Correlations and anticorrelations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies

By A. Weissenbacher, C. Kasess, F. Gerstl, R. Lanzenberger, E. Moser, C. Windischberger. NeuroImage, 2009

805 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/070ec1baa4d74fe63b7c6c450487e851e2523a36

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In Cognitive Neuroscience

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

GRETNA: a graph theoretical network analysis toolbox for imaging connectomics (2015, background): “However, negative correlations may also be introduced by global signal removal, a preprocessing step that is currently controversial (Fox et al., 2009; Murphy et al., 2009; Weissenbacher et al., 2009; Schölvinck et al., 2010).”
An Improved Framework for Confound Regression and Filtering for Control of Motion Artifact in the Preprocessing of Resting-State Functional Connectivity Data (2012, methodology): “Given the debate regarding the merits of including global signal in rsfc-MRI preprocessing (Fox et al., 2009; Murphy et al., 2009; Weissenbacher et al., 2009), and since not all studies include the global signal in confound regression, the presence of motion artifact and its effects on connectivity…”
Test-retest reliability of resting-state connectivity network characteristics using fMRI and graph theoretical measures (2012, methodology): “While it was demonstrated that (1) the inclusion of covariates such as CSF, WM ormotion provides a sufficient correction for patterns of non-neural origin (Fox et al., 2005; Weissenbacher et al., 2009) and (2) the patterns of low-frequency fluctuations and functional connectivity are similar across ”
Functional connectivity mapping of the human precuneus by resting state fMRI (2012, background): “However, recent studies suggested that the global signal regression, a common data preprocessing step in seed region based functional connectivity analyses, is a likely cause of anti-correlation functional networks (Murphy et al., 2009; Weissenbacher et al., 2009).”
Standardizing the intrinsic brain: Towards robust measurement of inter-individual variation in 1000 functional connectomes (2013, positive): “Global signal regression (GSR), a commonly used yet controversial practice in the R-fMRI field, yields substantial increases in negative correlations (Murphy et al., 2009; Weissenbacher et al., 2009) and may distort group differences in intrinsic functional connectivity (iFC) (Saad et al., 2012).”
A Brain-Wide Study of Age-Related Changes in Functional Connectivity. (2015, background): “In the literature, it has been shown that global signal regression can have both positive and negative effects on the analysis of functional connectivity (Murphy et al. 2009; Weissenbacher et al. 2009; Song et al. 2012).”
ICA-based artefact and accelerated fMRI acquisition for improved Resting State Network imaging (2014, methodology): “As BOLD signal related to neural activity should be predominantly in the grey matter, an alternative method is to regress out of the time series derived from just the white matter and/or CSF voxels (Weissenbacher et al., 2009).”
Exploring the brain network: a review on resting-state fMRI functional connectivity. (2010, methodology): “These methods include the subtraction of physiological signals out of the resting-state fMRI signal by monitoring physiological patterns during scanning and/or regressing non-gray matter signals out of the fMRI signal (Weissenbacher et al., 2009), as well as the use of high sampling rates to prevent”
Conn: A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks (2012, neutral): “Estimated voxel-to-voxel correlations using the raw BOLD signals typically show distributions with some degree of positive bias, and with large differences between sessions and subjects.”
Subregions of the human superior frontal gyrus and their connections (2013, neutral): “Additionally, we just focused on the positive rsFCs because it remains an unsettled debate on the negative rsFCs derived from the resting-state fMRI studies that whether the negative connectivity is an artifact of the global signal regression (Murphy et al., 2009; Weissenbacher et al., 2009) or refl”
Reduction of motion-related artifacts in resting state fMRI using aCompCor (2014, background): “Applying a temporal filter to constrain the data to frequencies more likely to represent neuronal-related variance is also common (Weissenbacher et al., 2009; Windischberger et al., 2002).”
Global Signal Regression Strengthens Association between Resting-State Functional Connectivity and Behavior (2019, background): “However, consistent with previous work (Murphy et al., 2009; Fox et al., 2009; Weissenbacher et al., 2009), GSR reduces functional connectivity across almost all ROI pairs.”
The Global Signal in fMRI: Nuisance or Information? (2017, neutral): “The concern about the spurious nature of the anticorrelations induced by GSR was echoed by several studies (Anderson et al., 2011; Weissenbacher et al., 2009; Saad et al., 2012) that used mathematical arguments and small-scale simulations similar to those of Murphy et al. (2009).”
Multi-level bootstrap analysis of stable clusters in resting-state fMRI (2009, methodology): “Data-driven noise correction has been reported to improve the detection of RSNs (Giove et al., 2009; Weissenbacher et al., 2009), yet the procedure that we used Perlbarg et al.”
Reliable Intrinsic Connectivity Networks: Test-Retest Evaluation Using ICA and Dual Regression Approach (2009, neutral): “Recently, concerns have arisen regarding the potential artifactual nature of findings of negative connectivity, resulting from global corrections procedures commonly employed in conjunction with seed-based approaches (Murphy et al., 2009; Fox et al., 2009; Weissenbacher et al., 2009).”
Methods to detect, characterize, and remove motion artifact in resting state fMRI (2013, result): “These results are consistent with previous analyses wherein WM and CSF signals explained little variance in the gray matter (Jo et al., 2010; Weissenbacher et al., 2009).”
Advances and Pitfalls in the Analysis and Interpretation of Resting-State FMRI Data (2010, neutral): “…effect, or rather artifi cially enhance ‘true’ negative relationships existing between cognitive control RSNs, remains contentious, as multiple studies have not reached identical conclusions on this issue (Chang and Glover, 2009; Fox et al., 2009; Weissenbacher et al., 2009; Van Dijk et al., 2010).”