Inducibility of human atrial fibrillation in an in silico model reflecting local acetylcholine distribution and concentration

The frequency of atrial fibrillatory waves is modulated by the spatiotemporal pattern of acetylcholine release: a 3D computational study

In atrial fibrillation (AF), the ECG P-wave, which represents atrial depolarization, is replaced with chaotic and irregular fibrillation waves (f waves). The f-wave frequency, F f, shows significant variations over time. Cardiorespiratory interactions regulated by the autonomic nervous system have been suggested to play a role in such variations. We conducted a simulation study to test whether the spatiotemporal release pattern of the parasympathetic neurotransmitter acetylcholine (ACh) modulates the frequency of atrial reentrant circuits. Understanding parasympathetic involvement in AF may guide more effective treatment approaches and could help to design autonomic markers alternative to heart rate variability (HRV), which is not available in AF patients. 2D tissue and 3D whole-atria models of human atrial electrophysiology in persistent AF were built. Different ACh release percentages (8% and 30%) and spatial ACh release patterns, including spatially random release and release from ganglionated plexi (GPs) and associated nerves, were considered. The temporal pattern of ACh release, ACh(t), was simulated following a sinusoidal waveform of frequency 0.125 Hz to represent the respiratory frequency. Different mean concentrations ( A C h ¯ ) and peak-to-peak ranges of ACh (ΔACh) were tested. We found that temporal variations in F f, F f(t), followed the simulated temporal ACh(t) pattern in all cases. The temporal mean of F f(t), F ¯ f , depended on the fibrillatory pattern (number and location of rotors), the percentage of ACh release nodes and A C h ¯ . The magnitude of F f(t) modulation, ΔF f, depended on the percentage of ACh release nodes and ΔACh. The spatial pattern of ACh release did not have an impact on F ¯ f and only a mild impact on ΔF f. The f-wave frequency, being indicative of vagal activity, has the potential to drive autonomic-based therapeutic actions and could replace HRV markers not quantifiable from AF patients.

Location of Parasympathetic Innervation Regions From Electrograms to Guide Atrial Fibrillation Ablation Therapy: An in silico Modeling Study

The autonomic nervous system (ANS) plays an essential role in the generation and maintenance of cardiac arrhythmias. The cardiac ANS can be divided into its extrinsic and intrinsic components, with the latter being organized in an epicardial neural network of interconnecting axons and clusters of autonomic ganglia called ganglionated plexi (GPs). GP ablation has been associated with a decreased risk of atrial fibrillation (AF) recurrence, but the accurate location of GPs is required for ablation to be effective. Although GP stimulation triggers both sympathetic and parasympathetic ANS branches, a predominance of parasympathetic activity has been shown. This study aims was to develop a method to locate atrial parasympathetic innervation sites based on measurements from a grid of electrograms (EGMs). Electrophysiological models representative of non-AF, paroxysmal AF (PxAF), and persistent AF (PsAF) tissues were developed. Parasympathetic effects were modeled by increasing the concentration of the neurotransmitter acetylcholine (ACh) in randomly distributed circles across the tissue. Different circle sizes of ACh and fibrosis geometries were considered, accounting for both uniform diffuse and non-uniform diffuse fibrosis. Computational simulations were performed, from which unipolar EGMs were computed in a 16 × 1 6 electrode mesh. Different distances of the electrodes to the tissue (0.5, 1, and 2 mm) and noise levels with signal-to-noise ratio (SNR) values of 0, 5, 10, 15, and 20 dB were tested. The amplitude of the atrial EGM repolarization wave was found to be representative of the presence or absence of ACh release sites, with larger positive amplitudes indicating that the electrode was placed over an ACh region. Statistical analysis was performed to identify the optimal thresholds for the identification of ACh sites. In all non-AF, PxAF, and PsAF tissues, the repolarization amplitude rendered successful identification. The algorithm performed better in the absence of fibrosis or when fibrosis was uniformly diffuse, with a mean accuracy of 0.94 in contrast with a mean accuracy of 0.89 for non-uniform diffuse fibrotic cases. The algorithm was robust against noise and worked for the tested ranges of electrode-to-tissue distance. In conclusion, the results from this study support the feasibility to locate atrial parasympathetic innervation sites from the amplitude of repolarization wave.

Site-Specific Epicardium-to-Endocardium Dissociation of Electrical Activation in a Swine Model of Atrial Fibrillation

OBJECTIVES

This study sought to define the extent and spatial distribution of endocardial-epicardial dissociation (EED) in a swine model.

BACKGROUND

The mechanisms underlying persistent atrial fibrillation (AF) remain unclear.

METHODS

Sixteen swine underwent simultaneous endocardial and epicardial mapping using 32-electrode grid catheters. This included 6 swine with rapid atrial pacing-induced atrial remodeling. Three right atrial (RA) and 3 left atrial (LA) regions were mapped during sinus rhythm, atrial pacing, acute or persistent AF, and AF in the presence of pericardial acetylcholine. Unipolar electrogram recordings over 10-s epochs underwent offline phase analysis using customized software. Regional activation patterns on paired surfaces and the instantaneous phase at each matched electrode location were analyzed. EED was defined as paired electrodes out of phase by ≥20 ms.

RESULTS

The mean distance between matched endocardial-epicardial electrode pairs was 4.4 ± 1.8 mm. During episodes of AF, rotational activations with ≥3 full rotations were not seen. EED was seen during 34.4 ± 16.4% of mapped time periods: LA > RA, persistent > acute AF in the LA, and acetylcholine-induced > acute AF in both atria (p < 0.05 for each). Most marked EED in persistent AF was in the LA appendage (47.2 ± 3.7%) and the LA posterior wall (50.3 ± 4.7%).

CONCLUSIONS

Marked EED was seen in a swine model of AF, particularly during persistent AF. There was significantly more EED in the LA than the RA and, particularly, in the LA PW and LAA. Mapping approaches limited to the endocardium may not sufficiently characterize the complexity of AF.

Understanding the Beat-to-Beat Variations of P-Waves Morphologies in AF Patients During Sinus Rhythm: A Scoping Review of the Atrial Simulation Studies

The remarkable advances in high-performance computing and the resulting increase of the computational power have the potential to leverage computational cardiology toward improving our understanding of the pathophysiological mechanisms of arrhythmias, such as Atrial Fibrillation (AF). In AF, a complex interaction between various triggers and the atrial substrate is considered to be the leading cause of AF initiation and perpetuation. In electrocardiography (ECG), P-wave is supposed to reflect atrial depolarization. It has been found that even during sinus rhythm (SR), multiple P-wave morphologies are present in AF patients with a history of AF, suggesting a higher dispersion of the conduction route in this population. In this scoping review, we focused on the mechanisms which modify the electrical substrate of the atria in AF patients, while investigating the existence of computational models that simulate the propagation of the electrical signal through different routes. The adopted review methodology is based on a structured analytical framework which includes the extraction of the keywords based on an initial limited bibliographic search, the extensive literature search and finally the identification of relevant articles based on the reference list of the studies. The leading mechanisms identified were classified according to their scale, spanning from mechanisms in the cell, tissue or organ level, and the produced outputs. The computational modeling approaches for each of the factors that influence the initiation and the perpetuation of AF are presented here to provide a clear overview of the existing literature. Several levels of categorization were adopted while the studies which aim to translate their findings to ECG phenotyping are highlighted. The results denote the availability of multiple models, which are appropriate under specific conditions. However, the consideration of complex scenarios taking into account multiple spatiotemporal scales, personalization of electrophysiological and anatomical models and the reproducibility in terms of ECG phenotyping has only partially been tackled so far.

Anti-arrhythmic strategies for atrial fibrillation: The role of computational modeling in discovery, development, and optimization

Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with increased risk of cerebrovascular stroke, and with several other pathologies, including heart failure. Current therapies for AF are targeted at reducing risk of stroke (anticoagulation) and tachycardia-induced cardiomyopathy (rate or rhythm control). Rate control, typically achieved by atrioventricular nodal blocking drugs, is often insufficient to alleviate symptoms. Rhythm control approaches include antiarrhythmic drugs, electrical cardioversion, and ablation strategies. Here, we offer several examples of how computational modeling can provide a quantitative framework for integrating multiscale data to: (a) gain insight into multiscale mechanisms of AF; (b) identify and test pharmacological and electrical therapy and interventions; and (c) support clinical decisions. We review how modeling approaches have evolved and contributed to the research pipeline and preclinical development and discuss future directions and challenges in the field.