Evidence map›Paper›PMID 33613173›Full record

ArticleFrontiers in neuroscience2020

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

Riccardo Pernice, Yuri Antonacci, Matteo Zanetti, Alessandro Busacca, Daniele Marinazzo, Luca Faes, Giandomenico Nollo

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Article in Frontiers in neuroscience, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.

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0cells of the map it votes in
10citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

10 citing papers in PubMed.

  1. Article
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  5. Dynamic fluctuations in ascending heart-to-brain communication under mental stress.American journal of physiology. Regulatory, integrative and comparative physiology · 2023
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  6. Review
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

7 authors.

Riccardo PerniceDepartment of Engineering, University of Palermo, Palermo, Italy.
Yuri AntonacciDepartment of Physics and Chemistry "Emilio Segrè," University of Palermo, Palermo, Italy.
Matteo ZanettiDepartment of Industrial Engineering, University of Trento, Trento, Italy.
Alessandro BusaccaDepartment of Engineering, University of Palermo, Palermo, Italy.
Daniele MarinazzoDepartment of Data Analysis, Ghent University, Ghent, Belgium.
Luca FaesDepartment of Engineering, University of Palermo, Palermo, Italy.
Giandomenico NolloDepartment of Industrial Engineering, University of Trento, Trento, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

brain–heart connectioncardiovascular oscillationsEEG wavesnetwork physiologyphysiological stresstime series analysiswearable devices

Identifiers

PMID33613173
PMCPMC7890264

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.