Non-white noise in fMRI: Does modelling have an impact?

By Torben Ellegaard Lund, Kristoffer Hougaard Madsen, Karam Sidaros, Wen-Lin Luo, Thomas E. Nichols. NeuroImage, 2006

458 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/fadd47a958c5495a3435c4aedb61c3a66618389b

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Citations in the corpus

The Statistical Analysis of fMRI Data. (2008, background): “If these terms are properly removed, there is evidence that the resulting error term corresponds to white noise (Lund et al., 2006).”
Methods for cleaning the BOLD fMRI signal (2016, unknown): “It has been shown that using volume-specific regressors, where a single physiological phase is assigned to the entire volume, offers similar performance to a more complex model considering segment-specific regressors as long as the timing of the regressor corresponds to the time at which the center ”
Resting state fMRI: A review of methods and clinical applications (2012, unknown): “thought to include high proportions of noise related to cardiac and respiratory signals.(21-24) Spatial smoothing and low-pass fil-”
Reduction of motion-related artifacts in resting state fMRI using aCompCor (2014, background): “These sources of nuisance can mimic and/or mask true functional connectivity, and theirminimization has been both a point of concern and controversy for rs-FC investigations since their inception (Bianciardi et al., 2009; Biswal et al., 1996; Bright and Murphy, 2012; Carbonell et al., 2011; Lund et ”
Modulation of aesthetic value by semantic context: An fMRI study (2009, unknown): “Having all four types of nuisance regressors in the design improves the assumption of independently and identically distributed errors (Lund et al., 2006).”
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging (2007, background): “and physiological fluctuations, such as cardiac or respiratory activity, account for a significant fraction of spontaneous BOLD variance in human dat”
How long to scan? The relationship between fMRI temporal signal to noise ratio and necessary scan duration (2007, methodology): “By going to higher resolution, SNR is inherently decreased and noise becomes more Gaussian-like thus reducing autocorrelation.”
The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? (2008, unknown): “j o u r n a l h o m e p a g e : w w w. e l s e v i e r. c o m / l o c a t e / y n i m g the vicinity of large blood vessels and throughout grey matter, obscuring spontaneous neuronal fluctuations (Lund et al., 2006).”
Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI (2006, methodology): “FNuisance Variable Regression_ (NVR) method proposed by Lund et al. (2006) for the removal of physiological noise and other regressors of no interest.”
A whole brain fMRI atlas generated via spatially constrained spectral clustering (2011, methodology): “0.41 mm). fMRI data were restricted to GM and denoised by regressing out motion parameters, WM timecourse, as well as CSF timecourse [Fox et al., 2005; Lund et al., 2006].”
The Nuisance of Nuisance Regression: Spectral Misspecification in a Common Approach to Resting-State fMRI Preprocessing Reintroduces Noise and Obscures Functional Connectivity (2013, background): “, 2009) that can lead to nuisance fluctuations over a broad range of frequencies due to aliasing (Lund et al., 2006).”
Analysis of a large fMRI cohort: Statistical and methodological issues for group analyses (2007, unknown): “, 2006), and more generally, what is usually called structured noise (Lund et al., 2006).”
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).”
Mapping Sources of Correlation in Resting State FMRI, with Artifact Detection and Removal (2010, unknown): “head motion, hardware drift signals) (Lund et al., 2006), (ii) physiological “noises” (true NMR signals with noise-like properties, caused by respiration and the heartbeat), and (iii) acute hardware malfunctions or instability.”
Low-frequency fluctuations in the cardiac rate as a source of variance in the resting-state fMRI BOLD signal (2007, methodology): “Some of the methods are designed for straightforward data correction (Glover et al., 2000) but most can be extended to perform ‘nuisance variable regression’ (Birn et al., 2006a; Lund et al., 2006 ) in which the physiological noise measures (or models derived from them) are included as regressors i”
Functional segmentation of the brain cortex using high model order group PICA (2009, background): “Artifactual non-RSNs were identified based on their motion-related location at the borders of the brain, in cerebrospinal fluid (CSF), at the proximity of large blood vessels, in white matter, and in the vicinity of areas shown to be susceptible to physiological pulsations with relatively increased ”
Correlations and anticorrelations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies (2009, unknown): “One such method allowing for correction against a number of confounds is multidimensional linear regression analysis and has been repeatedly applied to fMRI data sets (Lund et al., 2006).”
CORSICA: correction of structured noise in fMRI by automatic identification of ICA components. (2007, methodology): “[5], where a large panel of sources of structured noise (including low-frequency drifts, movements and physiological noise) were taken into account.”