Directionality of coupling of physiological subsystems: age-related changes of cardiorespiratory interaction during different sleep stages in babies

Abstract

2003.-Activity of many physiological subsystems has a wellexpressed rhythmic character. Often, a dependency between physiological rhythms is established due to interaction between the corresponding subsystems. Traditional methods of data analysis allow one to quantify the strength of interaction but not the causal interrelation that is indispensable for understanding the mechanisms of interaction. Here we present a recently developed method for quantification of coupling direction and apply it to an important problem. Namely, we study the mutual influence of respiratory and cardiovascular rhythms in healthy newborns within the first 6 mo of life in quiet and active sleep. We find an age-related change of the coupling direction: the interaction is nearly symmetric during the first days and becomes practically unidirectional (from respiration to heart rhythm) at the age of 6 mo. Next, we show that the direction of interaction is mainly determined by respiratory frequency. If the latter is less than Ϸ0.6 Hz, the interaction occurs dominantly from respiration to heart. With higher respiratory frequencies that only occur at very young ages, the dominating direction is less pronounced or even abolished. The observed dependencies are not related to sleep stage, suggesting that the coupling direction is determined by system-inherent dynamical processes, rather than by functional modulations. The directional analysis may be applied to other interacting narrow band oscillatory systems, e.g., in the central nervous system. Thus it is an important step forward in revealing and understanding causal mechanisms of interactions. direction of coupling; development; transmission dynamics INTERACTION BETWEEN PHYSIOLOGICAL (sub-) systems can be addressed by the analysis of the signals they generate. Traditional signal-processing techniques, e.g., cross-spectral (coherence) estimates (20) or computation of mutual information (25), provide symmetric measures of interaction strength. There were also several attempts to address the causality in interaction, based on theoretical information approach (11, 34) and methods of nonlinear dynamics (27, 33). We study directional interrelation using a technique that is particularly suited for the case of weakly interacting rhythmical systems. Thus we exclude from our consideration the input-output systems, i.e., we do not treat the case when, say, signal 1 can be regarded as delayed and/or (nonlinearly) filtered signal 2. We also do not consider the situation when an intervention is possible, i.e., when causal relation can be addressed by application of certain stimuli and analysis of the response to them (for description of such techniques, see, e.g., Ref. 31). In our approach, we restrict ourselves to the case when 1) the system can be only passively observed and 2) it can be modeled by two (or several) weakly coupled self-sustained oscillators, i.e., subsystems, having their own rhythms. Such coupled-oscillator models describe a variety of biological phenomena (9,10,24,38). In the framework...

In vivo cardiac phase response curve elucidates human respiratory heart rate variability

“…It has gained increased interest due to extensive application of ideas from nonlinear dynamics and information theory to medical problems, for example, to the use of heart rate variability (HRV) as an indicator of autonomic activity. As a result, many aspects of cardiorespiratory coordination, like the degree of locking and the directionality of interaction, have been investigated 26,28,29,[34][35][36][37][38][39][40] .…”

Synchronization Approach to Analysis of Biological Systems

“…So, tendency to phase locking of different order n : m was revealed in a group of young athletes [10,34] and in normal adults [12]. Strength and directionality of the cardiorespiratory interaction in healthy newborns was studied in [11,30,35] in dependence on the age and sleep stages, what revealed the dependence of the interaction parameters on the respiratory frequency and provided the information on the role of the kinetics involved in the vagal-atrial transmission. As further examples we mention the prediction and localization of epileptic activity [36,37], analysis of the role of interbrain synchronization and localization of pathological brain activity in Parkinson's disease [25,38], or nephron-nephron interaction [39].…”

Improving Reduced Variable Models for Complex Systems via Experiment

“…j = 2⇡/P j ), and P j ( ) is the change in the period as a function of phase di↵erence. Here, The coupling function can also be written as [8] 6) where Z( ) is the response function and h is the voltage perturbation h(x(t)) from Eq. (3.4) with x(t) expressed as the phase.…”

“…Extensive studies have been performed to understand, predict, and engineer behaviors such as synchrony in these oscillatory populations. Examples include coupled lasers [1], population dynamics [2], chemical reactions [3], superconducting Josephson junctions [4], and cardiorespiratory interactions [5,6], among others.…”

“…Examples include coupled lasers [1], population dynamics [2], chemical reactions [3], and cardiorespiratory interactions [5,6], among others. There are two ways to obtain a theoretical description of the system: either write the model equations for the coupled systems starting from the first principles, or reconstruct the model equations from observations.…”