Image‐based method for retrospective correction of physiological motion effects in fMRI: RETROICOR

By Gary H. Glover, Tie-Qiang Li, David Ress. Magnetic Resonance in Medicine, 2000

2,086 citations recorded by the source corpus.

Subfields: Atomic and Molecular Physics, and Optics, Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/28a03c69a92c98a61fd6ff67864e4cd74171661e

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Citations in the corpus

Relationship between respiration, end-tidal CO2, and BOLD signals in resting-state fMRI (2009, methodology): “2002), or retrospectively by filtering out a subspace of the voxel time series that is time-locked to the respiratory cycle (Glover et al., 2000).”
The effect of resting condition on resting-state fMRI reliability and consistency: A comparison between resting with eyes open, closed, and fixated (2013, methodology): “Preprocessing steps included rigid-body volume registration to reduce the influence of subjectmotion, implementation of RETROICOR to reduce the physiological noise introduced by the respiration and cardiac cycles (Glover et al., 2000), and slice timing correction.”
A quantitative comparison of NIRS and fMRI across multiple cognitive tasks (2010, positive): “Concurrent measurement of physiological responses could help to reduce such errors (Tachtsidis et al., 2009; Glover et al., 2000); also see Strangman et al., 2003) for a discussion of additional sources of error in NIRS signal.”
Time-frequency dynamics of resting-state brain connectivity measured with fMRI (2009, positive): “Images were corrected for physiological noise by (1) applying RETROICOR (Glover et al., 2000) for the removal of timelocked cardiac and respiratory artifacts, and (2) filtering of lowfrequency respiratory volume and heart rate effects using methods ( " RVHRCOR " ) described in (Chang and Glover, 200”
Association between heart rate variability and fluctuations in resting-state functional connectivity (2012, methodology): “As such, measurements of HRV are entangled with concurrent changes in respiratory parameters that may also cause changes in BOLD activity, either neural (Evans et al., 1999; Evans et al., 2009) or artifactual (Glover et al., 2000; Birn et al., 2006; Wise et al., 2004).”
Anticorrelations in resting state networks without global signal regression (2011, positive): “Chang and Glover (2009) tested the effects of removing time-locked cardiac and respiratory artifacts (RETROICOR) (Glover et al., 2000) and low-frequency respiratory and heart rate effects(Birnetal.,2008;Changetal.,2009).”
Influence of heart rate on the BOLD signal: the cardiac response function (2008, methodology): “sured by a photoplethysmograph and pneumatic belt, respectively (Deckers et al., 2006; Glover et al., 2000; Hu et al., 1995).”
Mapping Sources of Correlation in Resting State FMRI, with Artifact Detection and Removal (2010, positive): “X RI is the 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…”
Modulation of aesthetic value by semantic context: An fMRI study (2009, positive): “In order to correct for the structured noise induced by respiration and cardiac pulsation we included RETROICOR (RETROspective Image based CORrection method) nuisance covariates in the design matrix (Glover et al., 2000).”
Differentiating BOLD and Non-BOLD Signals in fMRI Time Series Using Multi-Echo EPI (2011, methodology): “For seed-based connectivity analysis with standard de-noising (i.e. motion parameters, RETROICOR, RVT, and band-pass filtering), a seed time course was first extracted for a region of interest from the de-noised and band passed data.”
On the Plurality of (Methodological) Worlds: Estimating the Analytic Flexibility of fMRI Experiments (2012, methodology): “…the effects of different strategies for coregistration between structural and functional images, for brain extraction and segmentation, for signal normalization, or for physiological noise reduction – e.g., as implemented in RETROICOR (Glover et al., 2000) or PHYCAA (Churchill et al., 2012c).”
Decoupling of the brain's default mode network during deep sleep (2009, background): “Physiologic noise was modeled as in Shmueli et al. (45), including RETROICOR (46), cardiac and respiration rates, and the respiration envelope.”
A map of abstract relational knowledge in the human hippocampal–entorhinal cortex (2017, methodology): “Models for the cardiac and respiratory phase and their aliased harmonics were based on RETROICOR (Glover et al., 2000).”
Brain Connectivity Related to Working Memory Performance (2006, background): “A variety of methods to remove noise arising from monitored physiological sources that are under-sampled in the imaging data have been described previously (Hu et al., 1995; Glover et al., 2000; Lund et al., 2006).”
Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression (2012, neutral): “…interest, such as motion estimates, or decompositions of signals aggregated over regions not exhibiting BOLD ﬂuctuations of interest (Beall, 2010; Behzadi et al., 2007; Birn et al., 2006, 2008; Chang and Glover, 2009b; Glover et al., 2000; Jo et al., 2010; Shmueli et al., 2007; Wise et al., 2004).”
How long to scan? The relationship between fMRI temporal signal to noise ratio and necessary scan duration (2007, methodology): “Similarly for lower resolutions, correction methods (such as RETROICOR (Glover et al., 2000) or prewhitening (Purdon and Weisskoff, 1998)) can be used to minimize the contribution of physiological noise thus making the overall noise more Gaussian-like (Lund et al., 2006).”
Very slow activity fluctuations in monkey visual cortex: implications for functional brain imaging. (2003, methodology): “While several methods have been developed to identify and remove artifacts caused by the periodic physiological signals (Glover et al., 2000; Chuang and Chen, 2001) these methods often rely upon high image acquisition rates in order to meet the Nyquist criterion for temporal sampling.”
The Human Connectome Project: A data acquisition perspective (2012, background): “On the other hand, there may be additional valuable gains, including an improved ability to model and remove physiological artifacts (Glover et al., 2000) including head motion (Power et al.”