The DOI matches its Crossref record; 62 of the 64 references it cites were found in a registry or an index.
The impact of global signal regression on resting state correlations: Are anti-correlated networks introduced?
Open accessAbstract
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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“Some physiological noises, such as cardiac and respiratory rates, can alias into the low-frequency domain of functional MRI signals (Strother, 2006;Murphy et al., 2009).”
From Identifying major depression using whole-brain functional connectivity: a multivariate pattern analysis (2012) · cites this paper as Murphy et al., 2009
In the citing paper
“The interpretation of negative correlations is especially controversial because they only become strongly evident after regressing out the global signal; therefore some argue that negative connections in functional data may be purely a statistical artifact (Murphy et al., 2009).”
From Characterizing heterogeneity in children with and without ADHD based on reward system connectivity (2015) · cites this paper as Murphy et al., 2009
In the citing paper
“Some experts have suggested that these techniques might introduce spurious negative correlations upon regressing out whole brain signals (Murphy et al, 2009;Van Dijk et al, 2009).”
From Understanding the Effects of Stimulant Medications on Cognition in Individuals with Attention-Deficit Hyperactivity Disorder: A Decade of Progress (2011) · cites this paper as Murphy et al, 2009
In the citing paper
“Though depression commonly co-occurs with PTSD in veteran populations (up to 80% by some estimations70)”
From Altered resting-state amygdala functional connectivity in men with posttraumatic stress disorder (2012) · cites this paper as [70]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
A default mode of brain function
Raichle, M. E., MacLeod, A. M., Snyder, A. Z., Powers, W. J., Gusnard, D. A., & Shulman, G. L. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.676 · Cited by 12,190
Co-Planar Stereotaxic Atlas of the Human Brain
Talairach, J., & Tournoux, P. (1988) · Medical Entomology and Zoology · Cited by 11,798
AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages
Cox, R. W. (1996) · Computers and Biomedical Research · doi:10.1006/cbmr.1996.0014 · Cited by 10,913
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
The Brain's Default Network: Anatomy, Function, and Relevance to Disease
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761
The human brain is intrinsically organized into dynamic, anticorrelated functional networks
Fox, M. D., Snyder, A. Z., Vincent, J. L., Corbetta, M., Van Essen, D. C., & Raichle, M. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0504136102 · Cited by 8,553
Co-planar stereotaxic atlas of the human brain
Laitinen, L. (1989) · Clinical Neurology and Neurosurgery · doi:10.1016/0303-8467(89)90128-5 · Cited by 7,565
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
Functional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
Consistent resting-state networks across healthy subjects
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601417103 · Cited by 4,326
Research works printed but not linked: 2
- Fox, M.D., Zhang, D., Snyder, A.Z., Raichle, M.E., 2008. Global signal regression and anticorrelations in resting state fMRI data. Proc. HBM, 575 W-AM.
- Weissenbacher, A., Lanzenberger, R., Moser, E., Windischberger, C., 2008. Increasing specificity of resting-state fMRI-data using multiple regression analysis. Proc. HBM, TH-AM.
Cited by
Sorted by citations
Items 1,001–1,008 of 1,008
Luo, Q., Deng, Z., Qin, J., Wei, D., Cun, L., Qiu, J., Hitchman, G., & Xie, P. (2015) · Scientific Reports · doi:10.1038/srep09710 · Cited by 42
Aberrant spontaneous and task-dependent functional connections in the anxious brain
MacNamara, A., DiGangi, J., & Phan, K. L. (2016) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2015.12.004 · Cited by 34
Sie, J.-H., Chen, Y.-H., Shiau, Y.-H., & Chu, W.-C. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2019.00369 · Cited by 35
Perera, M. P. N., Gotsis, E. S., Bailey, N. W., Fitzgibbon, B. M., & Fitzgerald, P. B. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae327 · Cited by 34
Hilger, K., & Fiebach, C. J. (2019) · Network Neuroscience · doi:10.1162/netn_a_00083 · Cited by 31
Intrinsic Connectivity Networks of Glutamate-Mediated Antidepressant Response: A Neuroimaging Review
Demchenko, I., Tassone, V. K., Kennedy, S. H., Dunlop, K., & Bhat, V. (2022) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2022.864902 · Cited by 29
“Global signal regression is frequently used in GBC analyses to preprocess the fMRI global signal and remove residual motion artifacts and physiological noise (187).”
BackgroundMethodWang, X., Cao, Q., Wang, J., Wu, Z., Wang, P., Sun, L., Cai, T., & Wang, Y. (2016) · Behaviour Research and Therapy · doi:10.1016/j.brat.2015.11.003 · Cited by 21
Mäki-Marttunen, V., Castro, M., Olmos, L., Leiguarda, R., & Villarreal, M. (2016) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2016.01.022 · Cited by 13
Metadata
- Authors
- Murphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B., & Bandettini, P. A.
- Affiliations
- National Institutes of Health (US)
- Corresponding author
- Bandettini, Peter A.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2009-02
- Volume
- 44
- Issue
- 3
- Pages
- 893-905
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC2750906
- OpenAlex
- W2169787465
- Cited by
- 2,387
- References
- 64
- Citations by year
- 2026: 118 · 2025: 104 · 2024: 109 · 2023: 78 · 2022: 121 · 2021: 133 · 2020: 173 · 2019: 157 · 2018: 166 · 2017: 204 · 2016: 187 · 2015: 188 · 2014: 181 · 2013: 168 · 2012: 114
- FWCI
- 31.87
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institutes of Health
- National Institute of Mental Health (Z01MH002783)
- 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, default mode network, attentional networks, posterior cingulate cortex, phase difference, breath-holding, visual task, seed-based correlation analysis
- MeSH terms
- Biological Clocks, Brain, Computer Simulation, Humans, Magnetic Resonance Imaging, Male, Models, Neurological, Nerve Net, Regression Analysis, Rest, Statistics as Topic, Young Adult
About these checks
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.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
