Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal

By Richard G. Wise, Kojiro Ide, Marc J. Poulin, Irene Tracey. NeuroImage, 2004

684 citations recorded by the source corpus.

Subfields: Neurology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/7720440e6cde2ee9fd9dedd5b06d8c4335775fb0

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Citations in the corpus

fMRI resting state networks define distinct modes of long-distance interactions in the human brain (2006, background): “Additional patterns related directly to vascular processes independent of cortical neuronal function have been identified as lowfrequency fluctuations in resting fMRI data (Kiviniemi et al., 2000; Wise et al., 2004).”
Functional Magnetic Resonance Imaging Phase Synchronization as a Measure of Dynamic Functional Connectivity (2012, neutral): “More recently, Kitzbichler et al. (2009) investigated the power-law scaling of synchronization metrics in the human brain for simulated and recorded MEG and fMRI ‘‘resting state’’ data.”
The respiration response function: The temporal dynamics of fMRI signal fluctuations related to changes in respiration (2008, background): “More recently, studies have shown that even subtle variations in breathing depth and rate that occur naturally during rest can result in significant signal changes (Birn et al., 2006; Wise et al., 2004).”
The role of physiological noise in resting-state functional connectivity (2012, neutral): “This increased sparseness and improved correspondence to task-deactivated areas may be the result of a reduction of the relatively global signal fluctuations induced by breathing variations, which tend to occur throughout gray matter (Wise et al., 2004).”
The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? (2008, neutral): “…low frequency BOLD fluctuations have been associated with changes in the level of arterial carbon dioxide (CO 2 ) in the frequency range 0–0.05 Hz (Wise et al., 2004) resulting from changes in the breathed-volume (respiration volume over time — RVT) (Birn et al., 2006) and changes in the rate of…”
Effects of model-based physiological noise correction on default mode network anti-correlations and correlations (2009, neutral): “…are known to modulate the BOLD signal (Biswal et al., 1993; Dagli et al., 1999; Jezzard et al., 1993; Shmueli et al., 2007; Weisskoff et al., 1993; Wise et al., 2004), and their effects can be reduced by monitoring respiratory and cardiac cycles throughout the scan and filtering them out of the…”
Building a Science of Individual Differences from fMRI. (2016, background): “Golestani AM, et al. Mapping the end-tidal CO2 response function in the resting-state BOLD fMRI signal: Spatial specificity, test-retest reliability and effect of fMRI sampling rate.”
Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI (2009, methodology): “Time shifts ranged from −10 s to 30 s in increments of 0.5 s, which represents the range of latency values previously observed between these signals and the fMRI time series (Birn et al., 2006; Birn et al., 2008; Wise et al., 2004).”
Visceral influences on brain and behavior. (2013, neutral): “These effects on brain vascular responses are perhaps not as marked as the modulation of cerebral perfusion by CO2 levels, an effect that, even at resting physiological ranges produces fluctuations in hemodynamic signals used to infer neural activity in neuroimaging studies (Wise et al., 2004).”
The Global Signal in fMRI: Nuisance or Information? (2017, neutral): “These in turn give rise to widespread fluctuations in the BOLD fMRI signal that can contribute to the global signal (Birn et al., 2006; Wise et al., 2004).”
Plasticity of Brain Networks in a Randomized Intervention Trial of Exercise Training in Older Adults (2010, neutral): “Finally, whereas global signal has been shown to correlate with cardiac and respirationinduced changes in the fMRI BOLD signal (Wise et al., 2004; Birn et al., 2006; Chang et al., 2009), we have shown that there is greater sensitivity to fitness-related variation in functional connectivity when…”