Vagal-dependent nonlinear variability in the respiratory pattern of anesthetized, spontaneously breathing rats

Abstract

Physiological rhythms, including respiration, exhibit endogenous variability associated with health, and deviations from this are associated with disease. Specific changes in the linear and nonlinear sources of breathing variability have not been investigated. In this study, we used information theory-based techniques, combined with surrogate data testing, to quantify and characterize the vagal-dependent nonlinear pattern variability in urethane-anesthetized, spontaneously breathing adult rats. Surrogate data sets preserved the amplitude distribution and linear correlations of the original data set, but nonlinear correlation structure in the data was removed. Differences in mutual information and sample entropy between original and surrogate data sets indicated the presence of deterministic nonlinear or stochastic non-Gaussian variability. With vagi intact (n = 11), the respiratory cycle exhibited significant nonlinear behavior in templates of points separated by time delays ranging from one sample to one cycle length. After vagotomy (n = 6), even though nonlinear variability was reduced significantly, nonlinear properties were still evident at various time delays. Nonlinear deterministic variability did not change further after subsequent bilateral microinjection of MK-801, an N-methyl-D-aspartate receptor antagonist, in the Kölliker-Fuse nuclei. Reversing the sequence (n = 5), blocking N-methyl-D-aspartate receptors bilaterally in the dorsolateral pons significantly decreased nonlinear variability in the respiratory pattern, even with the vagi intact, and subsequent vagotomy did not change nonlinear variability. Thus both vagal and dorsolateral pontine influences contribute to nonlinear respiratory pattern variability. Furthermore, breathing dynamics of the intact system are mutually dependent on vagal and pontine sources of nonlinear complexity. Understanding the structure and modulation of variability provides insight into disease effects on respiratory patterning.

Isoflurane and ketamine anesthesia have different effects on ventilatory pattern variability in rats

“…Mutual Information and Sample Entropy of raw and surrogate data were calculated as described previously (Dhingra et al, 2011). Mutual Information (MI) is a measure of statistical dependence in a data set that includes influences of both linear and nonlinear correlations (Fraser and Swinney, 1986; Shannon, 1997).…”

“…To construct a more complete picture of respiratory variability, we employed a set of analytic techniques that we developed recently (Dhingra et al, 2011; Jacono et al, 2010) to separate linear and nonlinear components of variability and to quantify deterministic variability in the ventilatory pattern. The following tools were used: coefficient of variation, a test which captures distributional variance in the pattern; mutual information, which quantifies statistical dependence; sample entropy, a measure of self-similarity in a time series; and surrogate data sets, which were constructed to reflect the linear but destroy the nonlinear sources of variability.…”

Pontine Mechanisms of Respiratory Control

“…Numerous experiments illustrate that IOS is disrupted after local blockade of NMDA-R in the KF-area or following systemic application of NMDA-R antagonists (34,43,48,71,79,99,120,121,125,127,160,161, 226,266,291–293,355). These findings are consistent with the dense expression of NMDA-Rs in KF neurons (151,207,259).…”

Linking Inflammation, Cardiorespiratory Variability, and Neural Control in Acute Inflammation via Computational Modeling

“…Without peripheral or central inputs, the respiratory CPG provides an output that is rhythmic and regular, i.e., characterized by low deterministic variability (Dhingra et al, 2011). Under normal conditions the intact cardio-respiratory system exhibits chaotic dynamics with deterministic variability (Sammon and Bruce, 1991).…”