Physiological noise in oxygenation‐sensitive magnetic resonance imaging

By G. Krüger, Gary H. Glover. Magnetic Resonance in Medicine, 2001

672 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/2f5cbf6cbba177692c8ba24cd17c04f2cd124698

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In Atomic and Molecular Physics, and Optics

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Citations in the corpus

Real-time fMRI neurofeedback: Progress and challenges (2013, background): “In this way, breathing artifacts should cancel out because they can be expected to have similar effects on both target regions (Krüger and Glover, 2001).”
On the Definition of Signal-To-Noise Ratio and Contrast-To-Noise Ratio for fMRI Data (2013, background): “When referring to the noise signal, we implicitly take into account all these sources, ignoring the specific influence or distribution of these sources [7].”
Resting fluctuations in arterial carbon dioxide induce significant low frequency variations in BOLD signal (2004, methodology): “In addition to such sources of noise, spontaneous low-frequency fluctuations in cerebral BOLD signal have been observed (Biswal et al., 1995; Kruger and Glover, 2001).”
Probabilistic independent component analysis for functional magnetic resonance imaging (2004, background): “suggested by [ 30 ]) a noise component combines non-additively with a signal source, then indeed the linear mixing model used here will be imperfect.”
CORSICA: correction of structured noise in fMRI by automatic identification of ICA components. (2007, methodology): “On short-TR datasets, we validate that the proposed method of noise reduction allows a substantial improvement of the signal-to-noise ratio evaluated at the cardiac and respiratory frequencies, even in the gray matter, while preserving the main fluctuations related to neural activity.”
Methods for cleaning the BOLD fMRI signal (2016, neutral): “A less clear distinction occurs for those components that are simultaneously coupled to changes in S0 (ΔS0) and R2* (ΔR2*) (i.e. showing a high rho and high kappa value), for instance certain cardiac and respiratoryrelated fluctuations showing a dependence on TE (Krüger and Glover, 2001).”
Evaluation of sliding window correlation performance for characterizing dynamic functional connectivity and brain states (2016, background): “The noise that we added was random, but actual noise is rsfMRI often arises from physiological sources (heart beat at ≈ 1 Hz, respiration at ≈ 0.3 Hz) and head motion (Kruger and Glover, 2001; Greve et al., 2013; Bright and Murphy, 2015) and is spatially structured.”
Increased sensitivity in neuroimaging analyses using robust regression (2005, background): “Heartbeat and breathing both induce pulsatile motion in the brain, which creates artifacts in the time series directly, by moving brain tissue with respect to the sampling grid, and indirectly, by inducing magnetic susceptibility artifacts (Frank et al., 1993, 2001; Kruger and Glover, 2001 ).”
Differentiating BOLD and Non-BOLD Signals in fMRI Time Series Using Multi-Echo EPI (2011, methodology): “Here, building on Peltier and Noll (2000) and Krüger and Glover (2001), we introduce a newmethod that employs multi-echo acquisition and a TE-dependence test to remove artifactual fluctuations more effectively than these previous approaches by cleanly separating BOLD and non-BOLD signal components…”
Non-white noise in fMRI: Does modelling have an impact? (2006, positive): “With physiological noise increasing with field strength (Krüger & Glover, 2001; Krüger et al., 2001), the full benefit in signal to noise ratio (SNR) by using high field scanners cannot be achieved without proper handling of physiological noise.”
Assessing functional connectivity in the human brain by fMRI. (2007, background): “One prominent confound in functional connectivity measurements is physiological noise in the BOLD signal, which can influence correlation measurements; in fact it dominates the signal at 3T [100].”
fMRI-adaptation and category selectivity in human ventral temporal cortex: regional differences across time scales. (2010, methodology): “To examine whether differences among ROIs and time scales were due to signal-to-noise differences according to anatomical location or experiment, we examined the time series signal-to-noise ratio (tSNR) (Kruger and Glover 2001) in each of the SL and LL paradigms.”
Multiplexed Echo Planar Imaging for Sub-Second Whole Brain FMRI and Fast Diffusion Imaging (2010, background): “[43,44,45]) and these fluctuations form the basis of R-fMRI.”
Resting-state FMRI confounds and cleanup (2013, neutral): “…of overall variance attributable to the underlying physiological noise increases with the signal-to-noise ratio (SNR) and so may be smaller or larger depending on voxel size and field strength (Bodurka et al., 2007; Kruger and Glover, 2001; Murphy et al., 2007; Triantafyllou et al., 2005).”