Linear and non-linear brain–heart and brain–brain interactions during sleep

Abstract

In this study, the physiological networks underlying the joint modulation of the parasympathetic component of heart rate variability (HRV) and of the different electroencephalographic (EEG) rhythms during sleep were assessed using two popular measures of directed interaction in multivariate time series, namely Granger causality (GC) and transfer entropy (TE). Time series representative of cardiac and brain activities were obtained in 10 young healthy subjects as the normalized high frequency (HF) component of HRV and EEG power in the δ, θ, α, σ, and β bands, measured during the whole duration of sleep. The magnitude and statistical significance of GC and TE were evaluated between each pair of series, conditional on the remaining series, using respectively a linear model-based approach exploiting regression models, and a nonlinear model-free approach combining nearest-neighbor entropy estimation with a procedure for dimensionality reduction. The contribution of nonlinear dynamics to the TE was also assessed using surrogate data. GC and TE consistently detected structured networks of physiological interactions, with links directed predominantly from HRV to the EEG waves in the brain-heart network, and from the σ and β EEG waves to the δ, θ, and α waves in the brain-brain network. While these common patterns supported the suitability of a linear model-based analysis, we also found a significant contribution of nonlinear dynamics, particularly involving the information transferred out of the δ node in the two networks. This suggested the importance of nonparametric TE estimation for evidencing the fine structure of the physiological networks underlying the autonomic regulation of cardiac and brain functions during sleep.

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

“…Given their high specificity, their efficient implementation via traditional multivariate regression analysis, and their demonstrated link with neural autonomic regulation, the proposed quantities are suitable candidates for large scale applications to clinical databases recorded under uncontrolled conditions. Future studies should be directed to extend the decompositions to model-free frameworks that assess the role of nonlinear physiological dynamics in information storage, transfer and modification [5,31], to explore novel partial decomposition approaches that separate synergetic and redundant information rather than providing their net balance [3,16,17], and to explore scenarios with more than two source processes [15]. Practical extensions should be devoted to evaluate the importance of these measures for the assessment of cardiovascular and cardiorespiratory interactions in diseased conditions.…”

“…Finally, from the point of view of their implementation, the outcome of analyses based on information dynamics can be strongly affected by the functional used to define and estimate information measures. Model-free approaches for the computation of these measures are more general but more difficult to implement, and often provide comparable results than simpler and less demanding model-based techniques [31,32]; even within the class of model-based approaches, prediction methods and entropy methods-though often used interchangeably to assess network dynamics-may lead to strongly different interpretations [16,30].…”

Combining electroencephalographic activity and instantaneous heart rate for assessing brain–heart dynamics during visual emotional elicitation in healthy subjects

“…More in general, the EEG θ power was associated with a general emotional response and states of relaxation and internal attention [63], whereas alterations of EEG dynamics in the θ band were found in case of ANS dysfunctions [55]. Cardiac and brain dynamics were also quantitatively assessed during sleep in the frame of dynamical information theory [65], highlighting the role of EEG low-frequency bands in the 'from-brain-to-heart' information transfer.…”

Advanced nonlinear approach to quantify directed interactions within EEG activity of children with temporal lobe epilepsy in their time course

“…By analyzing strength and direction of interactions within EEG recordings, Lehnertz and Dickten [40] argued that it remains unclear whether the dynamic of seizure-onset zone "indeed be characterized by an elevated local synchrony or elevated strength of interactions" and thus also guided the discussion to a more general view of "epileptic network" rather than "epileptic focus." Physiological networks are of great interest in many fields of neuroscience [41][42][43]. Our results contribute to the development of the field of network physiology [44] as well as nonlinear directed interactions.…”