A multiscale predictive digital twin for neurocardiac modulation

Abstract

Cardiac function is tightly regulated by the autonomic nervous system (ANS). Activation of the sympathetic nervous system increases cardiac output by increasing heart rate and stroke volume, while parasympathetic nerve stimulation instantly slows heart rate. Importantly, imbalance in autonomic control of the heart has been implicated in the development of arrhythmias and heart failure. Understanding of the mechanisms and effects of autonomic stimulation is a major challenge because synapses in different regions of the heart result in multiple changes to heart function. For example, nerve synapses on the sinoatrial node (SAN) impact pacemaking, while synapses on contractile cells alter contraction and arrhythmia vulnerability. Here, we present a multiscale neurocardiac modelling and simulator tool that predicts the effect of efferent stimulation of the sympathetic and parasympathetic branches of the ANS on the cardiac SAN and ventricular myocardium. The model includes a layered representation of the ANS and reproduces firing properties measured experimentally. Model parameters are derived from experiments and atomistic simulations. The model is a first prototype of a digital twin that is applied to make predictions across all system scales, from subcellular signalling to pacemaker frequency to tissue level responses. We predict conditions under which autonomic imbalance induces proarrhythmia and can be modified to prevent or inhibit arrhythmia. In summary, the multiscale model constitutes a predictive digital twin framework to test and guide high‐throughput prediction of novel neuromodulatory therapy. imageKey points A multi‐layered model representation of the autonomic nervous system that includes sympathetic and parasympathetic branches, each with sparse random intralayer connectivity, synaptic dynamics and conductance based integrate‐and‐fire neurons generates firing patterns in close agreement with experiment. A key feature of the neurocardiac computational model is the connection between the autonomic nervous system and both pacemaker and contractile cells, where modification to pacemaker frequency drives initiation of electrical signals in the contractile cells. We utilized atomic‐scale molecular dynamics simulations to predict the association and dissociation rates of noradrenaline with the β‐adrenergic receptor. Multiscale predictions demonstrate how autonomic imbalance may increase proclivity to arrhythmias or be used to terminate arrhythmias. The model serves as a first step towards a digital twin for predicting neuromodulation to prevent or reduce disease.

The promise of cardiac neuromodulation: can computational modelling bridge the gap?

“…In this issue of The Journal of Physiology, Yang et al (2023) present a novel model of neurocardiology that is not only multiscale but also multidisciplinary. This approach differs from prior studies that typically only model the downstream effects of sympathetic and parasympathetic modulation on cardiac myocytes, with pre-specified changes in heart rate, ion channel expression and calcium cycling.…”

“…This approach differs from prior studies that typically only model the downstream effects of sympathetic and parasympathetic modulation on cardiac myocytes, with pre-specified changes in heart rate, ion channel expression and calcium cycling. In their study, Yang et al (2023) directly simulate both neuronal and cardiac electrophysiology simultaneously. The neuronal electrophysiology models (Jolivet et al, 2004) for the sympathetic and parasympathetic cardiac nervous system are directly coupled to cardiac electrophysiology models incorporating both sinoatrial node (SAN) (Behar et al, 2016) and ventricular myocyte models (Iancu et al, 2007;Soltis & Saucerman, 2010).…”

“…Building the cardiac ANS model alone is a significant contribution. Yang et al (2023) base their ANS model on prior descriptions in the literature (Fukuda et al, 2015;Kember et al, 2011Kember et al, , 2017Shivkumar et al, 2016) to reproduce the multiple distinct networks of cardiac control consisting of three layers: the central nervous system (CNS), intrathoracic nervous system (ITNS) and the intrinsic cardiac nervous system (ICNS), which is further broken down into two subnetworks (the sympathetic S-ICNS and parasympathetic P-ICNS). Each of these networks is simulated in detail as individual neurons coupled at synapses, with bidirectional feedback between the ITNS, S-ICNS and P-ICNS subnetworks.…”

“…Multiscale modelling is an ambitious endeavour and the study by Yang et al (2023) has several limitations. The SAN and ventricular myocyte models used were from rabbit not human as a result of the availability of existing sympathetic/parasympathetic inputs.…”

“…For example, the model predicted an increased HR that reached a steady state with constant sympathetic stimulation, whereas experiments by Wang et al (2019) showed a gradual decline despite continued sympathetic activity. Yang et al (2023) discuss this limitation and hypothesize how the model could be improved to include β1-adrenergic receptor desensitization by protein kinase A as a possible contributing mechanism. The model also showed no prolongation of APD with isolated parasympathetic stimulation, which is inconsistent with Yamakawa et al (2014).…”