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 accessAbstract
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.
Overview
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
Cites
Statistics
Verification
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?
The cited source says what the claim attributes to it.
In the citing paper
“Along those lines, a 'positive-negative' axis of brain-behavior covariation describes a similar connectivity spectrum, distinguishing the DMN from sensory/motor regions [63].”
From Situating the default-mode network along a principal gradient of macroscale cortical organization (2016) · cites this paper as [63]
The cited source says what the claim attributes to it.
In the citing paper
“Precedent exists for using PCA to extract network-level patterns from high-dimensional neural data, including fMRI (Smith et al., 2015), EEG (Blankertz et al., 2011), and even LLM embeddings (Ethayarajh, 2019).”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
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-citationThe 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-citationThe 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-citationBinder, 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-citationCan 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
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).
Cited by
Sorted by citations
Local-global parcellation of the human cerebral cortex from intrinsic functional connectivity MRI
Schaefer, A., Kong, R., Gordon, E. M., Laumann, T. O., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., & Yeo, B. T. T. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhx179 · Cited by 3,657
Situating the default-mode network along a principal gradient of macroscale cortical organization
Margulies, D. S., Ghosh, S. S., Goulas, A., Falkiewicz, M., Huntenburg, J. M., Langs, G., Bezgin, G., Eickhoff, S. B., Castellanos, F. X., Petrides, M., et al. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1608282113 · Cited by 2,321
Multimodal population brain imaging in the UK Biobank prospective epidemiological study
Miller, K. L., Alfaro-Almagro, F., Bangerter, N. K., Thomas, D. L., Yacoub, E., Xu, J., Bartsch, A. J., Jbabdi, S., Sotiropoulos, S. N., Andersson, J. L. R., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4393 · Cited by 2,020
Reproducible brain-wide association studies require thousands of individuals
Marek, S. A., Tervo‐Clemmens, B., Calabro, F. J., Montez, D. F., Kay, B. P., Hatoum, A. S., Donohue, M. R., Foran, W., Miller, R. L., Hendrickson, T. J., et al. (2022) · Nature · doi:10.1038/s41586-022-04492-9 · Cited by 2,275
Self-citationThe Human Connectome Project's neuroimaging approach
Glasser, M. F., Smith, S. M., Marcus, D. S., Andersson, J. L. R., Auerbach, E. J., Behrens, T. E. J., Coalson, T. S., Harms, M. P., Jenkinson, M., Moeller, S., et al. (2016) · Nature Neuroscience · doi:10.1038/nn.4361 · Cited by 1,090
Self-citationCross-validation failure: Small sample sizes lead to large error bars
Varoquaux, G. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.061 · Cited by 897
“…of cognition (Haynes and Rees, 2006), information mapping (Kriegeskorte et al., 2006), prediction of individual differences –behavioral or clinical– (Smith et al., 2015), rich encoding models (Nishimoto et al., 2011), principled reverse inferences (Poldrack et al., 2009), etc. Replacing in-sample…”
BackgroundSex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants
Ritchie, S. J., Cox, S. R., Shen, X., Lombardo, M. V., Reus, L. M., Alloza, C., Harris, M. A., Alderson, H. L., Hunter, S., Neilson, E., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy109 · Cited by 855
“Before analysis of sex differences, we multiplied the strength of each connection by the sign of its group-mean (Smith et al. 2015).”
MethodBrain network dynamics are hierarchically organized in time
Vidaurre, D., Smith, S. M., & Woolrich, M. W. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1705120114 · Cited by 883
Self-citationTopographic organization of the human subcortex unveiled with functional connectivity gradients
Tian, Y., Margulies, D. S., Breakspear, M. J., & Zalesky, A. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00711-6 · Cited by 808
Sydnor, V. J., Larsen, B., Bassett, D. S., Alexander‐Bloch, A. F., Fair, D. A., Liston, C. M., Mackey, A. P., Milham, M. P., Pines, A. R., Roalf, D. R., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.06.016 · Cited by 750
Building a Science of Individual Differences from fMRI
Dubois, J., & Adolphs, R. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.03.014 · Cited by 644
Kong, R., Li, J., Orbán, C., Sabuncu, M. R., Liu, H., Schaefer, A., Sun, N., Zuo, X., Holmes, A. J., Eickhoff, S. B., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy123 · Cited by 615
Makropoulos, A., Robinson, E. C., Schuh, A., Wright, R., Fitzgibbon, S. P., Božek, J., Counsell, S. J., Steinweg, J. K., Vecchiato, K., Passerat‐Palmbach, J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.01.054 · Cited by 571
Self-citationDistinct Global Brain Dynamics and Spatiotemporal Organization of the Salience Network
Chen, T., Cai, W., Ryali, S., Supekar, K., & Menon, V. (2016) · PLOS Biology · doi:10.1371/journal.pbio.1002469 · Cited by 480
Linked dimensions of psychopathology and connectivity in functional brain networks
Xia, C. H., Ma, Z., Ciric, R., Gu, S., Betzel, R. F., Kaczkurkin, A. N., Calkins, M. E., Cook, P. A., García de la Garza, A., Vandekar, S. N., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-05317-y · Cited by 437
Li, J., Kong, R., Liégeois, R., Orban, C., Tan, Y., Sun, N., Holmes, A. J., Sabuncu, M. R., Ge, T., & Yeo, B. T. T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.04.016 · Cited by 380
Noble, S., Spann, M. N., Tokoglu, F., Shen, X., Constable, R. T., & Scheinost, D. (2017) · Cerebral Cortex · doi:10.1093/cercor/bhx230 · Cited by 378
Metric learning with spectral graph convolutions on brain connectivity networks
Ktena, S. I., Parisot, S., Ferrante, E., Rajchl, M., Lee, M., Glocker, B., & Rueckert, D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.052 · Cited by 382
Cui, Z., & Gong, G. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.06.001 · Cited by 367
Somerville, L. H., Bookheimer, S. Y., Buckner, R. L., Burgess, G. C., Curtiss, S. W., Dapretto, M., Elam, J. S., Gaffrey, M. S., Harms, M. P., Hodge, C., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.08.050 · Cited by 373
Self-citationCan brain state be manipulated to emphasize individual differences in functional connectivity?
Finn, E. S., Scheinost, D., Finn, D. M., Shen, X., Papademetris, X., & Constable, R. T. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.064 · Cited by 344
The relation between statistical power and inference in fMRI
Cremers, H. R., Wager, T. D., & Yarkoni, T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0184923 · Cited by 356
Discovering dynamic brain networks from big data in rest and task
Vidaurre, D., Abeysuriya, R., Becker, R., Quinn, A. J., Alfaro‐Almagro, F., Smith, S. M., & Woolrich, M. W. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.077 · Cited by 347
Self-citationEvaluating the evidence for biotypes of depression: Methodological replication and extension of
Dinga, R., Schmaal, L., Penninx, B. W. J. H., van Tol, M. J., Veltman, D. J., van Velzen, L., Mennes, M., van der Wee, N. J. A., & Marquand, A. F. (2019) · NeuroImage Clinical · doi:10.1016/j.nicl.2019.101796 · Cited by 313
Benchmarking functional connectome-based predictive models for resting-state fMRI
Dadi, K., Rahim, M., Abraham, A., Chyzhyk, D., Milham, M. P., Thirion, B., & Varoquaux, G. P. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.02.062 · Cited by 349
Statistical Challenges in “Big Data” Human Neuroimaging
Smith, S. M., & Nichols, T. E. (2018) · Neuron · doi:10.1016/j.neuron.2017.12.018 · Cited by 333
Self-citationFaskowitz, J., Esfahlani, F. Z., Jo, Y., Sporns, O., & Betzel, R. F. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00719-y · Cited by 341
A distributed brain network predicts general intelligence from resting-state human neuroimaging data
Dubois, J., Galdi, P., Paul, L. K., & Adolphs, R. (2018) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2017.0284 · Cited by 328
Advancing functional connectivity research from association to causation
Reid, A., Headley, D. B., Mill, R. D., Sánchez-Romero, R., Uddin, L. Q., Marinazzo, D., Lurie, D. J., Valdes-Sosa, P. A., Hanson, S. J., Biswal, B. B., et al. (2019) · Nature Neuroscience · doi:10.1038/s41593-019-0510-4 · Cited by 303
The Human Connectome Project: A retrospective
Elam, J. S., Glasser, M. F., Harms, M. P., Sotiropoulos, S. N., Andersson, J., Burgess, G. C., Curtiss, S. W., Oostenveld, R., Larson‐Prior, L. J., Schoffelen, J., et al. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118543 · Cited by 340
Self-citationResting brain dynamics at different timescales capture distinct aspects of human behavior
Liégeois, R., Li, J., Kong, R., Orbán, C., Ville, D. V. D., Ge, T., Sabuncu, M. R., & Yeo, B. T. T. (2019) · Nature Communications · doi:10.1038/s41467-019-10317-7 · Cited by 300
Cheng, W., Rolls, E. T., Ruan, H., & Feng, J. (2018) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2018.1941 · Cited by 291
Building connectomes using diffusion MRI: why, how and but
Sotiropoulos, S. N., & Zalesky, A. (2019) · NMR in Biomedicine · doi:10.1002/nbm.3752 · Cited by 290
Brain network communication: concepts, models and applications
Seguin, C., Sporns, O., & Zalesky, A. (2023) · Nature reviews. Neuroscience · doi:10.1038/s41583-023-00718-5 · Cited by 329
Network-Level Structure-Function Relationships in Human Neocortex
Mišić, B., Betzel, R. F., de Reus, M. A., van den Heuvel, M. P., Berman, M. G., McIntosh, A. R., & Sporns, O. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw089 · Cited by 293
Somatosensory-Motor Dysconnectivity Spans Multiple Transdiagnostic Dimensions of Psychopathology
Kebets, V., Holmes, A. J., Orbán, C., Tang, S., Li, J., Sun, N., Kong, R., Poldrack, R. A., & Yeo, B. T. T. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.06.013 · Cited by 281
The normative modeling framework for computational psychiatry
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. (2022) · Nature Protocols · doi:10.1038/s41596-022-00696-5 · Cited by 296
He, T., Kong, R., Holmes, A. J., Nguyen, M., Sabuncu, M. R., Eickhoff, S. B., Bzdok, D., Feng, J., & Yeo, B. T. T. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2019.116276 · Cited by 271
The human connectome in Alzheimer disease — relationship to biomarkers and genetics
Yu, M., Sporns, O., & Saykin, A. J. (2021) · Nature Reviews Neurology · doi:10.1038/s41582-021-00529-1 · Cited by 265
Data Quality Influences Observed Links Between Functional Connectivity and Behavior
Siegel, J. S., Mitra, A., Laumann, T. O., Seitzman, B. A., Raichle, M. E., Corbetta, M., & Snyder, A. Z. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhw253 · Cited by 254
Individual-Specific Areal-Level Parcellations Improve Functional Connectivity Prediction of Behavior
Kong, R., Yang, Q., Gordon, E. M., Xue, A., Yan, X., Orbán, C., Zuo, X., Spreng, R. N., Ge, T., Holmes, A. J., et al. (2021) · Cerebral Cortex · doi:10.1093/cercor/bhab101 · Cited by 261
From regions to connections and networks: new bridges between brain and behavior
Mišić, B., & Sporns, O. (2016) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2016.05.003 · Cited by 252
Heart-brain connections: Phenotypic and genetic insights from magnetic resonance images
Zhao, B., Li, T., Fan, Z., Yang, Y., Shu, J., Yang, X., Wang, X., Luo, T., Tang, J., Xiong, D., et al. (2023) · Science · doi:10.1126/science.abn6598 · Cited by 235
Wang, H.-T., Smallwood, J., Mourão-Miranda, J., Xia, C. H., Satterthwaite, T. D., Bassett, D. S., & Bzdok, D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116745 · Cited by 252
Dong, H.-M., Margulies, D. S., Zuo, X., & Holmes, A. J. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2024448118 · Cited by 253
Childhood trauma history is linked to abnormal brain connectivity in major depression
Yu, M., Linn, K. A., Shinohara, R. T., Oathes, D. J., Cook, P. A., Duprat, R., Moore, T. M., Oquendo, M. A., Phillips, M. L., McInnis, M. G., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900801116 · Cited by 230
Trait-like variants in human functional brain networks
Seitzman, B. A., Gratton, C., Laumann, T. O., Gordon, E. M., Adeyemo, B., Dworetsky, A., Kraus, B. T., Gilmore, A. W., Berg, J. J., Ortega, M., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1902932116 · Cited by 219
The relationship between spatial configuration and functional connectivity of brain regions
Bijsterbosch, J. D., Woolrich, M. W., Glasser, M. F., Robinson, E. C., Beckmann, C. F., Essen, D. C. V., Harrison, S. J., & Smith, S. M. (2018) · eLife · doi:10.7554/elife.32992 · Cited by 237
Self-citationA technical review of canonical correlation analysis for neuroscience applications
Zhuang, X., Yang, Z., & Cordes, D. (2020) · Human Brain Mapping · doi:10.1002/hbm.25090 · Cited by 210
Functional brain networks related to individual differences in human intelligence at rest
Hearne, L. J., Mattingley, J. B., & Cocchi, L. (2016) · Scientific Reports · doi:10.1038/srep32328 · Cited by 217
The Neuroscience of Intelligence
Haier, R. J. (2016) · Cambridge University Press eBooks · doi:10.1017/9781316105771 · Cited by 209
Supekar, K., Cai, W., Krishnadas, R., Palaniyappan, L. K., & Menon, V. (2018) · Biological Psychiatry · doi:10.1016/j.biopsych.2018.07.020 · Cited by 205
Linking interindividual variability in brain structure to behaviour
Genon, S., Eickhoff, S. B., & Masouleh, S. K. (2022) · Nature reviews. Neuroscience · doi:10.1038/s41583-022-00584-7 · Cited by 193
Defining Individual-Specific Functional Neuroanatomy for Precision Psychiatry
Gratton, C., Kraus, B. T., Greene, D. J., Gordon, E. M., Laumann, T. O., Nelson, S. M., Dosenbach, N. U. F., & Petersen, S. E. (2019) · Biological Psychiatry · doi:10.1016/j.biopsych.2019.10.026 · Cited by 202
Lynch, C. J., Gunning, F. M., & Liston, C. M. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.01.012 · Cited by 193
A mechanistic model of connector hubs, modularity and cognition
Bertolero, M. A., Yeo, B. T. T., Bassett, D. S., & D’Esposito, M. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0420-6 · Cited by 213
Trait paranoia shapes inter-subject synchrony in brain activity during an ambiguous social narrative
Finn, E. S., Corlett, P. R., Chen, G., Bandettini, P. A., & Constable, R. T. (2018) · Nature Communications · doi:10.1038/s41467-018-04387-2 · Cited by 179
Towards Reproducible Brain-Wide Association StudiesPreprint
Marek, S. A., Tervo-Clemmens, B., Calabro, F., Montez, D., Kay, B., Hatoum, A., Donohue, M., Foran, W., Miller, R. L., Feczko, E., et al. (2020) · bioRxiv · doi:10.1101/2020.08.21.257758 · Cited by 196
Self-citationToward a connectivity gradient-based framework for reproducible biomarker discovery
Hong, S., Xu, T., Nikolaidis, A., Smallwood, J., Margulies, D. S., Bernhardt, B. C., Vogelstein, J. T., & Milham, M. P. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117322 · Cited by 194
A mechanistic model of connector hubs, modularity and cognition
Bertolero, M. A., Yeo, B. T. T., Bassett, D. S., & D’Esposito, M. (2018) · RePEc: Research Papers in Economics · Cited by 181
Prediction complements explanation in understanding the developing brain
Rosenberg, M. D., Casey, B. J., & Holmes, A. J. (2018) · Nature Communications · doi:10.1038/s41467-018-02887-9 · Cited by 169
Cognitive task information is transferred between brain regions via resting-state network topology
Ito, T., Kulkarni, K. R., Schultz, D. H., Mill, R. D., Chen, R. H., Solomyak, L. I., & Cole, M. W. (2017) · Nature Communications · doi:10.1038/s41467-017-01000-w · Cited by 181
Yoo, K., Rosenberg, M. D., Hsu, T. W., Zhang, S., Li, C. R., Scheinost, D., Constable, R. T., & Chun, M. M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.11.010 · Cited by 174
Toward Neurosubtypes in Autism
Hong, S., Vogelstein, J. T., Gozzi, A., Bernhardt, B. C., Yeo, B. T. T., Milham, M. P., & Martino, A. D. (2020) · Biological Psychiatry · doi:10.1016/j.biopsych.2020.03.022 · Cited by 175
Automated processing pipeline for neonatal diffusion MRI in the developing Human Connectome Project
Bastiani, M., Andersson, J., Cordero‐Grande, L., Deprez, M., Hutter, J. M., Price, A. N., Makropoulos, A., Fitzgibbon, S. P., Hughes, E., Rueckert, D., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.05.064 · Cited by 173
Self-citationChallenges and future directions for representations of functional brain organization
Bijsterbosch, J. D., Harrison, S. J., Jbabdi, S., Woolrich, M. W., Beckmann, C. F., Smith, S. M., & Duff, E. P. (2020) · Nature Neuroscience · doi:10.1038/s41593-020-00726-z · Cited by 167
Self-citationEffective degrees of freedom of the Pearson's correlation coefficient under autocorrelation
Afyouni, S., Smith, S. M., & Nichols, T. E. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.05.011 · Cited by 169
Self-citationThe secret life of predictive brains: what’s spontaneous activity for?
Pezzulo, G., Zorzi, M., & Corbetta, M. (2021) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.05.007 · Cited by 173
Methods for analysis of brain connectivity: An IFCN-sponsored review
Rossini, P. M., Iorio, R. D., Bentivoglio, M., Bertini, G., Ferreri, F., Gerloff, C. P., Ilmoniemi, R. J., Miraglia, F., Nitsche, M. A., Pestilli, F., et al. (2019) · Clinical Neurophysiology · doi:10.1016/j.clinph.2019.06.006 · Cited by 169
Functional connectomics of affective and psychotic pathology
Baker, J. T., Dillon, D. G., Patrick, L. M., Roffman, J. L., Brady, R. O., Pizzagalli, D. A., Öngür, D., & Holmes, A. J. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1820780116 · Cited by 156
Functional corticostriatal connection topographies predict goal-directed behaviour in humans
Marquand, A. F., Haak, K. V., & Beckmann, C. F. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0146 · Cited by 153
Cai, N., Choi, K. W., & Fried, E. I. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa115 · Cited by 160
Positive affect, surprise, and fatigue are correlates of network flexibility
Betzel, R. F., Satterthwaite, T. D., Gold, J. I., & Bassett, D. S. (2017) · Scientific Reports · doi:10.1038/s41598-017-00425-z · Cited by 178
Longitudinal test-retest neuroimaging data from healthy young adults in southwest China
Liu, W., Wei, D., Chen, Q., Yang, W., Meng, J., Wu, G., Bi, T., Zhang, Q., Zuo, X., & Qiu, J. (2017) · Scientific Data · doi:10.1038/sdata.2017.17 · Cited by 152
Connectivity Fingerprints: From Areal Descriptions to Abstract Spaces
Mars, R. B., Passingham, R. E., & Jbabdi, S. (2018) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2018.08.009 · Cited by 157
Prediction of brain age and cognitive age: quantifying brain and cognitive maintenance in aging
Anatürk, M., Kaufmann, T., Cole, J. H., Suri, S., Griffanti, L., Zsoldos, E., Filippini, N., Singh‐Manoux, A., Kivimäki, M., Westlye, L. T., et al. (2021) · Human Brain Mapping · doi:10.1002/hbm.25316 · Cited by 132
Perry, A., Roberts, G. M. P., Mitchell, P. B., & Breakspear, M. J. (2018) · Molecular Psychiatry · doi:10.1038/s41380-018-0267-2 · Cited by 152
Extensive sampling for complete models of individual brains
Naselaris, T., Allen, E., & Kay, K. (2021) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2020.12.008 · Cited by 151
Subspecialization within default mode nodes characterized in 10,000 UK Biobank participants
Kernbach, J. M., Yeo, B. T. T., Smallwood, J., Margulies, D. S., de Schotten, M. T., Walter, H., Sabuncu, M. R., Holmes, A. J., Gramfort, A., Varoquaux, G. P., et al. (2018) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1804876115 · Cited by 142
Within and between-person correlates of the temporal dynamics of resting EEG microstates
Zanesco, A. P., King, B. G., Skwara, A. C., & Saron, C. D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116631 · Cited by 150
Individual Brain Charting, a high-resolution fMRI dataset for cognitive mapping
Pinho, A. L., Amadon, A., Ruest, T., Fabre, M., Dohmatob, E., Denghien, I., Ginisty, C., Becuwe-Desmidt, S., Roger, S., Laurier, L., et al. (2018) · Scientific Data · doi:10.1038/sdata.2018.105 · Cited by 158
The relationship between baseline pupil size and intelligence
Tsukahara, J. S., Harrison, T. L., & Engle, R. W. (2016) · Cognitive Psychology · doi:10.1016/j.cogpsych.2016.10.001 · Cited by 145
Characterizing Attention with Predictive Network Models
Rosenberg, M. D., Finn, E. S., Scheinost, D., Constable, R. T., & Chun, M. M. (2017) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2017.01.011 · Cited by 151
Palacios, E. M., Yuh, E. L., Chang, Y.-S., Yue, J. K., Schnyer, D. M., Okonkwo, D. O., Valadka, A. B., Gordon, W. A., Maas, A. I. R., Vassar, M. J., et al. (2017) · Journal of Neurotrauma · doi:10.1089/neu.2016.4752 · Cited by 148
The role of RDoC in future classification of mentaldisorders
Cuthbert, B. N. (2020) · Dialogues in Clinical Neuroscience · doi:10.31887/dcns.2020.22.1/bcuthbert · Cited by 135
Associations between Neighborhood SES and Functional Brain Network Development
Tooley, U. A., Mackey, A. P., Ćirić, R., Ruparel, K., Moore, T. M., Gur, R. C., Gur, R. E., Satterthwaite, T. D., & Bassett, D. S. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhz066 · Cited by 142
Marek, S. A., Tervo‐Clemmens, B., Nielsen, A. N., Wheelock, M. D., Miller, R. L., Laumann, T. O., Earl, E., Foran, W., Cordova, M., Doyle, O. J., et al. (2019) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2019.100706 · Cited by 127
Self-citationMultimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study
de Lange, A. G., Anatürk, M., Suri, S., Kaufmann, T., Cole, J. H., Griffanti, L., Zsoldos, E., Jensen, D. E. A., Filippini, N., Singh‐Manoux, A., et al. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117292 · Cited by 129
Heritability of the human connectome: A connectotyping study
Miranda-Domínguez, Ó., Feczko, E., Grayson, D. S., Walum, H., Nigg, J. T., & Fair, D. A. (2017) · Network Neuroscience · doi:10.1162/netn_a_00029 · Cited by 124
Ooi, L. Q. R., Chen, J., Zhang, S., Kong, R., Tam, A., Li, J., Dhamala, E., Zhou, J. H., Holmes, A. J., & Yeo, B. T. T. (2022) · NeuroImage · doi:10.1016/j.neuroimage.2022.119636 · Cited by 125
The heritability of multi-modal connectivity in human brain activity
Colclough, G. L., Smith, S. M., Nichols, T. E., Winkler, A. M., Sotiropoulos, S. N., Glasser, M. F., Essen, D. C. V., & Woolrich, M. W. (2017) · eLife · doi:10.7554/elife.20178 · Cited by 127
Self-citationTurnbull, A. G., Wang, H.-T., Schooler, J. W., Jefferies, E., Margulies, D. S., & Smallwood, J. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.09.069 · Cited by 131
Yamada, T., Hashimoto, R., Yahata, N., Ichikawa, N., Yoshihara, Y., Okamoto, Y., Kato, N., Takahashi, H., & Kawato, M. (2017) · The International Journal of Neuropsychopharmacology · doi:10.1093/ijnp/pyx059 · Cited by 137
The idiosyncratic nature of confidence
Navajas, J., Hindocha, C., Foda, H., Keramati, M., Latham, P. E., & Bahrami, B. (2017) · Nature Human Behaviour · doi:10.1038/s41562-017-0215-1 · Cited by 136
High-resolution connectomic fingerprints: Mapping neural identity and behavior
Sina Mansour, L., Tian, Y., Yeo, B. T. T., Cropley, V., & Zalesky, A. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2020.117695 · Cited by 110
Llera, A., Wolfers, T., Mulders, P. C. R., & Beckmann, C. F. (2019) · eLife · doi:10.7554/elife.44443 · Cited by 111
Seguin, C., Tian, Y., & Zalesky, A. (2020) · Network Neuroscience · doi:10.1162/netn_a_00161 · Cited by 120
Classification and treatment of antisocial individuals: From behavior to biocognition
Brazil, I. A., van Dongen, J. D. M., Maes, J. H. R., Mars, R. B., & Baskin-sommers, A. R. (2016) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.10.010 · Cited by 135
Topography and behavioral relevance of the global signal in the human brain
Li, J., Bolt, T., Bzdok, D., Nomi, J. S., Yeo, B. T. T., Spreng, R. N., & Uddin, L. Q. (2019) · Scientific Reports · doi:10.1038/s41598-019-50750-8 · Cited by 101
Hilger, K., Ekman, M., Fiebach, C. J., & Basten, U. (2016) · Intelligence · doi:10.1016/j.intell.2016.11.001 · Cited by 113
Metadata
- 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
- Venue
- Nature Neuroscience
- Published
- 2015-09-28
- Volume
- 18
- Issue
- 11
- Pages
- 1565-1567
- ISSN
- 1097-6256 (ISSN-L)
- 1546-1726
- 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
