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

Abstract

This study provides a comprehensive investigation of the spontaneous short-term regulatory mechanisms affecting cardiovascular and cardiorespiratory interactions during supine rest and in response to postural stress. The direct causality measure of conditional transfer entropy was applied to beat-to-beat heart period, arterial pressure, respiration, and arterial compliance variability series assessed in thirty-nine healthy subjects during the supine resting state and the orthostatic challenge. The inferred physiological networks behind these two conditions reveal well-known regulatory mechanisms, such as the tilt-induced decreased respiratory sinus arrhythmia (RSA) and increased baroreflex, as well as less explored interactions such as those involving compliance, which suggest striking physiological responses. Specifically, we found tight relationships between compliance and heart period, arterial pressure and respiration, which advocate the non negligible involvement of this cardio-vascular parameter into the intricate hank of the most studied physiological interconnections. Furthermore, the joint use of parametric and model-free estimation approaches allowed us to infer the prevalence of linear and nonlinear dynamics, as well as the effects on the inferred directed links of low- and high-frequency oscillations reflecting autonomic modulation. In conclusion, our study proves that direct causality measures are crucial to assess the characteristic links of complex cardiovascular networks and infer the many underlying short-term regulatory mechanisms. NEW & NOTEWORHTYWhile short-term regulatory mechanisms involving heart period, respiration and arterial pressure have been widely investigated, the way they produce and buffer cardiovascular oscillations in different physiological states is not fully understood. This study proposes a thorough investigation of a four-node physiological network, including the less explored arterial compliance variability, and provides insights into the linear vs. nonlinear characterization and spectral content of the causal dynamics representing each link within the network.