Cardiac Heterogeneity Prediction by Cardio-Neural Network Simulation

Area of Science:

• Neurocardiology
• Computational Neuroscience
• Biophysics

Background:

• The autonomic nervous system mediates complex bidirectional communication between the brain and heart.
• Existing computational models struggle to capture intricate ion channel dynamics and cellular heterogeneity in brain and heart.
• This limits the ability to understand organ-level electrical responses from cellular electrophysiology.

Purpose of the Study:

• To propose a unified model of excitable cells adaptable to adrenergic modulation for simulating brain-heart interactions.
• To investigate the role of cardiac myocyte electrical heterogeneity in heart rate variability (HRV).
• To explore the potential of spiking neural networks (SNNs) for analyzing brain-heart electrophysiological signals and predicting morbidity.

Main Methods:

• Development of a unified excitable cell model with adrenergic modulation capabilities.
• Simulation of a sparsely coupled cardio-neural network comprising one thousand nodes.
• Analysis of electrophysiological recordings using SNNs to assess HRV and its determinants.

Main Results:

• Cardiac myocyte electrical heterogeneity is identified as the primary driver of heart rate variability.
• Brain-heart interplay via electrical pulses contains crucial information analyzable by SNNs.
• SNNs can predict and monitor HRV from electrophysiological data, linking it to tachycardia and bradycardia via myocyte polarization.

Conclusions:

• The proposed unified model and SNN analysis provide a framework for understanding and predicting brain-heart interactions.
• This approach facilitates early morbidity prediction through the analysis of electrophysiological signals.
• Future advancements in nano-electronics may enable the development of implantable brain-heart interfaces for real-time monitoring and stimulation.