Higher-Order Interactions in Physiological Networks
A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Signal and Data Analysis".
Deadline for manuscript submissions: 25 February 2026 | Viewed by 60
Special Issue Editor
Interests: time series analysis; complex systems; network physiology; EEG
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advancements in biomedical signal processing are revolutionizing the way we model complex physiological systems. A key aspect of this progress is network analysis, which enables the representation of physiological systems through models constructed from the dynamic interactions of their components. Traditional network science approaches primarily rely on pairwise connectivity measures, which, while informative, fail to capture higher-order interactions involving three or more elements. These interactions are characterized by non-factorizability, meaning that the joint behavior of multiple components cannot be expressed as the sum of individual or pairwise contributions. This limitation poses significant challenges for accurately describing the intricate dependencies observed in physiological networks.
This Special Issue invites contributions that develop, refine, or apply different methodologies in computational neuroscience, physiological data analysis, and related fields. Special emphasis will be placed on data-efficient estimators, methodological advancements for analyzing both amplitude- and event-based physiological signals, and extensions to time- and frequency-resolved network descriptions. We welcome both theoretical advancements and applications to experimental data, fostering interdisciplinary approaches to better understand the complex information flow in physiological networks.
Dr. Yuri Antonacci
Guest Editor
Manuscript Submission Information
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Keywords
- higher-order interactions
- information theory
- network science
- redundancy and synergy
- multivariate time series analysis
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