Quantification of Different Regulatory Pathways Contributing to Heartbeat Dynamics during Multiple Stimuli: a Proof of the Concept

Abstract

The dynamical interplay between brain and heart is mediated by several feedback mechanisms including the central autonomic network and baroreflex loop at a peripheral level, also for a short-term regulation. State of the art focused on the characterization of each regulatory pathway through a single stressor elicitation. However, no studies targeted the actual quantification of different mediating routes leading to the generation of heartbeat dynamics, particularly in case of combined exogenous stimuli. In this study, we propose a new approach based on computational modeling to quantify the contribution of multiple concurrent stimuli in modulating cardiovascular dynamics. In this preliminary attempt, the model estimates the high-frequency power of heartbeat dynamics, and derives disentangling coefficients quantifying the effect of multiple elicitations. Model evaluation is performed on healthy rate variability (HRV) series from fourteen healthy subjects undergoing physical (tilt-table) and mental stressors (aritmetics), as well as their combined administration. Results indicate that, at a group-wise level, in base of concurrent physical and mental elicitations, the physical stressor contributes for the 85% of the resulting heartbeat dynamics. These findings are in agreement with the current knowledge on heartbeat regulatory systems, providing valuable perspectives on the quantification of underlying generative mechanisms of HRV.

Quantifying multidimensional control mechanisms of cardiovascular dynamics during multiple concurrent stressors

“…The experimental protocol comprised physical (i.e., cold pressor and tilt-up tests) and cognitive/emotional stressors (i.e., mental arithmetics and emotional videos) that were assumed to elicit selectively the peripheral and the central regulatory pathways, respectively. Preliminary results of this study can be found in [31]. The values of α p and β p to obtain this plot are selected as the average between the α p and β p outputs obtained from the two datasets according to the The model comprises two main equations (3 and 4) that were obtained after a data exploration for the choice of the best function, and the presence of the constrains leads to the quadratic degree formulation.…”