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Open AccessJournal ArticleDOI

The Human Organism as an Integrated Interaction Network: Recent Conceptual and Methodological Challenges.

Klaus Lehnertz, +2 more
- 21 Dec 2020 - 
- Vol. 11, pp 598694-598694
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TLDR
In this paper, a variety of bivariate or multivariate time-series analysis techniques are employed and the resulting time-dependent networks can then be further characterized with methods from network theory.
Abstract
The field of Network Physiology aims to advance our understanding of how physiological systems and sub-systems interact to generate a variety of behaviors and distinct physiological states, to optimize the organism's functioning, and to maintain health. Within this framework, which considers the human organism as an integrated network, vertices are associated with organs while edges represent time-varying interactions between vertices. Likewise, vertices may represent networks on smaller spatial scales leading to a complex mixture of interacting homogeneous and inhomogeneous networks of networks. Lacking adequate analytic tools and a theoretical framework to probe interactions within and among diverse physiological systems, current approaches focus on inferring properties of time-varying interactions-namely strength, direction, and functional form-from time-locked recordings of physiological observables. To this end, a variety of bivariate or, in general, multivariate time-series-analysis techniques, which are derived from diverse mathematical and physical concepts, are employed and the resulting time-dependent networks can then be further characterized with methods from network theory. Despite the many promising new developments, there are still problems that evade from a satisfactory solution. Here we address several important challenges that could aid in finding new perspectives and inspire the development of theoretic and analytical concepts to deal with these challenges and in studying the complex interactions between physiological systems.

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Measuring the Rate of Information Exchange in Point-Process Data With Application to Cardiovascular Variability

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A New Framework for the Time- and Frequency-Domain Assessment of High-Order Interactions in Networks of Random Processes

TL;DR: In this paper , the authors define the so-called O-information rate (OIR) as a new metric to assess HOIs for multivariate time series and present a framework to decompose the OIR into measures quantifying Granger-causal and instantaneous influences, as well as to expand all measures in the frequency domain.
References
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