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Rotational Activity around an Obstacle in 2D Cardiac Tissue in Presence of Cellular Heterogeneity

Author

Listed:
  • Pavel Konovalov

    (Institute of Immunology and Physiology, Ural Branch of Russian Academy of Sciences, 620049 Ekaterinburg, Russia)

  • Daria Mangileva

    (Institute of Immunology and Physiology, Ural Branch of Russian Academy of Sciences, 620049 Ekaterinburg, Russia
    Laboratory of Computational Biology and Medicine, Ural Federal University, 620075 Ekaterinburg, Russia)

  • Arsenii Dokuchaev

    (Institute of Immunology and Physiology, Ural Branch of Russian Academy of Sciences, 620049 Ekaterinburg, Russia)

  • Olga Solovyova

    (Institute of Immunology and Physiology, Ural Branch of Russian Academy of Sciences, 620049 Ekaterinburg, Russia
    Laboratory of Computational Biology and Medicine, Ural Federal University, 620075 Ekaterinburg, Russia)

  • Alexander V. Panfilov

    (Laboratory of Computational Biology and Medicine, Ural Federal University, 620075 Ekaterinburg, Russia
    Department of Physics and Astronomy, Ghent University, 9000 Ghent, Belgium
    World-Class Research Center “Digital Biodesign and Personalized Healthcare”, Sechenov University, 119146 Moscow, Russia)

Abstract

Waves of electrical excitation rotating around an obstacle is one of the important mechanisms of dangerous cardiac arrhythmias occurring in the heart damaged by a post-infarction scar. Such a scar is also surrounded by the region of heterogeneity called a gray zone. In this paper, we perform the first comprehensive numerical study of various regimes of wave rotation around an obstacle surrounded by a gray zone. We use the TP06 cellular ionic model for human cardiomyocytes and study how the period and the pattern of wave rotation depend on the radius of a circular obstacle and the width of a circular gray zone. Our main conclusions are the following. The wave rotation regimes can be subdivided into three main classes: (1) functional rotation, (2) scar rotation and the newly found (3) gray zone rotation regimes. In the scar rotation regime, the wave rotates around the obstacle, while in the gray zone regime, the wave rotates around the gray zone. As a result, the period of rotation is determined by the perimeter of the scar, or gray zone perimeter correspondingly. The transition from the scar to the gray rotation regimes can be determined from the minimal period principle, formulated in this paper. We have also observed additional regimes associated with two types of dynamical instabilities which may affect or not affect the period of rotation. The results of this study can help to identify the factors determining the period of arrhythmias in post-infarction patients.

Suggested Citation

  • Pavel Konovalov & Daria Mangileva & Arsenii Dokuchaev & Olga Solovyova & Alexander V. Panfilov, 2021. "Rotational Activity around an Obstacle in 2D Cardiac Tissue in Presence of Cellular Heterogeneity," Mathematics, MDPI, vol. 9(23), pages 1-15, November.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:23:p:3090-:d:692203
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    References listed on IDEAS

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    1. Arne Defauw & Ivan V Kazbanov & Hans Dierckx & Peter Dawyndt & Alexander V Panfilov, 2013. "Action Potential Duration Heterogeneity of Cardiac Tissue Can Be Evaluated from Cell Properties Using Gaussian Green's Function Approach," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-12, November.
    2. Nele Vandersickel & Ivan V Kazbanov & Anita Nuitermans & Louis D Weise & Rahul Pandit & Alexander V Panfilov, 2014. "A Study of Early Afterdepolarizations in a Model for Human Ventricular Tissue," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-19, January.
    3. Gabriel Balaban & Brian P Halliday & Wenjia Bai & Bradley Porter & Carlotta Malvuccio & Pablo Lamata & Christopher A Rinaldi & Gernot Plank & Daniel Rueckert & Sanjay K Prasad & Martin J Bishop, 2019. "Scar shape analysis and simulated electrical instabilities in a non-ischemic dilated cardiomyopathy patient cohort," PLOS Computational Biology, Public Library of Science, vol. 15(10), pages 1-18, October.
    4. Hermenegild J. Arevalo & Fijoy Vadakkumpadan & Eliseo Guallar & Alexander Jebb & Peter Malamas & Katherine C. Wu & Natalia A. Trayanova, 2016. "Arrhythmia risk stratification of patients after myocardial infarction using personalized heart models," Nature Communications, Nature, vol. 7(1), pages 1-8, September.
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