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Global signal regression strengthens association between resting-state functional connectivity and behavior
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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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“We utilized NBS to localize edge-level group differences in both positive and negative connectivity using two sample t tests(P<0.001), controlling for age, gender, and scanner(58).”
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Cites
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
Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002) · NeuroImage · doi:10.1006/nimg.2002.1132 · Cited by 11,888
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
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-citationFischl, 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
Flagged first, then by citations
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Items 301–369 of 369
Pavlovich, K., Pang, J. C. S., & Fornito, A. (2025) · Network Neuroscience · doi:10.1162/netn.a.534 · Cited by 1
Global cortical arousal effects in fMRI reveal brain markers of state and trait anxiety
Kundert-Obando, K., Lee, T., Martin, C. G., Kaur, K., Lagándara, J. G., Li, Y., Harding, J. M., Wang, S.-Y., Song, R., Yang, R., et al. (2026) · Cerebral Cortex · doi:10.1093/cercor/bhag008
Li, Y., Zhou, Z., Li, Q., Li, T., Julian, I. N., Guo, H., & Chen, J. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.889105 · Cited by 1
Gao, J., Zhao, L., Zhong, T., Li, C., He, Z., Wei, Y., Zhang, S., Guo, L., Liu, T., Han, J., et al. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43895-0_27 · Cited by 1
The fluctuating brain—essentials of neural variability
Northoff, G. (2024) · Elsevier eBooks · doi:10.1016/b978-0-12-821935-5.00001-6 · Cited by 1
Wang, S. R., Li, T., He, H., & Li, Y. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae263 · Cited by 1
Hirsch, F., Bumanglag, Â., Zhang, Y., & Wohlschlaeger, A. M. (2024) · NeuroImage Clinical · doi:10.1016/j.nicl.2024.103657 · Cited by 2
Wang, Y., Chen, Z., Cai, Z., Ao, W., Li, Q., Xu, M., & Zhou, S. (2025) · Brain and Behavior · doi:10.1002/brb3.70386 · Cited by 1
Wang, X., & Li, L. (2021) · Frontiers in Genetics · doi:10.3389/fgene.2021.728913 · Cited by 1
Genon, S., & Li, J. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91688-2.00001-1 · Cited by 1
Self-citationResting state fMRI connectivity mapping across species: Challenges and opportunities
Pagani, M., Gutierrez‐Barragan, D., de Guzman, A. E., & Gozzi, A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91688-2.00009-6 · Cited by 1
Brain network organization as the computational architecture of cognitionPreprint
Ito, T., Hearne, L. J., Mill, R. D., Cocuzza, C. V., & Cole, M. W. (2019) · arXiv (Cornell University) · Cited by 1
Reply: Looking beyond indirect lesion network mapping of prosopagnosia: direct measures required
Cohen, A. L., & Fox, M. (2021) · Brain · doi:10.1093/brain/awab277 · Cited by 1
Sex differences in the functional network underpinnings of psychotic-like experiences in childrenPreprint
Dhamala, E., Chopra, S., Ooi, L. Q. R., Rubio, J. M., Yeo, B. T. T., Malhotra, A. K., & Holmes, A. J. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.04.22.590660 · Cited by 2
Self-citationKorponay, C. H., Cohen‐Gilbert, J. E., Kumar, P., Harnett, N. G., Medina, A., Cheng, Y., Forester, B. P., Ressler, K. J., Demsar, J., Frederick, B. B., et al. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.11.659091 · Cited by 1
Bolton, T. A. W., Schöttner, M., Patel, J. H., Fluhr, H., Alemán‐Gómez, Y., & Hagmann, P. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.20.671319 · Cited by 1
A new fMRI quality metric using multi-echo information: Theory, validation and implicationsPreprint
González-Castillo, J., Caballero‐Gaudes, C., Handwerker, D. A., & Bandettini, P. A. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.03.19.712948 · Cited by 1
Pavlović, D. M., Guillaume, B., Afyouni, S., & Nichols, T. E. (2020) · Statistica Neerlandica · doi:10.1111/stan.12219
Moutoussis, M., Garzón, B., Neufeld, S., Bach, D. R., Rigoli, F., Guitart‐Masip, M., & Dolan, R. J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.20.259697
Jäncke, L. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.735830092.793560898
Schaper, F. (. L. W. V. J. ). (2022) · doi:10.26481/dis.20221103fs
A test of time-resolved functional MRI with sub-second event durationsPreprint
Wong, A. P. H., Wu, E., Rogers, B. P., & Asplund, C. L. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.30.550770
Parsing Brain Network Specialization: A Replication and Expansion of Wang et al. (2014)Preprint
Peterson, M., Floris, D. L., & Nielsen, J. A. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.13.580153
Hou, J., King, T. Z., Chen, H., Wang, Q., Xie, Y., Mao, H. Z., Wang, L., & Cheng, L. (2024) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2024.110937
Huang, K. (2024) · Applied and Computational Engineering · doi:10.54254/2755-2721/57/20241313
Li, X., Wei, W., Qian, L., Li, X., Li, M., Kakkos, I., Wang, Q., Yu, H., Guo, W., Ma, X., et al. (2025) · Brain Research Bulletin · doi:10.1016/j.brainresbull.2025.111238
eLife Assessment: Heritability of movie-evoked brain activity and connectivity
Sprooten, E. (2025) · eLife · doi:10.7554/elife.106081.1.sa3
Lu, F., Li, L., Wang, J., Chen, X., Yang, H., Xiaoli Li, A., Yao, L., & Liang, Z. (2025) · Frontiers in Neuroscience · doi:10.3389/fnins.2025.1576535
Blakstad, E., Sneve, M. H., Dídac Vidal-piñeiro, A., Walhovd, K. B., Fjell, A. M., & Grydeland, H. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1312
Multimodal neuroimaging data boosts the prediction of multifaceted cognitionPreprint
Wu, J., Li, J., Jung, K., Eickhoff, S. B., Yeo, B. T. T., & Genon, S. (2025) · Research Square · doi:10.21203/rs.3.rs-7721822/v1
Self-citationWang, Q., Liu, B., Sun, Y., Tian, X., Du, L., & Fan, L. (2025) · International Conference on Bioinformatics and Biomedical Technology (ICBBT) · doi:10.1109/icbbt65815.2025.11276717
Schulz, M., Malherbe, C., Cheng, B., Thomalla, G., & Schlemm, E. (2021) · Figshare · doi:10.6084/m9.figshare.14538908
Additional file 1 of Reduced lateralization of multiple functional brain networks in autistic males
Peterson, M., Prigge, M. B. D., Floris, D. L., Bigler, E. D., Zielinski, B. A., King, J. B., LANGE, N. D., Alexander, A. L., Lainhart, J. E., & Nielsen, J. A. (2024) · Figshare · doi:10.6084/m9.figshare.25779401
Brain resilience to targeted attack of resting BOLD networks as a measure of cognitive reserve
Argiris, G., Stern, Y., & Habeck, C. G. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.1065
Ai, M., Wu, K., Song, M., Huo, B., Wu, Z., Jia, H., & Ning, Y. (2026) · Sleep Medicine · doi:10.1016/j.sleep.2026.108769
Transfer learning from 2D natural images to 4D fMRI brain images via geometric mapping
Gao, K., Wang, L., Li, L., Chen, X., Li, B., Wang, Y.-W., Li, X.-Y., Wang, Z.-H., Li, H.-X., Liao, Y., et al. (2026) · Medical Image Analysis · doi:10.1016/j.media.2026.103949
Yang, C., Liu, Y., Liu, X., Wu, D., Cheng, Y., & Yin, H. (2026) · International Journal of Psychophysiology · doi:10.1016/j.ijpsycho.2026.113331
Untamed: Unconstrained Tensor Decomposition and Graph Node Embedding for Cortical Parcellation
Liu, Y., Li, J., Wisnowski, J. L., & Leahy, R. M. (2026) · Human Brain Mapping · doi:10.1002/hbm.70483
Self-citationChen, X., Xu, P., Ye, S., Zhao, H., Li, Y., Ye, X., Zhang, S., Gui, Y., Wu, R., Jiang, C., et al. (2026) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2026.03.015
Ye, X., Ye, S., Wei, S., Chen, X., Zhou, Z., Zhao, H., Zhang, S., Li, Y., Xu, P., Wu, R. Y., et al. (2026) · npj Mental Health Research · doi:10.1038/s44184-026-00206-5
Ji, J., Liu, H. R., Cheng, S., Li, Y., Huang, X., Yao, Q., Yu, H., Liu, Y., Zhang, X.-B., & Sun, H. (2026) · BMC Psychiatry · doi:10.1186/s12888-026-08115-9
Investigating the impact of stressful life events on neuroanatomy across adolescencePreprint
Wiersch, L., Brosch, K., Christensen, E., & Dhamala, E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.06.723376
Li, F., Bo, Q., Chen, Z., Wang, C., & Zhang, Z. (2026) · BMC Psychiatry · doi:10.1186/s12888-026-08267-8
Reward network state dynamics track ASD symptom severity but not DiagnosisPreprint
Bressgott, J., & Arató, J. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.12.731591
Li, Y., Li, L., Jia, X., Xu, L., & Liang, P. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1734858
Jarusek, J., Uberti, M., Bhattarai, A., Peng, D., Bade, A. N., & Liu, Y. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1815092
Local brain connectome parameters across the spectrum of clinical cognitive decline
Dal, D. Y., Kilic, B. A., & Gamgam, G. (2026) · Frontiers in Neuroscience · doi:10.3389/fnins.2026.1840382
Infra-slow brain fluctuations: A taxonomy of mechanistic hypotheses
Mäki-Marttunen, V. (2026) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2026.106825
Layer-specific wide-field calcium imaging of neocortical activityPreprint
Lorenzo, D. A., Gallero-Salas, Y., Panzeri, M., Wahl, A.-S., Gilad, A., Lewis, C., & Helmchen, F. (2026) · eLife · doi:10.7554/elife.109521
Layer-specific wide-field calcium imaging of neocortical activityPreprint
Lorenzo, D. A., Gallero-Salas, Y., Panzeri, M., Wahl, A.-S., Gilad, A., Lewis, C., & Helmchen, F. (2026) · eLife · doi:10.7554/elife.109521.1
Aberrant large- and mesoscale network segregation and integration in bulimia nervosa
Zhang, P., Tang, L., Li, W., Wang, M., Jia, Y., Yu, F., Li, Z., Wang, Z., Wang, J., Wu, G., et al. (2026) · Journal of Eating Disorders · doi:10.1186/s40337-026-01671-1
He, H., Fang, Y., Jiang, J. C., Lu, D., Ji, L., Liu, B., Jiang, Y., Cao, J., Mei, B., & Zhang, J. (2026) · Brain Sciences · doi:10.3390/brainsci16070695
Characteristic Analysis of the Resting-State fMRI Global Signal in Schizophrenia
Yang, Z., Chang, X., Chen, J., Pei, H., Liao, J., Luo, X., He, H., Duan, M., Rodríguez-Labrada, R., Jiang, S., et al. (2026) · International Journal of Neural Systems · doi:10.1142/s0129065727500080
Author response: Heritability of movie-evoked brain activity and connectivity
Gruskin, D. C., Vieira, D. J., Lee, J. K., & Patel, G. H. (2026) · eLife · doi:10.7554/elife.106081.2.sa0
Wu, J., Li, J., Eickhoff, S. B., Yeo, B. T. T., & Genon, S. (2026) · Research Square · doi:10.21203/rs.3.rs-10604176/v1
Self-citationResting state brain activity and association with transfer of cognitive training gains
Faraza, S., Dyrba, M., Wolf, D., Fischer, F. U., Knaepen, K., Riedel, D., Kollmann, B., Binder, H., Mierau, A., Fellgiebel, A., et al. (2026) · Neuroimage Reports · doi:10.1016/j.ynirp.2026.100389
Gillig, A., Jobard, G., Cremona, S., & Joliot, M. (2026) · Network Neuroscience · doi:10.1162/netn.a.600
Jing, Y., Jing, L., Chen, X., Xu, J., li, D., Li, R., Li, X., Liu, J., Zhang, X., Zhou, J., et al. (2026) · Translational Psychiatry · doi:10.1038/s41398-026-04304-y
Sun, X., Lin, Y., Zhang, J., Fang, W., Chen, Z., Cao, P., & Zheng, Y. (2026) · Diagnostics · doi:10.3390/diagnostics16162546
Zhang, Z., Chen, Z., Wang, Y., Zhao, L., Feng, L., Xing, Y., Li, T., Bo, Q., & Wang, C. (2026) · CNS Neuroscience & Therapeutics · doi:10.1002/cns.71083
Zheng, F., Zhang, S., Tang, X., & Li, G. (2026) · Brain Sciences · doi:10.3390/brainsci16080884
Sihn, D., Kim, J., & Kim, S.-P. (2026) · Neuroscience · doi:10.1016/j.neuroscience.2026.08.048
Holmes, A., Pavlovich, K., Qian, X., Zhou, J. H., Chong, Y. S., Tan, A. P., Meaney, M. J., Eriksson, J. G., Margulies, D. S., Pang, J. C. S., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.09.11.750874
Self-citationMasaoka, K., Li, R., Kasagi, A., & Matsui, T. (2026) · Neuroscience Research · doi:10.1016/j.neures.2026.105127
Excessive censoring degrades individual-specific cortical parcellations and personalized TMS targets
Tan, T. W. K., Kong, R., Xue, A., Cheng, J., Burgher, B., Cocchi, L., Siddiqi, S., Nichols, T. E., Mejia, A. F., Sartorius, N., et al. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1422
Self-citationConnectome-wide dysconnectivity signature tracks clinical status in major depressive disorder
Gruber, M., Libedinsky, I., Ahrens, K. F., Rehm, P., Staub, H., Fischer, S., Timm, F., Thanarajah, S. E., Flinkenflügel, K., Borgers, T., et al. (2026) · Nature Mental Health · doi:10.1038/s44220-026-00747-w
Metadata
- 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
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2019-08
- Volume
- 196
- Pages
- 126-141
- ISSN
- 1053-8119 (ISSN-L)
- 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
- 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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