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

• DOI: 10.48550/arxiv.2401.05556
• Publication Date: 2024-01-01
• Authors: Gorana Mijatović; Laura Sparacino; Yuri Antonacci; Michal Javorka; Daniele Marinazzo; Sebastiano Strama...; Luca Faes
• Keywords: Network Analysis; Dynamic network analysis; Statistical Inference

The network representation is becoming increasingly popular for the description of cardiovascular interactions based on analysis multiple simultaneously collected variables. However, traditional methods to assess links pairwise interaction measures cannot reveal high-order effects involving more than two nodes, and are not appropriate infer underlying topology. To address these limitations, here we introduce a framework which combines assessment with statistical inference characterization functional sustaining physiological networks. develops information-theoretic quantifying how nodes interact in redundant or synergistic way rest network, employs reconstructing structure network. implemented both static dynamic networks mapped respectively by random variables processes using plug-in model-based entropy estimators. validation theoretical numerical simulated documents ability represent as detect structures associated cascade, common drive target effects. application beat-to-beat variability heart rate, respiration, arterial pressure, cardiac output vascular resistance allowed noninvasive several mechanisms control operating resting state during orthostatic stress. Our approach brings new comprehensive complements existing strategies classification pathophysiological states.