Information-theoretic assessment of cardiovascular-brain networks during sleep

Abstract

This study was aimed at detecting the structure of the physiological network underlying the regulation of the cardiovascular and brain systems during normal sleep. To this end, we measured from the polysomnographic recordings of 10 healthy subjects the normalized spectral power of heart rate variability in the high frequency band (HF) and the EEG power in the δ, θ, α, σ, and β bands. Then, the causal statistical dependencies within and between these six time series were assessed in terms of internal information (conditional self entropy, CSE) and information transfer (transfer entropy, TE) computed via a linear method exploiting multiple regression models and a nonlinear method combining nearest neighbour entropy estimation with dimensionality reduction. The statistical significance of CSE and TE was assessed using an F-test for the linear method, and an empirical randomization test for the nonlinear method. Both approaches consistently detected structured networks of physiological interactions, revealing (i) strong internal information in all systems; (ii) information transfer directed predominantly from heart to brain; (iii) bidirectional interactions between HF and β EEG power. Moreover, the nonlinear method evidenced higher information flowing out of the δ node. These results highlight the potential of the information-theoretic framework to assess linear and nonlinear dynamics manifested in the functional network that underlies the autonomic regulation of cardiovascular and brain functions during sleep