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 201–300 of 859

On the Nature of Explanations Offered by Network Science: A Perspective From and for Practicing Neuroscientists
Bertolero, M. A., & Bassett, D. S. (2020) · Topics in Cognitive Science · doi:10.1111/tops.12504 · Cited by 54
Hypertension is associated with reduced hippocampal connectivity and impaired memory
Feng, R., Rolls, E. T., Cheng, W., & Feng, J. (2020) · EBioMedicine · doi:10.1016/j.ebiom.2020.103082 · Cited by 49
A new model for simultaneous dimensionality reduction and time-varying functional connectivity estimation
Vidaurre, D. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008580 · Cited by 47
Self-citation
Patterns of brain dynamic functional connectivity are linked with attention-deficit/hyperactivity disorder-related behavioral and cognitive dimensions
Luo, L., Chen, L., Wang, Y., Li, Q., He, N., Li, Y., You, W., Wang, Y., Long, F., Guo, L., et al. (2023) · Psychological Medicine · doi:10.1017/s0033291723000089 · Cited by 54
An exemplar-based approach to individualized parcellation reveals the need for sex specific functional networks
Salehi, M., Karbasi, A., Shen, X., Scheinost, D., & Constable, R. T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.068 · Cited by 47
Decreased brain connectivity in smoking contrasts with increased connectivity in drinking
Cheng, W., Rolls, E. T., Robbins, T. W., Gong, W., Liu, Z., Lv, W., Du, J., Wen, H., Ma, L., Quinlan, E. B., et al. (2019) · eLife · doi:10.7554/elife.40765 · Cited by 48
Childhood socio‐economic disadvantage predicts reduced myelin growth across adolescence and young adulthood
Ziegler, G., Moutoussis, M., Hauser, T. U., Fearon, P. R. M., Bullmore, E. T., Goodyer, I. M., Fonagy, P., Jones, P. B., Lindenberger, U., & Dolan, R. J. (2020) · Human Brain Mapping · doi:10.1002/hbm.25024 · Cited by 47
A double-hit of stress and low-grade inflammation on functional brain network mediates posttraumatic stress symptoms
Kim, J., Yoon, S. J., Lee, S. L., Hong, H., Ha, E., Joo, Y., Lee, E. H., & Lyoo, I. K. (2020) · Nature Communications · doi:10.1038/s41467-020-15655-5 · Cited by 50
Changes in brain metabolic connectivity underlie autistic-like social deficits in a rat model of autism spectrum disorder
Cho, H., Kim, C. H., Knight, E. Q., Oh, H.-W., Park, B., Kim, D. G., & Park, H. (2017) · Scientific Reports · doi:10.1038/s41598-017-13642-3 · Cited by 49
Precision Inhibitory Stimulation of Individual-Specific Cortical Hubs Disrupts Information Processing in Humans
Lynch, C. J., Breeden, A. L., Gordon, E. M., Cherry, J. B. C., Turkeltaub, P. E., & Vaidya, C. J. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy270 · Cited by 47
Linked patterns of biological and environmental covariation with brain structure in adolescence: a population-based longitudinal study
Modabbernia, A., Reichenberg, A., Ing, A., Moser, D. A., Doucet, G. E., Artiges, É., Banaschewski, T., Barker, G. J., Becker, A., Bokde, A. L. W., et al. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-0757-x · Cited by 47
Brain–behavior patterns define a dimensional biotype in medication-naïve adults with attention-deficit hyperactivity disorder
Lin, H., Cocchi, L., Zalesky, A., Lv, J., Perry, A., Tseng, W. I., Kundu, P., Breakspear, M. J., & Gau, S. S. (2018) · Psychological Medicine · doi:10.1017/s0033291718000028 · Cited by 49
Multi-modal latent factor exploration of atrophy, cognitive and tau heterogeneity in Alzheimer’s disease
Sun, N., Mormino, E. C., Chen, J., Sabuncu, M. R., & Yeo, B. T. T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116043 · Cited by 47
Test-retest reliability of brain responses to risk-taking during the balloon analogue risk task
Li, X., Pan, Y., Fang, Z., Lei, H., Zhang, X., Shi, H., Ma, N., Raine, P., Wetherill, R. R., Kim, J., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116495 · Cited by 48
A structural brain network of genetic vulnerability to psychiatric illness
Taquet, M., Smith, S. M., Prohl, A. K., Peters, J. M., Warfield, S. K., Scherrer, B., & Harrison, P. J. (2020) · Molecular Psychiatry · doi:10.1038/s41380-020-0723-7 · Cited by 49
Self-citation
Trajectories of brain system maturation from childhood to older adulthood: Implications for lifespan cognitive functioning.
Petrican, R., Taylor, M. J., & Grady, C. L. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.09.025 · Cited by 43
Comparison between gradients and parcellations for functional connectivity prediction of behavior
Kong, R., Tan, Y. R., Wulan, N., Ooi, L. Q. R., Farahibozorg, S.-R., Harrison, S. J., Bijsterbosch, J. D., Bernhardt, B. C., Eickhoff, S. B., & Yeo, B. T. T. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.120044 · Cited by 49
Phenotype discovery from population brain imaging
Gong, W., Beckmann, C. F., & Smith, S. M. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102050 · Cited by 46
Self-citation
Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain
Madan, C. R. (2021) · Neuroinformatics · doi:10.1007/s12021-021-09519-6 · Cited by 48
Cognitive Profiles and Hub Vulnerability in Parkinson's Disease
Lin, S., Baumeister, T. R., Garg, S., & McKeown, M. J. (2018) · Frontiers in Neurology · doi:10.3389/fneur.2018.00482 · Cited by 46
Bedside EEG predicts longitudinal behavioural changes in disorders of consciousness
Bareham, C. A., Roberts, N., Allanson, J. E., Hutchinson, P. J., Pickard, J. D., Menon, D. K., & Chennu, S. (2020) · NeuroImage Clinical · doi:10.1016/j.nicl.2020.102372 · Cited by 45
Individual variability in functional connectivity architecture of the mouse brain
Bergmann, E., Gofman, X., Kavushansky, A., & Kahn, I. (2020) · Communications Biology · doi:10.1038/s42003-020-01472-5 · Cited by 48
Functional connectome biotypes of chemotherapy-related cognitive impairment
Kesler, S. R., Petersen, M. E., Rao, V., Harrison, R. A., & Palesh, O. G. (2020) · Journal of Cancer Survivorship · doi:10.1007/s11764-020-00863-1 · Cited by 43
Metabolic and functional connectivity provide unique and complementary insights into cognition-connectome relationships
Voigt, K., Liang, E. X., Mišić, B., Ward, P. G. D., Egan, G. F., & Jamadar, S. D. (2022) · Cerebral Cortex · doi:10.1093/cercor/bhac150 · Cited by 44
Mapping correlated neurological deficits after stroke to distributed brain networks
Siegel, J. S., Shulman, G. L., & Corbetta, M. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02525-7 · Cited by 44
Differences in the functional brain architecture of sustained attention and working memory in youth and adults
Kardan, O., Stier, A. J., Cardenas‐Iniguez, C., Pruin, J., Schertz, K. E., Deng, Y., Chamberlain, T. A., Meredith, W. J., Zhang, X.-H., Bowman, J. E., et al. (2022) · PLoS Biology · doi:10.1371/journal.pbio.3001938 · Cited by 45
Effects of eight neuropsychiatric copy number variants on human brain structure
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. (2021) · Translational Psychiatry · doi:10.1038/s41398-021-01490-9 · Cited by 45
Brain network modularity predicts cognitive training-related gains in young adults
Baniqued, P. L., Gallen, C. L., Kranz, M. B., Kramer, A. F., & D’Esposito, M. (2019) · Neuropsychologia · doi:10.1016/j.neuropsychologia.2019.05.021 · Cited by 44
Gray matter covariations and core symptoms of autism: the EU-AIMS Longitudinal European Autism Project
Mei, T., Llera, A., Floris, D. L., Forde, N. J., Tillmann, J., Durston, S., Moessnang, C., Banaschewski, T., Holt, R. J., Baron‐Cohen, S., et al. (2020) · Molecular Autism · doi:10.1186/s13229-020-00389-4 · Cited by 43
An integrated multimodal model of alcohol use disorder generated by data-driven causal discovery analysis
Rawls, E., Kummerfeld, E., & Zilverstand, A. (2021) · Communications Biology · doi:10.1038/s42003-021-01955-z · Cited by 45
Alterations of local functional connectivity in lifespan: A resting‐state fMRI study
Wen, X., He, H., Dong, L., Chen, J., Yang, J., Guo, H., Luo, C., & Yao, D. (2020) · Brain and Behavior · doi:10.1002/brb3.1652 · Cited by 41
Multivariate brain-behaviour associations in psychiatric disorders
Vieira, S., Bolton, T. A. W., Schöttner, M., Baecker, L., Marquand, A. F., Mechelli, A., & Hagmann, P. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02954-4 · Cited by 47
Handedness-dependent functional organizational patterns within the bilateral vestibular cortical network revealed by fMRI connectivity based parcellation
Kirsch, V., Boegle, R., Keeser, D., Kierig, E., Ertl-Wagner, B. B., Brandt, T., & Dieterich, M. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.018 · Cited by 47
Multidimensional encoding of brain connectomes
Caiafa, C. F., & Pestilli, F. (2017) · Scientific Reports · doi:10.1038/s41598-017-09250-w · Cited by 46
White matter changes in psychosis risk relate to development and are not impacted by the transition to psychosis
Biase, M. A. D., Karayumak, S. C., Lyall, A. E., Zalesky, A., Cho, K. I. K., Zhang, F., Kubicki, M. R., Yogesh Rathi, A., Lyons, M., Sylvain Bouix, A., et al. (2021) · Molecular Psychiatry · doi:10.1038/s41380-021-01128-8 · Cited by 43
The predictive nature of spontaneous brain activity across scales and species
Dimakou, A., Pezzulo, G., Zangrossi, A., & Corbetta, M. (2025) · Neuron · doi:10.1016/j.neuron.2025.02.009 · Cited by 49
Resting-State Functional Connectivity and Network Analysis of Cerebellum with Respect to IQ and Gender
Pezoulas, V. C., Zervakis, M. E., Michelogiannis, S., & Klados, M. A. (2017) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2017.00189 · Cited by 38
Different shades of default mode disturbance in schizophrenia: Subnodal covariance estimation in structure and function
Lefort‐Besnard, J., Bassett, D. S., Smallwood, J., Margulies, D. S., Derntl, B., Gruber, O., Aleman, A., Jardri, R., Varoquaux, G. P., Thirion, B., et al. (2017) · Human Brain Mapping · doi:10.1002/hbm.23870 · Cited by 41
Agito ergo sum: Correlates of spatio-temporal motion characteristics during fMRI
Bolton, T. A. W., Kebets, V., Glerean, E., Zöller, D., Li, J., Yeo, B. T. T., Caballero‐Gaudes, C., & Ville, D. V. D. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116433 · Cited by 40
Approaches to Defining Common and Dissociable Neurobiological Deficits Associated With Psychopathology in Youth
Kaczkurkin, A. N., Moore, T. M., Sotiras, A., Xia, C. H., Shinohara, R. T., & Satterthwaite, T. D. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.12.015 · Cited by 42
Neural synchronization predicts marital satisfaction
Li, L., Huang, X., Xiao, J., Zheng, Q., Shan, X., He, C., Liao, W., Chen, H., Menon, V., & Duan, X. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2202515119 · Cited by 39
Toward Mapping Neurobehavioral Heterogeneity of Psychedelic Neurobiology in Humans
Moujaes, F., Preller, K. H., Ji, J. L., Murray, J. D., Berkovitch, L., Vollenweider, F. X., & Anticevic, A. (2022) · Biological Psychiatry · doi:10.1016/j.biopsych.2022.10.021 · Cited by 38
Latent Clinical-Anatomical Dimensions of Schizophrenia
Kirschner, M., Shafiei, G., Markello, R. D., Makowski, C., Talpalaru, A., Hodzic-Santor, B. H., Devenyi, G. A., Paquola, C., Bernhardt, B. C., Lepage, M., et al. (2020) · Schizophrenia Bulletin · doi:10.1093/schbul/sbaa097 · Cited by 38
A Whole-Brain and Cross-Diagnostic Perspective on Functional Brain Network Dysfunction
Spronk, M., Keane, B. P., Ito, T., Kulkarni, K. R., Ji, J. L., Anticevic, A., & Cole, M. W. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa242 · Cited by 43
Transient neural network dynamics in cognitive ageing
Tibon, R., Tsvetanov, K. A., Price, D., Nesbitt, D. J., Can, C., & Henson, R. N. (2021) · Neurobiology of Aging · doi:10.1016/j.neurobiolaging.2021.01.035 · Cited by 41
Subject specificity of the correlation between large-scale structural and functional connectivity
Zimmermann, J., Griffiths, J. D., Schirner, M., Ritter, P., & McIntosh, A. R. (2018) · Network Neuroscience · doi:10.1162/netn_a_00055 · Cited by 40
Nutritional status, brain network organization, and general intelligence
Zamroziewicz, M. K., Talukdar, M. T., Zwilling, C. E., & Barbey, A. K. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.08.043 · Cited by 39
Modelling subject variability in the spatial and temporal characteristics of functional modes
Harrison, S. J., Bijsterbosch, J. D., Segerdahl, A. R., Fitzgibbon, S. P., Farahibozorg, S.-R., Duff, E. P., Smith, S. M., & Woolrich, M. W. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117226 · Cited by 42
Self-citation
Covariance patterns between sleep health domains and distributed intrinsic functional connectivity
Wang, Y., Genon, S., Dong, D., Zhou, F., Li, C., Yu, D., Yuan, K., He, Q., Qiu, J., Feng, T., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-42945-5 · Cited by 39
Heterogeneous factors influence social cognition across diverse settings in brain health and age-related diseases
Fittipaldi, S., Legaz, A., Maito, M. A., Hernandez, H., Altschuler, F., Canziani, V., Moguilner, S., Gillan, C. M., Castillo, J., Lillo, P., et al. (2024) · Nature Mental Health · doi:10.1038/s44220-023-00164-3 · Cited by 44
Improved estimation of subject-level functional connectivity using full and partial correlation with empirical Bayes shrinkage
Mejia, A. F., Nebel, M. B., Barber, A. D., Choe, A. S., Pekar, J. J., Caffo, B. S., & Lindquist, M. A. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.029 · Cited by 39
Brain-behaviour modes of covariation in healthy and clinically depressed young people
Mihalik, Á., Ferreira, F. S., Rosa, M. J., Moutoussis, M., Ziegler, G., Monteiro, J. M., Portugal, L. C. L., Adams, R. A., Romero-García, R., Vértes, P. E., et al. (2019) · Scientific Reports · doi:10.1038/s41598-019-47277-3 · Cited by 36
Task modulations and clinical manifestations in the brain functional connectome in 1615 fMRI datasets
Kaufmann, T., Alnæs, D., Brandt, C. L., Doan, N. T., Kauppi, K., Bettella, F., Lagerberg, T. V., Berg, A. O., Djurovic, S., Agartz, I., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.11.073 · Cited by 41
Differential contributions of static and time-varying functional connectivity to human behavior
Eichenbaum, A., Pappas, I., Lurie, D. J., Cohen, J. R., & D’Esposito, M. (2020) · Network Neuroscience · doi:10.1162/netn_a_00172 · Cited by 37
Whole-brain estimates of directed connectivity for human connectomics
Frässle, S., Manjaly, Z. M., Tri, C., Kasper, L., Pruessmann, K. P., & Stephan, K. E. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117491 · Cited by 35
Individual Differences in Cognitive Performance Are Better Predicted by Global Rather Than Localized BOLD Activity Patterns Across the Cortex
Zhao, W., Palmer, C. E., Thompson, W. K., Chaarani, B., Garavan, H., Casey, B. J., Jernigan, T. L., Dale, A. M., & Fan, C. C. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa290 · Cited by 35
Shared and Anxiety-Specific Pediatric Psychopathology Dimensions Manifest Distributed Neural Correlates
Linke, J. O., Abend, R., Kircanski, K., Clayton, M., Stavish, C. M., Benson, B. E., Brotman, M. A., Renaud, O., Smith, S. M., Nichols, T. E., et al. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.10.018 · Cited by 37
Self-citation
Brain pathology recapitulates physiology: A network meta-analysis
Vanasse, T. J., Fox, P. T., Fox, P. M., Cauda, F., Costa, T., Smith, S. M., Eickhoff, S. B., & Lancaster, J. L. (2021) · Communications Biology · doi:10.1038/s42003-021-01832-9 · Cited by 35
Self-citation
Psychiatric Neural Networks and Precision Therapeutics by Machine Learning
Komatsu, H., Watanabe, E., & Fukuchi, M. (2021) · Biomedicines · doi:10.3390/biomedicines9040403 · Cited by 39
Latent brain state dynamics and cognitive flexibility in older adults
Lee, B., Cai, W., Young, C. B., Yuan, R., Ryman, S. G., Kim, J., Santini, V. E., Henderson, V. W., Poston, K. L., & Menon, V. (2021) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2021.102180 · Cited by 37
JIVE integration of imaging and behavioral data
Yu, Q., Risk, B. B., Zhang, K., & Marron, J. S. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.02.072 · Cited by 36
Mapping altered brain connectivity and its clinical associations in adult moyamoya disease: A resting-state functional MRI study
Kazumata, K., Tha, K. K., 内野晴登, Ito, M., Nakayama, N., & Abumiya, T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0182759 · Cited by 36
Joint functional brain network atlas estimation and feature selection for neurological disorder diagnosis with application to autism
Mhiri, I., & Rekik, I. (2019) · Medical Image Analysis · doi:10.1016/j.media.2019.101596 · Cited by 37
Morbigenous brain region and gene detection with a genetically evolved random neural network cluster approach in late mild cognitive impairment
Bi, X.-A., Liu, Y., Xie, Y., Hu, X., & Jiang, Q. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btz967 · Cited by 36
BOLD cofluctuation ‘events’ are predicted from static functional connectivity
Ladwig, Z., Seitzman, B. A., Dworetsky, A., Yu, Y., Adeyemo, B., Smith, D. M., Petersen, S. E., & Gratton, C. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119476 · Cited by 32
Compensatory Hyperconnectivity in Developing Brains of Young Children With Type 1 Diabetes
Saggar, M., Tsalikian, E., Mauras, N., Mazaika, P. K., White, N. H., WEINZIMER, S. A., Buckingham, B. A., Hershey, T. G., & Reiss, A. L. (2016) · Diabetes · doi:10.2337/db16-0414 · Cited by 37
Network functional connectivity and whole-brain functional connectomics to investigate cognitive decline in neurodegenerative conditions
Dipasquale, O. (2016) · Functional Neurology · doi:10.11138/fneur/2016.31.4.191 · Cited by 36
Tracking mood fluctuations with functional network patterns
Mirchi, N., Betzel, R. F., Bernhardt, B. C., Dagher, A., & Mišić, B. (2018) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsy107 · Cited by 39
Mapping brain–behavior networks using functional and structural connectome fingerprinting in the HCP dataset
Lin, Y., Baete, S. H., Wang, X. H., & Boada, F. E. (2020) · Brain and Behavior · doi:10.1002/brb3.1647 · Cited by 33
Minimal specifications for non-human primate MRI: Challenges in standardizing and harmonizing data collection
Autio, J. A., Zhu, Q., Li, X., Glasser, M. F., Schwiedrzik, C. M., Fair, D. A., Zimmermann, J., Yacoub, E. S., Menon, R. S., Essen, D. C. V., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118082 · Cited by 39
Self-citation
Maternal parenting behavior and functional connectivity development in children: A longitudinal fMRI study
Pozzi, E., Vijayakumar, N., Byrne, M. L., Bray, K. O., Seal, M. L., Richmond, S., Zalesky, A., & Whittle, S. (2021) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2021.100946 · Cited by 39
Mapping brain-behavior space relationships along the psychosis spectrum
Ji, J. L., Helmer, M., Fonteneau, C., Burt, J. B., Tamayo, Z., Demšar, J., Adkinson, B. D., Savić, A., Preller, K. H., Moujaes, F., et al. (2021) · eLife · doi:10.7554/elife.66968 · Cited by 42
Social cognitive network neuroscience
Krendl, A. C., & Betzel, R. F. (2022) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsac020 · Cited by 40
Thresholding functional connectomes by means of mixture modeling
Bielczyk, N., Walocha, F., Ebel, P., Haak, K. V., Llera, A., Buitelaar, J. K., Glennon, J. C., & Beckmann, C. F. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.003 · Cited by 36
Atlas55+: Brain Functional Atlas of Resting-State Networks for Late Adulthood
Doucet, G. E., Labache, L., Thompson, P. M., Joliot, M., Frangou, S., & Initiative, A. D. N. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa321 · Cited by 34
Modulation of the functional connectome in major depressive disorder by ketamine therapy
Sahib, A. K., Loureiro, J. R., Vasavada, M. M., Anderson, C., Kubicki, A., Wade, B., Joshi, S. H., Woods, R. P., Congdon, E. L., Espinoza, R. T., et al. (2020) · Psychological Medicine · doi:10.1017/s0033291720004560 · Cited by 35
Recent developments in representations of the connectome
Bijsterbosch, J. D., Valk, S. L., Wang, D., & Glasser, M. F. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118533 · Cited by 34
Self-citation
The Normative Modeling Framework for Computational PsychiatryPreprint
Rutherford, S. E., Kia, S. M., Wolfers, T., Fraza, C., Zabihi, M., Dinga, R., Berthet, P., Worker, A., Verdi, S., Ruhé, H. G., et al. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.08.08.455583 · Cited by 34
Network attributes underlying intellectual giftedness in the developing brain
Ma, J., Kang, H. J., Kim, J., Jeong, H. S., Im, J. J., Namgung, E., Kim, M. J., Lee, S. L., Kim, T. D., Oh, J. K., et al. (2017) · Scientific Reports · doi:10.1038/s41598-017-11593-3 · Cited by 34
Risk-taking in humans and the medial orbitofrontal cortex reward system
Rolls, E. T., Wan, Z., Cheng, W., & Feng, J. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.118893 · Cited by 34
Associations Between Daily Mood States and Brain Gray Matter Volume, Resting-State Functional Connectivity and Task-Based Activity in Healthy Adults
Ismaylova, E., Sante, J. D., Gouin, J., Pomares, F. B., Vitaro, F., Tremblay, R. E., & Booij, L. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00168 · Cited by 31
Functional Connectivity’s Degenerate View of Brain Computation
Marrelec, G., Messé, A., Giron, A., & Rudrauf, D. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005031 · Cited by 32
Quantitative Identification of Functional Connectivity Disturbances in Neuropsychiatric Lupus Based on Resting-State fMRI: A Robust Machine Learning Approach
Simos, N. J., Dimitriadis, S. I., Kavroulakis, E., Manikis, G. C., Bertsias, G. K., Simos, P. G., Maris, T. G., & Papadaki, E. Z. (2020) · Brain Sciences · doi:10.3390/brainsci10110777 · Cited by 31
Reliability modelling of resting-state functional connectivity
Teeuw, J., Pol, H. E. H., Boomsma, D. I., & Brouwer, R. M. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.117842 · Cited by 33
The individuality of shape asymmetries of the human cerebral cortex
Chen, Y., Arnatkevičiūtė, A., McTavish, E., Pang, J. C. S., Chopra, S., Suo, C., Fornito, A., Aquino, K. M., & Initiative, A. D. N. (2022) · eLife · doi:10.7554/elife.75056 · Cited by 32
Dyslipidemia induced large-scale network connectivity abnormality facilitates cognitive decline in the Alzheimer’s disease
Wang, Q., Zang, F., He, C., Zhang, Z., Xie, C., & Initiative, A. D. N. (2021) · Journal of Translational Medicine · doi:10.1186/s12967-022-03786-w · Cited by 32
Distinct resting-state functional connections associated with episodic and visuospatial memory in older adults
Suri, S., Topiwala, A., Filippini, N., Zsoldos, E., Mahmood, A., Sexton, C. E., Singh‐Manoux, A., Mika Kivimaki, B., Mackay, C. E., Smith, S. M., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.049 · Cited by 35
Self-citation
Individual variation in brain network topology is linked to emotional intelligence
Ling, G., Lee, I., Guimond, S., Lutz, O., Tandon, N., Nawaz, U., Öngür, D., Eack, S. M., Lewandowski, K. E., Keshavan, M. S., et al. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.01.013 · Cited by 32
Multi-modal brain fingerprinting: A manifold approximation based framework
Kumar, K., Toews, M., Chauvin, L., Olivier Colliot, A., & Desrosiers, C. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.006 · Cited by 35
Brain Connectome Mapping of Complex Human Traits and Their Polygenic Architecture Using Machine Learning
Maglanoc, L. A., Kaufmann, T., van der Meer, D., Marquand, A. F., Wolfers, T., Jonassen, R., Hilland, E., Andreassen, O. A., Landrø, N. I., & Westlye, L. T. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.10.011 · Cited by 31
Disrupted Functional Network Connectivity Predicts Cognitive Impairment in Presbycusis Patients
Xing, C., Zhang, J., Cui, J., Yong, W., Hu, J.-H., Yin, X., Wu, Y., & Chen, Y. (2020) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2020.00246 · Cited by 31
Avalanche criticality in individuals, fluid intelligence, and working memory
Xu, L., Feng, J., & Yu, L. (2022) · Human Brain Mapping · doi:10.1002/hbm.25802 · Cited by 35
Relating brain connectivity with persistent symptoms in pediatric concussion
Iyer, K. K., Barlow, K. M., Brooks, B. L., Ofoghi, Z., Zalesky, A., & Cocchi, L. (2019) · Annals of Clinical and Translational Neurology · doi:10.1002/acn3.764 · Cited by 31
Generalizable and replicable brain-based predictions of cognitive functioning across common psychiatric illness
Chopra, S., Dhamala, E., Lawhead, C., Ricard, J. A., Orchard, E. R., An, L., Chen, P., Wulan, N., Kumar, P., Rubenstein, A., et al. (2024) · Science Advances · doi:10.1126/sciadv.adn1862 · Cited by 38
Resting-State Functional Connectivity in the Human Connectome Project: Current Status and Relevance to Understanding Psychopathology
Barch, D. M. (2017) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000166 · Cited by 31
Self-citation
Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques
Sallet, J., Noonan, M. P., Thomas, A. G., O’Reilly, J. X., Anderson, J., Papageorgiou, G., Neubert, F. X., Ahmed, B., Smith, J. E. T., Bell, A. H., et al. (2020) · PLoS Biology · doi:10.1371/journal.pbio.3000605 · Cited by 29
Genetic and Environmental Influence on the Human Functional Connectome
Reineberg, A. E., Hatoum, A. S., Hewitt, J. K., Banich, M. T., & Friedman, N. P. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz225 · Cited by 31
Analysing brain networks in population neuroscience: a case for the Bayesian philosophy
Bzdok, D., Floris, D. L., & Marquand, A. F. (2020) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2019.0661 · Cited by 30
Multivariate associative patterns between the gut microbiota and large-scale brain network connectivity
Kohn, N., Szopinska-Tokov, J., Llera, A., Beckmann, C. F., Vásquez, A. A., & Aarts, E. (2021) · Gut Microbes · doi:10.1080/19490976.2021.2006586 · Cited by 33
Toward a functional future for the cognitive neuroscience of human aging
Mooraj, Z., Salami, A., Campbell, K. L., Dahl, M. J., Kosciessa, J. Q., Nassar, M. R., Werkle‐Bergner, M., Craik, F. I. M., Lindenberger, U., Mayr, U., et al. (2025) · Neuron · doi:10.1016/j.neuron.2024.12.008 · Cited by 39

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