Abstract
In order to characterize the variability and correlation properties of spontaneous breathing in humans, the breathing pattern of 16 seated healthy subjects was studied during 40 min of quiet breathing using opto-electronic plethysmography, a contactless technology that measures total and compartmental chest wall volumes without interfering with the subjects breathing. From these signals, tidal volume (VT), respiratory time (TTOT) and the other breathing pattern parameters were computed breath-by-breath together with the end-expiratory total and compartmental (pulmonary rib cage and abdomen) chest wall volume changes. The correlation properties of these variables were quantified by detrended fluctuation analysis, computing the scaling exponentα. VT, TTOT and the other breathing pattern variables showed α values between 0.60 (for minute ventilation) to 0.71 (for respiratory rate), all significantly lower than the ones obtained for end-expiratory volumes, that ranged between 1.05 (for rib cage) and 1.13 (for abdomen) with no significant differences between compartments. The much stronger long-range correlations of the end expiratory volumes were interpreted by a neuromechanical network model consisting of five neuron groups in the brain respiratory center coupled with the mechanical properties of the respiratory system modeled as a simple Kelvin body. The model-based α for VT is 0.57, similar to the experimental data. While the α for TTOT was slightly lower than the experimental values, the model correctly predicted α for end-expiratory lung volumes (1.045). In conclusion, we propose that the correlations in the timing and amplitude of the physiological variables originate from the brain with the exception of end-expiratory lung volume, which shows the strongest correlations largely due to the contribution of the viscoelastic properties of the tissues. This cycle-by-cycle variability may have a significant impact on the functioning of adherent cells in the respiratory system.
Highlights
Most studies on physiological control systems have been based on measures of the average output over some period of time, and little attention has been paid to the mechanisms that determine how these control systems regulate their output in order to maintain a stable homeostatic internal environment
It has been shown that the tidal volume of the total chest wall measured by Optoelectronic Plethysmography (OEP) is extremely similar to the tidal volume measured by spirometry, with the coefficient of variations of the differences between techniques being below 5% [9], we assume that all breathing pattern parameters derived by the total chest wall volume measured by OEP accurately resembles the ones measured by spirometry or body plethismography
The most important finding of this study is that the long-term correlation properties of end-expiratory volume (EEV) parameters are significantly different than those of all other respiratory indices such as VT or TTOT
Summary
Most studies on physiological control systems have been based on measures of the average output over some period of time, and little attention has been paid to the mechanisms that determine how these control systems regulate their output in order to maintain a stable homeostatic internal environment. In an effort to account for the correlated variability in VT observed in babies, Cernelc et al [4] proposed to add noise to the neural network model of the brain respiratory oscillator put forth by Botros and Bruce [8] Their modeling results showed that the phrenic output of the model generated a cyclic pattern with breath-to-breath variations that mimicked the correlation properties of VT [4]. The respiratory system is composed of several mechanical structures such as the lung and the chest wall and its abdominal and rib cage compartments It is not known whether the centrally controlled signal from the phrenic nerve results in different variabilities of these compartments. The role of the passive mechanical properties of the lung and chest wall in the breath-to-breath variations of respiratory parameters has not been determined
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