Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI

By Catie Chang, Gary H. Glover. NeuroImage, 2009

299 citations recorded by the source corpus.

A significant component of BOLD fMRI physiological noise is caused by variations in the depth and rate of respiration. It has previously been demonstrated that a breath-to-breath metric of respiratory variation (respiratory volume per time; RVT), computed from pneumatic belt measurements of chest expansion, has a strong linear relationship with resting-state BOLD signals across the brain. RVT is believed to capture breathing-induced changes in arterial CO2, which is a cerebral vasodilator; indeed, separate studies have found that spontaneous fluctuations in end-tidal CO2 (PETCO2) are correlated with BOLD signal time series. The present study quantifies the degree to which RVT and PETCO2 measurements relate to one another and explain common aspects of the resting-state BOLD signal. It is found that RVT (particularly when convolved with a particular impulse response, the “respiration response function”) is highly correlated with PETCO2, and that both explain remarkably similar spatial and temporal BOLD signal variance across the brain. In addition, end-tidal O2 is shown to be largely redundant with PETCO2. Finally, the latency at which PETCO2 and respiration belt measures are correlated with the time series of individual voxels is found to vary across the brain and may reveal properties of intrinsic vascular response delays.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/6a4236ac63004e714454369b22a21b1889562084

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Citations in the corpus

Dynamic models of large-scale brain activity (2017, background): “Whereas first-order physiological signals—respira-tion and heart rate—yield substantial confounds in the blood oxygen level–dependent (BOLD) signal 113 , second-order effects such as heart rate variability co-vary with specific activity in interoceptive cortex, such as the insula, and herald…”
Sources and implications of whole-brain fMRI signals in humans (2016, background): “, 2004) and more recent studies emphasizing gray matter changes (Birn et al., 2006, 2008; Chang and Glover, 2009).”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, background): “…for resting-state analysis, as they reside predominantly in the low frequencies (<0.1 Hz) and tend to cause synchronous global modulations of the fMRI time series owing to their influence on arterial CO 2 levels and cerebral blood flow (Chang and Glover, 2009; Peng et al., 2013; Wise et al., 2004).”
The role of physiological noise in resting-state functional connectivity (2012, unknown): “Furthermore, includingmeasures of end-tidal CO2 explains even more noise, suggesting that the different measurements (respiration volume, heart rate, and end-tidal CO2) to a certain degree explain different aspects of the noise (Chang and Glover, 2009).”
Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression (2012, unknown): “Numerous studies since Murphy et al. (2009) have focused on whether or not anticorrelations are solely an artifact of GSReg with conclusions pointing in both directions (Anderson et al., 2011b; Fox et al., 2009; Chai et al., 2012; Chang and Glover, 2009a).”
Time-resolved resting-state brain networks (2014, methodology): “To estimate the statistic’s distribution under the null hypothesis of stationarity, 250 null data sets were generated using stable vector autoregressive (VAR) models (34) that approximately preserved the power and cross-spectrum of the actual rsfMRI data (Materials and Methods and SI Appendix, Fig.”