Half of the patients suffering from atrial fibrillation (AF) cannot be treated adequately, today. This book presents multi-scale computational methods to advance our understanding of patho-mechanisms, to improve the diagnosis of patients harboring an arrhythmogenic substrate, and to tailor therapy. The modeling pipeline ranges from ion channels on the subcellular level up to the ECG on the body surface. The tailored therapeutic approaches carry the potential to reduce the burden of AF.
Half of the patients suffering from atrial fibrillation (AF) cannot be treated adequately, today. This book presents multi-scale computational methods to advance our understanding of patho-mechanisms, to improve the diagnosis of patients harboring an arrhythmogenic substrate, and to tailor therapy. The modeling pipeline ranges from ion channels on the subcellular level up to the ECG on the body surface. The tailored therapeutic approaches carry the potential to reduce the burden of AF. This work was published by Saint Philip Street Press pursuant to a Creative Commons license permitting commercial use. All rights not granted by the work's license are retained by the author or authors.
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An early detection and diagnosis of atrial fibrillation sets the course for timely intervention to prevent potentially occurring comorbidities. Electrocardiogram data resulting from electrophysiological cohort modeling and simulation can be a valuable data resource for improving automated atrial fibrillation risk stratification with machine learning techniques and thus, reduces the risk of stroke in affected patients.
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' information of the substrate.
Multiscale modeling of cardiac electrophysiology helps to better understand the underlying mechanisms of atrial fibrillation, acute cardiac ischemia and pharmacological treatment. For this purpose, measurement data reflecting these conditions have to be integrated into models of cardiac electrophysiology. Several methods for this model adaptation are introduced in this thesis. The resulting effects are investigated in multiscale simulations ranging from the ion channel up to the body surface.AbstractEnglisch = Multiscale modeling of cardiac electrophysiology helps to better understand the underlying mechanisms of atrial fibrillation, acute cardiac ischemia and pharmacological treatment. For this purpose, measurement data reflecting these conditions have to be integrated into models of cardiac electrophysiology. Several methods for this model adaptation are introduced in this thesis. The resulting effects are investigated in multiscale simulations ranging from the ion channel up to the body surface.
Atrial fibrillation (AF) affects 2.5 million people per year in the U.S., and is associated with high morbidity and mortality. Both electrical and structural remodeling on the cardiac cell- and tissue-level contribute to AF, but the precise molecular pathways that lead to atrial fibrillation pathogenesis are not well understood. Mathematical modelling is ideally suited to understand the molecular basis of cardiac arrhythmias by allowing simulation of complex biological systems based on integrated data from individual populations of ion channels. A cross-platform multi-threaded graphical user interface LongQt was developed for advanced computational cardiac electrophysiology studies in sinoatrial, atrial, and ventricular cells to help bridge the gap between experimental and theoretical techniques in cardiac electrophysiology. A method of parameter sensitivity analysis is discussed to help define the contribution of individual ion channels to cell membrane excitability and action potential properties. Finally, a computational model of the human atrial cell was used to determine the mechanism for increased susceptibility to arrhythmogenic events in patients with defects in Ca2+/calmodulin-dependent protein kinase signaling pathways. An atrial computational model was extended to explore CaMKII activation of late sodium current (INa,L) and phosphorylation of downstream targets (L-type Ca2+ channel, phospholamban, ryanodine receptor). Both LongQt and the method of parameter sensitivity analysis were used to help identify the cellular pathway responsible for disrupted ion homeostasis and afterdepolarizations in atrial cells. Intracellular Ca2+ and Na+ accumulation, increased phosphorylation of RyR2 by CaMKII, and abnormal Ca2+ dynamics [e.g. beat-to-beat alteration (alternans)], and afterdepolarizations (early and late phase) was observed in mathematical models of the atrial cell with constitutive increase in INa,L, compared to wildtype control. These simulations define roles for previously unexplored ion channel regulation of CaMKII-mediated INa,L in the context of atrial fibrillation. Although INa,L is
This thesis describes the development of biophysically detailed computer models of the human atria and torso to study the underlying mechanisms of cardiac diseases, some of the most common causes of morbidity and mortality. This is a cross-disciplinary project, involving fundamentals of cardiac electrophysiology, physics of excitable media, applied mathematics and high performance scientific computing and visualisation. The author uses computer models to provide insights into the underlying mechanisms of the genesis of atrial fibrillation and develops novel techniques for the monitoring of atrial tachycardia.
