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

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Self-citation
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Self-citation
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Vachha, B. A., Kumar, V. A., Pillai, J. J., Shimony, J. S., Tanabe, J., & Sair, H. I. (2024) · American Journal of Roentgenology · doi:10.2214/ajr.24.32163 · Cited by 12
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Golestani, A., & Chen, J. J. (2024) · Frontiers in Neuroscience · doi:10.3389/fnins.2024.1223230 · Cited by 11
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Hb A1c: a review on its structure, biosynthesis, clinical significance and methods of assay.
Garel, M. C., Blouquit, Y., Molko, F., & Rosa, J. (1979) · PubMed · Cited by 11
Sex differences of signal complexity at resting-state functional magnetic resonance imaging and their associations with the estrogen-signaling pathway in the brain
Zhao, C., Hou, W., Jia, Y., Sahakian, B. J., & Luo, Q. (2023) · Cognitive Neurodynamics · doi:10.1007/s11571-023-09954-y · Cited by 10
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Khosravani, S., Palm, S. T., Drew, W., Frandsen, S. B., Lin, C., Tirrell, E., Hindley, L. E., Schineller, M. E., Garimella, A. H., Chiulli, N., et al. (2025) · Molecular Psychiatry · doi:10.1038/s41380-025-03153-3 · Cited by 10
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Developmental coupling of brain iron and intrinsic activity in infants during the first 150 days
Ji, L., Yoon, Y. B., Hendrix, C. L., Kennelly, E., Majbri, A., Bhatia, T., Taylor, A., & Thomason, M. E. (2023) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2023.101326 · Cited by 10
Distinct brain network features predict internalizing and externalizing traits in children, adolescents and adults
Qu, Y. L., Chen, J., Tam, A., Ooi, L. Q. R., Dhamala, E., Cocuzza, C. V., Zhang, S., Zeng, T., Lawhead, C., Yeo, B. T. T., et al. (2025) · Nature Mental Health · doi:10.1038/s44220-025-00388-5 · Cited by 10
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Garzón, B., Lövdén, M., de Boer, L., Axelsson, J., Riklund, K., Bäckman, L., Nyberg, L. H., & Guitart‐Masip, M. (2021) · Brain Structure and Function · doi:10.1007/s00429-020-02205-4 · Cited by 10
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Dipasquale, O., Cohen, A. D., Martins, D., Zelaya, F. O., Turkheimer, F., Veronese, M., Mehta, M. A., Williams, S., Yang, B., Banerjee, S., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-38573-0 · Cited by 10
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Ma, X., Li, J., Yang, Y., Qiu, X., Sheng, J., Han, N., Wu, C., Xu, G., Jiang, G., Tian, J., et al. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120762 · Cited by 9
Self-citation
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He, Z., Li, W., Liu, Y., Liu, X., Han, J., Zhang, T., & Yuan, Y. (2024) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2024.3506734 · Cited by 12
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Klein, S. D., Collins, P. F., Wun, V. L., Grund, P. M., & Luciana, M. M. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.1233-23.2025 · Cited by 12
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Lou, J., Liu, K., Wen, J., He, Y., Sun, Y., Tian, X., Hu, K., Deng, Y., Liu, B., & Wen, G. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115551 · Cited by 9

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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