Direct causality measures unravel complex networks of cardiovascular dynamics and their modifications with postural stress

TL;DR

This study explores how cardiovascular systems interact during rest and stress using new network analysis methods.

Contribution

The study introduces a novel network perspective using direct causality measures to explore cardiovascular dynamics.

Findings

Arterial compliance shows significant influence on arterial pressure during postural stress.

Conditional causality measures reveal decreased respiratory sinus arrhythmia and increased baroreflex during tilt.

Model-based and model-free approaches clarify the linear and nonlinear nature of cardiovascular interactions.

Abstract

This study provides a novel network perspective on the spontaneous short-term regulatory mechanisms underlying cardiovascular and cardiorespiratory interactions during different physiological states. The direct causality measure of conditional transfer entropy was estimated employing linear model-based and nonlinear model-free approaches and applied to the network of beat-to-beat heart period, arterial pressure, respiration, and arterial compliance variability series assessed in thirty-nine young healthy subjects monitored in the supine resting state and during orthostatic stress. The network analysis retrieved well-known regulatory mechanisms significant in most of the subjects, such as the tilt-induced decreased respiratory sinus arrhythmia and increased baroreflex, and uncovered less explored interaction pathways involving compliance. Specifically, we found a decreased effect of heart period and arterial pressure on compliance, as well as a stronger causal influence from compliance to arterial pressure and a decreased influence of compliance on respiration. The joint use of model-based and model-free approaches allowed us to infer the linear or nonlinear nature of these interactions. Our study advocates the main role played by arterial compliance into the intricate hank of cardiovascular interactions, and documents the need to employ direct causality measures to infer the many complex mechanisms generating short-term cardiovascular oscillations.

Short-term regulatory mechanisms involving well-known cardiovascular parameters such as heart period, respiration and arterial pressure have been widely investigated in data-driven cardiovascular research. Nevertheless, the way they produce and buffer cardiovascular variability in different physiological states is not fully understood, especially in contexts where the effects of unobserved variables may play a key role. For instance, the causal interplay between arterial compliance variability and both heart rate and arterial pressure variabilities has never been explored from a network physiology perspective, and the mutual effects between these variables remain poorly understood. To fill this gap, we propose a thorough investigation of a physiological network comprising multiple cardiovascular variables, including the less explored arterial compliance beat-to-beat time series, in young healthy subjects monitored in the resting state and during postural stress. We provide novel insights into the characterization of link-specific physiological mechanisms through conditional causality measures estimated using both linear model-based and model-free approaches. We highlight the remarkable role of arterial compliance in governing cardiovascular variability during the supine rest and orthostatic challenge.

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