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Dynamic Digital Biomarkers of Motor and Cognitive Function in Parkinson's Disease
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Investigating Dynamic High-Order Interactions in Physiological Networks through Predictive Information Decomposition.

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    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    Area of Science:

    • Physiology
    • Information Theory
    • Network Science

    Background:

    • Understanding complex interactions within physiological systems is crucial for diagnosing health conditions.
    • Existing methods may not fully capture the nuanced interplay of multivariate physiological processes.
    • Information theory offers powerful tools for quantifying relationships in dynamic systems.

    Purpose of the Study:

    • To develop and validate an information-theoretic framework for assessing redundant and synergistic interactions in network systems.
    • To decompose shared information into unique, redundant, and synergistic contributions from system subgroups.
    • To apply this method to physiological data for a deeper understanding of regulatory mechanisms.

    Main Methods:

    • Utilized information-theoretic analysis to quantify shared information among multivariate physiological processes.
    • Developed a strategy to decompose information into unique, redundant, and synergistic components based on subgroup dynamics.
    • Illustrated the method with linearly interacting Gaussian processes and applied it to human cardiorespiratory data.

    Main Results:

    • Redundancy and synergy in Gaussian processes correlate with unidirectional and bidirectional coupling, respectively.
    • In healthy subjects, redundancy dominates cardiorespiratory interactions, while synergy is prominent in cardiovascular interactions at rest.
    • Postural stress enhances predictive information and redundancy within physiological interactions.

    Conclusions:

    • The proposed information-theoretic approach effectively quantifies complex interactions in network systems.
    • Redundancy and synergy play distinct roles in physiological regulation, varying with system type and physiological state.
    • This framework provides novel insights into the dynamics of cardiorespiratory and cardiovascular control.