Challenges Associated with Interpreting Mechanisms of AF

Abstract

Determining optimal treatment strategies for complex arrhythmogenesis in AF is confounded by the lack of consensus regarding the mechanisms causing AF. Studies report different mechanisms for AF, ranging from hierarchical drivers to anarchical multiple activation wavelets. Differences in the assessment of AF mechanisms are likely due to AF being recorded across diverse models using different investigational tools, spatial scales and clinical populations. The authors review different AF mechanisms, including anatomical and functional re-entry, hierarchical drivers and anarchical multiple wavelets. They then describe different cardiac mapping techniques and analysis tools, including activation mapping, phase mapping and fibrosis identification. They explain and review different data challenges, including differences between recording devices in spatial and temporal resolutions, spatial coverage and recording surface, and report clinical outcomes using different data modalities. They suggest future research directions for investigating the mechanisms underlying human AF.

Pro-Arrhythmic Signaling of Thyroid Hormones and Its Relevance in Subclinical Hyperthyroidism

“…Concerning AF, the cardiomyocyte sleeves overlapping the pulmonary veins along with Ca 2+ handling disorders are the sources of the ectopic electrical activity, while re-entry circuits are promoted by the atrial tissue heterogeneity and disorders in the intercellular electrical coupling mediated by connexin (Cx) channels [3,5,8,9,[30][31][32][33][34]. The association of AF with the atrial Cx37 and Cx40 gene polymorphisms [35], as well as somatic mutations in GJA5 (encoding Cx40), have been identified in AF [36,37].…”

“…Atrial fibrillation (AF) and ventricular fibrillation (VF) are clinically relevant and potentially life-threatening arrhythmias. Despite some differences, both AF and VF have been assumed to occur due to abnormalities in the electrical activity involved in impulse initiation and impulse propagation [1][2][3][4][5][6][7][8][9]. The former is due to an enhanced automaticity of the cardiomyocytes (i.e., pacemaker-like activity) or triggered activity expressed as early after-depolarization (EAD) or delayed after-depolarization (DAD).…”

Directed graph mapping exceeds phase mapping in discriminating true and false rotors detected with a basket catheter in a complex in-silico excitation pattern

“…However, many other different explanations were proposed as possible mechanisms of AF: the multiple wavelet theory [ [21] , [22] , [23] , [24] ], the double layer hypothesis [ [25] , [26] , [27] , [28] , [29] ], mother rotor fibrillation [ 30 , 31 ], focal sources [ 32 , 33 ], and more recently, micro-anatomical intramural reentry [ 34 , 35 ]. To demonstrate the complexity in the field, a crosstalk provided room for all the different viewpoints [ [36] , [37] , [38] , [39] , [40] ] and the discussion remains ongoing [ 41 , 42 ].…”

Rotor Localization and Phase Mapping of Cardiac Excitation Waves Using Deep Neural Networks

“…These methods have been applied to both simulations of VF (Fenton and Karma, 1998;Bray et al, 2001;Clayton et al, 2006) and AF (Hwang et al, 2016;Rodrigo et al, 2017), as well as experimental data, including electrode recordings of human VF (Nash et al, 2006;Umapathy et al, 2010) and human AF (Kuklik et al, 2015;Podziemski et al, 2018;Abad et al, 2021) optical maps of the transmembrane potential during VF (Gray et al, 1998;Iyer and Gray, 2001;Bray and Wikswo, 2002;Rogers, 2004; and AF (Yamazaki et al, 2012;Guillem et al, 2016) in isolated hearts, optical maps of action potential spiral waves in cardiac cell cultures (Bursac et al, 2004;Entcheva and Bien, 2006;Munoz et al, 2007;Umapathy et al, 2010;You et al, 2017), and time-varying 3D maps of mechanical strain waves measured during VF in isolated hearts using ultrasound . However, phase maps and PS are prone to measurement artifacts and deficits caused by inadequate processing of the measurement data, particularly when the data is noisy or sparse (King et al, 2017;Kuklik et al, 2017;Rodrigo et al, 2017;Roney et al, 2017Roney et al, , 2019You et al, 2017). Noise and motion artifacts are a frequent issue when analyzing optical mapping recordings (Zou et al, 2002;Christoph and Luther, 2018).…”

“…Mapping fibrillatory wave phenomena at low resolutions can lead to misrepresentation of the underlying dynamics. For example, low resolution phase mapping has been shown to create false positive detections of PS (King et al, 2017;Kuklik et al, 2017;Roney et al, 2017Roney et al, , 2019You et al, 2017), contributing to much uncertainty in the imaging-based diagnosis of AF, a field in which rotors remain a highly controversial concept (Aronis et al, 2017;Nattel et al, 2017;Schotten et al, 2020). Mapping of AF would greatly benefit from computational methods, which could account for low spatial resolution and produce reliable visualizations of electrical phenomena from sparse and noisy spatio-temporal electrical signals.…”