Dual network structure of the AV node

TL;DR

This study introduces the first functional network analysis of the cardiac atrioventricular node (AVN), revealing its small-world properties and resilience, with implications for understanding cardiac conduction and potential disease mechanisms.

Contribution

It provides the first characterization of the AVN as a functional network, demonstrating its small-world architecture and resilience through calcium imaging and network modeling.

Findings

AVN network exhibits small-world properties similar to brain networks.

Network resilience observed after sodium-calcium exchange transporter knockout.

Removal of global action potential alters network efficiency and robustness.

Abstract

Biological systems, particularly the brain, are frequently analyzed as networks, conveying mechanistic insights into their function and pathophysiology. This is the first study of a functional network of cardiac tissue. We use calcium imaging to obtain two functional networks in a subsidiary but essential pacemaker of the heart, the atrioventricular node (AVN). The AVN is a small cellular structure with dual functions: a) to delay the pacemaker signal passing from the sinoatrial node (SAN) to the ventricles, and b) to serve as a back-up pacemaker should the primary SAN pacemaker fail. Failure of the AVN can lead to syncope and death. We found that the shortest path lengths and clustering coefficients of the AVN are remarkably similar to those of the brain. The network is ``small-world," thus optimized for energy use vs transmission efficiency. We further study the network properties of AVN tissue with knock-out of the sodium-calcium exchange transporter. In this case, the average shortest path-lengths remained nearly unchanged showing network resilience, while the clustering coefficient was somewhat reduced, similar to schizophrenia in brain networks. When we removed the global action potential using principal component analysis (PCA) in wild-type model, the network lost its ``small-world" characteristics with less information-passing efficiency due to longer shortest path lengths but more robust signal propagation resulting from higher clustering. These two wild-type networks (with and without global action potential) may correspond to fast and slow conduction pathways. Laslty, a one-parameter non-linear preferential attachment model is a good fit to all three AVN networks.