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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doaj-82c88c7ffbaa4f219452f9d845269ebd2021-02-04T07:55:34ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2021-02-011410.3389/fnins.2020.602584602584Multivariate Correlation Measures Reveal Structure and Strength of Brain–Body Physiological Networks at Rest and During Mental StressRiccardo Pernice0Yuri Antonacci1Matteo Zanetti2Alessandro Busacca3Daniele Marinazzo4Luca Faes5Giandomenico Nollo6Department of Engineering, University of Palermo, Palermo, ItalyDepartment of Physics and Chemistry “Emilio Segrè,” University of Palermo, Palermo, ItalyDepartment of Industrial Engineering, University of Trento, Trento, ItalyDepartment of Engineering, University of Palermo, Palermo, ItalyDepartment of Data Analysis, Ghent University, Ghent, BelgiumDepartment of Engineering, University of Palermo, Palermo, ItalyDepartment of Industrial Engineering, University of Trento, Trento, ItalyIn 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.https://www.frontiersin.org/articles/10.3389/fnins.2020.602584/fullnetwork physiologybrain–heart connectioncardiovascular oscillationsEEG wavesphysiological stresstime series analysis |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Riccardo Pernice Yuri Antonacci Matteo Zanetti Alessandro Busacca Daniele Marinazzo Luca Faes Giandomenico Nollo |
spellingShingle |
Riccardo Pernice Yuri Antonacci Matteo Zanetti Alessandro Busacca Daniele Marinazzo Luca Faes Giandomenico Nollo Multivariate Correlation Measures Reveal Structure and Strength of Brain–Body Physiological Networks at Rest and During Mental Stress Frontiers in Neuroscience network physiology brain–heart connection cardiovascular oscillations EEG waves physiological stress time series analysis |
author_facet |
Riccardo Pernice Yuri Antonacci Matteo Zanetti Alessandro Busacca Daniele Marinazzo Luca Faes Giandomenico Nollo |
author_sort |
Riccardo Pernice |
title |
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_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_sort |
multivariate correlation measures reveal structure and strength of brain–body physiological networks at rest and during mental stress |
publisher |
Frontiers Media S.A. |
series |
Frontiers in Neuroscience |
issn |
1662-453X |
publishDate |
2021-02-01 |
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. |
topic |
network physiology brain–heart connection cardiovascular oscillations EEG waves physiological stress time series analysis |
url |
https://www.frontiersin.org/articles/10.3389/fnins.2020.602584/full |
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