A method for removal of global effects from fMRI time series

By P. Macey, K. E. Macey, Rajesh Kumar, R. Harper. NeuroImage, 2004

436 citations recorded by the source corpus.

Subfields: Cognitive Neuroscience, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/b8d10b73ecebe75b8a0e790298ee7292d5929453

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Citations in the corpus

Functional connectivity of human striatum: a resting state FMRI study. (2008, background): “As the global signal is thought to reflect a combination of physiological processes (such as cardiac and respiratory fluctuations) and scanner drift, it was included as a nuisance signal to minimize the influence of such factors (Gavrilescu et al. 2002; Macey et al. 2004; Birn et al. 2006).”
Neural basis of global resting-state fMRI activity (2010, background): “Resting-state fMRI studies routinely remove the spontaneous blood oxygenation level-dependent (BOLD) fluctuations common to large regions of the brain to focus on covariation within so-called functional networks (12, 48).”
Functional connectivity of default mode network components: correlation, anticorrelation, and causality (2008, background): “The global signal, thought to reflect a combination of physiological processes (such as cardiac and respiratory fluctuations) and scanner drift, was included in the GLM to minimize the influence of such factors [Birn et al., 2006; Gavrilescu et al., 2002; Macey et al., 2004].”
The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced? (2008, positive): “In this technique, the global signal, calculated by averaging the time series over all voxels in the brain, is used as a regressor in a general linear model (GLM) to remove the associated variance (Desjardins et al., 2001; Macey et al., 2004).”
Trouble at Rest: How Correlation Patterns and Group Differences Become Distorted After Global Signal Regression (2012, neutral): “The global signal (GS) is a time course obtained by averaging resting-state time series over the entire brain (Desjar-dins et al., 2001; Macey et al., 2004).”
Assessing functional connectivity in the human brain by fMRI. (2007, methodology): “[115] developed a technique in which a linear model of the global signal is determined and removed from each voxel individually.”
Anticorrelations in resting state networks without global signal regression (2011, neutral): “…fMRI are neurobiologically valid or are instead arti ﬁ cial-ly introduced by global signal regression, a preprocessing technique for removing physiological and other noise in fMRI BOLD time series (Aguirre et al., 1997, 1998; Desjardins et al., 2001; Macey et al., 2004; Zarahn et al., 1997).”
A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks (2008, methodology): “A third experiment examined whether the network switching mechanism could be observed during task-free resting state where there was no overt task and no behavioral response (4).”
The Global Signal in fMRI: Nuisance or Information? (2017, neutral): “In contrast to the prior work where both task and global signal covariates were employed, Macey et al. (2004) did not use a task covariate and simply regressed the global signal out of each voxel time series.”
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging (2007, background): “, regressing out signals that are common to all voxels (the global signal”
Benchmarking of participant‐level confound regression strategies for the control of motion artifact in studies of functional connectivity (2017, methodology): “Finally, the inclusion of global signal regression (GSR) (Macey et al., 2004) in confound regression models remains a source of controversy (Fox et al., 2009; Murphy et al., 2009; Chai et al., 2012; Saad et al., 2012; Yan et al., 2013b; Murphy, in press).”
Resting-state FMRI confounds and cleanup (2013, positive): “Global signal regression (GSReg) removes the global mean BOLD signal computed across all voxels in the brain (Desjardins et al., 2001; Greicius et al., 2003; Macey et al., 2004).”