The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
Anticorrelations in resting state networks without global signal regression
Open accessAbstract
Anticorrelated relationships in spontaneous signal fluctuation have been previously observed in resting-state functional magnetic resonance imaging (fMRI). In particular, it was proposed that there exists two systems in the brain that are intrinsically organized into anticorrelated networks, the default mode network, which usually exhibits task-related deactivations, and the task-positive network, which usually exhibits task-related activations during tasks that demands external attention. However, it is currently under debate whether the anticorrelations observed in resting state fMRI were valid or were instead artificially introduced by global signal regression, a common preprocessing technique to remove physiological and other noise in resting-state fMRI signal. We examined positive and negative correlations in resting-state connectivity using two different preprocessing methods: a component base noise reduction method (CompCor, Behzadi et al., 2007), in which principal components from noise regions-of-interest were removed, and the global signal regression method. Robust anticorrelations between a default mode network seed region in the medial prefrontal cortex and regions of the task-positive network were observed under both methods. Specificity of the anticorrelations was similar between the two methods. Specificity and sensitivity for positive correlations were higher under CompCor compared to the global regression method. Our results suggest that anticorrelations observed in resting-state connectivity are not an artifact introduced by global signal regression and might have biological origins, and that the CompCor method can be used to examine valid anticorrelations during rest.
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.
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
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
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
Conn : A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks
Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012) · Brain Connectivity · doi:10.1089/brain.2012.0073 · Cited by 4,885
A component based noise correction method (CompCor) for BOLD and perfusion based fMRI
Behzadi, Y., Restom, K., Liau, J., & Liu, T. T. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.04.042 · Cited by 4,502
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
Investigations into resting-state connectivity using independent component analysis
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1634 · Cited by 3,398
Murphy, K., Birn, R. M., Handwerker, D. A., Jones, T. B., & Bandettini, P. A. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2008.09.036 · Cited by 2,387
Cited by
Conn : A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks
Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012) · Brain Connectivity · doi:10.1089/brain.2012.0073 · Cited by 4,885
Satterthwaite, T. D., Elliott, M. A., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M. E., Eickhoff, S. B., Hakonarson, H., Gur, R. C., Gur, R. E., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.052 · Cited by 1,881
Fox, M. D., Buckner, R. L., White, M. P., Greicius, M. D., & Pascual-Leone, A. (2012) · Biological Psychiatry · doi:10.1016/j.biopsych.2012.04.028 · Cited by 1,270
The brain’s default network: updated anatomy, physiology and evolving insights
Buckner, R. L., & DiNicola, L. M. (2019) · Nature Reviews Neuroscience · doi:10.1038/s41583-019-0212-7 · Cited by 1,058
Towards a consensus regarding global signal regression for resting state functional connectivity MRI
Murphy, K., & Fox, M. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.052 · Cited by 1,048
Metadata
- Authors
- Chai, X. J., Castañón, A. N., Öngür, D., & Whitfield-Gabrieli, S.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-01
- Volume
- 59
- Issue
- 2
- Pages
- 1420-1428
- ISSN
- 1053-8119 (ISSN-L)
- OpenAlex
- W2082714165
- Cited by
- 1,144
- References
- 18
- FWCI
- 22.01
- Type
- journal article
- Language
- en
- Open access
- green
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Anticorrelations in resting state networks without global signal regression. https://app.cochat.ai/papers/10.1016/j.neuroimage.2011.08.048
- The paper
- Chai, X. J., Castañón, A. N., Öngür, D., & Whitfield-Gabrieli, S. (2012). Anticorrelations in resting state networks without global signal regression. NeuroImage. https://doi.org/10.1016/j.neuroimage.2011.08.048
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
