ArticleFrontiers in neuroscience2020
Multivariate Correlation Measures Reveal Structure and Strength of Brain-Body Physiological Networks at Rest and During Mental Stress.
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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10 citing papers in PubMed.
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- Lobe-wise cognitive load detection using empirical Fourier decomposition and optimized machine learning.Frontiers in physiology · 2025Article
- Detection of respiratory frequency rhythm in human alpha phase shifts: topographic distributions in wake and drowsy states.Frontiers in physiology · 2024Article
- Dynamic fluctuations in ascending heart-to-brain communication under mental stress.American journal of physiology. Regulatory, integrative and comparative physiology · 2023Article
- Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends.Bioengineering (Basel, Switzerland) · 2023Review
- Time-varying information measures: an adaptive estimation of information storage with application to brain-heart interactions.Frontiers in network physiology · 2023Article
- Assessment of Cardiorespiratory Interactions during Apneic Events in Sleep via Fuzzy Kernel Measures of Information Dynamics.Entropy (Basel, Switzerland) · 2021Article
- Estimation of Granger causality through Artificial Neural Networks: applications to physiological systems and chaotic electronic oscillators.PeerJ. Computer science · 2021Article
- The New Field of Network Physiology: Building the Human Physiolome.Frontiers in network physiology · 2021Article
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7 authors.
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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.
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