6533b7ddfe1ef96bd1273631
RESEARCH PRODUCT
Multivariate correlation measures reveal structure and strength of brain–body physiological networks at rest and during mental stress
Luca FaesRiccardo PerniceYuri AntonacciMatteo ZanettiDaniele MarinazzoGiandomenico NolloAlessandro Busaccasubject
Multivariate statisticsTechnology and EngineeringElectroencephalographybrain-heart connectionNetwork topologynetwork physiologylcsh:RC321-571Correlation03 medical and health sciences0302 clinical medicinewearable devicesMedicine and Health SciencesmedicineMultiple correlationSubnetworklcsh:Neurosciences. Biological psychiatry. Neuropsychiatryinformation theory030304 developmental biologyMathematicsOriginal Researchphysiological stressbrain-body interactionsNetwork physiology brain–heart connection cardiovascular oscillations EEG waves physiological stress time series analysis wearable devices0303 health sciencesnetwork physiology; brain-heart connection; cardiovascular oscillations; EEG waves; physiological stressmedicine.diagnostic_testPulse (signal processing)General NeuroscienceCardiorespiratory fitnessbrain–heart connectionMathematics and Statisticscardiovascular oscillationsnetworkstime series analysisphysiologySettore ING-INF/06 - Bioingegneria Elettronica E InformaticaNeuroscience030217 neurology & neurosurgeryEEG wavesNeurosciencedescription
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 delta, theta, alpha, and beta electroencephalographic (EEG) wave amplitudes, and of heart rate, respiration amplitude, and pulse arrival time (PAT) variability. 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 heart rate-respiration and delta-theta, theta-alpha, alpha-beta), but also statistically significant interactions between the two subnetworks (mainly heart rate-beta and heart rate-delta). 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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2021-02-01 |