Hidden Structures of Information Transport Underlying Spiral Wave Dynamics

TL;DR

This paper introduces a hybrid geometric and information-theoretic method to quantify spiral wave dynamics in excitable media, revealing hidden structures of information transport that could aid in clinical interventions.

Contribution

The paper presents a novel approach combining geometry and information theory to analyze spiral wave dynamics, applicable across various physical, chemical, and biological systems.

Findings

Hidden coherent structures of information flow are identified in spiral wave dynamics.

Longer observation periods enhance the clarity of these coherent structures.

The method is computationally efficient and broadly applicable.

Abstract

A spiral wave is a macroscopic dynamic of excitable media that plays an important role in several distinct systems, including the Belousov-Zhabotinsky reaction, seizures in the brain, and lethal arrhythmia in the heart. Because spiral wave dynamics can exhibit a wide spectrum of behaviors, its precise quantification can be challenging. Here we present a hybrid geometric and information-theoretic approach to quantifying spiral wave dynamics. We demonstrate the effectiveness of our approach by applying it to numerical simulations of a two-dimensional excitable medium with different numbers and spatial patterns of spiral waves. We show that, by defining information flow over the excitable medium, hidden coherent structures emerge that effectively quantify the information transport underlying spiral wave dynamics. Most importantly, we find that some coherent structures become more clearly defined over a longer observation period. These findings validate our approach to quantitatively characterize spiral wave dynamics by focusing on information transport. Our approach is computationally efficient and is applicable to many excitable media of interest in distinct physical, chemical and biological systems. Our approach could ultimately contribute to an improved therapy of clinical conditions such as seizures and cardiac arrhythmia by identifying potential targets of interventional therapies.