Key result
Information measures detect high-order cardiovascular network behaviors while prediction metrics capture synergy.
Why the study?
Assessing the synergistic high-order behaviors that emerge from underlying structural mechanisms is crucial to characterize complex systems.
Quantifying high-order behaviors through whole-minus-sum measures can provide new biomarkers to assess physio-pathological alterations of cardiovascular networks.
No immediate implications for clinical cardiology; leaves open novel approaches to quantify synergistic behaviors in physiological networks.
OBJECTIVE: Assessing the synergistic high-order behaviors (HOBs) that emerge from underlying structural mechanisms is crucial to characterize complex systems. This work leverages the combined use of predictability and information-theoretic measures to detect and quantify HOBs in synthetic and physiological network systems. METHODS: After providing formal definitions of mechanisms and behaviors in a complex system, measures of statistical synergy are defined as the whole-minus-sum (WMS) excess of mutual predictability (Δ MP) or mutual information (Δ MI) observed when considering the system as a whole rather than as a combination of its units. The two measures are computed using model-free methods based on nonlinear prediction and entropy estimation. RESULTS: The application to simulated linear Gaussian systems and nonlinear deterministic and stochastic dynamic systems shows that Δ MP tends to vanish for target variables influenced by additive effects of single independent source variables and is positive in the presence of group interactions between sources, while Δ MI exhibits a higher propensity to display positive values. Then, the analysis of physiological variables shows significant values of Δ MI when investigating the additive effect of systolic and diastolic arterial pressure on mean arterial pressure, and of both Δ MP and Δ MI when assessing how diastolic pressure is modulated by pre-ejection period and left-ventricular ejection time. CONCLUSION: HOBs can be more clearly identified by information-theoretic WMS measures, while prediction WMS measures appear more sensitive to synergy arising from the governing rules of the system analyzed rather than from pure statistical dependencies. SIGNIFICANCE: Quantifying HOBs through WMS measures sensitive to complex structural mechanisms can provide new biomarkers to assess physio-pathological alterations of cardiovascular networks.0.
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Bara et al. (2026) studied Cardiovascular interactions. Predictability and information-theoretic measures was evaluated on Detection and quantification of high-order behaviors. Information-theoretic whole-minus-sum measures identified high-order behaviors in cardiovascular networks, while prediction measures were more sensitive to synergy from governing rules.
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