, 49-58. doi:10.1016/j.yjmcc.2017.02.004Goldhaber, J. I., Xie, L.-H., Duong, T., Motter, C., Khuu, K., & Weiss, J. N. (2005). Action Potential Duration Restitution and Alternans in Rabbit Ventricular Myocytes. Circulation Research, 96(4), 459-466. doi:10.1161/01.res.0000156891.66893.83Walmsley, J., Rodriguez, J. F., Mirams, G. R., Burrage, K., Efimov, I. R., & Rodriguez, B. (2013). mRNA Expression Levels in Failing Human Hearts Predict Cellular Electrophysiological Remodeling: A Population-Based Simulation Study. PLoS ONE, 8(2), e56359. doi:10.1371/journal.pone.0056359Narayan, S. M., Bayer, J. D., Lalani, G., & Trayanova, N. A. (2008). Action Potential Dynamics Explain Arrhythmic Vulnerability in Human Heart Failure. Journal of the American College of Cardiology, 52(22), 1782-1792. doi:10.1016/j.jacc.2008.08.037Livshitz, L. M., & Rudy, Y. (2007). Regulation of Ca2+ and electrical alternans in cardiac myocytes: role of CAMKII and repolarizing currents. American Journal of Physiology-Heart and Circulatory Physiology, 292(6), H2854-H2866. doi:10.1152/ajpheart.01347.2006WILSON, L. D., WAN, X., & ROSENBAUM, D. S. (2006). Cellular Alternans: A Mechanism Linking Calcium Cycling Proteins to Cardiac Arrhythmogenesis. Annals of the New York Academy of Sciences, 1080(1), 216-234. doi:10.1196/annals.1380.018Wilson, L. D., Jeyaraj, D., Wan, X., Hoeker, G. S., Said, T. H., Gittinger, M., … Rosenbaum, D. S. (2009). Heart failure enhances susceptibility to arrhythmogenic cardiac alternans. Heart Rhythm, 6(2), 251-259. doi:10.1016/j.hrthm.2008.11.008Cutler, M. J., Wan, X., Plummer, B. N., Liu, H., Deschenes, I., Laurita, K. R., … Rosenbaum, D. S. (2012). Targeted Sarcoplasmic Reticulum Ca 2+ ATPase 2a Gene Delivery to Restore Electrical Stability in the Failing Heart. Circulation, 126(17), 2095-2104. doi:10.1161/circulationaha.111.071480Bayer, J. D., Narayan, S. M., Lalani, G. G., & Trayanova, N. A. (2010). Rate-dependent action potential alternans in human heart failure implicates abnormal intracellular calcium handling. Heart Rhythm, 7(8), 1093-1101. doi:10.1016/j.hrthm.2010.04.008Wang, L., Myles, R. C., De Jesus, N. M., Ohlendorf, A. K. P., Bers, D. M., & Ripplinger, C. M. (2014). Optical Mapping of Sarcoplasmic Reticulum Ca 2+ in the Intact Heart. Circulation Research, 114(9), 1410-1421. doi:10.1161/circresaha.114.302505Rovetti, R., Cui, X., Garfinkel, A., Weiss, J. N., & Qu, Z. (2010). Spark-Induced Sparks As a Mechanism of Intracellular Calcium Alternans in Cardiac Myocytes. Circulation Research, 106(10), 1582-1591. doi:10.1161/circresaha.109.213975Tomek, J., Tomková, M., Zhou, X., Bub, G., & Rodriguez, B. (2018). Modulation of Cardiac Alternans by Altered Sarcoplasmic Reticulum Calcium Release: A Simulation Study. Frontiers in Physiology, 9. doi:10.3389/fphys.2018.01306Hammer, K. P., Ljubojevic, S., Ripplinger, C. M., Pieske, B. M., & Bers, D. M. (2015). Cardiac myocyte alternans in intact heart: Influence of cell–cell coupling and β-adrenergic stimulation. Journal of Molecular and Cellular Cardiology, 84, 1-9. doi:10.1016/j.yjmcc.2015.03.012Majumder, R., Engels, M. C., de Vries, A. A. F., Panfilov, A. V., & Pijnappels, D. A. (2016). Islands of spatially discordant APD alternans underlie arrhythmogenesis by promoting electrotonic dyssynchrony in models of fibrotic rat ventricular myocardium. Scientific Reports, 6(1). doi:10.1038/srep24334Shiferaw, Y., & Karma, A. (2006). Turing instability mediated by voltage and calcium diffusion in paced cardiac cells. Proceedings of the National Academy of Sciences, 103(15), 5670-5675. doi:10.1073/pnas.0511061103Sato, D., Shiferaw, Y., Garfinkel, A., Weiss, J. N., Qu, Z., & Karma, A. (2006). Spatially Discordant Alternans in Cardiac Tissue. Circulation Research, 99(5), 520-527. doi:10.1161/01.res.0000240542.03986.e

Reduction of power line interference in electrocardiographic signals by dual Kalman filtering

[EN] This paper presents a filter for reducing powerline interference in electrocardiographic signals (ECG), based on dual parameter and state estimation using with a Kalman filter. Two models were used to represent power-line interference and ECG signal. Both models were combined to simulate the ECG signal whose state was estimated for separating the ECG signal from the interference. The proposed algorithm was fine-tuned and compared using a set of tests relying on the QT arrhythmia database. Tuning tests were done for tracking clean ECG; these results were used for setting the algorithm¿s parameters for later filtering tests. Exhaustive filtering tests were carried out on artificially corrupted database registers for given signal to noise ratios; performance curves were thus obtained, leading to comparing the proposed algorithm with other filtering methods. The proposed algorithm was compared to an recursive infinite impulse response filter (IIR) and a Kalman filter based on a simpler model. A filtering algorithm was thus obtained which is robust for changes in interference amplitude and keeps these properties for different types of ECG morphologies.[ES] En este artículo se presenta el desarrollo de un filtro para la reducción de la interferencia de línea de potencia en señales electrocardiográficas (ECG), basado en estimación dual de parámetros y de estado, empleando la filtración Kalman, en el cual se consideran modelos independientes entre la interferencia de línea de potencia y la señal ECG. Ambos modelos son combinados para simular la señal ECG medida sobre la que se realiza la estimación de estado para separar la señal de la interferencia. El algoritmo propuesto es sintonizado y comparado en un conjunto de pruebas realizadas sobre la base de datos QT de electrocardiografía. Inicialmente se hacen pruebas de sintonización del algoritmo para el rastreo de la señal ECG limpia, cuyos resultados son utilizados después para las pruebas de filtrado. Luego se llevan a cabo pruebas exhaustivas sobre la base de datos QT en la filtración de interferencia de línea de potencia, la cual ha sido introducida artificialmente en los registros, para una relación de señal a ruido (SNR) dada, obteniendo así curvas del desempeño del algoritmo, que permiten a su vez comparar con el desempeño de otros algoritmos de filtración, a saber, un filtro notch recursivo de respuesta infinita al impulso (IIR) y un filtro de Kalman, basado en un modelo más simple para la señal ECG. Como resultado, se demuestra que el algoritmo de filtrado obtenido es robusto a los cambios de amplitud de la interferencia; además, conserva sus propiedades para los diferentes tipos de morfologías de señales ECG normales y patológicas.Este trabajo se realiza en el marco del proyecto de la DIMA Técnicas de Computación de Alto Rendimiento en la Interpretación Automatizada de Imágenes Médicas y Bioseñales.Avendaño-Valencia, LD.; Avendaño, LE.; Ferrero De Loma-Osorio, JM.; Castellanos-Domínguez, G. (2007). Reducción de interferencia de línea de potencia en señales electrocardiográficas mediante el filtro dual de Kalman. Ingeniería e Investigación. 27(3):77-88. http://hdl.handle.net/10251/150636S778827

Ca2+ alternans in human heart failure and its modulation by fibroblasts

The functional coupling between myocytes and fibroblasts can alter the electrophysiology of myocytes, which is a major problem in heart failure. The code provides the mathematical model to simulate this interaction, and combines a modified version of the ORd human ventricular action potential model (O'Hara el al. 2011, Mora el al. 2017) with the electrophysiological fibroblast model formulated by MacCannell el al. (2009). The model includes the kinetics of 14 myocyte ion currents, 4 fibroblast ion currents and the current flowing between both cells. The electrophisiological remodeling of heart failure is also included and simulations can be performed in normal or failing conditions. The code has been implemented in MATLAB and can be excecuted in R2016b without problems. This code can be used to reproduce the results obtained in the paper entitled "Mechanistic investigation of Ca2+ alternans in human heart failure and its modulation by fibroblasts" (DOI: 10.1371/journal.pone.0217993).Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016” from the Ministerio de Economía, Industria y Competitividad of Spain and Fondo Europeo de Desarrollo Regional (FEDER) DPI2016-75799-R (AEI/FEDER, UE) y Universitat Politècnica de València, PAID-01-17 (UPV)Mora Fenoll, MT.; Gómez García, JF.; Ferrero De Loma-Osorio, JM.; Trénor Gomis, BA. (2019). Ca2+ alternans in human heart failure and its modulation by fibroblasts. Universitat Politècnica de València. http://hdl.handle.net/10251/12153

Comparison between Hodgkin-Huxley and Markov formulations of cardiac ion channels

When simulating the macroscopic current flowing through cardiac ion channels, two mathematical formalisms can be adopted: the Hodgkin–Huxley model (HHM) formulation, which describes openings and closings of channel ‘gates’, or the Markov model (MM) formulation, based on channel ‘state’ transitions. The latter was first used in 1995 to simulate the effects of mutations in ionic currents and, since then, its use has been extended to wild-type channels also. While the MMs better describe the actual behavior of ion channels, they are mathematically more complex than HHMs in terms of parameter estimation and identifiability and are computationally much more demanding, which can dramatically increase computational time in large-scale (e.g. whole heart) simulations. We hypothesize that a HHM formulation obtained from classical patch-clamp protocols in wild-type and mutant ion channels can be used to correctly simulate cardiac action potentials and their static and dynamic properties. To validate our hypothesis, we selected two pivotal cardiac ionic currents (the rapid delayed rectifier Kþ current, IKr, and the inward Naþ current, INa) and formulated HHMs for both wild-type and mutant channels (LQT2- linked T474I mutation for IKr and LQT3-linked ΔKPQ mutation for INa). Action potentials were then simulated using the MM and HHM versions of the currents, and the action potential waveforms, biomarkers and action potential duration rate dependence properties were compared in control conditions and in the presence of physiological variability. While small differences between ionic currents were found between the two models (correlation coefficient ρ40.92), the simulations yielded almost identical action potentials (ρ40.99), suggesting that HHMs may also be valid to simulate the effects of mutations affecting IKr and INa on the action potential.This work was partially supported by the "VI Plan Nacional de Investigacion Cientifica, Desarrollo e Innovacion Tecnologica" from the Ministerio de Economia y Competitividad of Spain (TIN2012-37546-C03-01) and the European Commission (European Regional Development Funds - ERDF - FEDER).Carbonell Pascual, B.; Godoy, EJ.; Ferrer Albero, A.; Romero Pérez, L.; Ferrero De Loma-Osorio, JM. (2016). Comparison between Hodgkin-Huxley and Markov formulations of cardiac ion channels. Journal of Theoretical Biology. 399:92-102. doi:10.1016/j.jtbi.2016.03.039S9210239

Why Does Extracellular Potassium Rise in Acute Ischemia? Insights from Computational Smilations

[EN] Hyperkalemia, acidosis and hypoxia are the three main components of acute myocardial ischemia. In particular, the increase of extracellular K+ concentration (hyperkalemia), has been proved to be very proarrhythmic because it sets the stage for ventricular fibrillation. However, the intimate mechanisms remain partially unknown. The aim of this work was to investigate, using computational simulation, the relationship between the different phases of hiperkalemia, the activity of the ion channels and the changes related to the action potential in the absence of coronary flow. Our results show that the partial inhibition of the sodium-potassium pump is the main cause of extracellular potassium accumulation. However, the cause of the plateau phase could be due to the appearance of action potential alternans, which reduces the net potassium efflux and limits the increase of extracellular potassium concentration.This work was partially supported by the "Programa Salvador de Madariaga 2018" of the Spanish Ministry of Science, Innovation and Universities (Grant Reference PRX18/00489).González-Ascaso, A.; Olcina, P.; Garcia-Daras, M.; Rodriguez Matas, JF.; Ferrero De Loma-Osorio, JM. (2019). Why Does Extracellular Potassium Rise in Acute Ischemia? Insights from Computational Smilations. IEEE. 1-4. https://doi.org/10.22489/CinC.2019.088S1

Analysis of vulnerability to reentry in acute myocardial ischemia using a realistic human heart model

[EN] Electrophysiological alterations of the myocardium caused by acute ischemia constitute a pro-arrhythmic substrate for the generation of potentially lethal arrhythmias. Experimental evidence has shown that the main components of acute ischemia that induce these electrophysiological alterations are hyperkalemia, hypoxia (or anoxia in complete artery occlusion), and acidosis. However, the influence of each ischemic component on the likelihood of reentry is not completely established. Moreover, the role of the His-Purkinje system (HPS) in the initiation and maintenance of arrhythmias is not completely understood. In the present work, we investigate how the three components of ischemia affect the vulnerable window (VW) for reentry using computational simulations. In addition, we analyze the role of the HPS on arrhythmogenesis. A 3D biventricular/torso human model that includes a realistic geometry of the central and border ischemic zones with one of the most electrophysiologically detailed model of ischemia to date, as well as a realistic cardiac conduction system, were used to assess the VW for reentry. Four scenarios of ischemic severity corresponding to different minutes after coronary artery occlusion were simulated. Our results suggest that ischemic severity plays an important role in the generation of reentries. Indeed, this is the first 3D simulation study to show that ventricular arrhythmias could be generated under moderate ischemic conditions, but not in mild and severe ischemia. Moreover, our results show that anoxia is the ischemic component with the most significant effect on the width of the VW. Thus, a change in the level of anoxia from moderate to severe leads to a greater increment in the VW (40 ms), in comparison with the increment of 20 ms and 35 ms produced by the individual change in the level of hyperkalemia and acidosis, respectively. Finally, the HPS was a necessary element for the generation of approximately 17% of reentries obtained. The retrograde conduction from the myocardium to HPS in the ischemic region, conduction blocks in discrete sections of the HPS, and the degree of ischemia affecting Purkinje cells, are suggested as mechanisms that favor the generation of ventricular arrhythmias.This work was supported by the Secretaría de Educacion ¿ Superior, Ciencia, Tecnología e Innovacion ¿ (SENESCYT) of Ecuador CIBAE-023- 2014, by Grant PID2019-104356RB-C41 funded by MCIN/AEI/ 10.13039/501100011033, by the European Union¿s Horizon 2020 research and innovation programme under grant agreement No 101016496, by Direccion ¿ General de Política Científica de la Generalitat Valenciana (PROMETEO 2020/043), and by the ¿Programa Salvador de Madariaga 2018¿¿ of the Spanish Ministry of Science, Innovation and Universities (Grant Reference PRX18/00489).Carpio, EF.; Gómez, JF.; Rodríguez-Matas, JF.; Trenor Gomis, BA.; Ferrero De Loma-Osorio, JM. (2022). Analysis of vulnerability to reentry in acute myocardial ischemia using a realistic human heart model. Computers in Biology and Medicine. 141:1-15. https://doi.org/10.1016/j.compbiomed.2021.10503811514

Interaction of Specialized Cardiac Conduction System With Antiarrhythmic Drugs: A Simulation Study

[EN] The use of antiarrhythmic drugs is common to treat heart rhythm disorders. Computational modeling and simulation are promising tools that could be used to investigate the effects of specific drugs on cardiac electrophysiology. In this paper, we study the multiscale effects of dofetilide, a drug that blocks IKr, from cellular to organ level paying special attention to its effect on heart structures, in particular the specialized cardiac conduction system (CCS). We include a model of the CCS in a patient-specific anatomical ventricular model and study the drug effects in simulations with and without a CCS. Results confirmed the expected effects of dofetilide at cellular level, increasing the action potential duration, and at organ level, prolonging the QT segment. Notable differences are shown between models with and without the CCS on action potential duration distributions. These techniques show the importance of heart heterogeneity and the global effects of the interaction of drugs with cardiac structures. © 2006 IEEE.This work was supported in part by the Ministry of Science and Innovation, TEC-2008-02090 (FPI grant BES-2009-016071), in part by the Programa de Apoyo a la Investigacion y Desarrollo (PAID-06-09-2843) de la Universitat Politecnica de Valencia, and in part by the Direccion General de Politica Cientifica de la Generalitat Valenciana (GV/2010/078). Asterisk indicates corresponding author.Dux-Santoy Hurtado, L.; Sebastián Aguilar, R.; Felix-Rodriguez, J.; Ferrero De Loma-Osorio, JM.; Saiz Rodríguez, FJ. (2011). Interaction of Specialized Cardiac Conduction System With Antiarrhythmic Drugs: A Simulation Study. IEEE Transactions on Biomedical Engineering. 58(12):3475-3478. https://doi.org/10.1109/TBME.2011.2165213S34753478581

Blocking L-Type Calcium Current Reduces Vulnerability to Re-Entry in Human iPSC-Derived Cardiomyocytes Tissue

[EN] Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) have proven to be crucial in pharmacological assessment. Nevertheless, their response to drugs when coupled forming a tissue is not fully understood. Thus, the aim of this study was to determine whether blocking L-type Ca+2 current (¿"#) in a hiPSCCMs tissue could be considered as a potential antiarrhythmic procedure. To analyze the effects of ¿"# block, the maximum conductance of ¿"# (¿"#) was decreased (block conditions) and compared to control. In both situations, control and block, the tissue was stimulated following a cross-field protocol to generate re-entries. A phase analysis was performed and specific parameters, such as re-entry frequency (¿'(()*'+), excitation wavelength, vulnerable window (VW), and cellular excitability, were evaluated. Induced re-entries, where ¿"#$ was reduced by 70% showed a 6.9% and a 47.83% decrease in ¿'(()*'+ and in the width of the VW, respectively. Our results suggest that blocking calcium channels could be considered as an antiarrhythmic strategy in a hiPSC-CMs tissue.Dasi, A.; Climent, AM.; Martínez, L.; Gómez, JF.; Ferrero De Loma-Osorio, JM.; Trenor Gomis, BA. (2019). Blocking L-Type Calcium Current Reduces Vulnerability to Re-Entry in Human iPSC-Derived Cardiomyocytes Tissue. IEEE. 1-4. https://doi.org/10.22489/CinC.2019.113S1

Análisis computacional de vulnerabilidad a reentrada en isquemia miocárdica aguda

La influencia de cada componente isquémico (hipoxia, hiperpotasemia y acidosis) sobre la arritmogénesis es controvertida y difícil de estudiar experimentalmente. En el presente estudio, investigamos cómo los diferentes componentes isquémicos afectan la ventana vulnerable (VW) para la reentrada mediante simulaciones computacionales. Las simulaciones se realizaron en un modelo biventricular 3D que incluye una región isquémica realista y el sistema de conducción His-Purkinje. A nivel celular, utilizamos una versión modificada del modelo de potencial de acción de O’Hara adaptado para simular isquemia aguda. Se simularon tres niveles diferentes de isquemia: leve, moderada y grave. Se analizaron los efectos sobre el ancho del VW de cada parámetro isquémico. El modelo nos permitió obtener un patrón reentrante realista correspondiente a la taquicardia ventricular en todas las simulaciones. Los resultados sugieren que el nivel isquémico juega un papel importante en la generación de reentradas. Además, la hipoxia tiene el efecto más significativo en el ancho del VW. La presencia del sistema Purkinje es clave para la generación de reentradas.The influence of each ischemic component (hypoxia, hyperkalemia, and acidosis) on arrhythmogenesis is controversial and difficult to study experimentally. In the present study, we investigate how the different ischemic components affect the vulnerable window (VW) for reentry using computational simulations. Simulations were performed in a 3D biventricular model that includes a realistic ischemic region and the His-Purkinje conduction system. At the cellular level, we used a modified version of the O’Hara action potential model adapted to simulate acute ischemia. Three different levels of ischemia were simulated: mild, moderate, and severe. The effects on the width of the VW of each ischemic parameter were analyzed. The model allowed us to obtain a realistic reentrant pattern corresponding to ventricular tachycardia in all simulations. Results suggest that the ischemic level plays an important role in the generation of reentries. Furthermore, hypoxia has the most significant effect on the width of the VW. The presence of Purkinje system is key to the generation of reentries.Rimin