Cited by 380Functional Brain Connectivity StudiesAdvanced MRI Techniques and ApplicationsAdvanced Neuroimaging Techniques and Applications

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Global signal regression strengthens association between resting-state functional connectivity and behavior

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Li, Jingwei, Kong, Ru, Liégeois, Raphaël, Orban, Csaba, Tan, Yanrui, Sun, Nanbo, Holmes, Avram J., Sabuncu, Mert R., Ge, Tian, Yeo, B.T. Thomas·2019·NeuroImage

DOI 10.1016/j.neuroimage.2019.04.016PMC PMC6585462

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Abstract

Global signal regression (GSR) is one of the most debated preprocessing strategies for resting-state functional MRI. GSR effectively removes global artifacts driven by motion and respiration, but also discards globally distributed neural information and introduces negative correlations between certain brain regions. The vast majority of previous studies have focused on the effectiveness of GSR in removing imaging artifacts, as well as its potential biases. Given the growing interest in functional connectivity fingerprinting, here we considered the utilitarian question of whether GSR strengthens or weakens associations between resting-state functional connectivity (RSFC) and multiple behavioral measures across cognition, personality and emotion. By applying the variance component model to the Brain Genomics Superstruct Project (GSP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 47% across 23 behavioral measures after GSR. In the Human Connectome Project (HCP), we found that behavioral variance explained by whole-brain RSFC increased by an average of 40% across 58 behavioral measures, when GSR was applied after ICA-FIX de-noising. To ensure generalizability, we repeated our analyses using kernel regression. GSR improved behavioral prediction accuracies by an average of 64% and 12% in the GSP and HCP datasets respectively. Importantly, the results were consistent across methods. A behavioral measure with greater RSFC-explained variance (using the variance component model) also exhibited greater prediction accuracy (using kernel regression). A behavioral measure with greater improvement in behavioral variance explained after GSR (using the variance component model) also enjoyed greater improvement in prediction accuracy after GSR (using kernel regression). Furthermore, GSR appeared to benefit task performance measures more than self-reported measures. Since GSR was more effective at removing motion-related and respiratory-related artifacts, GSR-related increases in variance explained and prediction accuracies were unlikely the result of motion-related or respiratory-related artifacts. However, it is worth emphasizing that the current study focused on whole-brain RSFC, so it remains unclear whether GSR improves RSFC-behavioral associations for specific connections or networks. Overall, our results suggest that at least in the case for young healthy adults, GSR strengthens the associations between RSFC and most (although not all) behavioral measures. Code for the variance component model and ridge regression can be found here: https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/preprocessing/Li2019_GSR.

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Advances in functional and structural MR image analysis and implementation as FSL
Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E. J., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., et al. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.07.051 · Cited by 13,630
Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888
FSL
Jenkinson, M., Beckmann, C. F., Behrens, T. E. J., Woolrich, M. W., & Smith, S. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.09.015 · Cited by 11,314
Cortical Surface-Based Analysis
Dale, A. M., Fischl, B., & Sereno, M. I. (1999) · NeuroImage · doi:10.1006/nimg.1998.0395 · Cited by 10,910
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
FreeSurfer
Fischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.021 · Cited by 9,526
The organization of the human cerebral cortex estimated by intrinsic functional connectivity
Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182
Self-citation
Whole Brain Segmentation
Fischl, B., Salat, D. H., Busa, E., Albert, M., Dieterich, M., Haselgrove, C., van der Kouwe, A., Killiany, R., Kennedy, D., Klaveness, S., et al. (2002) · Neuron · doi:10.1016/s0896-6273(02)00569-x · Cited by 8,610
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
GCTA: a tool for genome-wide complex trait analysis
Yang, J., Lee, S. H., Goddard, M. E., & Visscher, P. M. (2011) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2010.11.011 · Cited by 8,279
Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion
Power, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429
Dissociable intrinsic connectivity networks for salience processing and executive control
Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., Reiss, A. L., & Greicius, M. D. (2007) · The Journal of Neuroscience · doi:10.1523/jneurosci.5587-06.2007 · Cited by 7,426
The organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304
Self-citation
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
Cortical Surface-Based Analysis
Fischl, B., Sereno, M. I., & Dale, A. M. (1999) · NeuroImage · doi:10.1006/nimg.1998.0396 · Cited by 6,147
The WU-Minn Human Connectome Project: An overview
Van Essen, D. C., Smith, S. M., Barch, D. M., Behrens, T. E. J., Yacoub, E., & Ugurbil, K. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.041 · Cited by 5,651
The minimal preprocessing pipelines for the Human Connectome Project
Glasser, M. F., Sotiropoulos, S. N., Wilson, J. A., Coalson, T. S., Fischl, B., Andersson, J. L., Xu, J., Jbabdi, S., Webster, M., Polimeni, J. R., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.127 · Cited by 5,253
Correspondence of the brain's functional architecture during activation and rest
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150
Methods to detect, characterize, and remove motion artifact in resting state fMRI
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.048 · Cited by 3,748
Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhx179 · Cited by 3,657
Self-citation
Accurate and robust brain image alignment using boundary-based registration
Greve, D. N., & Fischl, B. R. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.06.060 · Cited by 3,553
The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism
Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., Anderson, J. S., Assaf, M., Bookheimer, S. Y., Dapretto, M., et al. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.78 · Cited by 2,780
Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity
Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., & Constable, R. T. (2015) · Nature Neuroscience · doi:10.1038/nn.4135 · Cited by 2,747
The Human Connectome Project: A data acquisition perspective
Essen, D. C. V., Uǧurbil, K., Auerbach, E. J., Barch, D. M., Behrens, T. E. J., Bucholz, R. D., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.018 · Cited by 2,638
The impact of global signal regression on resting state correlations: are anti-correlated networks introduced?
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
The influence of head motion on intrinsic functional connectivity MRI
Van Dijk, K. R. A., Sabuncu, M. R., & Buckner, R. L. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.07.044 · Cited by 2,344
Self-citation
A hybrid approach to the skull stripping problem in MRI
Ségonne, F., Dale, A. M., Busa, E., Glessner, M., Salat, D. H., Hahn, H., & Fischl, B. R. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.03.032 · Cited by 2,227
The Jackknife and Bootstrap
Shao, J., & Tu, D. (1995) · Springer Series in Statistics · doi:10.1007/978-1-4612-0795-5 · Cited by 1,985
An improved framework for confound regression and filtering for control of motion artifact in the preprocessing of resting-state functional connectivity data
Satterthwaite, T. D., Elliott, M. A., Gerraty, R. T., Ruparel, K., Loughead, J., Calkins, M. E., Eickhoff, S. B., Hakonarson, H. H., Gur, R. C., Gur, R. E., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.08.052 · Cited by 1,982
The global signal and observed anticorrelated resting state brain networks
Fox, M. D., Zhang, D., Snyder, A. Z., & Raichle, M. E. (2009) · Journal of Neurophysiology · doi:10.1152/jn.90777.2008 · Cited by 1,877
Automatic denoising of functional MRI data: Combining independent component analysis and hierarchical fusion of classifiers
Salimi-Khorshidi, G., Douaud, G., Beckmann, C. F., Glasser, M. F., Griffanti, L., & Smith, S. M. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.11.046 · Cited by 1,770
Automated manifold surgery: constructing geometrically accurate and topologically correct models of the human cerebral cortex
Fischl, B. R., Liu, A., & Dale, A. M. (2001) · IEEE Transactions on Medical Imaging · doi:10.1109/42.906426 · Cited by 1,755
Function in the human connectome: task-fMRI and individual differences in behavior
Barch, D. M., Burgess, G. C., Harms, M. P., Petersen, S. E., Schlaggar, B. L., Corbetta, M., Glasser, M. F., Curtiss, S., Dixit, S., Feldt, C., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.033 · Cited by 1,529
Resting-state fMRI in the human connectome project
Smith, S. M., Beckmann, C. F., Andersson, J., Auerbach, E. J., Bijsterbosch, J., Douaud, G., Duff, E., Feinberg, D. A., Griffanti, L., Harms, M. P., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.039 · Cited by 1,502
Generation and evaluation of a cortical area parcellation from resting-state correlations
Gordon, E. M., Laumann, T. O., Adeyemo, B., Huckins, J. F., Kelley, W. M., & Petersen, S. E. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhu239 · Cited by 1,463
A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics
Yan, C.-G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R. C., Di Martino, A., Li, Q., Zuo, X.-N., Castellanos, F. X., & Milham, M. P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.004 · Cited by 1,461
Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI
Birn, R. M., Diamond, J. B., Smith, M. A., & Bandettini, P. A. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.02.048 · Cited by 1,381
ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging
Griffanti, L., Salimi‐Khorshidi, G., Beckmann, C. F., Auerbach, E. J., Douaud, G., Sexton, C. E., Zsoldos, E., Ebmeier, K. P., Filippini, N., Mackay, C. E., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.034 · Cited by 1,273
Anticorrelations in resting state networks without global signal regression
Chai, X. J., Nieto-Castañón, A., Öngür, D., & Whitfield, S. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.08.048 · Cited by 1,193
Individual Variability in Functional Connectivity Architecture of the Human Brain
Mueller, S., Wang, D., Fox, M., Yeo, B. T. T., Sepulcre, J., Sabuncu, M. R., Shafee, R., Lu, J., & Liu, H. (2013) · Neuron · doi:10.1016/j.neuron.2012.12.028 · Cited by 1,168
Self-citation
Impact of in-scanner head motion on multiple measures of functional connectivity: relevance for studies of neurodevelopment in youth
Satterthwaite, T. D., Wolf, D. H., Loughead, J., Ruparel, K., Elliott, M. A., Hakonarson, H., Gur, R. C., & Gur, R. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.063 · Cited by 1,125
A neuromarker of sustained attention from whole-brain functional connectivity
Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., & Chun, M. M. (2015) · Nature Neuroscience · doi:10.1038/nn.4179 · Cited by 1,051
Towards a consensus regarding global signal regression for resting state functional connectivity MRI
Murphy, K., & Fox, M. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.052 · Cited by 1,044
Benchmarking of participant-level confound regression strategies for the control of motion artifact in studies of functional connectivity
Ciric, R., Wolf, D. H., Power, J. D., Roalf, D. R., Baum, G. L., Ruparel, K., Shinohara, R. T., Elliott, M. A., Eickhoff, S. B., Davatzikos, C., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.020 · Cited by 1,042
Geometrically Accurate Topology-Correction of Cortical Surfaces Using Nonseparating Loops
Ségonne, F., Pacheco, J., & Fischl, B. R. (2007) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2006.887364 · Cited by 995
Neural basis of global resting-state fMRI activity
Schölvinck, M. L., Maier, A., Ye, F. Q., Duyn, J. H., & Leopold, D. A. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0913110107 · Cited by 939
Opportunities and limitations of intrinsic functional connectivity MRI
Buckner, R. L., Krienen, F. M., & Yeo, B. T. T. (2013) · Nature Neuroscience · doi:10.1038/nn.3423 · Cited by 912
Self-citation
Functional system and areal organization of a highly sampled individual human brain
Laumann, T. O., Gordon, E. M., Adeyemo, B., Snyder, A. Z., Joo, S. J., Chen, M.-Y., Gilmore, A. W., McDermott, K. B., Nelson, S. M., Dosenbach, N. U. F., et al. (2015) · Neuron · doi:10.1016/j.neuron.2015.06.037 · Cited by 911
A positive-negative mode of population covariation links brain connectivity, demographics and behavior
Smith, S. M., Nichols, T. E., Vidaurre, D., Winkler, A. M., Behrens, T. E. J., Glasser, M. F., Ugurbil, K., Barch, D. M., Van Essen, D. C., & Miller, K. L. (2015) · Nature Neuroscience · doi:10.1038/nn.4125 · Cited by 859
Trouble at rest: how correlation patterns and group differences become distorted after global signal regression
Saad, Z. S., Gotts, S. J., Murphy, K., Chen, G., Jo, H. J., Martin, A., & Cox, R. W. (2012) · Brain Connectivity · doi:10.1089/brain.2012.0080 · Cited by 859
Assessing and tuning brain decoders: Cross-validation, caveats, and guidelines
Varoquaux, G. P., Raamana, P. R., Engemann, D. A., Hoyos-Idrobo, A., Schwartz, Y., & Thirion, B. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.038 · Cited by 848
Genome-wide association studies of brain imaging phenotypes in UK Biobank
Elliott, L. T., Sharp, K., Alfaro-Almagro, F., Shi, S., Miller, K. L., Douaud, G., Marchini, J., & Smith, S. M. (2018) · Nature · doi:10.1038/s41586-018-0571-7 · Cited by 834
An evaluation of the efficacy, reliability, and sensitivity of motion correction strategies for resting-state functional MRI
Parkes, L., Fulcher, B., Yücel, M., & Fornito, A. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.073 · Cited by 830
Correlations and anticorrelations in resting-state functional connectivity MRI: A quantitative comparison of preprocessing strategies
Weissenbacher, A., Kasess, C., Gerstl, F., Lanzenberger, R., Moser, E., & Windischberger, C. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.05.005 · Cited by 776
The nuisance of nuisance regression: spectral misspecification in a common approach to resting-state fMRI preprocessing reintroduces noise and obscures functional connectivity
Hallquist, M. N., Hwang, K., & Luna, B. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.116 · Cited by 753
Developmental and oncogenic programs in H3K27M gliomas dissected by single-cell RNA-seq
Filbin, M. G., Tirosh, I., Hovestadt, V., Shaw, M. L., Escalante, L. E., Mathewson, N. D., Neftel, C., Frank, N., Pelton, K., Hebert, C. M., et al. (2018) · Science · doi:10.1126/science.aao4750 · Cited by 694
Resting-state fMRI confounds and cleanup
Murphy, K., Birn, R. M., & Bandettini, P. A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.001 · Cited by 666
Building a Science of Individual Differences from fMRI
Dubois, J., & Adolphs, R. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.03.014 · Cited by 644
Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion
Kong, R., Li, J., Orbán, C., Sabuncu, M. R., Liu, H., Schaefer, A., Sun, N., Zuo, X., Holmes, A. J., Eickhoff, S. B., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy123 · Cited by 615
Self-citation
Identifying the brain's most globally connected regions
Cole, M. W., Pathak, S., & Schneider, W. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.11.001 · Cited by 572
The modular and integrative functional architecture of the human brain
Bertolero, M. A., Yeo, B. T. T., & D’Esposito, M. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1510619112 · Cited by 572
Self-citation
Sources and implications of whole-brain fMRI signals in humans
Power, J. D., Plitt, M. H., Laumann, T. O., & Martin, A. D. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.038 · Cited by 534
The ADHD-200 Consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience
Milham, M. P., Fair, D. A., Mennes, M., & Mostofsky, S. H. (2012) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00062 · Cited by 514
Imaging-based parcellations of the human brain
Eickhoff, S. B., Yeo, B. T. T., & Genon, S. (2018) · Nature reviews. Neuroscience · doi:10.1038/s41583-018-0071-7 · Cited by 484
Self-citation
Functional Specialization and Flexibility in Human Association Cortex
Yeo, B. T. T., Krienen, F. M., Eickhoff, S. B., Yaakub, S. N., Fox, P. T., Buckner, R. L., Asplund, C. L., & Chee, M. W. L. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhu217 · Cited by 448
Self-citation
Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures
Holmes, A. J., Hollinshead, M. O., O’Keefe, T. M., Petrov, V. I., Fariello, G., Wald, L. L., Fischl, B. R., Rosen, B. R., Mair, R. W., Roffman, J. L., et al. (2015) · Scientific Data · doi:10.1038/sdata.2015.31 · Cited by 443
Self-citation
A phenome-wide examination of neural and cognitive function
Poldrack, R. A., Congdon, E. L., Triplett, W. T., Gorgolewski, K. J., Karlsgodt, K. H., Mumford, J. A., Sabb, F. W., Freimer, N. B., London, E. D., Cannon, T. D., et al. (2016) · Scientific Data · doi:10.1038/sdata.2016.110 · Cited by 421
The global signal in fMRI: Nuisance or Information?
Liu, T. T., Nalci, A., & Falahpour, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.036 · Cited by 405
Identification of reproducible individualized targets for treatment of depression with TMS based on intrinsic connectivity
Fox, M., Liu, H., & Pascual‐Leone, Á. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.10.082 · Cited by 395
Local-Global Parcellation of the Human Cerebral Cortex From Intrinsic Functional Connectivity MRIPreprint
Schaefer, A., Kong, R., Gordon, E. M., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/135632 · Cited by 379
Self-citation
Altered global brain signal in schizophrenia
Yang, G., Murray, J. D., Repovš, G., Cole, M. W., Savić, A., Glasser, M. F., Pittenger, C., Krystal, J. H., Wang, X., Pearlson, G. D., et al. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1405289111 · Cited by 372
Functional connectivity between task-positive and task-negative brain areas and its relation to working memory performance
Hampson, M., Driesen, N. R., Roth, J. K., Gore, J. C., & Constable, R. T. (2010) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2010.03.021 · Cited by 363
A wavelet method for modeling and despiking motion artifacts from resting-state fMRI time series
Patel, A. X., Kundu, P., Rubinov, M., Jones, P. S., Vértes, P. E., Ersche, K. D., John Suckling, A., & Bullmore, E. T. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.03.012 · Cited by 354
Tracking brain arousal fluctuations with fMRI
Chang, C., Leopold, D. A., Schölvinck, M. L., Mandelkow, H., Picchioni, D., Liu, X., Ye, F. Q., Turchi, J. N., & Duyn, J. H. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1520613113 · Cited by 306
The amplitude of the resting-state fMRI global signal is related to EEG vigilance measures
Wong, C. W., Olafsson, V. T., Tal, O., & Liu, T. T. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.057 · Cited by 291
Effective Preprocessing Procedures Virtually Eliminate Distance-Dependent Motion Artifacts in Resting State FMRI
Jo, H. J., Gotts, S. J., Reynolds, R. C., Bandettini, P. A., Martin, A. D., Cox, R. W., & Saad, Z. S. (2013) · Journal of Applied Mathematics · doi:10.1155/2013/935154 · Cited by 277
Functional connectivity during rested wakefulness predicts vulnerability to sleep deprivation
Yeo, B. T. T., Tandi, J., & Chee, M. W. L. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.018 · Cited by 272
Self-citation
Ridding fMRI data of motion-related influences: Removal of signals with distinct spatial and physical bases in multiecho data
Power, J. D., Plitt, M. H., Gotts, S. J., Kundu, P., Voon, V., Bandettini, P. A., & Martin, A. D. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1720985115 · Cited by 261
Using temporal ICA to selectively remove global noise while preserving global signal in functional MRI data
Glasser, M. F., Coalson, T. S., Bijsterbosch, J. D., Harrison, S. J., Harms, M. P., Anticevic, A., Essen, D. C. V., & Smith, S. M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.04.076 · Cited by 260
The perils of global signal regression for group comparisons: a case study of Autism Spectrum Disorders
Gotts, S. J., Saad, Z. S., Jo, H. J., Wallace, G. L., Cox, R. W., & Martin, A. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00356 · Cited by 255
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Hesterberg, T., Shao, J., & Tu, D. (1997) · Technometrics · doi:10.2307/1271515 · Cited by 149
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Authors: Li, J., Kong, R., Liégeois, R., Orban, C., Tan, Y., Sun, N., Holmes, A. J., Sabuncu, M. R., Ge, T., & Yeo, B. T. T.
Affiliations: National University of Singapore (SG)
Yale University (US)
Cornell University (US)
Massachusetts General Hospital (US)
Corresponding author: Yeo, B.T. Thomas
ORCID: Li, Jingwei
Kong, Ru
Liégeois, Raphaël
Sun, Nanbo
Holmes, Avram J.
Sabuncu, Mert R.
Ge, Tian
Yeo, B.T. Thomas
Venue: NeuroImage
Publisher: Elsevier BV
Published: 2019-08
Volume: 196
Pages: 126-141
ISSN: 1053-8119 (ISSN-L)
DOI: 10.1016/j.neuroimage.2019.04.016
PMC: PMC6585462
OpenAlex: W2953136735
Cited by: 380
References: 108
Citations by year: 2026: 44 · 2025: 59 · 2024: 55 · 2023: 48 · 2022: 54 · 2021: 52 · 2020: 47 · 2019: 19
FWCI: 24.82
Type: journal article
Language: en
Open access: bronze
doi.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
www.elsevier.com/open-access/userlicense/1.0/ (am, 256 d)
Locations: NeuroImage (acceptedVersion)
bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Medical Research Council (CBRG/0088/2015)
National University of Singapore (R185000271720, DPRT/944/09/14)
NIH Blueprint for Neuroscience Research
Harvard University
Massachusetts General Hospital
National Research Foundation Singapore
Ministry of Education - Singapore (MOE2014-T2-2-016)
McDonnell Center for Systems Neuroscience
National Institutes of Health (K99AG054573)
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, behavior prediction, variance component models, kernel regression, whole-brain connectivity, Human Connectome Project
MeSH terms: Adolescent, Adult, Brain, Brain Mapping, Cognition, Emotions, Female, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Male, Neural Pathways, Personality, Signal Processing, Computer-Assisted, Artifacts, Young Adult

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Global signal regression strengthens association between resting-state functional connectivity and behavior

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