Correlated variability in the breathing pattern and end-expiratory lung volumes in conscious humans.
Correlated variability in the breathing pattern and end-expiratory lung volumes in conscious humans.
复制标题
DOI：
10.1371/journal.pone.0116317
复制
发表时间：
2015
期刊：
影响因子：
3.7
通讯作者：
Suki B
中科院分区：
文献类型：
--
作者：
Dellaca RL;Aliverti A;Lo Mauro A;Lutchen KR;Pedotti A;Suki B
为了表征人类自主呼吸的变异性和相关性，使用光电体积描记法研究了16名健康受试者在40分钟安静呼吸期间的呼吸模式，光电体积描记法是一种非接触式技术，可测量总胸壁容积和隔室胸壁容积，而不会干扰受试者的呼吸。根据这些信号，逐个呼吸计算潮气量（VT）、呼吸时间（TTOT）和其他呼吸模式参数以及呼气末总和房室（肺肋骨和腹部）胸壁容积变化。这些变量的相关性通过去趋势波动分析进行量化，计算标度指数α。VT、TTOT和其他呼吸模式变量显示α值在0.60（每分钟通气量）至0.71（呼吸频率）之间，均显著低于呼气末容积获得的α值，后者在1.05（胸腔）至1.13（腹部）之间，室间无显著差异。呼气末容积的长程相关性更强的解释由一个神经力学网络模型组成的五个神经元群在大脑呼吸中枢耦合的机械性能的呼吸系统建模为一个简单的开尔文体。VT的基于模型的α为0.57，与实验数据相似。虽然TTOT的α略低于实验值，但该模型正确预测了呼气末肺容量的α（1.045）。总之，我们提出，在时间和幅度的生理变量的相关性起源于大脑的呼气末肺容量，这表明最强的相关性，主要是由于组织的粘弹性的贡献除外。这种周期间的变异性可能对呼吸系统中贴壁细胞的功能产生显著影响。
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.
登录
查看更多内容
在有和没有哮喘的超重和肥胖受试者中睡眠期间的呼吸力学变异性。
影响因子：
2.3
作者：
Campana, L. M.;Owens, R. L.;Butler, J. P.;Suki, B.;Malhotra, A.
通讯作者：
Malhotra, A.
影响因子：
3.3
作者：
Frey, U;Silverman, M;Suki, B
通讯作者：
Suki, B
影响因子：
8.8
作者：
Dellacà, RL;Aliverti, A;Gattinoni, L
通讯作者：
Gattinoni, L
影响因子：
3.3
作者：
Cala, SJ;Kenyon, CM;Rochester, DF
通讯作者：
Rochester, DF
影响因子：
3.3
作者：
Fadel, PJ;Barman, SM;Gebber, GL
通讯作者：
Gebber, GL