Cited by 859Functional Brain Connectivity StudiesAdvanced Neuroimaging Techniques and ApplicationsMental Health Research Topics

The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.

A positive-negative mode of population covariation links brain connectivity, demographics and behavior

Open access

Smith, Stephen M, Nichols, Thomas E, Vidaurre, Diego, Winkler, Anderson M, Behrens, Timothy E J, Glasser, Matthew F, Ugurbil, Kamil, Barch, Deanna M, Van Essen, David C, Miller, Karla L·2015·Nature Neuroscience

DOI 10.1038/nn.4125PMC PMC4625579

Open this paper in CoChat — free account

Abstract

We investigated the relationship between individual subjects' functional connectomes and 280 behavioral and demographic measures in a single holistic multivariate analysis relating imaging to non-imaging data from 461 subjects in the Human Connectome Project. We identified one strong mode of population co-variation: subjects were predominantly spread along a single 'positive-negative' axis linking lifestyle, demographic and psychometric measures to each other and to a specific pattern of brain connectivity.

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 · 10.1038/nn.4125

Cites

27 of 27 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.

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
Self-citation
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
"General Intelligence," Objectively Determined and Measured
Spearman, C. (1904) · The American Journal of Psychology · doi:10.2307/1412107 · Cited by 6,207
Cortical Surface-Based Analysis
Fischl, B., Sereno, M. I., & Dale, A. M. (1999) · NeuroImage · doi:10.1006/nimg.1998.0396 · Cited by 6,147
RELATIONS BETWEEN TWO SETS OF VARIATES
Hotelling, H. (1936) · Biometrika · doi:10.1093/biomet/28.3-4.321 · Cited by 6,041
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
Self-citation
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
Self-citation
Where is the semantic system? A critical review and meta-analysis of 120 functional neuroimaging studies
Binder, J. R., Desai, R. H., Graves, W. W., & Conant, L. L. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhp055 · Cited by 4,060
Probabilistic Independent Component Analysis for Functional Magnetic Resonance Imaging
Beckmann, C. F., & Smith, S. M. (2004) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2003.822821 · Cited by 2,707
Self-citation
Can cognitive processes be inferred from neuroimaging data?
POLDRACK, R. (2006) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2005.12.004 · Cited by 2,249

Flagged first, then by citations

Show all 27 references

Research works printed but not linked: 3

Winkler, A. et al. Neuroimage published online, 10.1016/j.neuroimage.2015.05.092 (11 June 2015). · DOI
Thomson, G.H. Br. J. Psychol. 8, 271–281 (1916).
Van der Waerden, B.L. Proc. K. Ned. Akad. Wet. A. 55, 453–458 (1952).

Sorted by citations

Items 601–700 of 859

Risk for Adolescent Substance Use Initiation: Associations with Large-Scale Brain Network Recruitment During Emotional Inhibitory Control
Cohen‐Gilbert, J. E., Sneider, J. T., Oot, E. N., Seraikas, A. M., Schuttenberg, E. M., Harris, S. K., Nickerson, L. D. H., & Silveri, M. M. (2025) · Behavioral Sciences · doi:10.3390/bs15101407 · Cited by 3
Ultra-high field brain MRI for functional neurological disorder: opportunities and challenges
Myren, S., Jungilligens, J., Diez, I. P., Bøen, E., Elvsåshagen, T., Forstmann, B. U., Ziaei, M., & Doan, T. P. (2026) · NeuroImage Clinical · doi:10.1016/j.nicl.2026.103972 · Cited by 4
Development of non-spatial grid-like neural codes tracks inference and intelligence
Qu, Y., Ou, J., Pang, L., Wu, S., Luo, Y., Behrens, T. E. J., & Liu, Y. (2026) · Cell · doi:10.1016/j.cell.2026.02.028 · Cited by 9
Self-citation
Interoceptive performance is unrelated to mental health symptoms in a large multi-domain psychophysical investigation
Banellis, L., Nikolova, N. E., Ehmsen, J. F., Courtin, A. S., Vejlø, M., Tyrer, A., Böhme, R. A., Bavato, F., Hoogervorst, K., Fardo, F., et al. (2026) · Nature Mental Health · doi:10.1038/s44220-026-00688-4 · Cited by 3
Brain network changes in adult victims of violence
Shymanskaya, A., Kohn, N., Habel, U., & Wagels, L. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1040861 · Cited by 3
Childhood adversities characterize the heterogeneity in the brain pattern of individuals during neurodevelopment
Kashyap, R., Holla, B., Bhattacharjee, S., Sharma, E., Mehta, U. M., Vaidya, N., Bharath, R. D., Murthy, P., Basu, D., Nanjayya, S. B., et al. (2024) · Psychological Medicine · doi:10.1017/s0033291724000710 · Cited by 3
Integration of estimated regional gene expression with neuroimaging and clinical phenotypes at biobank scale
Hoang, N., Sardaripour, N., Ramey, G. D., Schilling, K., Liao, E., Chen, Y.-T., Park, J. H., Bledsoe, X., Landman, B. A., Gamazon, E. R., et al. (2024) · PLoS Biology · doi:10.1371/journal.pbio.3002782 · Cited by 3
FDP control in mass-univariate linear models using the residual bootstrap
Davenport, S., Thirion, B., & Neuvial, P. (2025) · Electronic Journal of Statistics · doi:10.1214/25-ejs2354 · Cited by 3
The self-awareness brain network: Construction, characterization, and alterations in schizophrenia and major depressive disorder
Xia, X., Gao, F., Xu, S., Li, K., Zhu, Q., He, Y., Zeng, X., Hua, L., Huang, S., & Yuan, Z. (2025) · NeuroImage · doi:10.1016/j.neuroimage.2025.121205 · Cited by 3
Deep learning reveals that multidimensional social status drives population variation in 11,875 US participant cohort
Marotta, J., Aggarwal, S., Osayande, N., Saltoun, K., Kopál, J., Holmes, A. J., Yip, S. W., & Bzdok, D. (2025) · PLoS ONE · doi:10.1371/journal.pone.0327729 · Cited by 4
Multiscale modes of functional brain connectivity
Farahibozorg, S.-R., Harrison, S. J., Bijsterbosch, J. D., Woolrich, M. W., & Smith, S. M. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.1031 · Cited by 4
Self-citation
Functional random effects modeling of brain shape and connectivity
Lila, E., & Aston, J. A. D. (2022) · The Annals of Applied Statistics · doi:10.1214/21-aoas1572 · Cited by 3
Individualized prediction of future cognition based on developmental changes in cortical anatomy
Khundrakpam, B. S., Booij, L., Jeon, S., Karama, S., Tohka, J., & Evans, A. C. (2022) · Neuroimage Reports · doi:10.1016/j.ynirp.2022.100127 · Cited by 3
Tree representations of brain structural connectivity via persistent homology
Li, D., Nguyen, P. H., Zhang, Z., & Dunson, D. B. (2023) · Frontiers in Neuroscience · doi:10.3389/fnins.2023.1200373 · Cited by 3
Predictable navigation through spontaneous brain states with cognitive-map-like representations
Li, S., Li, Z., Liu, Q., Ren, P., Sun, L., Cui, Z., & Liang, X. (2024) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2024.102570 · Cited by 3
A cautionary tale on the effects of different covariance structures in linear mixed effects modeling of fMRI data
van der Horn, H. J., Erhardt, E. B., Dodd, A. B., Nathaniel, U., Wick, T. V., McQuaid, J. R., Ryman, S. G., Vakhtin, A. A., Meier, T. B., & Mayer, A. R. (2024) · Human Brain Mapping · doi:10.1002/hbm.26699 · Cited by 3
Neuroanatomical, transcriptomic, and molecular correlates of math ability and their prognostic value for predicting learning outcomes
Liu, J., Supekar, K., El-Said, D., Angeles, C. D. L., Zhang, Y., Chang, H., & Menon, V. (2024) · Science Advances · doi:10.1126/sciadv.adk7220 · Cited by 4
Behavior can be decoded across the cortex when individual differences are considered
Nakuci, J., Yeon, J., Kim, J. H., Kim, S.-P., & Rahnev, D. (2024) · Imaging Neuroscience · doi:10.1162/imag_a_00359 · Cited by 4
IDH Status in Brain Gliomas Can Be Predicted by the Spherical Mean MRI Technique
Sedlák, V., Němý, M., Májovský, M., Bubeníková, A., Nordin, L. E., Moravec, T., Engelová, J., Síla, D., Konečná, D., Belsan, T., et al. (2024) · American Journal of Neuroradiology · doi:10.3174/ajnr.a8432 · Cited by 3
TR (Acking) Individuals Down: Exploring the Effect of Temporal Resolution in Resting‐State Functional MRI Fingerprinting
Cassone, B., Saviola, F., Tambalo, S., Amico, E., Hübner, S., Sarubbo, S., Ville, D. V. D., & Jovicich, J. (2025) · Human Brain Mapping · doi:10.1002/hbm.70125 · Cited by 4
Contexts of educational neuroscience
Joldersma, C. W., & Herwegen, J. V. (2022) · UNESCO MGIEP eBooks · doi:10.56383/runc9656 · Cited by 4
Information fusion for cocaine dependence recognition using fMRI
Faria, F. A., Cappabianco, F. A. M., Li, C. R., & Ide, J. S. (2016) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.2016.7899784 · Cited by 4
Functional hierarchy of oculomotor and visual motion subnetworks within the human cortical optokinetic system
Ruehl, R. M., Hoffstaedter, F., Reid, A., Eickhoff, S. B., & zu Eulenburg, P. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1788-9 · Cited by 3
The Handbook of Wellness Medicine
Snellings, J. E. (2021) · Family Medicine · doi:10.22454/fammed.2021.204951 · Cited by 3
Functional Imaging: Magnetic Resonance Imaging
Bandettini, P. A. (2022) · Springer eBooks · doi:10.1007/978-3-030-88832-9_150 · Cited by 3
Neuropsychiatric copy number variants exert shared effects on human brain structurePreprint
Modenato, C., Kumar, K., Moreau, C. A., Martin‐Brevet, S., Huguet, G., Schramm, C., Jean‐Louis, M., Martin, C.-O., Younis, N., Tamer, P., et al. (2020) · medRxiv · doi:10.1101/2020.04.15.20056531 · Cited by 3
Brain/MINDS Beyond Human Brain MRI Project: A Protocol for Multi-Site Harmonization across Brain Disorders Throughout the LifespanPreprint
Koike, S., Tanaka, S. C., Okada, T., Aso, T., Asano, M., Maikusa, N., Morita, K., Okada, N., Fukunaga, M., Uematsu, A., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.05.05.076273 · Cited by 3
Self-citation
Cortical Morphological Brain Networks: Phenotypic Associations and Microbiological SubstratesPreprint
Li, Z., Li, J., Wang, N., Lv, Y., Zou, Q., & Wang, J. (2022) · SSRN Electronic Journal · doi:10.2139/ssrn.4094329 · Cited by 3
Rationale and design of the Healthy Brain Study: an accessible resource for understanding the human brain and how it dynamically and individually operates in its bio-social contextPreprint
Overbeek, L. I. H. (2021) · OSF Preprints (OSF Preprints) · doi:10.31219/osf.io/fqmdu · Cited by 2
Static and dynamic functional connectomes represent largely similar informationPreprint
Matkovič, A., Anticevic, A., Murray, J. D., & Repovš, G. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.01.24.525348 · Cited by 3
A Transdiagnostic fMRI Dataset With 300+ Deeply Phenotyped Subjects Across Resting and Task StatesPreprint
Samardzija, A., Shen, X., Greene, A. S., Mehta, S., Iannone, S., Simon, A. J., Parra, F., Luo, W., Arora, J., Tokoglu, F., et al. (2025) · medRxiv · doi:10.1101/2025.07.15.25331595 · Cited by 9
Can "consciousness" be observed from large language model (LLM) internal states? Dissecting LLM representations obtained from Theory of Mind test with Integrated Information Theory and Span Representation analysis
Li, J. (2025) · Natural Language Processing Journal · doi:10.1016/j.nlp.2025.100163 · Cited by 3
Adaptive-to-maladaptive gradient of emotion regulation tendencies are embedded in the functional–structural hybrid connectome
Kim, W., & Kim, M. J. (2024) · Psychological Medicine · doi:10.1017/s0033291724000473 · Cited by 3
A pattern-learning algorithm associates copy number variations with brain structure and behavioural variables in an adolescent population cohort
Kopál, J., Huguet, G., Marotta, J., Aggarwal, S., Osayande, N., Kumar, K., Saci, Z., Jean‐Louis, M., Chai, X. J., Ge, T., et al. (2025) · Nature Biomedical Engineering · doi:10.1038/s41551-025-01454-0 · Cited by 3
Spontaneous brain regional dynamics contribute to generalizable brain–behaviour associations
Tian, X., Peng, Y., Liu, S., Shafiei, G., Wang, M., Sun, Y., Lou, J., Xian, J., Hu, K., He, Y., et al. (2025) · Nature Human Behaviour · doi:10.1038/s41562-025-02332-0 · Cited by 4
Martial arts induce quasicritical brain states: a unified, multiscale, and mechanistic theory of brain health optimization
Gabriel, C. A. (2025) · Frontiers in Psychology · doi:10.3389/fpsyg.2025.1661566 · Cited by 4
Brain morphological pattern is associated with the presence, severity and transition of transdiagnostic psychiatric disorders in preadolescents
Kuang, N., Hammond, C. J., Salmeron, B. J., Xiao, X., Wang, D., Murray, L., Gu, H., Zhai, T., Zheng, H., Hill, J., et al. (2026) · Nature Mental Health · doi:10.1038/s44220-026-00704-7 · Cited by 3
Gene coexpression modules in human cognition
Werling, D. M., & Sanders, S. (2016) · Nature Neuroscience · doi:10.1038/nn.4226 · Cited by 2
Combining Multiple Connectomes via Canonical Correlation Analysis Improves Predictive Models
Gao, S., Greene, A. S., Constable, R. T., & Scheinost, D. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-00931-1_40 · Cited by 2
Predicting Individual Differences in Cognitive Ability from Brain Imaging and Genetics
Anderson, K. M., & Holmes, A. J. (2021) · Cambridge University Press eBooks · doi:10.1017/9781108635462.021 · Cited by 3
Dimensionality Reduction: Foundations and Applications in Clinical Neuroscience
Kernbach, J. M., Ort, J., Hakvoort, K., Clusmann, H., Delev, D., & Neuloh, G. (2021) · Acta neurochirurgica. Supplementum · doi:10.1007/978-3-030-85292-4_8 · Cited by 2
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
Multivariate associations between dopamine receptor availability and risky investment decision-making across adulthood
Green, M. A., Crawford, J. L., Kuhnen, C. M., Samanez-Larkin, G. R., & Seaman, K. L. (2023) · Cerebral Cortex Communications · doi:10.1093/texcom/tgad008 · Cited by 2
covSTATIS: A multi-table technique for network neuroscience
Baracchini, G., Yu, J., Rieck, J. R., Beaton, D., Guillemot, V., Grady, C. L., Abdi, H., & Spreng, R. N. (2024) · Aperture Neuro · doi:10.52294/001c.124518 · Cited by 2
Intrinsic Functional Connectivity Associated with γ‑Aminobutyric Acid and Glutamate/Glutamine in the Lateral Prefrontal Cortex and Internalizing Psychopathology in Adolescents
Wang, K., Smolker, H. R., Brown, M. S., Snyder, H. R., Cheng, Y., Hankin, B. L., & Banich, M. T. (2025) · Neuroscience Bulletin · doi:10.1007/s12264-025-01408-1 · Cited by 2
Adaptive proximity to criticality underlies amplification of ultra-slow fluctuations during free recall
Yellin, D., Siegel, N., Malach, R., & Shriki, O. (2025) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1013528 · Cited by 2
The Use of Ultrahigh Field Functional MRI in Neuroscience Applications
Yacoub, E. S., Martino, F. D., & Uǧurbil, K. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-813068-1.00016-6 · Cited by 2
The Wellness Treatment Plan
Perera, D. M., & Moe, J. L. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108650182.005 · Cited by 2
Wellness Interventions in the Workplace
Chernoff, R. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108650182.020 · Cited by 2
Nature and Pets
Sharma‐Brymer, V., Dashper, K. L., & Brymer, E. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108650182.034 · Cited by 2
Can an online battery match in‐person cognitive testing in predicting age‐related cortical changes
Thienel, R., Borne, L., Faucher, C., Robinson, G. A., Fripp, J., Giorgio, J., Ceslis, A., McAloney, K., Adsett, J., Galligan, D., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.074476 · Cited by 2
Predicting conversational satisfaction of face-to-face conversation through interpersonal similarity in resting-state functional connectivity
Ikeda, S., Jeong, H., Sasaki, Y., Sakaki, K., Yamazaki, S., Nozawa, T., & Kawashima, R. (2024) · Scientific Reports · doi:10.1038/s41598-024-56718-7 · 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
Functional Connectivity within and beyond the Face Network Is Related to Reduced Discrimination of Degraded Faces in Young and Older Adults
Grady, C. L., Rieck, J. R., Nichol, D., & Garrett, D. D. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa179 · Cited by 2
Activity in the Fronto-Parietal Multiple-Demand Network is Robustly Associated with Individual Differences in Working Memory and Fluid IntelligencePreprint
Assem, M., Blank, I., Mineroff, Z., Ademoglu, A., & Fedorenko, E. G. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/110270 · Cited by 3
Cortical processing of breathing perceptions in the athletic brainPreprint
Harrison, O. K., Cox, P. J., & Pattinson, K. T. S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/174052 · Cited by 1
Inferring Macroscopic Brain Connectomes via Group-Sparse Factorization
Aminmansour, F. (2020) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3-60g5-ym61 · Cited by 2
Interactions between the neural correlates of dispositional internally directed thought and visual imageryPreprint
Karapanagiotidis, T., Jefferies, E., & Smallwood, J. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/857490 · Cited by 2
Towards understanding interindividual differences in cortical morphological brain networksPreprint
Li, Z., Li, J., Wang, N., & Wang, J. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.12.21.423884 · Cited by 2
A snapshot of brain and cognition in healthy mid-life and older adultsPreprint
Borne, L., Lupton, M. K., Guo, C. C., Mosley, P. E., Adam, R. J., Ceslis, A., Bourgeat, P., Fazlollahi, A., Maruff, P. T., Rowe, C. C., et al. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.01.20.476706 · Cited by 2
Traces of impaired social communication and cognitive ability in the youth brain are shared across diagnostic boundariesPreprint
Voldsbekk, I., Kjelkenes, R., Wolfers, T., Dahl, A., Lund, M. J., Kaufmann, T., Fernandez, S., de Lange, A. G., Tamnes, C. K., Andreassen, O. A., et al. (2022) · doi:10.31234/osf.io/h4env · Cited by 2
Large-Scale Functional Hyperconnectivity Patterns Characterizing Trauma-Related Dissociation: A rs-fMRI Study of PTSD and its Dissociative SubtypePreprint
Shaw, S. B., Terpou, B. A., Densmore, M., Théberge, J., Frewen, P., McKinnon, M. C., & Lanius, R. A. (2022) · doi:10.31234/osf.io/b6kdm · Cited by 2
Disruptions of Hierarchical Cortical Organisation in Early Psychosis and SchizophreniaPreprint
Holmes, A., Levi, P. T., Chen, Y., Chopra, S., Aquino, K. M., Pang, J. C. S., & Fornito, A. (2023) · medRxiv · doi:10.1101/2023.05.02.23289376 · Cited by 2
Multivariate Associations Between Structural Brain Networks, Genetics, Environments, and Cognitive-Psychopathological Traits in Children
Seo, J., Lee, E., Kim, B., Kim, G., Joo, Y. Y., & Cha, J. (2026) · Imaging Neuroscience · doi:10.1101/2023.07.24.23293075 · Cited by 2
Post-stroke apathy biotypes and their relation to the effort-based reward decision network: a resting-state fMRI studyPreprint
Sun, W., Fang, Y., Wang, J., Yin, D., Jiang, S., Chao, X., Zhang, F., Yan, D., Zhang, P., Wang, P., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3248784/v1 · Cited by 2
Deciphering complex brain spatiotemporal dynamics shaping diverse human behaviorPreprint
Liu, B., Tian, X., Peng, Y., Wang, M., Sun, Y., Lou, J., Xian, J., He, Y., Hu, K., Wang, Q., et al. (2023) · Research Square · doi:10.21203/rs.3.rs-3344208/v1 · Cited by 2
Personalized circuit modeling captures variation in cortical functional connectivityPreprint
Cooper, R. A., Demirtaş, M., Burt, J. B., Howell, A. M., Ji, J., Repovš, G., Sotiropoulos, S. N., Anticevic, A., & Murray, J. D. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.02.19.581085 · Cited by 2
Testing the Tests: Using Connectome-Based Predictive Models to Reveal the Systems Standardized Tests and Clinical Symptoms are ReflectingPreprint
Samardzija, A., Shen, X., Luo, W., Greene, A. S., Mehta, S., Tokoglu, F., Arora, J., Woods, S. W., Katz, R. R., Sanacora, G., et al. (2024) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2024.10.24.619737 · Cited by 2
A Functional Network Perspective on the Role of the Frontal Lobes in Executive Cognition
Hampshire, A. (2017) · Elsevier eBooks · doi:10.1016/b978-0-12-803676-1.00004-0 · Cited by 2
Well-Being and Work–Life Balance
Bolton, M. A., Lobben, I., Pruzinsky, T., & Stern, T. A. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108650182.046 · Cited by 1
Forgiveness, Gratitude, and Spirituality
Idicula, S. A., Thrower, N., & Lomax, J. W. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108650182.029 · Cited by 1
Engaging the Five Senses
Bokarius, V., & Bokarius, A. V. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108650182.037 · Cited by 2
Wellness Interventions in Patients Living with Chronic Medical Conditions
Steiner, A. J., Aguilar-Hernandez, L., Pizano, D. R., Dascal, J., & IsHak, W. W. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108650182.014 · Cited by 1
Linking functional and structural brain organisation with behaviour in healthy adults
Forde, N. J., Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2023) · Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition · doi:10.58530/2022/2472 · Cited by 1
The neural basis responsible for the relationship between trait anxiety and upward counterfactual thinking: the role of functional connectivity between precentral gyrus and dmPFC
Suo, T., Chen, C., Yin, H., Feng, T., Wang, L., & Wang, M. (2024) · Current Psychology · doi:10.1007/s12144-024-06472-w · 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
Time-resolved instantaneous functional loci estimation (TRIFLE): Estimating time-varying allocation of spatially overlapping sources in functional magnetic resonance imaging
de Kloe, T., Fazal, Z., Kohn, N., Norris, D. G., Menon, R. S., Llera, A., & Beckmann, C. F. (2025) · Imaging Neuroscience · doi:10.1162/imag.a.58 · Cited by 1
Multifaceted Neuroimaging Data Integration via Analysis of Subspaces
Ackerman, A., Zhang, Z., Hannig, J., Prothero, J., & Marron, J. S. (2025) · Psychometrika · doi:10.1017/psy.2025.10020 · Cited by 4
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
The Specificity of Metacognition Questionnaire‐30 Subdimensions: Findings From Connectome‐Based Predictive Modeling
Hu, R., Yu, M., Li, L., He, H., Wei, S., Ma, J., Gu, Y., & Dai, Z. (2025) · Depression and Anxiety · doi:10.1155/da/5581270 · Cited by 1
GBrain:A Graph Signal Processing Approach to EEG Based Attention Detection
Majanini, S. A., Khalil, M. A., Kabbara, A., & Sheikh, H. A. (2025) · International Conference on Advances in Biomedical Engineering (ICABME) · doi:10.1109/icabme66883.2025.11211822 · Cited by 1
Structured Artificial Neural Networks for Chronological Brain Age Prediction using Functional Connectivity Matrices
Duarte, K. T. N., Sidhu, A. S., de Barros, M. C., Gobbi, D. G., McCreary, C. R., Bento, M., & Frayne, R. (2025) · International Symposium on Medical Information Processing and Analysis (SIPAIM) · doi:10.1109/sipaim67325.2025.11283257 · Cited by 1
An integrated framework for synergistic multi-element, cross-media pollution control in organic solid waste treatment system
Yang, Q., Chen, C., Zhang, J., & Wen, Z. (2026) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2026.147701 · Cited by 1
Study of sex differences in the whole brain white matter using diffusion MRI tractography and suprathreshold fiber cluster statistics
Zhang, F., Rushmore, J., Li, Y., Karayumak, S. C., Song, Y., Cai, W., Westin, C., Levitt, J. J., Makris, N., Yogesh Rathi, A., et al. (2026) · NeuroImage Clinical · doi:10.1016/j.nicl.2026.103979 · Cited by 1
Multivariate relationships between social cognitive performance and functional connectivity during task and rest across schizophrenia spectrum disorders and healthy controls
Oliver, L. D., Yu, J., Hawco, C. S., Calarco, N., Tan, V., Moxon‐Emre, I., Tang, S. X., Gold, J. M., Foussias, G., DeRosse, P., et al. (2026) · Molecular Psychiatry · doi:10.1038/s41380-026-03504-8 · Cited by 1
Effects of Age on Resting‐State Cortical Networks
Gohil, C., Kohl, O., Pitt, J., van Es, M. W. J., Quinn, A. J., Vidaurre, D., Turner, M. R., Nobre, A. C., & Woolrich, M. W. (2026) · Human Brain Mapping · doi:10.1002/hbm.70516 · Cited by 6
Self-citation
Aberrant inter-subject functional heterogeneity in frontoparietal and attention networks marks tobacco use disorder
Xu, X., & Dong, G.-H. (2026) · Progress in Neuro-Psychopharmacology and Biological Psychiatry · doi:10.1016/j.pnpbp.2026.111695 · Cited by 1
The dynamic interplay between brain entropy and functional connectivity
Xin, X., Yu, J., Wang, C., & Gao, X. (2026) · NeuroImage · doi:10.1016/j.neuroimage.2026.121919 · Cited by 1
Bayesian hierarchical modeling on covariance valued data
Acharyya, S., Zhang, Z., Bhattacharya, A., & Pati, D. (2022) · Stat · doi:10.1002/sta4.534 · Cited by 1
Massage, Humor, and Music
Ooi, S. L., & Pak, S. C. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108650182.033 · Cited by 1
Healing and Wellness
Dieppe, P. A., Goldingay, S., & Warber, S. L. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108650182.042 · Cited by 3
Emotional Intelligence and Its Role in Sustaining Fulfillment in Life
Sax, H. C., & Gewertz, B. L. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108650182.038 · Cited by 1
Family Relations, Friendships, and Love
Alsup, S., Weisbaum, E., Vogel, T., & Siegel, D. J. (2020) · Cambridge University Press eBooks · doi:10.1017/9781108650182.047 · Cited by 1
Inter-species cortical registration between macaques and humans using a functional network property under a spherical demons framework
Nam, H., Pae, C., Eo, J., Oh, M., & Park, H. (2021) · PLoS ONE · doi:10.1371/journal.pone.0258992 · Cited by 1
Funções cognitivas de mulheres diagnosticadas com câncer de mama em tratamento quimioterápico adjuvante
Mello, T. D. L., Kupske, J. W., Zanella, J. D. F. P., Schneider, I. J. C., & Krug, M. D. R. (2021) · Pensando Psicología · doi:10.16925/2382-3984.2021.02.02 · Cited by 1
Big Data in Cognitive Neuroscience: Opportunities and Challenges
Dadi, K., & Bapi, R. S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-24094-2_2 · Cited by 1
Brain networks atlases
Genon, S., & Li, J. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-91688-2.00001-1 · Cited by 1
The Ideal Doctor Image in Asian Countries: A Qualitative Study of Gen Z Patient's Perspective
Rosa, E. Y., & Sugandi, M. S. (2023) · Eduvest - Journal Of Universal Studies · doi:10.59188/eduvest.v3i7.865 · Cited by 1
Hexa-Net Framework: A Fresh ADHD-Specific Model for Identifying ADHD Based on Integrating Brain Atlases
Al-Ubaidi, D. A., Samah, A. A., & Jasim, M. N. (2024) · Communications in computer and information science · doi:10.1007/978-981-99-9592-9_7 · Cited by 1
Voxelwise Multivariate Analysis of Brain-Psychosocial Associations in Adolescents Reveals 6 Latent Dimensions of Cognition and Psychopathology
Adams, R. A., Zor, C., Mihalik, Á., Tsirlis, K., Brudfors, M., Chapman, J., Ashburner, J., Paulus, M. P., & Mourão-Miranda, J. (2024) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2024.03.006 · Cited by 3

Authors: 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.
Affiliations: University of Oxford (GB)
University of Warwick (GB)
Washington University in St. Louis (US)
Corresponding author: Smith, Stephen M
ORCID: Smith, Stephen M
Nichols, Thomas E
Vidaurre, Diego
Winkler, Anderson M
Behrens, Timothy E J
Glasser, Matthew F
Barch, Deanna M
Miller, Karla L
Venue: Nature Neuroscience
Publisher: Springer Science and Business Media LLC
Published: 2015-09-28
Volume: 18
Issue: 11
Pages: 1565-1567
ISSN: 1097-6256 (ISSN-L)
1546-1726
DOI: 10.1038/nn.4125
PMC: PMC4625579
OpenAlex: W1828334443
Cited by: 859
References: 27
Citations by year: 2026: 44 · 2025: 70 · 2024: 73 · 2023: 58 · 2022: 73 · 2021: 100 · 2020: 162 · 2019: 82 · 2018: 81 · 2017: 70 · 2016: 41 · 2015: 5
FWCI: 39.51
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.springer.com/tdm (tdm)
Locations: Nature Neuroscience (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
ScholarWorks @ UTRGV (The University of Texas Rio Grande Valley) (submittedVersion)
Open Repository and Bibliography (University of Liège) (submittedVersion, other-oa)
Funders: Wellcome Trust (104765/Z/14/Z, 098369/Z/12/Z, 098369, 104765, 100309/A/12/Z, 091509/Z/10/Z, 100309)
National Institutes of Health (5u54mh091657-05)
Engineering and Physical Sciences Research Council (EP/J005444/1)
National Institute of Mental Health (1U54 MH091657, U54-MH091657, F30MH097312)
National Institute of Neurological Disorders and Stroke (P30NS057091)
National Institute of Biomedical Imaging and Bioengineering (EB015894, P41EB015894)
National Center for Research Resources (P41RR008079, P41RR08079)
Topics: Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
Social Sciences › Psychology › Experimental and Cognitive Psychology › Mental Health Research Topics
Keywords: brain functional connectivity, lifestyle, psychometric measures, behavioral measures, Human Connectome Project
MeSH terms: Adult, Behavior, Brain, Demography, Female, Humans, Magnetic Resonance Imaging, Male, Models, Neurological, Nerve Net, Cluster Analysis, Young Adult, Connectome

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

A positive-negative mode of population covariation links brain connectivity, demographics 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