Studying Cardiac Neural Network Dynamics: Challenges and Opportunities for Scientific Computing

Abstract

Neural control of the heart involves continuous modulation of cardiac mechanical and electrical activity to meet the organism's demand for blood flow. The closed-loop control scheme consists of interconnected neural networks with central and peripheral components working cooperatively with each other. These components have evolved to cooperate control of various aspects of cardiac function, which produce measurable "functional" outputs such as heart rate and blood pressure. In this review, we will outline fundamental studies probing the cardiac neural control hierarchy. We will discuss how computational methods can guide improved experimental design and be used to probe how information is processed while closed-loop control is operational. These experimental designs generate large cardio-neural datasets that require sophisticated strategies for signal processing and time series analysis, while presenting the usual large-scale computational challenges surrounding data sharing and reproducibility. These challenges provide unique opportunities for the development and validation of novel techniques to enhance understanding of mechanisms of cardiac pathologies required for clinical implementation.

Keywords: cardiac function; cardiac nervous system; closed-loop control; neurocardiology; sudden cardiac death (SCD).

FIGURE 1

(A) Fundamental difference between open-loop and closed-loop systems is that the system output is regulated by feedback in closed-loop systems, while open-loop systems have no feedback. (B) Cardiac nervous system represents a three-tier closed-loop control hierarchy where each tier exhibits neural inputs resulting in functional outputs such as the electrocardiography (ECG) or blood pressure (BP). (C) Open source algorithm development and novel cardioneural recording technologies supported by histologic studies will propel the field of neurocardiology forward.