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

By Rasmus M. Birn, Monica A. Smith, Tyler B. Jones, Peter A. Bandettini. NeuroImage, 2008

664 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/4690d66c2d0ae915c16b4847d8b02a83fd3c557e

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Citations in the corpus

Intrinsic Brain Connectivity in Fibromyalgia is Associated with Chronic Pain Intensity (2010, neutral): “11), as well as regressors representing cardiac and respiratory variability, defined by convolving the heart rate time series and respiratory variations with appropriate cardiac and respiratory transfer functions, respectively, as suggested by Chang et al (33) and Birn et al (34).”
Influence of heart rate on the BOLD signal: the cardiac response function (2008, methodology): “…RRF is indeed an effective a priori RV response function, which is further demonstrated by our result showing that the RRF correction impacts maps of the DMN, a finding that extends Birn's results from correction based on crosscorrelation (Birn et al., 2006) to use of the RRF (Birn et al., 2008b).”
Methods for cleaning the BOLD fMRI signal (2016, positive): “…then be used to define the phase of the quasi-periodic fluctuations induced by the heart rate and the respiratory rate as it is done in RETROICOR, as well as to characterize variations in the cardiac rate (Shmueli et al., 2007; Chang et al., 2009) or respiratory volume (Birn et al., 2006; 2008).”
Clinical applications of the functional connectome (2013, neutral): “For example, patterns of functional connectivity derived from respiratory signals have been shown to resemble those typically observed in the default network (Birn et al., 2006, 2008a).”
Time-frequency dynamics of resting-state brain connectivity measured with fMRI (2009, neutral): “response to a single deep breath is well-modeled by a function that decays to 0 after approximately 40 s (Birn et al., 2008), then discarding the first 1 min of data would largely remove the effects of the BH.”
Functional connectivity of the insula in the resting brain (2011, neutral): “Whereas physiological noise is correlated with rsFC patterns (Birn et al., 2006, 2008), the regression of nuisance correlations that can be estimated from the data (via white-matter, ventricular, and whole-brain”
Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI (2009, methodology): “In addition, RVT(t) was convolved with the RRF (Birn et al., 2008), forming a separate waveform (RVTRRF(t)).”
Mapping Sources of Correlation in Resting State FMRI, with Artifact Detection and Removal (2010, neutral): “…N×13 matrix of eight slice-wise RETROICOR (Glover et al., 2000; Jones et al., 2008) regressors that model the effect of respiration and heart cycle, and five respiration volume per time (RVT) regressors that model slow blood oxygenation level fluctuations (Birn et al., 2008; Chang et al., 2009).”
Spatiotemporal dynamics of the brain at rest - Exploring EEG microstates as electrophysiological signatures of BOLD resting state networks (2012, methodology): “…and their first harmonics were removed using the RETROICOR method (Glover et al., 2000). fMRI time series fluctuations related to the respiration volume per time (RVT) were also removed with delays of 0, 5, 10, 15 and 20 s using the method of Birn et al. (Birn et al., 2006; Birn et al., 2008).”
Sources and implications of whole-brain fMRI signals in humans (2016, methodology): “…those studies have linked breathing patterns at rest to fMRI signal changes across much of the brain, with Wise and colleagues reporting modulation in gray and white matter (Wise et al., 2004) and more recent studies emphasizing gray matter changes (Birn et al., 2006, 2008; Chang and Glover, 2009).”
The Nuisance of Nuisance Regression: Spectral Misspecification in a Common Approach to Resting-State fMRI Preprocessing Reintroduces Noise and Obscures Functional Connectivity (2013, methodology): “measurable noise sources such as scanner drift and headmotion, aswell as periodic physiological signals (e.g., heartbeat and respiration; Birn et al., 2008; Chang et al., 2009), whereas bandpass filtering suppresses all variability in a range of frequencies that are a priori not of interest.”
Two systems of resting state connectivity between the insula and cingulate cortex (2009, background): “…some of the low-frequency fluctuations are due to non-neuronal physiological fluctuations, such as cardiac, respiratory, and blood CO2 fluctuations, or scanner instability (Birn et al., 2006; Birn et al., 2008a; Birn et al., 2008b; Fox and Raichle, 2007; Shmueli et al., 2007; Wise et al., 2004).”
The Influence of Head Motion on Intrinsic Functional Connectivity MRI (2011, background): “…motion behave as a trait and present a potential confound when exploring individual differences. swallowing and jaw clenching; cardiac cycles and respiration are also known to contribute to motion artifacts (Birn et al., 2008a; Enzmann and Pelc, 1992; Glover et al., 2000; Huettel et al., 2004).”
Dynamic functional connectivity: Promise, issues, and interpretations (2013, background): “…can arise from cardiac pulsation, shifts in the main magnetic field caused by motion of the body during respiration, and variations in the respiratory volume/ rate and cardiac rate that evoke changes in BOLD contrast (Birn et al., 2008; Chang et al., 2009; Dagli et al., 1999; Shmueli et al., 2007).”
Effects of model-based physiological noise correction on default mode network anti-correlations and correlations (2009, neutral): “Briefly, RVHRCOR utilizes the following steps: First, a “respiratory variations” (RV) time series is computed as the root-mean-square amplitude of the respiration waveform across a 6 s sliding window, and is then convolved with an impulse response known as the “respiration response function” (Birn e”
Small-world and scale-free organization of voxel-based resting-state functional connectivity in the human brain (2008, neutral): “1 Hz), making the resting-state correlations less specific (Birn et al., 2008).”
Exploring the brain network: a review on resting-state fMRI functional connectivity. (2010, methodology): “As a result, these higher frequent cardiac and respiratory patterns might shape the BOLD time-series of anatomically separate brain regions in a similar way, introducing artificial correlations between the time-series of these regions (Birn et al., 2006; Birn et al., 2008; Chang et al., 2009; Shmuel”