Ventilatory variability induced by spontaneous variations of PaCO2 in humans

Modarreszadeh, M.; Bruce, Eugene N.

doi:10.1152/jappl.1994.76.6.2765

Cited by 52 publications

(39 citation statements)

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“…In anesthetized, vagotomized spontaneously breathing rats, Khatib and associates (25) found that inhalation of 4070 carbon dioxide caused 3 of 3 animals to have positive correlations between the VTof one breath with that of the next, whereas VT was negatively correlated between adjoining breaths in 5 of 7 animals during air breathing. Additional evidence supporting the importance of chemoreceptor activity as a source of ventilatory variability is provided by recent data of Modarreszadeh and Bruce (33). They used a computer-controlled buffering technique to reduce spontaneous variability in arterial carbon dioxide tension, and observed a corresponding decrease in breath-to-breath variation in ventilation.…”

Section: Implications Concerning Respiratory Controlmentioning

confidence: 90%

Interrelationship of breath components in neighboring breaths of normal eupneic subjects.

Tobin¹,

Yang²,

Jubran³

et al. 1995

Am J Respir Crit Care Med

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To determine the fraction of variational activity that is correlated on a breath-to-breath basis from uncorrelated random fluctuations, we performed autocorrelation analysis in 33 normal subjects during resting breathing. A calibrated inductive plethysmograph was used to nonobtrusively record 700 breaths in each subject. The group mean autocorrelation coefficients at a lag of 1 breath for each of the three primary breath components, tidal volume (VT), inspiratory time (TI), and expiratory time (TE), were significantly different from zero (p < 0.001). The autocorrelation coefficients for VT, 0.295 +/- 0.148 (SD), and TE, 0.259 +/- 0.121, were greater than that for TI, 0.201 +/- 0.135 (p < 0.001 and p < 0.01, respectively). The autocorrelation coefficients for each breath component remained significant for approximately 3 consecutive breaths (p < 0.001), indicating the presence of "short-term memory." Cross-correlation analysis revealed significant interrelationships (p < 0.001) for all component irrespective of which component was leading or following, with the exception of the pairing of VT in the leading breath and TI in the subsequent breath. In conclusion, in resting healthy subjects breath components display considerable breath-to-breath variability that is not completely random in nature, but which, instead, has a significant fraction of structured correlated variational activity.

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Section: Implications Concerning Respiratory Controlmentioning

confidence: 90%

Interrelationship of breath components in neighboring breaths of normal eupneic subjects.

Tobin¹,

Yang²,

Jubran³

et al. 1995

Am J Respir Crit Care Med

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“…Furthermore, these breathing-related fluctuations are particularly problematic for resting-state connectivity analyses, which rely on the correlation of time-series between brain regions to infer a functional connection. As demonstrated in previous studies, the fluctuations in breathing during rest generally occur at similar frequencies (~0.03 Hz) and in similar brain regions as those implicated in resting-state default-mode network activity (Birn et al, 2006;Modarreszadeh et al, 1994;Van den Aardweg et al, 2002;Wise et al, 2004). Therefore, in order to obtain resting-state activity maps that reflect fluctuations in neuronal activity exclusively, it is vital that these respiration-induced fluctuations are modeled or removed from the data.…”

Section: Introductionmentioning

confidence: 92%

The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration

et al. 2008

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Changes in the subject's breathing rate or depth, such as a breath-hold challenge, can cause significant MRI signal changes. However, the response function that best models breath-holding induced signal changes, as well as those resulting from a wider range of breathing variations including those occurring during rest, has not yet been determined. Respiration related signal changes appear to be slower than neuronally-induced BOLD signal changes and are not modeled accurately using the typical hemodynamic response functions used in fMRI. In this study, we derive a new response function to model the average MRI signal changes induced by variations in the respiration volume (breath-to-breath changes in the respiration depth and rate). This was done by averaging the response to a series of single deep breaths performed once every 40s amongst otherwise constant breathing. The new "respiration response function" consists of an early overshoot followed by a later undershoot (peaking at approximately 16s), and accurately models the MRI signal changes resulting from breath holding as well as cued depth and rate changes.

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“…Furthermore, several authors have employed system identification techniques based on autoregressive models that make use of the breath-to-breath dependence of ventilation on arterial blood gas tensions (via chemoreceptors) as well as dependence of O 2 /CO 2 on ventilation (due to gas exchange) (Khoo and Marmarelis, 1989; Mitsis et al, 2009; Nemati et al, 2011). Lastly, many researchers have used autoregressive modeling in the presence of exogenous stimulations (e.g., pseudorandom binary sequences of inhaled CO 2 ) in an attempt to estimate LG during sleep (Ghazanshahi and Khoo, 1997; Modarreszadeh and Bruce, 1994; Modarreszadeh et al, 1995). However, none of these techniques has emerged as a useful and reliable tool for noninvasively estimating LG, possibly because recordings of respiratory variables are noisy and non-stationary.…”

Section: Discussionmentioning

confidence: 99%

Model-based estimation of loop gain using spontaneous breathing: A validation study

Gederi¹,

Nemati²,

Edwards³

et al. 2014

Respiratory Physiology & Neurobiology

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Non-invasive assessment of ventilatory control stability or loop gain (which is a key contributor in a number of sleep-related breathing disorders) has proven to be cumbersome. We present a novel multivariate autoregressive model that we hypothesize will enable us to make time-varying measurements of loop gain using nothing more than spontaneous fluctuations in ventilation and CO2. The model is adaptive to changes in the feedback control loop and therefore can account for system non-stationarities (e.g. changes in sleep state) and it is resistant to artifacts by using a signal quality measure. We tested this method by assessing its ability to detect a known increase in loop gain induced by proportional assist ventilation (PAV). Subjects were studied during sleep while breathing on continuous positive airway pressure (CPAP) alone (to stabilize the airway) or on CPAP + PAV. We show that the method tracked the PAV-induced increase in loop gain, demonstrating its time-varying capabilities, and it remained accurate in the face of measurement related artifacts. The model was able to detect a statistically significant increase in loop gain from 0.14 ± 10 on CPAP alone to 0.21 ± 0.13 on CPAP + PAV (p < 0.05). Furthermore, our method correctly detected that the PAV-induced increase in loop gain was predominantly driven by an increase in controller gain. Taken together, these data provide compelling evidence for the validity of this technique.

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Ventilatory variability induced by spontaneous variations of PaCO2 in humans

Cited by 52 publications

References 0 publications

Interrelationship of breath components in neighboring breaths of normal eupneic subjects.

Interrelationship of breath components in neighboring breaths of normal eupneic subjects.

The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration

Model-based estimation of loop gain using spontaneous breathing: A validation study

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