The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced?

By Kevin Murphy, Rasmus M. Birn, Daniel A. Handwerker, Tyler B. Jones, Peter A. Bandettini. NeuroImage, 2008

2,435 citations recorded by the source corpus.

Low-frequency fluctuations in fMRI signal have been used to map several consistent resting state networks in the brain. Using the posterior cingulate cortex as a seed region, functional connectivity analyses have found not only positive correlations in the default mode network but negative correlations in another resting state network related to attentional processes. The interpretation is that the human brain is intrinsically organized into dynamic, anti-correlated functional networks. Global variations of the BOLD signal are often considered nuisance effects and are commonly removed using a general linear model (GLM) technique. This global signal regression method has been shown to introduce negative activation measures in standard fMRI analyses. The topic of this paper is whether such a correction technique could be the cause of anti-correlated resting state networks in functional connectivity analyses. Here we show that, after global signal regression, correlation values to a seed voxel must sum to a negative value. Simulations also show that small phase differences between regions can lead to spurious negative correlation values. A combination breath holding and visual task demonstrates that the relative phase of global and local signals can affect connectivity measures and that, experimentally, global signal regression leads to bell-shaped correlation value distributions, centred on zero. Finally, analyses of negatively correlated networks in resting state data show that global signal regression is most likely the cause of anti-correlations. These results call into question the interpretation of negatively correlated regions in the brain when using global signal regression as an initial processing step.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/6c64889a707a15702f0abb047be5cd14d8a3dfb4

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Citations in the corpus

The Development of Human Amygdala Functional Connectivity at Rest from 4 to 23 Years: a cross-sectional study (2014, positive): “Although it has been clearly shown that global signal regression can induce spurious negative correlations between regions' signals (Saad et al. 2012; Weissenbacher et al. 2009; Murphy et al. 2009), this regression provides several important advantages.”
Toward reliable characterization of functional homogeneity in the human brain: Preprocessing, scan duration, imaging resolution and computational space (2012, background): “Global signal regression is more controversial due to potential introduction of negative correlations and potential to artifactually impact inter-individual differences in connectivity (Chai et al., 2012; Fox et al., 2009; Murphy et al., 2009; Saad et al., 2012).”
Is the ADHD brain wired differently? A review on structural and functional connectivity in attention deficit hyperactivity disorder (2010, background): “The use of such a preprocessing step can induce false negative correlations between brain regions [Murphy et al., 2009].”
Functional segmentation of the brain cortex using high model order group PICA (2009, methodology): “The use of ICA avoids the problematic regression of the global mean signal, although the PCA mean subtraction step in preprocessing of PICA also alters the data to some extent [Murphy et al., 2009].”
Benchmarking functional connectome‐based predictive models for resting‐state fMRI (2019, positive): “The box displays the median and quartiles, while the whiskers give the 5 th and 95 th percentiles. global signal mean regression (Fox et al., 2009; Murphy et al., 2009; Power et al., 2012).”
Graph analysis of the human connectome: Promise, progress, and pitfalls (2013, neutral): “Though the contributions of pre-processing techniques to the emergence of negative correlations in fMRI data remains a topic of debate (Fox et al., 2009; Murphy et al., 2009; Saad et al., 2012), it is probable that not all negative correlations are artifacts (Chang and Glover, 2009).”
An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI (2017, methodology): “The method also changes the distribution of functional connectivity estimates in the brain so that it is approximately centred on zero, which causes the emergence of negative correlations which may be artefactual (Fox et al., 2009; Murphy et al., 2009).”
A Comprehensive Assessment of Regional Variation in the Impact of Head Micromovements on Functional Connectomics (2013, neutral): “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) andmaydistort groupdifferences in intrinsic functional connectivity (iFC) (Saad et al., 2012).”