Multivariate Linear and Nonlinear Central-Cardiorespiratory Coupling Pathways in Healthy Subjects

Advances in nonlinear dynamics and information theory facilitate a multivariate study of information transfer between physiological systems and sub-systems aiming to characterize healthy and diseased physiological network states. In this study, we investigated the central-cardiorespiratory network (CCRN) applying linear and nonlinear causal coupling approaches (normalized short time partial directed coherence, multivariate transfer entropy) in 21 healthy subjects. From all participants, continuous heart rate (successive beat-to-beat intervals, BBI), synchronized calibrated respiratory inductive plethysmography signal (respiratory frequency, RESP), and the mean power P_EEG from a 64-channel EEG were recorded for 15 minutes under resting conditions. We found that the central-cardiorespiratory coupling is a bidirectional one, with central driving mechanisms towards BBI (PEEG→BBI), and respiratory driving towards P_EEG (RESP→PEEG). The central-cardiac (P_EEG-BBI) and central-respiratory coupling (P_EEG-RESP) seem to be stronger generated by linear process than nonlinear ones. We obtained a different CCRN behavior in healthy subjects providing a further step towards a more comprehensive understanding of the interplay of neuronal and autonomic regulatory processes.