Trouble at rest: how correlation patterns and group differences become distorted after global signal regression
Open accessAbstract
Resting-state functional magnetic resonance imaging (RS-FMRI) holds the promise of revealing brain functional connectivity without requiring specific tasks targeting particular brain systems. RS-FMRI is being used to find differences between populations even when a specific candidate target for traditional inferences is lacking. However, the problem with RS-FMRI is a lacking definition of what constitutes noise and signal. RS-FMRI is easy to acquire but is not easy to analyze or draw inferences from. In this commentary we discuss a problem that is still treated lightly despite its significant impact on RS-FMRI inferences; global signal regression (GSReg), the practice of projecting out signal averaged over the entire brain, can change resting-state correlations in ways that dramatically alter correlation patterns and hence conclusions about brain functional connectedness. Although Murphy et al. in 2009 demonstrated that GSReg negatively biases correlations, the approach remains in wide use. We revisit this issue to argue the problem that GSReg is more than negative bias or the interpretability of negative correlations. Its usage can fundamentally alter interregional correlations within a group, or their differences between groups. We used an illustrative model to clearly convey our objections and derived equations formalizing our conclusions. We hope this creates a clear context in which counterarguments can be made. We conclude that GSReg should not be used when studying RS-FMRI because GSReg biases correlations differently in different regions depending on the underlying true interregional correlation structure. GSReg can alter local and long-range correlations, potentially spreading underlying group differences to regions that may never have had any. Conclusions also apply to substitutions of GSReg for denoising with decompositions of signals aggregated over the network's regions to the extent they cannot separate signals of interest from noise. We touch on the need for careful accounting of nuisance parameters when making group comparisons of correlation maps.
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There is the possibility that GSR altered connectivity patterns, which in turn may change the degree of similarity of individuals and thus the resulting community designations.
“There is the possibility that GSR altered connectivity patterns [57], [58], which in turn may change the degree of similarity of individuals and thus the resulting community designations.”
From Organizing Heterogeneous Samples Using Community Detection of GIMME-Derived Resting State Functional Networks (2014) · cites this paper as [58]
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McIntosh, R. C., Hoshi, R. A., & Timpano, K. R. (2020) · Respiratory Physiology & Neurobiology · doi:10.1016/j.resp.2020.103427 · Cited by 14 · see citing works
Xu, Y., Liu, H., Liu, H., Lin, D., Wu, S., & Peng, Z. (2025) · Behavioral Sciences · doi:10.3390/bs15040488 · Cited by 2 · see citing works
- Authors
- Saad, Z. S., Gotts, S. J., Murphy, K., Chen, G., Jo, H. J., Martin, A., & Cox, R. W.
- Affiliations
- National Institutes of Health (US)
- Cardiff University (GB)
- Corresponding author
- Saad, Ziad S.
- Venue
- Brain Connectivity
- Publisher
- SAGE Publications
- Published
- 2012-02
- Volume
- 2
- Issue
- 1
- Pages
- 25-32
- ISSN
- 2158-0014 (ISSN-L)
- 2158-0022
- PMC
- PMC3484684
- OpenAlex
- W2071300176
- Cited by
- 859
- References
- 29
- Citations by year
- 2026: 31 · 2025: 35 · 2024: 28 · 2023: 31 · 2022: 45 · 2021: 47 · 2020: 60 · 2019: 79 · 2018: 70 · 2017: 85 · 2016: 88 · 2015: 95 · 2014: 78 · 2013: 77 · 2012: 9
- FWCI
- 42.46
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Brain Connectivity (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Wellcome Trust (090199)
- National Institutes of Health (Z99 MH999999)
- National Institute of Mental Health
- National Institute of Neurological Disorders and Stroke
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- global signal regression, resting-state functional connectivity, correlation bias, noise estimation, interregional correlation, group differences, nuisance parameters, signal denoising, long-range correlations
- MeSH terms
- Brain, Brain Mapping, Computer Simulation, Humans, Magnetic Resonance Imaging, Models, Anatomic, Rest, Signal-To-Noise Ratio
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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