Key result
This review outlines fundamental studies probing the cardiac neural control hierarchy and discusses how computational methods can guide improved experimental design and data analysis.
Why the study?
Large cardio-neural datasets present challenges for signal processing, time series analysis, data sharing, and reproducibility that require computational methods to enhance understanding of cardiac pathologies.
This review highlights the evolution of experimental and computational methods for studying the cardiac neural control hierarchy, emphasizing the shift from single-unit to multi-unit recordings and the need for advanced data analysis strategies.
Highlights cooperative neural networks for cardiac regulation; leaves open targeted interventions in autonomic heart disease.
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.
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Gurel et al. (2022) conducted a review in Cardiac neural network dynamics. This review outlines fundamental studies probing the cardiac neural control hierarchy and discusses how computational methods can guide improved experimental design and data analysis.
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