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Multivariate Correlation Measures Reveal Structure and Strength of Brain–Body Physiological Networks at Rest and During Mental Stress

In this work, we extend to the multivariate case the classical correlation analysis used in the field of network physiology to probe dynamic interactions between organ systems in the human body. To this end, we define different correlation-based measures of the multivariate interaction (MI) within a...

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Autores principales: Pernice, Riccardo, Antonacci, Yuri, Zanetti, Matteo, Busacca, Alessandro, Marinazzo, Daniele, Faes, Luca, Nollo, Giandomenico
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890264/
https://www.ncbi.nlm.nih.gov/pubmed/33613173
http://dx.doi.org/10.3389/fnins.2020.602584
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author Pernice, Riccardo
Antonacci, Yuri
Zanetti, Matteo
Busacca, Alessandro
Marinazzo, Daniele
Faes, Luca
Nollo, Giandomenico
author_facet Pernice, Riccardo
Antonacci, Yuri
Zanetti, Matteo
Busacca, Alessandro
Marinazzo, Daniele
Faes, Luca
Nollo, Giandomenico
author_sort Pernice, Riccardo
collection PubMed
description In this work, we extend to the multivariate case the classical correlation analysis used in the field of network physiology to probe dynamic interactions between organ systems in the human body. To this end, we define different correlation-based measures of the multivariate interaction (MI) within and between the brain and body subnetworks of the human physiological network, represented, respectively, by the time series of δ, θ, α, and β electroencephalographic (EEG) wave amplitudes, and of heart rate, respiration amplitude, and pulse arrival time (PAT) variability (η, ρ, π). MI is computed: (i) considering all variables in the two subnetworks to evaluate overall brain–body interactions; (ii) focusing on a single target variable and dissecting its global interaction with all other variables into contributions arising from the same subnetwork and from the other subnetwork; and (iii) considering two variables conditioned to all the others to infer the network topology. The framework is applied to the time series measured from the EEG, electrocardiographic (ECG), respiration, and blood volume pulse (BVP) signals recorded synchronously via wearable sensors in a group of healthy subjects monitored at rest and during mental arithmetic and sustained attention tasks. We find that the human physiological network is highly connected, with predominance of the links internal of each subnetwork (mainly η−ρ and δ−θ, θ−α, α−β), but also statistically significant interactions between the two subnetworks (mainly η−β and η−δ). MI values are often spatially heterogeneous across the scalp and are modulated by the physiological state, as indicated by the decrease of cardiorespiratory interactions during sustained attention and by the increase of brain–heart interactions and of brain–brain interactions at the frontal scalp regions during mental arithmetic. These findings illustrate the complex and multi-faceted structure of interactions manifested within and between different physiological systems and subsystems across different levels of mental stress.
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spelling pubmed-78902642021-02-19 Multivariate Correlation Measures Reveal Structure and Strength of Brain–Body Physiological Networks at Rest and During Mental Stress Pernice, Riccardo Antonacci, Yuri Zanetti, Matteo Busacca, Alessandro Marinazzo, Daniele Faes, Luca Nollo, Giandomenico Front Neurosci Neuroscience In this work, we extend to the multivariate case the classical correlation analysis used in the field of network physiology to probe dynamic interactions between organ systems in the human body. To this end, we define different correlation-based measures of the multivariate interaction (MI) within and between the brain and body subnetworks of the human physiological network, represented, respectively, by the time series of δ, θ, α, and β electroencephalographic (EEG) wave amplitudes, and of heart rate, respiration amplitude, and pulse arrival time (PAT) variability (η, ρ, π). MI is computed: (i) considering all variables in the two subnetworks to evaluate overall brain–body interactions; (ii) focusing on a single target variable and dissecting its global interaction with all other variables into contributions arising from the same subnetwork and from the other subnetwork; and (iii) considering two variables conditioned to all the others to infer the network topology. The framework is applied to the time series measured from the EEG, electrocardiographic (ECG), respiration, and blood volume pulse (BVP) signals recorded synchronously via wearable sensors in a group of healthy subjects monitored at rest and during mental arithmetic and sustained attention tasks. We find that the human physiological network is highly connected, with predominance of the links internal of each subnetwork (mainly η−ρ and δ−θ, θ−α, α−β), but also statistically significant interactions between the two subnetworks (mainly η−β and η−δ). MI values are often spatially heterogeneous across the scalp and are modulated by the physiological state, as indicated by the decrease of cardiorespiratory interactions during sustained attention and by the increase of brain–heart interactions and of brain–brain interactions at the frontal scalp regions during mental arithmetic. These findings illustrate the complex and multi-faceted structure of interactions manifested within and between different physiological systems and subsystems across different levels of mental stress. Frontiers Media S.A. 2021-02-04 /pmc/articles/PMC7890264/ /pubmed/33613173 http://dx.doi.org/10.3389/fnins.2020.602584 Text en Copyright © 2021 Pernice, Antonacci, Zanetti, Busacca, Marinazzo, Faes and Nollo. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Pernice, Riccardo
Antonacci, Yuri
Zanetti, Matteo
Busacca, Alessandro
Marinazzo, Daniele
Faes, Luca
Nollo, Giandomenico
Multivariate Correlation Measures Reveal Structure and Strength of Brain–Body Physiological Networks at Rest and During Mental Stress
title Multivariate Correlation Measures Reveal Structure and Strength of Brain–Body Physiological Networks at Rest and During Mental Stress
title_full Multivariate Correlation Measures Reveal Structure and Strength of Brain–Body Physiological Networks at Rest and During Mental Stress
title_fullStr Multivariate Correlation Measures Reveal Structure and Strength of Brain–Body Physiological Networks at Rest and During Mental Stress
title_full_unstemmed Multivariate Correlation Measures Reveal Structure and Strength of Brain–Body Physiological Networks at Rest and During Mental Stress
title_short Multivariate Correlation Measures Reveal Structure and Strength of Brain–Body Physiological Networks at Rest and During Mental Stress
title_sort multivariate correlation measures reveal structure and strength of brain–body physiological networks at rest and during mental stress
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7890264/
https://www.ncbi.nlm.nih.gov/pubmed/33613173
http://dx.doi.org/10.3389/fnins.2020.602584
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