Creating an interactive computer model of left atrium electrophysiology to study the underlying mechanisms leading to atrial fibrillation

Tarleton State University received a $464,502 Project Grant award from the National Heart, Lung, and Blood Institute (NHLBI) under the Cardiovascular Diseases Research program (CFDA 93.837), effective September 1, 2025, through August 31, 2028. The project deliverable is an advanced interactive computer model of left atrium electrophysiology designed to investigate the underlying mechanisms of atrial fibrillation (AF). The research addresses a critical public health need, as AF is the most prevalent atrial arrhythmia and carries a five-fold increased stroke risk for patients. To achieve the project objectives, the research team will construct an anatomically accurate three-dimensional lattice of the left atrium using computed tomography angiography imaging and incorporate electrochemical and stochastic processes into the model to enable investigation of chaotic mechanisms characteristic of AF.

The project incorporates sub-awards to establish clinical and imaging expertise. Baylor College of Medicine, as a sub-awardee (awarded April 21, 2026), provides patient selection leadership, expert clinical guidance on model construction from imaging data, and simulation result interpretation to connect the computational work to electrophysiologist clinical needs. The University of Houston System (awarded February 28, 2026) contributes co-investigator support for imaging data export, three-dimensional model development from imaging datasets, and collaborative interpretation of simulation results. Project deliverables include peer-reviewed journal publications, presentations at national cardiology and computational mathematics conferences, and follow-up grant submissions.

Abstract

Heart disease and strokes are the leading causes of death worldwide. Atrial arrhythmias are significant contributors to strokes, heart failure, and, in some cases, acute myocardial infarction. Atrial fibrillation (AF), the most prevalent but least understood atrial arrhythmia, is associated with a five-fold increased risk of stroke for patients. Therefore, IT is imperative that, in our pursuit of building healthier lives free of cardiovascular diseases and strokes, we focus on reducing the occurrence of atrial arrhythmias, especially AF. The left atrium (LA) is the most complex of the heart's four chambers, and IT is where most complex arrhythmias arise. AF exhibits many characteristics of chaos. If AF is truly chaotic, IT cannot be studied analytically; rather, IT must be examined through computational mathematics and simulations. While our digital twin of the LA has been utilized to simulate and study many textbook atrial arrhythmias, IT lacks the complexity necessary to investigate the underlying mechanisms of AF. True chaos can only manifest on high-dimensional landscapes and must be driven by nonlinear functions. The primary objective of this work will be to introduce the requisite complexity to the model to enable the study of the underlying mechanisms responsible for AF. This will be accomplished through two specific aims. Aim 1: utilize computed tomography angiography (CTA) images to construct an anatomically accurate three- dimensional lattice of the LA. This approach will facilitate the creation of a high-dimensional landscape where chaos can emerge. Aim 2: incorporate electrochemical and stochastic processes into the model, and enhance the representation of bachmann's bundle from a single pulse node to a branching tree of specialized myocytes. This will generate a nonlinear, dynamically evolving landscape conducive to chaos. After achieving each aim, the improved model will undergo testing against textbook arrhythmias. If the enhancement is deemed to contribute to the understanding of the underlying mechanisms behind left atrium arrhythmias, IT will be integrated into the model. Otherwise, IT will not be included. The objective is to refrain from adding complexity that does not enhance utility, and the failure of one aim should not hinder the implementation of others. The ultimate goal is to employ the final model to investigate the root causes of atrial fibrillation and develop strategies for its elimination to save lives.