Information Theory in Network Physiology: State of the Art and New Perspectives

Information Theory in Network Physiology is an emerging interdisciplinary field that integrates principles from information theory with the study of physiological networks. This area of research seeks to understand how information is processed, stored, and transferred within complex biological systems. Despite significant advancements, there remain critical gaps in our understanding of the dynamic interactions within these networks. Current studies have provided insights into the complexity and interconnectivity of physiological systems, yet many questions about the underlying mechanisms and their implications for health and disease remain unanswered. Recent research has employed both model-based and model-free approaches to estimate system complexity and has applied univariate, bivariate, and multivariate measures to dynamic networks. However, there is a pressing need for more comprehensive methodologies and a deeper exploration of high-order interactions and information dynamics.

This Research Topic aims to advance our understanding of Information Theory in Network Physiology by addressing key questions and testing new hypotheses. Specifically, it seeks to explore how information is dynamically processed within physiological networks, the role of high-order interactions, and the implications of these processes for health and disease. The goal is to foster a deeper understanding of the mechanisms underlying physiological information processing and to identify new directions for future research. By doing so, we hope to stimulate discussion and promote further investigation within the Network Physiology community and the broader field of Information Theory.

To gather further insights in the application of Information Theory to Network Physiology, we welcome articles addressing, but not limited to, the following themes:
- Model-based and model-free estimation of system complexity
- Univariate, bivariate, and multivariate measures applied to dynamic networks
- Methods for the analysis of information dynamics in network systems
- High-order interactions: methodologies and applications
- Information storage and information transfer in physiological systems
- Granger causality analysis
- Information-theoretic analysis and spectral analysis of time series
- Statistically validated networks
- Graph theory applied to Network Physiology
- State of the art and future directions in the application of information theory to brain, physiological, and biological networks

Keywords: Information Theory, network physiology, dynamic systems, biology, neuroscience, physiological systems, complexity, models, Granger Causality

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.