ArticlePloS one2015
Correlated variability in the breathing pattern and end-expiratory lung volumes in conscious humans.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Diagnostic Accuracy Of Forced Oscillation Technique To Detect Lung Function Anomalies
A Bayesian, Operationally Seamless Adaptive Phase 2A/2B, Open-label, Multi-center, Randomized, Two-part Trial to Evaluate the Safety, Efficacy, and Optimal Tidal Volume Distribution of Variable Ventilation (VV) in Participants Undergoing Mechanical Ventilatory Support for Acute Respiratory Distress Syndrome
Who cites it
8 citing papers in PubMed, 20 citations in OpenAlex.
- Day-to-Day Variability of Respiratory Resistance in Asthma and COPD: Influence of Intra-Breath Data Sampling and Observation Period.IEEE journal of translational engineering in health and medicine · 2026Article
- Mitochondrial Dynamics: Working with the Cytoskeleton and Intracellular Organelles to Mediate Mechanotransduction.Aging and disease · 2023Review
- The Constrained Disorder Principle Accounts for the Variability That Characterizes Breathing: A Method for Treating Chronic Respiratory Diseases and Improving Mechanical Ventilation.Advances in respiratory medicine · 2023Review
- Mechanisms involved in brain dysfunction in mechanically ventilated critically ill patients: implications and therapeutics.Annals of translational medicine · 2018Review
- Regulation of Mitochondrial Structure and Dynamics by the Cytoskeleton and Mechanical Factors.International journal of molecular sciences · 2017Review
- Estimation of respiratory volume from thoracoabdominal breathing distances: comparison of two models of machine learning.European journal of applied physiology · 2017Article
- Systems Biology and Clinical Practice in Respiratory Medicine. The Twain Shall Meet.American journal of respiratory and critical care medicine · 2016Article
- Regulatory Roles of Fluctuation-Driven Mechanotransduction in Cell Function.Physiology (Bethesda, Md.) · 2016Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 1 institution in 1 country.
Funding
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 exponenta. 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.