Scope

The Information Theory section of Frontiers in Network Physiology aims to pave the bridge between the general field of Shannon information theory and the study of networks in computational physiology and neuroscience. Originally developed at the intersection of probability theory, statistical physics and communication engineering, information theory is witnessing a surge of interest in the application of its tools to the analysis of the dynamics of complex systems. Following this impetus, this section aims to foster, increment, and support the development and the use of information-theoretic methods for the definition, detection, and interpretation of physiological networks from the cellular to the organ level. We actively seek for high-quality contributions covering theoretical, methodological and applied research across all aspects of information theory in Network Physiology, including but not limited to:

·        Entropy measures to quantify the generation, storage, and transmission of information within and between physiological systems

·       Complexity in physiological systems

·       Causality and directed information transfer in Network Physiology

·       Approaches to go beyond the framework of pairwise interactions and to assess higher-order interactions in physiological networks 

·       Multiscale and frequency-domain representation of networks

·       Linear/nonlinear parametric models for the prediction of network dynamics

·       Time-varying and non-stationary representation of information dynamics

·       Graph-theoretical and higher-order representations of networks derived from information measures

·       Applications at the microscale or mesoscale level (e.g., cellular/neuronal networks, multi-electrode arrays)

·       Single organ applications (e.g., spatially distributed brain dynamics (EEG, MEG, fMRI, fNIRS) or cardiac dynamics)

·       Multiple organ applications (e.g., brain-heart and brain-periphery interactions, cardiovascular and cardiorespiratory interactions)

While submissions describing advances in methodology and/or underlying theory with experimental verification are particularly welcome, contributions not related to the information-theoretic description of networks (e.g., fractal analysis, phase dynamics) are better suited to other sections of this journal. Nevertheless, as the information-theoretic analysis of physiological networks is not restricted to fundamental work, this section operates in close collaboration with other sections of Frontiers in Network Physiology.

Submission

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