Measuring High-Order Interactions in Rhythmic Processes Through Multivariate Spectral Information Decomposition

Many complex systems in physics, biology and engineering are modeled as dynamical networks and described using multivariate time series analysis. Recent developments have shown that the emergent dynamics of a network system are significantly affected by interactions involving multiple network nodes...

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Autores principales:	Yuri Antonacci, Ludovico Minati, Davide Nuzzi, Gorana Mijatovic, Riccardo Pernice, Daniele Marinazzo, Sebastiano Stramaglia, Luca Faes
Formato:	article
Lenguaje:	EN
Publicado:	IEEE 2021
Materias:	Time series analysis information theory information dynamics spectral analysis high-order interactions EEG analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971
Acceso en línea:	https://doaj.org/article/3374702d52b8411b8d77058b03158e18
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id	oai:doaj.org-article:3374702d52b8411b8d77058b03158e18
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spelling	oai:doaj.org-article:3374702d52b8411b8d77058b03158e182021-11-18T00:03:02ZMeasuring High-Order Interactions in Rhythmic Processes Through Multivariate Spectral Information Decomposition2169-353610.1109/ACCESS.2021.3124601https://doaj.org/article/3374702d52b8411b8d77058b03158e182021-01-01T00:00:00Zhttps://ieeexplore.ieee.org/document/9597508/https://doaj.org/toc/2169-3536Many complex systems in physics, biology and engineering are modeled as dynamical networks and described using multivariate time series analysis. Recent developments have shown that the emergent dynamics of a network system are significantly affected by interactions involving multiple network nodes which cannot be described using pairwise links. While these higher-order interactions can be probed using information-theoretic measures, a rigorous framework to describe them in the frequency domain is still lacking. This work presents an approach for the spectral decomposition of multivariate information measures, capable of identifying higher-order synergistic and redundant interactions between oscillatory processes. We show theoretically that synergy and redundancy can coexist at different frequencies among the output signals of a network system and can be detected only using the proposed spectral method. To demonstrate the broad applicability of the framework, we provide parametric and non-parametric data-efficient estimators for the spectral information measures, and employ them to describe multivariate interactions in three complex systems producing rich oscillatory dynamics, namely the human brain, a ring of electronic oscillators, and the global climate system. In these systems, we show that the use of our framework for the spectral decomposition of information measures reveals multivariate and higher-order interactions not detectable in the time domain. Our results are exemplary of how the frequency-specific analysis of multivariate dynamics can aid the implementation of assessment and control strategies in real-world network systems.Yuri AntonacciLudovico MinatiDavide NuzziGorana MijatovicRiccardo PerniceDaniele MarinazzoSebastiano StramagliaLuca FaesIEEEarticleTime series analysisinformation theoryinformation dynamicsspectral analysishigh-order interactionsEEG analysisElectrical engineering. Electronics. Nuclear engineeringTK1-9971ENIEEE Access, Vol 9, Pp 149486-149505 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Time series analysis information theory information dynamics spectral analysis high-order interactions EEG analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971
spellingShingle	Time series analysis information theory information dynamics spectral analysis high-order interactions EEG analysis Electrical engineering. Electronics. Nuclear engineering TK1-9971 Yuri Antonacci Ludovico Minati Davide Nuzzi Gorana Mijatovic Riccardo Pernice Daniele Marinazzo Sebastiano Stramaglia Luca Faes Measuring High-Order Interactions in Rhythmic Processes Through Multivariate Spectral Information Decomposition
description	Many complex systems in physics, biology and engineering are modeled as dynamical networks and described using multivariate time series analysis. Recent developments have shown that the emergent dynamics of a network system are significantly affected by interactions involving multiple network nodes which cannot be described using pairwise links. While these higher-order interactions can be probed using information-theoretic measures, a rigorous framework to describe them in the frequency domain is still lacking. This work presents an approach for the spectral decomposition of multivariate information measures, capable of identifying higher-order synergistic and redundant interactions between oscillatory processes. We show theoretically that synergy and redundancy can coexist at different frequencies among the output signals of a network system and can be detected only using the proposed spectral method. To demonstrate the broad applicability of the framework, we provide parametric and non-parametric data-efficient estimators for the spectral information measures, and employ them to describe multivariate interactions in three complex systems producing rich oscillatory dynamics, namely the human brain, a ring of electronic oscillators, and the global climate system. In these systems, we show that the use of our framework for the spectral decomposition of information measures reveals multivariate and higher-order interactions not detectable in the time domain. Our results are exemplary of how the frequency-specific analysis of multivariate dynamics can aid the implementation of assessment and control strategies in real-world network systems.
format	article
author	Yuri Antonacci Ludovico Minati Davide Nuzzi Gorana Mijatovic Riccardo Pernice Daniele Marinazzo Sebastiano Stramaglia Luca Faes
author_facet	Yuri Antonacci Ludovico Minati Davide Nuzzi Gorana Mijatovic Riccardo Pernice Daniele Marinazzo Sebastiano Stramaglia Luca Faes
author_sort	Yuri Antonacci
title	Measuring High-Order Interactions in Rhythmic Processes Through Multivariate Spectral Information Decomposition
title_short	Measuring High-Order Interactions in Rhythmic Processes Through Multivariate Spectral Information Decomposition
title_full	Measuring High-Order Interactions in Rhythmic Processes Through Multivariate Spectral Information Decomposition
title_fullStr	Measuring High-Order Interactions in Rhythmic Processes Through Multivariate Spectral Information Decomposition
title_full_unstemmed	Measuring High-Order Interactions in Rhythmic Processes Through Multivariate Spectral Information Decomposition
title_sort	measuring high-order interactions in rhythmic processes through multivariate spectral information decomposition
publisher	IEEE
publishDate	2021
url	https://doaj.org/article/3374702d52b8411b8d77058b03158e18
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Measuring High-Order Interactions in Rhythmic Processes Through Multivariate Spectral Information Decomposition

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