Dispersion of Recovery and Vulnerability to Re-entry in a Model of Human Atrial Tissue With Simulated Diffuse and Focal Patterns of Fibrosis

Abstract

Fibrosis in atrial tissue can act as a substrate for persistent atrial fibrillation, and can be focal or diffuse. Regions of fibrosis are associated with slowed or blocked conduction, and several approaches have been used to model these effects. In this study a computational model of 2D atrial tissue was used to investigate how the spatial scale of regions of simulated fibrosis influenced the dispersion of action potential duration (APD) and vulnerability to re-entry in simulated normal human atrial tissue, and human tissue that has undergone remodeling as a result of persistent atrial fibrillation. Electrical activity was simulated in a 10 × 10 cm square 2D domain, with a spatially varying diffusion coefficient as described below. Cellular electrophysiology was represented by the Courtemanche model for human atrial cells, with the model parameters set for normal and remodeled cells. The effect of fibrosis was modeled with a smoothly varying diffusion coefficient, obtained from sampling a Gaussian random field (GRF) with length scales of between 1.25 and 10.0 mm. Twenty samples were drawn from each field, and used to allocate a value of diffusion coefficient between 0.05 and 0.2 mm2/ms. Dispersion of APD was assessed in each sample by pacing at a cycle length of 1,000 ms, followed by a premature beat with a coupling interval of 400 ms. Vulnerability to re-entry was assessed with an aggressive pacing protocol with pacing cycle lengths decreasing from 450 to 250 ms in 25 ms intervals for normal tissue and 300–150 ms for remodeled tissue. Simulated fibrosis at smaller spatial scales tended to lengthen APD, increase APD dispersion, and increase vulnerability to sustained re-entry relative to fibrosis at larger spatial scales. This study shows that when fibrosis is represented by smoothly varying tissue diffusion, the spatial scale of fibrosis has important effects on both dispersion of recovery and vulnerability to re-entry.

In silico investigation of the mechanisms underlying atrial fibrillation due to impaired Pitx2

“…Thirdly, fibrosis was modelled as nonconducting tissue in this study as consistent with our previous studies [52,53] and others [54]. In contrast, fibrosis was electrically coupled with healthy myocytes in some modelling studies [55]. Although different methodologies were used to model fibrosis in the past, they all draw the similar conclusions with regard to the role of fibrosis in AF [52,[56][57][58].…”

Epicardial Fibrosis Explains Increased Endo–Epicardial Dissociation and Epicardial Breakthroughs in Human Atrial Fibrillation

“…To generate patchy fibrosis patterns, we generated a spatially correlated, anatomy-tailored random field. A similar procedure for generating spatially correlated fibrosis was also proposed by Clayton (2018), but on a 2D tissue. In this work, we also account for the topology of the atria, and fibrosis is generated in such a way that geometrically close but anatomically distinct regions will not be correlated as they would just by considering the Euclidean distance.…”

Acute effects of alcohol on cardiac electrophysiology and arrhythmogenesis: Insights from multiscale in silico analyses

“…In this study, the S 1 was initiated from left to right and the S 2 was applied to the upper-left quadrant of the tissue. The patterns of fibrosis were generated randomly using a Gaussian Random Field approach [21], with or without border-zone (BZ)-associated remodeling. In the presence of BZ, both previously reported cellular electrophysiological changes and alterations in conduction velocity were applied to the HF cardiomyocyte model [20,22,23].…”