EMBC Special Issue: Investigating High-Order Behaviors in Multivariate Cardiovascular Interactions via Nonlinear...

Area of Science:

• Complex Systems Science
• Network Theory
• Information Theory

Background:

• Characterizing complex systems requires assessing synergistic high-order behaviors (HOBs) emerging from underlying structural mechanisms.
• Predictability and information-theoretic measures offer novel approaches to detect and quantify HOBs.

Purpose of the Study:

• To develop and apply whole-minus-sum (WMS) measures, based on mutual predictability ($\Delta _\rm {MP}$) and mutual information ($\Delta _\rm {MI}$), for detecting and quantifying HOBs.
• To evaluate the utility of these measures in synthetic and physiological network systems.

Main Methods:

• Formal definitions of mechanisms and behaviors in complex systems were established.
• Statistical synergy was quantified using WMS excess of mutual predictability ($\Delta _\rm {MP}$) and mutual information ($\Delta _\rm {MI}$).
• Model-free methods utilizing nonlinear prediction and entropy estimation were employed.

Main Results:

• In simulated systems, $\Delta _\rm {MP}$ was sensitive to group interactions, while $\Delta _\rm {MI}$ showed higher propensity for positive values.
• Physiological data revealed significant $\Delta _\rm {MI}$ for arterial pressure regulation and both $\Delta _\rm {MP}$ and $\Delta _\rm {MI}$ for diastolic pressure modulation.

Conclusions:

• Information-theoretic WMS measures ($\Delta _\rm {MI}$) are superior for identifying HOBs compared to prediction-based measures ($\Delta _\rm {MP}$).
• WMS measures sensitive to complex structural mechanisms can serve as novel biomarkers for assessing physio-pathological alterations in cardiovascular networks.