Identifying locations susceptible to micro-anatomical reentry using a spatial network representation of atrial fibre maps

Micro-anatomical reentry has been identified as a potential driver of atrial fibrillation (AF). In this paper, we introduce a novel computational method which aims to identify which atrial regions are most susceptible to micro-reentry. The approach, which considers the structural basis for micro-reentry only, is based on the premise that the accumulation of electrically insulating interstitial fibrosis can be modelled by simulating percolation-like phenomena on spatial networks. Our results suggest that at high coupling, where micro-reentry is rare, the micro-reentrant substrate is highly clustered in areas where the atrial walls are thin and have convex wall morphology. However, as transverse connections between fibres are removed, mimicking the accumulation of interstitial fibrosis, the substrate becomes less spatially clustered, and the bias to forming in thin, convex regions of the atria is reduced. Comparing our algorithm on image-based models with and without atrial fibre structure, we find that strong longitudinal fibre coupling can suppress the micro-reentrant substrate, whereas regions with disordered fibre orientations have an enhanced risk of micro-reentry. We suggest that with further development, these methods may have future potential for patient-specific risk stratification, taking a longitudinal view of the development of the micro-reentrant substrate. Author summary Atrial fibrillation (AF) is the most common abnormal heart rhythm, yet, despite extensive research, treatment success rates remain poor. In part, this is because there is an incomplete understanding of the mechanistic origin of AF. In this paper, we investigate one proposed mechanism of AF, the formation of “micro-reentrant circuits”, which can be thought of as a “short circuit”, forming when electrically insulating fibrosis (structural repair tissue) infiltrates the space between heart muscle cells. Previously, such circuits have been found in experimental hearts, but identifying these circuits clinically is difficult. Here, we aim to take a small step towards developing computational methods for identifying where in the atria these circuits are most likely to form, drawing on techniques from network science. Our approach indicates that a number of factors are key to determining where circuits form, most notably the thickness of the heart muscle, and the alignment of muscle fibres