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

The DOI matches its Crossref record; 108 of the 108 references it cites were found in a registry or an index.

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

Open access

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

Published version of a preprint

Open this paper in CoChat — free account

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.

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

Crossref · 10.1016/j.neuroimage.2019.04.016

Cites

108 of 108 references verified0 citation errors · 0 not checkable

Statistics

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

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

Flagged first, then by citations

Show all 108 references

Sorted by citations

Items 201–300 of 369

The efficacy of resting-state fMRI denoising pipelines for motion correction and behavioural prediction
Pavlovich, K., Pang, J. C. S., Holmes, A., Constable, T., & Fornito, A. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.97 · Cited by 9
Self-citation
Cross‐Dataset Evaluation of Dementia Longitudinal Progression Prediction Models
Zhang, C., An, L., Wulan, N., Nguyen, K., Orbán, C., Chen, P., Chen, C., Zhou, J. H., Liu, K., Yeo, B. T. T., et al. (2025) · Human Brain Mapping · doi:10.1002/hbm.70280 · Cited by 9
Self-citation
Hypothalamic connectivities predict individual differences in ADT-elicited changes in working memory and quality of life in prostate cancer patients
Chaudhary, S., Roy, A. M., Summers, C., Zhornitsky, S., Ahles, T. A., Li, C.-S. R., & Chao, H. H. (2022) · Scientific Reports · doi:10.1038/s41598-022-13361-4 · Cited by 9
Reconciling Dimensional and Categorical Models of Autism Heterogeneity: a Brain Connectomics & Behavioral StudyPreprint
Tang, S., Sun, N., Floris, D. L., Zhang, X., Martino, A. D., & Yeo, B. T. T. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/692772 · Cited by 9
Self-citation
Functional dysconnectivity of visual and somatomotor networks yields a simple and robust biomarker for psychosis
Keane, B. P., Abrham, Y. T., Cole, M. W., Johnson, B. A., Hu, B., & Cocuzza, C. V. (2024) · Molecular Psychiatry · doi:10.1038/s41380-024-02767-3 · Cited by 10
Cortico-hippocampal interactions underlie schema-supported memory encoding in older adults
Huang, S., Bogdan, P. C., Howard, C. M., Gillette, K. D., Deng, L., Welch, E., McAllister, M. L., Giovanello, K. S., Davis, S. W., & Cabeza, R. E. L. (2025) · Cerebral Cortex · doi:10.1093/cercor/bhaf211 · Cited by 8
Abnormal Static and Dynamic Local Functional Connectivity in First-Episode Schizophrenia: A Resting-State fMRI Study
Zhou, J., Jiao, X., Hu, Q., Du, L., Wang, J., & Sun, J. (2024) · IEEE Transactions on Neural Systems and Rehabilitation Engineering · doi:10.1109/tnsre.2024.3368697 · Cited by 8
The functional and structural connectomes associated with geriatric depression and anxiety symptoms in mild cognitive impairment: Cross-syndrome overlap and generalization
Yu, J., Rawtaer, I., Feng, L., Kua, E. H., & Mahendran, R. (2021) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2021.110329 · Cited by 8
Functional Connectivity and Compensation of Phonemic Fluency in Aging
Mohanty, R., Gonzalez‐Burgos, L., Díaz‐Flores, L., Muehlboeck, J., Barroso, J., Ferreira, D., & Westman, E. (2021) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2021.644611 · Cited by 8
Beyond BOLD: Evidence for diffusion fMRI contrast in the human brain distinct from neurovascular responsePreprint
Olszowy, W., Diao, Y., & Jelescu, I. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.05.16.444253 · Cited by 8
Preoperative brain connectome predicts postoperative changes in processing speed in moyamoya disease
Gao, M., Lam, C. L. M., Lui, W., Lau, K. K., & Lee, T. M. C. (2022) · Brain Communications · doi:10.1093/braincomms/fcac213 · Cited by 7
Stable individual differences from dynamic patterns of function: brain network flexibility predicts openness/intellect, intelligence, and psychoticism
Sassenberg, T. A., Safron, A., & DeYoung, C. G. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhae391 · Cited by 8
Hypothalamic resting-state functional connectivity and self-evaluated aggression in young adults
Yao, Y. J., Chen, Y., & Li, C.-S. R. (2025) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2025.01.045 · Cited by 7
Temporal and spatial variability of large-scale dynamic brain networks in ASD
Yin, S., Sun, S., Li, J., Feng, Y., Zheng, L., Chen, K., Ma, J., Xu, F., Yao, D., Xu, P., et al. (2025) · European Child & Adolescent Psychiatry · doi:10.1007/s00787-025-02679-9 · Cited by 7
Self-citation
Determining four confounding factors in individual cognitive traits prediction with functional connectivity: an exploratory study
Feng, P., Jiang, R., Wei, L., Calhoun, V. D., Jing, B., Li, J., & Jing Sui （Beijing Normal University）， my correct affiliation is beijing normal university, N. Q. U. O. S. A. T. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac189 · Cited by 7
Self-citation
Beyond the Snapshot: Brain Tokenized Graph Transformer for Longitudinal Brain Functional Connectome Embedding
Dong, Z., Wu, Y., Yu, X., Chong, J. S. X., Jin, Y., & Zhou, J. H. (2023) · Lecture notes in computer science · doi:10.1007/978-3-031-43904-9_34 · Cited by 7
Motion-Dependent Effects of Functional Magnetic Resonance Imaging Preprocessing Methodology on Global Functional Connectivity
Desalvo, M. N. (2020) · Brain Connectivity · doi:10.1089/brain.2020.0854 · Cited by 7
The intrinsic functional connectivity of psychopathy and its relation to self-control
Kng, J. H., Luo, L., Choy, O., & Yu, J. (2024) · Biological Psychology · doi:10.1016/j.biopsycho.2024.108979 · Cited by 6
Age-dependent cortical overconnectivity in Shank3 mice is reversed by anesthesia
Montagni, E., Ambrosone, M., Martello, A., Curti, L., Polverini, F., Baroncelli, L., Mannaioni, G., Pavone, F. S., Masi, A., & Mascaro, A. L. A. (2025) · Translational Psychiatry · doi:10.1038/s41398-025-03377-5 · Cited by 6
Shared functional connections within and between cortical networks predict cognitive abilities in adult males and femalesPreprint
Dhamala, E., Jamison, K. W., Jaywant, A., & Kuceyeski, A. F. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.02.17.431670 · Cited by 6
Atlas of functional connectivity relationships across rare and common genetic variants, traits, and psychiatric conditionsPreprint
Moreau, C. A., Kumar, K., Harvey, A., Huguet, G., Urchs, S. G. W., Douard, E. A., Schultz, L. M., Sharmarke, H., Jizi, K., Martin, C.-O., et al. (2021) · medRxiv · doi:10.1101/2021.05.21.21257604 · Cited by 8
Voxel-based texture similarity networks reveal individual variability and correlate with biological ontologies
Lin, L., Chang, Z., Zhang, Y., Xue, K., Xie, Y., Wei, L., Li, X., Zhao, Z., Luo, Y., Dong, H., et al. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120688 · Cited by 5
Network-based near-scalp personalized brain stimulation targets
Kong, R., Xue, A., Cheng, J., Ooi, L. Q. R., Asplund, C. L., Tan, X. W., Goh, S. E., Lee, J. J., Koh, J. Z. J., Tan, R. S. Y., et al. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1222 · Cited by 5
Self-citation
Investigating topological alterations in procedural memory network across neuropsychiatric disorders using rs-fMRI and graph theory
Mohammadkhanloo, M., Sharini, H., Yousefpour, M., & Pooyan, M. (2025) · BMC Neuroscience · doi:10.1186/s12868-025-00979-z · Cited by 5
Task-evoked functional connectivity exhibits novel and strengthened relationships with executive function relative to the resting state
Mitchell, M. E., Feczko, E., Fair, D. A., & Cohen, J. R. (2026) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2516929123 · Cited by 5
Age‐specificity and generalization of behavior‐associated structural and functional networks and their relevance to behavioral domains
Yu, J., & Fischer, N. L. (2022) · Human Brain Mapping · doi:10.1002/hbm.25759 · Cited by 6
Neural Correlates of Novelty-Evoked Distress in 4-Month-Old Infants: A Synthetic Cohort Study
Filippi, C. A., Winkler, A. M., Kanel, D., Elison, J. T., Hardiman, H., Sylvester, C. M., Pine, D. S., & Fox, N. A. (2024) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2024.03.008 · Cited by 5
Explainable deep-learning framework: decoding brain states and prediction of individual performance in false-belief task at early childhood stage
Bhavna, K., Akhter, A., Banerjee, R., & Roy, D. (2024) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2024.1392661 · Cited by 5
Morning resting hypothalamus-dorsal striatum connectivity predicts individual differences in diurnal sleepiness accumulation
Mao, T., Guo, B., Quan, P., Deng, Y., Chai, Y., Xu, J., Jiang, C., Zhang, Q., Lu, Y., Goel, N., et al. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120833 · Cited by 5
Investigating resting-state functional connectivity changes within procedural memory network across neuropsychiatric disorders using fMRI
Mohammadkhanloo, M., Pooyan, M., Sharini, H., & Yousefpour, M. (2025) · BMC Medical Imaging · doi:10.1186/s12880-024-01527-7 · Cited by 5
The functional overlap between respiration and global signal and its behavioral relevance
Yuan, J., Luo, Y., & Zhang, J. (2025) · Communications Biology · doi:10.1038/s42003-025-08260-z · Cited by 5
SupReMix: Supervised contrastive learning for medical imaging regression with mixup
Wu, Y., Dong, Z., Chen, C., Zhou, W., & Zhou, J. H. (2025) · Medical Image Analysis · doi:10.1016/j.media.2025.103909 · Cited by 5
Construction and Multiple Feature Classification Based on a High-Order Functional Hypernetwork on fMRI Data
Li, Y., Li, Q., Li, T., Zhou, Z., Xu, Y. J., Yang, Y., Chen, J., & Guo, H. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.848363 · Cited by 5
Spontaneous brain activity, graph metrics, and head motion related to prospective post-traumatic stress disorder trauma-focused therapy response
van Lutterveld, R., Varkevisser, T., Kouwer, K., van Rooij, S. J. H., Kennis, M., Hueting, M., van Montfort, S. J. T., van Dellen, E., & Geuze, E. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.730745 · Cited by 6
Integrative role of attention networks in frequency-dependent modular organization of human brain
Neşe, H., Harı, E., Ay, U., Demıralp, T., & Ademoğlu, A. (2024) · Brain Structure and Function · doi:10.1007/s00429-024-02847-8 · Cited by 5
Functional Connectivity Biomarkers in Schizophrenia
Howell, A. M., & Anticevic, A. (2024) · Advances in neurobiology · doi:10.1007/978-3-031-69491-2_10 · Cited by 5
Hippocampal resting-state functional connectivity with the mPFC and DLPFC moderates and mediates the association between education level and memory function in subjective cognitive decline
Xu, S., Sun, Q., Li, M., Luo, J., Cai, G., Chen, R., Zhang, L., & Liu, J. (2021) · Brain Science Advances · doi:10.26599/bsa.2021.9050013 · Cited by 5
Differential Effect of Global Signal Regression Between Awake and Anesthetized Conditions in Mice
Wang, D., Li, H., Xu, M., Bo, B., Pei, M., Liang, Z., & Thompson, G. J. (2023) · Brain Connectivity · doi:10.1089/brain.2023.0032 · Cited by 5
The Effect Analysis of Atlas and Global Signal Regression in Classification based on Brain Network for Major Depression Disorders
Long, D., Liu, Y., Chen, Z., Xie, J., Shi, L., & Luo, C. (2022) · Journal of Imaging Science and Technology · doi:10.2352/j.imagingsci.technol.2022.66.4.040413 · Cited by 5
Beyond functional connectivity: deep learning applied to resting-state fMRI time series in the prediction of 58 human traits in the HCPPreprint
Vieira, B. H., Schöttner, M., Calhoun, V. D., & Salmón, C. E. G. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.03.07.583858 · Cited by 5
Finger motor representation supports the autonomy in arithmetic: neuroimaging evidence from abacus training.
Wang, Y., Zhang, Y., Xu, T.-Y., Han, X., Ge, X., & Chen, F.-Y. (2024) · Cerebral Cortex · doi:10.1093/cercor/bhad524 · Cited by 5
Individual suicide risk factors with resting-state brain functional connectivity patterns in bipolar disorder patients based on latent Dirichlet allocation model
Wei, X., Shao, J., Wang, H., Wang, X., Li, X., Yan, R., Wang, X., Yao, Z., & Lü, Q. (2024) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2024.111117 · Cited by 4
Functional connectivity patterns of the fronto-parietal and cingulo-opercular networks demonstrate distinct associations with individual differences in cognitive control during early adolescence
Smith, L. L., Friedman, N. P., Luciana, M. M., & Banich, M. T. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121454 · Cited by 5
Neuroanatomy reflects individual variability in impulsivity in youth
Dhamala, E., Christensen, E., Hanson, J. L., Ricard, J. A., Arcaro, N., Bhola, S., Wiersch, L., Brosch, K., Yeo, B. T. T., Holmes, A. J., et al. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03526-2 · Cited by 4
Self-citation
Role of Hormonal Change after Start and Discontinuation of the Pill in Women’s Functional Brain Architecture and Mental Health
Kimmig, A.-C. S., Friedrich, P., Drotleff, B., Lämmerhofer, M., Poromaa, I. S., Susanne Weis Group Leader, & Derntl, B. (2024) · Psychoneuroendocrinology · doi:10.1016/j.psyneuen.2023.106731 · Cited by 4
An evaluation of the efficacy of single-echo and multi-echo fMRI denoising strategies
Constable, T., Tiego, J., Pavlovich, K., Sangchooli, A., Levi, P. T., Hartshorn, B., Kwee, J., Fortune, K., Cooper, K., Brown, S., et al. (2026) · Network Neuroscience · doi:10.1162/netn.a.547 · Cited by 4
Impaired Topographic Organization in Patients With Idiopathic Blepharospasm
Hou, Y., Zhang, L., Wei, Q., Ou, R., Yang, J., Gong, Q., & Shang, H. (2022) · Frontiers in Neurology · doi:10.3389/fneur.2021.708634 · Cited by 4
Evidence for a compensatory relationship between left- and right-lateralized brain networks
Peterson, M., Braga, R. M., Floris, D. L., & Nielsen, J. A. (2025) · Imaging Neuroscience · doi:10.1162/imag_a_00437 · Cited by 5
F2TNet: FMRI to T1w MRI Knowledge Transfer Network for Brain Multi-phenotype Prediction
He, Z., Li, W., Jiang, Y. L., Peng, Z., Wang, P., Li, X., Liu, T., Han, J., Zhang, T., & Yuan, Y. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-72120-5_25 · Cited by 4
Individual-Specific Connectome Fingerprint Based Classification of Temporal Lobe Epilepsy
Meng, Y., Xiao, J., Yang, S., Xu, Q., Zhang, Z., & Liao, W. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87358-5_61 · Cited by 4
A Benchmark of Preprocessing Strategies for Autism Classification from Resting-State Functional Magnetic Resonance Imaging
Cancino, W., Africano, G., & Pertuz, S. D. (2021) · Symposium on Image, Signal Processing and Artificial Vision (STSIVA) · doi:10.1109/stsiva53688.2021.9592011 · Cited by 4
Brain functional connectivity and anatomical features as predictors of cognitive behavioral therapy outcome for anxiety in youths
Zugman, A., Ringlein, G., Finn, E. S., Lewis, K. M., Berman, E., Silverman, W. K., Lebowitz, E. R., Pine, D. S., & Winkler, A. M. (2025) · Psychological Medicine · doi:10.1017/s0033291724003131 · Cited by 3
Brain network analysis in Alzheimer’s disease and mild cognitive impairment using high-density diffuse optical tomography
Butters, E., Collins-Jones, L. H., Mesquita, R. C., Acharya, D., McKiernan, E. F., Laurell, A. A. S., Low, A., Srinivasan, S., O'Brien, J. T., Su, L., et al. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1208 · Cited by 4
Different Brain Network Connectomic Relationships Subserve Hand Dexterity During Task Versus Resting States in People with Brain Tumors
Boerger, T. F., Taquet, L., Goetschel, K., Young, S. C., Connelly, J., Binder, J. R., Schmit, B. D., & Krucoff, M. O. (2025) · Brain and Behavior · doi:10.1002/brb3.71032 · Cited by 3
Personalized high-intensity temporal interference stimulation decouples cerebellar networks to enhance implicit learning
Tang, D., Qin, L., Hu, L., Jian, Y., Gao, S., Huang, Z., Cai, L., Shao, X., Chang, C., Gang, Z. H. A. O., et al. (2026) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/s12984-025-01865-9 · Cited by 3
Predicting rTMS treatment efficacy in depression based on modular flexibility of functional connectivity
Shao, Y., Zhou, Z., Mao, J., Xu, P., Luo, Y., Yu, F., & Wang, S. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.121273 · Cited by 3
Prediction of Cognitive Scores by Movie-Watching FMRI Connectivity and Eye Movement Via Spectral Graph Convolutions
Gao, J., Li, C., He, Z., Wei, Y., Guo, L., Han, J., Zhang, S., & Zhang, T. (2022) · IEEE International Symposium on Biomedical Imaging (ISBI) · doi:10.1109/isbi52829.2022.9761565 · Cited by 3
Trait absorption is not reliably associated with brain structure or resting-state functional connectivity
Girn, M., Spreng, R. N., Margulies, D. S., van Elk, M., & Lifshitz, M. (2023) · Neuroimage Reports · doi:10.1016/j.ynirp.2023.100171 · Cited by 4
Neurobiological fingerprints of negative symptoms in schizophrenia identified by connectome‐based modeling
Gao, Z., Xiao, Y., Zhu, F., Bo, T., Zhao, Q., Yu, W., Bishop, J. R., Gong, Q., & Lui, S. (2025) · Psychiatry and Clinical Neurosciences · doi:10.1111/pcn.13782 · Cited by 3
Longitudinal Changes in Infant Attention-Related Brain Networks and Fearful Temperament
Filippi, C. A., Massera, A., Xing, J., Park, H. G., Valadez, E. A., Elison, J. T., Kanel, D., Pine, D. S., Fox, N. A., & Winkler, A. M. (2025) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1101/2025.01.24.25321085 · Cited by 3
Global signal regression reduces connectivity patterns related to physiological signals and does not alter EEG-derived connectivity
Xifra‐Porxas, A., Kassinopoulos, M., Prokopiou, P. C., Boudrias, M., & Mitsis, G. D. (2025) · Frontiers in Neuroimaging · doi:10.3389/fnimg.2025.1653206 · Cited by 3
Differences in basic psychological needs-related resting-state functional connectivity between individuals with high and low life satisfaction
Kwon, J. H., Kim, H. E., Kim, J., Kim, E. J., & Kim, J. (2021) · Neuroscience Letters · doi:10.1016/j.neulet.2021.135798 · Cited by 3
Seed-based dual regression: An illustration of the impact of dual regression's inherent filtering of global signal
Robert E. Kelly, Jr., Hoptman, M. J., Lee, S., Alexopoulos, G. S., Gunning, F. M., & McKeown, M. J. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2021.109410 · Cited by 3
A visualization tool for assessment of spinal cord functional magnetic resonance imaging data quality
Hemmerling, K. J., & Bright, M. G. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630903 · Cited by 3
Provisioning a risk predictor model for Alzheimers disease using an improved deep network model
Murugesh, V., Janarthanan, P., Kavitha, A., Sivakumar, N., Jaganathan, S. C. B., & Suriyan, K. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16858-w · Cited by 3
Dimensional subtyping of first-episode drug-naïve major depressive disorder: A multisite resting-state fMRI study
Zhang, B., Li, Y., Shen, Y., Zhao, W., Yu, Y., & Tang, J. T. (2023) · Psychiatry Research · doi:10.1016/j.psychres.2023.115598 · Cited by 3
Differential spatial working memory–related functional network reconfiguration in young and older adults
Yue, W. L., Ng, K. K., Liu, S., Qian, X., Chong, J. S. X., Koh, A. J., Ong, M. Q. W., Ting, S. K. S., Ng, A. S. L., Kandiah, N., et al. (2024) · Network Neuroscience · doi:10.1162/netn_a_00358 · Cited by 3
Self-citation
Challenges in multi-task learning for fMRI-based diagnosis: Benefits for psychiatric conditions and CNVs would likely require thousands of patients
Harvey, A., Moreau, C. A., Kumar, K., Huguet, G., Urchs, S. G. W., Sharmarke, H., Jizi, K., Martin, C.-O., Younis, N., Tamer, P., et al. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00222 · Cited by 3
Identification of high-functioning autism spectrum disorders based on gray-white matter functional network connectivity
Yang, Y., Tang, D., Wang, Z., Liu, Y., Chen, F., Jie, B., Ni, T., Xu, C., Li, J., & Wang, C. (2024) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2024.08.006 · Cited by 3
Self-citation
Frequent absent mindedness and the neural mechanism trapped by mobile phone addiction
Zhang, J., Feng, Q., & Qiu, J. (2024) · Neuroscience · doi:10.1016/j.neuroscience.2024.10.042 · Cited by 4
Enhancing prediction of human traits and behaviors through ensemble learning of traditional and novel resting-state fMRI connectivity analyses
Yoshimoto, T., Tokunaga, K., & Chikazoe, J. (2024) · NeuroImage · doi:10.1016/j.neuroimage.2024.120911 · Cited by 3
Identifying imaging genetic associations via regional morphometricity estimation
Bao, J., Wen, Z., Kim, M., Saykin, A. J., Thompson, P. M., Zhao, Y., & Shen, L. (2021) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811250477_0010 · Cited by 3
Network segregation during episodic memory shows age-invariant relations with memory performance from 7 to 82 years
Grydeland, H., Sneve, M. H., Roe, J. M., Raud, L., Ness, H. T., Folvik, L., Amlien, I. K., Geier, O. M., Sørensen, Ø., Dídac Vidal-piñeiro, A., et al. (2025) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2025.01.004 · Cited by 3
Association between functional alterations and specific transcriptional expression patterns in craniocervical dystonia
Liu, G., Zhang, J., Zhang, H., Cheng, Q., Zhang, X., Liu, J., Luo, Y., Zhong, L., Yang, Z., Zhang, Y., et al. (2025) · Parkinsonism & Related Disorders · doi:10.1016/j.parkreldis.2025.107315 · Cited by 2
Improved whole-brain reconfiguration efficiency reveals mechanisms of speech rehabilitation in cleft lip and palate patients: an fMRI study
Zhang, W., Si, Q., Guan, Z., Cao, L., Wang, M., Zhao, C., Sun, L., Zhang, X., Zhang, Z., Li, C., et al. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1536658 · Cited by 2
Development of areal-level individualized homologous functional parcellations in youth
Li, J., Zhang, Y., Xin, W., Xue, M., Wang, Z., Lv, S., Yang, R., Zhu, W., Li, X., Yan, T., et al. (2025) · Communications Biology · doi:10.1038/s42003-025-08509-7 · Cited by 2
Self-citation
Altered functional connectivity of the hippocampus in cortico-subcortical networks in early-stage and emerging psychosis
Brunner, G., Gajwani, R., Groß, J., Gumley, A. I., Krishnadas, R., Lawrie, S. M., Schwannauer, M., Schultze‐Lutter, F., Uhlhaas, P. J., & Fracasso, A. (2025) · European Archives of Psychiatry and Clinical Neuroscience · doi:10.1007/s00406-025-02079-9 · Cited by 2
Atypical cortical regional homogeneity observed in grapheme-colour synaesthesia and its subtypes
YEO, B. P., & Yu, J. (2025) · Cortex · doi:10.1016/j.cortex.2025.09.003 · Cited by 2
Self-citation
Disrupted integration–segregation balance in the intact hemisphere in chronic spatial neglect
Takamura, Y., Lunven, M., Serhan, Y., Bourlon, C., Ovadia‐Caro, S., & Bartolomeo, P. (2026) · Brain Structure and Function · doi:10.1007/s00429-026-03137-1 · Cited by 2
SuperAging functional connectomics from resting-state functional MRI
Diamond, B. R., Sridhar, J., Maier, J., Martersteck, A., & Rogalski, E. J. (2024) · Brain Communications · doi:10.1093/braincomms/fcae205 · Cited by 2
Test-retest reliability of dynamic functional connectivity parameters for a two-state model
Fang, X., & Marxen, M. (2025) · Network Neuroscience · doi:10.1162/netn_a_00437 · Cited by 2
Optimizing functional connectivity scanning conditions for predicting autistic traits
Horien, C. L., Mandino, F., Greene, A. S., Shen, X., Powell, K. K., Vernetti, A., O'Connor, D., Adkinson, B. D., Tejavibulya, L., McPartland, J. C., et al. (2026) · Nature Mental Health · doi:10.1038/s44220-026-00623-7 · Cited by 2
Time-varying functional connectivity as Wishart processes
Kampman, O. P., Ziminski, J., Afyouni, S., van der Wilk, M., & Kourtzi, Z. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00184 · Cited by 2
Dynamic Brain Network States during Suspenseful Film ViewingPreprint
Bezdek, M. A., Keilholz, S. D., & Schumacher, E. H. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.10.19.465030 · Cited by 2
Subject-specific brain functional fingerprints are preserved during general anesthesiaPreprint
Liu, H., Fu, X., Ren, J., Wei, W., Li, S., Wang, D., Wang, Y., Yang, Z., Zhang, Y., Zhang, P., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3332757/v1 · Cited by 2
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) · Research Square · doi:10.21203/rs.3.rs-5167657/v1 · Cited by 2
Self-citation
Shared Neural Signatures of Socioeconomic Status, Scarcity, and Neighborhood Threat in YouthPreprint
Brosch, K., Wiersch, L., Christensen, E., Rakesh, D., Hanson, J. L., Schwartz, R. M., & Dhamala, E. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.06.11.659188 · Cited by 2
The progression of sex differences in brain networks across the lifespanPreprint
Huang, K., Jamison, K. W., Jacobs, E. G., Miolane, N., & Kuceyeski, A. F. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.01.30.702608 · Cited by 5
Human brain R2* transitions across birth from the womb to early infancy
Ji, L., Chen, B., Menu, I., Trentacosta, C. J., & Thomason, M. E. (2025) · Communications Biology · doi:10.1038/s42003-025-08932-w · Cited by 1
Uncovering the neurological substrates underlying restlessness in cluster headache - A functional MRI study
Chen, S., Chiang, C., Chen, Y.-L., Tseng, S.-Y., Chen, M., Lau, C. I., & Wu, J. (2025) · The Journal of Headache and Pain · doi:10.1186/s10194-025-02209-7 · Cited by 1
Identifying brain activation regions and functional connectivity changes in stroke rehabilitation using fNIRS
Wang, T., Dai, J., Tang, J., Xi, X., Maofeng, W., Luo, Z., & Li, L. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.109262 · Cited by 1
Accurately modelling the resting-state functional connectivity with eigen-based connected-graph diffusion model
Ma, J., Luo, J., Liu, D., Wu, J., Wang, Y., & Li, X. (2026) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2026.115091 · Cited by 1
Time Persistence of the FMRI Resting-State Functional Brain Networks
Guo, S., Levy, O., Dvir, H., Kang, R., Li, D., Havlin, S., & Axelrod, V. (2025) · Journal of Neuroscience · doi:10.1523/jneurosci.1570-24.2025 · Cited by 1
Reproducible Effects of Sex and Acquisition Order on Multiple Global Signal Metrics: Implications for Functional Connectivity Studies of Phenotypic Individual Differences Using fMRI
Chase, H. W., Hafeman, D., Ghane, M., Skeba, A. S., Brady, T., Aslam, H. A., Stiffler, R. S., Bonar, L. K., Graur, S., Bebko, G. M., et al. (2025) · Brain and Behavior · doi:10.1002/brb3.70141 · Cited by 1
Graph-Theoretic Analysis of Human Brain Networks
Fornito, A. (2025) · Neuromethods · doi:10.1007/978-1-0716-4438-6_10 · Cited by 1
Functional Coupling and Longitudinal Outcome Prediction in First-Episode Psychosis
Pope, I. Z., Chopra, S., Holmes, A., Francey, S. M., O'Donoghue, B. N., Cropley, V., Nelson, B., Yuen, H. P., Allott, K., Álvarez‐Jiménez, M., et al. (2025) · Biological Psychiatry Global Open Science · doi:10.1016/j.bpsgos.2025.100589 · Cited by 1
Self-citation
Effects of measurement errors on relationships between resting-state functional connectivity and psychological phenotypes
Haitani, T., Sakai, Y., & Tanaka, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-13105-0 · Cited by 1
Voxel‐Wise or Region‐Wise Nuisance Regression for Functional Connectivity Analyses: Does It Matter?
Muganga, T., Sasse, L., Larabi, D. I., Nieto, N., Caspers, J., Eickhoff, S. B., & Patil, K. R. (2025) · Human Brain Mapping · doi:10.1002/hbm.70323 · Cited by 1
Susceptibility to visual hallucinations in the amyotrophic lateral sclerosis-frontotemporal dementia spectrum: The role of dysfunctional attentional networks
Tse, N. Y., Orlando, I. F., O’Callaghan, C., Taylor, N. L., Shine, J. M., Zalesky, A., Tu, S., Ahmed, R. M., Halliday, G. M., Piguet, O., et al. (2025) · Cortex · doi:10.1016/j.cortex.2025.08.014 · Cited by 1
Spatiotemporal transition of resting-state brain networks associates with human cognitive abilities
Zhou, L., Jiang, Z., Chang, Z., Wang, R., & Wu, Y. (2025) · Cognitive Neurodynamics · doi:10.1007/s11571-025-10347-6 · Cited by 1

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

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

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

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks