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The network representation is becoming increasingly popular for thedescription of cardiovascular interactions based on the analysis of multiplesimultaneously collected variables. However, the traditional methods to assessnetwork links based on pairwise interaction measures cannot reveal high-ordereffects involving more than two nodes, and are not appropriate to infer theunderlying network topology. To address these limitations, here we introduce aframework which combines the assessment of high-order interactions withstatistical inference for the characterization of the functional linkssustaining physiological networks. The framework develops information-theoreticmeasures quantifying how two nodes interact in a redundant or synergistic waywith the rest of the network, and employs these measures for reconstructing thefunctional structure of the network. The measures are implemented for bothstatic and dynamic networks mapped respectively by random variables and randomprocesses using plug-in and model-based entropy estimators. The validation ontheoretical and numerical simulated networks documents the ability of theframework to represent high-order interactions as networks and to detectstatistical structures associated to cascade, common drive and common targeteffects. The application to cardiovascular networks mapped by the beat-to-beatvariability of heart rate, respiration, arterial pressure, cardiac output andvascular resistance allowed noninvasive characterization of several mechanismsof cardiovascular control operating in resting state and during orthostaticstress. Our approach brings to new comprehensive assessment of physiologicalinteractions and complements existing strategies for the classification ofpathophysiological states.

Assessing High-Order Links in Cardiovascular and Respiratory Networks via Static and Dynamic Information Measures