Information dynamics of brain–heart physiological networks during sleep

Abstract

This study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain-heart interaction networks during sleep. With this approach, the time series of cardiac vagal autonomic activity and brain wave activities measured respectively as the normalized high frequency component of heart rate variability and the EEG power in the δ, θ, α, σ, and β bands, are considered as realizations of the stochastic processes describing the dynamics of the heart system and of different brain sub-systems. Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissect this information into a part actively stored in the system and a part transferred to it from the other connected systems. The application of this approach to polysomnographic recordings of ten healthy subjects led us to identify a structured network of sleep brain-brain and brain-heart interactions, with the node described by the β EEG power acting as a hub which conveys the largest amount of information flowing between the heart and brain nodes. This network was found to be sustained mostly by the transitions across different sleep stages, as the information transfer was weaker during specific stages than during the whole night, and vanished progressively when moving from light sleep to deep sleep and to REM sleep.

Characterization of physiological networks in sleep apnea patients using artificial neural networks for Granger causality computation

“…This is reported in previous studies as Faes et al, in [9]. The information flow from heart to brain in the δ band is in a unidirectional way.…”

“…Examples of analysis of the brain-hearth physiological networks can be found in works like the developed by Faes et al, where TE calculations were applied to quantify the transmitted information in bidirectional ways between the CNS and cardiac system in with healthy people, during sleep [9]. Likewise, under the same point of view the transmitted information was measured between CNS subsystems, assuming that each of the EEG sub-bands (ẟ, θ, α, β, γ) represents a subsystem of the nervous system.…”

Multiscale Information Decomposition: Exact Computation for Multivariate Gaussian Processes

“…These elements essentially reflect the new information produced at each moment in time about a target system in the network [2], the information stored in the target system [3,4], the information transferred to it from the other connected systems [5,6] and the modification of the information flowing from multiple source systems to the target [7,8]. The measures of information dynamics have gained more and more importance in both theoretical and applicative studies in several fields of science [9][10][11][12][13][14][15][16][17][18]. While the information-theoretic approaches to the definition and quantification of new information, information storage and information transfer are well understood and widely accepted, the problem of defining, interpreting and using measures of information modification has not been fully addressed in the literature.…”

Information Decomposition in Multivariate Systems: Definitions, Implementation and Application to Cardiovascular Networks

“…Operational definitions of these concepts have been proposed in recent years, which allow to quantify predictive information through measures of prediction entropy or full-predictability [11,12], information storage through the self-entropy or self-predictability [11,13], information transfer through transfer entropy or Granger causality [14], and information modification through entropy and prediction measures of net redundancy/synergy [11,15] or separate measures derived from partial information decomposition [16,17]. All these measures have been successfully applied in diverse fields of science ranging from cybernetics to econometrics, climatology, neuroscience and others [6,7,[18][19][20][21][22][23][24][25][26][27][28]. In particular, recent studies have implemented these measures in cardiovascular physiology to study the short-term dynamics of the cardiac, vascular and respiratory systems in terms of information storage, transfer and modification [12,13,29].…”