Understanding the interplay between baroreflex gain, low frequency oscillations, and pulsatility in the neural baroreflex

Ringwood, John V.; Hare, Hasana Bagnall

doi:10.1016/j.bbe.2020.07.008

Cited by 2 publications

(1 citation statement)

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“…In particular, it has been shown that both mental load and physiological stress produce repeatable variations not only in the brain activity ( Gevins et al, 1998 ; Berka et al, 2007 ; Al-shargie et al, 2018 ), but also in the dynamic control of the cardiovascular function and heart rate variability (HRV) ( Petrowski et al, 2017 ; Kim et al, 2018 ; Pernice et al, 2018 , 2019a ); these effects can be of clinical relevance as they can ultimately increase the risk of heart attacks and stroke ( Steptoe and Kivimäki, 2013 ; Al-Shargie et al, 2016 ). Moreover, besides the interplay between brain and heart, the network of interactions sub-serving the regulation of the homeostatic function encompasses other physiological rhythms, such as the respiratory drive ( Pfurtscheller et al, 2019 ; Javorka et al, 2020 ), the cardiovascular and baroreflex functions ( Krohova et al, 2019 , 2020 ; Ringwood and Bagnall-Hare, 2020 ), and other less studied but significant vital signs, e.g., including muscular and ocular activities ( Ivanov et al, 2017 ; Boonstra et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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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