Effects of maturation and acidosis on the chaos-like complexity of the neural respiratory output in the isolated brainstem of the tadpole, Rana esculenta.

Human ventilation at rest exhibits mathematical chaos-like complexity that can be described as long-term unpredictability mediated (in whole or in part) by some low-dimensional nonlinear deterministic process. Although various physiological and pathological situations can affect respiratory complexity, the underlying mechanisms remain incompletely elucidated. If such chaos-like complexity is an intrinsic property of central respiratory generators, it should appear or increase when these structures mature or are stimulated. To test this hypothesis, we employed the isolated tadpole brainstem model [Rana (Pelophylax) esculenta] and recorded the neural respiratory output (buccal and lung rhythms) of pre- (n = 8) and postmetamorphic tadpoles (n = 8), at physiologic (7.8) and acidic pH (7.4). We analyzed the root mean square of the cranial nerve V or VII neurograms. Development and acidosis had no effect on buccal period. Lung frequency increased with development (P < 0.0001). It also increased with acidosis, but in postmetamorphic tadpoles only (P < 0.05). The noise-titration technique evidenced low-dimensional nonlinearities in all the postmetamorphic brainstems, at both pH. Chaos-like complexity, assessed through the noise limit, increased from pH 7.8 to pH 7.4 (P < 0.01). In contrast, linear models best fitted the ventilatory rhythm in all but one of the premetamorphic preparations at pH 7.8 (P < 0.005 vs. postmetamorphic) and in four at pH 7.4 (not significant vs. postmetamorphic). Therefore, in a lower vertebrate model, the brainstem respiratory central rhythm generator accounts for ventilatory chaos-like complexity, especially in the postmetamorphic stage and at low pH. According to the ventilatory generators homology theory, this may also be the case in mammals.

Effects of inspiratory loading on the chaotic dynamics of ventilatory flow in humans.

Human ventilation at rest exhibits complexity and chaos. The aim of this study was to determine whether suprapontine interferences with the automatic breathing control could contribute to ventilatory chaos. We conducted a post hoc analysis of a previous study performed in awake volunteers exhibiting cortical pre-motor potentials during inspiratory loading. In eight subjects, flow was recorded at rest, while breathing against inspiratory threshold loads (median 21.5 cm H(2)O) and resistive loads (50 cm H(2)Ol(-1)s(-1)) loads, and while inhaling 7% CO(2)-93% O(2). Chaos was identified through noise titration (noise limit, NL) and the sensitivity to initial conditions was assessed through the largest Lyapunov exponent (LLE). Breath-by-breath variability was evaluated using the coefficient of variation of several ventilatory variables. Chaos was consistently present in ventilatory flow recordings, but mechanical loading did not alter NL, LLE, or variability. In contrast, CO(2) altered chaos and reduced variability. In conclusion, inspiratory loading - and any resultant respiratory-related cortical activity - were not associated with changes in ventilatory chaos in this study, arguing against suprapontine contributions to ventilatory complexity.

Respiratory inductive plethysmography to assess respiratory variability and complexity in humans.

Human ventilation is aperiodic, exhibiting a breath-by-breath variability and a complexity of which the characteristics may be interesting physiologically and clinically. In the present study, we tested the ability of respiratory inductive plethysmography (RIP) to describe these properties. Indeed, RIP does not have the effects on ventilation described with mouthpiece measurements. We compared the ventilatory flow recorded with a pneumotachograph (V'PNT) and the ventilatory flow derived from the mathematical treatment of the thoracoabdominal motion signals obtained from a particular type of RIP (V'RIP, Visuresp, Meylan, France) in 8 freely breathing normal subjects. Using the Z correlation coefficient, Passing-Bablock regressions and Bland and Altman graphical analyses, we compared the coefficients of variation of the main discrete respiratory variables determined with V'PNT and V'RIP and a set of nonlinear descriptors including the noise limit (chaotic nature of the signal), largest Lyapunov exponent (sensitivity to initial conditions), the Kolmogorov-Sinai entropy (unpredictability) and the correlation dimension (irregularity). When the recordings were obtained with the two techniques simultaneously, all the measurements were correlated and interchangeable. RIP can be safely used to quantify the breath-by-breath variability of ventilation and to study the complexity and the chaotic behavior of the ventilatory flow.