Energy landscape analysis of cardiac fibrillation wave dynamics using pairwise maximum entropy model

TL;DR

This study applies a pairwise maximum entropy model to simulate and analyze the complex wave dynamics of cardiac fibrillation, revealing hierarchical energy landscapes that characterize different fibrillation states.

Contribution

It introduces an energy landscape framework using MEM to understand the nonlinear dynamics of cardiac fibrillation from simulation data.

Findings

MEM accurately describes sinus rhythm and stable rotor dynamics

Energy landscapes reveal unique minima and barriers for fibrillation states

The approach helps identify potential 'drivers' like rotors or sinus nodes

Abstract

Cardiac fibrillation is characterized by chaotic and disintegrated spiral wave dynamics patterns, whereas sinus rhythm shows synchronized excitation patterns. To determine functional correlations among cardiomyocytes during complex fibrillation states, we applied a pairwise maximum entropy model (MEM) to the 2D numerical simulation data of human atrial fibrillation. We then constructed an energy landscape and estimated a hierarchical structure among the different local minima (attractors) to explain the dynamic properties of cardiac fibrillation. The MEM could describe the wave dynamics of sinus rhythm, single stable rotor, and single rotor with wavebreak (both accuracy and reliability>0.9), but not the multiple random wavelet case. The energy landscapes exhibited unique profiles of local minima and energy barriers, characterizing the spatiotemporal patterns of cardiac fibrillation dynamics. The pairwise MEM-based energy landscape analysis provides reliable explanations of complex nonlinear dynamics of cardiac fibrillation, which might be determined by the presence of a 'driver' such as a sinus node or rotor.