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Toward discovery science of human brain function
Open accessAbstract
Although it is being successfully implemented for exploration of the genome, discovery science has eluded the functional neuroimaging community. The core challenge remains the development of common paradigms for interrogating the myriad functional systems in the brain without the constraints of a priori hypotheses. Resting-state functional MRI (R-fMRI) constitutes a candidate approach capable of addressing this challenge. Imaging the brain during rest reveals large-amplitude spontaneous low-frequency (<0.1 Hz) fluctuations in the fMRI signal that are temporally correlated across functionally related areas. Referred to as functional connectivity, these correlations yield detailed maps of complex neural systems, collectively constituting an individual's "functional connectome." Reproducibility across datasets and individuals suggests the functional connectome has a common architecture, yet each individual's functional connectome exhibits unique features, with stable, meaningful interindividual differences in connectivity patterns and strengths. Comprehensive mapping of the functional connectome, and its subsequent exploitation to discern genetic influences and brain-behavior relationships, will require multicenter collaborative datasets. Here we initiate this endeavor by gathering R-fMRI data from 1,414 volunteers collected independently at 35 international centers. We demonstrate a universal architecture of positive and negative functional connections, as well as consistent loci of inter-individual variability. Age and sex emerged as significant determinants. These results demonstrate that independent R-fMRI datasets can be aggregated and shared. High-throughput R-fMRI can provide quantitative phenotypes for molecular genetic studies and biomarkers of developmental and pathological processes in the brain. To initiate discovery science of brain function, the 1000 Functional Connectomes Project dataset is freely accessible at www.nitrc.org/projects/fcon_1000/.
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“Over the last decade many studies have found that spontaneous BOLD fluctuations are not random noise, but specifically organized in the resting human brain (Biswal et al., 2010).”
From Oxytocin effects on complex brain networks are moderated by experiences of maternal love withdrawal (2013) · cites this paper as Biswal et al., 2010
In the citing paper
“The Thousand Functional Connectomes (and related) projects [74, 75], led by Mike Milham, are seeking to generate even larger numbers of subjects than the HCP, bringing together data from a wide range of imaging studies and scanners, and covering a wider range of subject groups, including a range of pathologies.”
From Functional connectomics from resting-state fMRI (2013) · cites this paper as [74]
In the citing paper
“While general brain organization measured with RSFC is similar across thousands of different individuals [12] and stable and robust over the short-term (hourly) and long-term (monthly) [86], there are small individual differences in connectivity, which can be reliably observed across time.”
From Predicting Brain Functional Connectivity Using Mobile Sensing (2020) · cites this paper as [12]
Citations
Cites
Functional connectivity in the motor cortex of resting human brain using echo‐planar mri
Biswal, B., Zerrin Yetkin, F., Haughton, V. M., & Hyde, J. S. (1995) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910340409 · Cited by 9,812
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
Finding the missing heritability of complex diseases
Manolio, T. A., Collins, F. S., Cox, N. J., Goldstein, D. B., Hindorff, L. A., Hunter, D. J., McCarthy, M. I., Ramos, E. M., Cardon, L. R., Chakravarti, A., et al. (2009) · Nature · doi:10.1038/nature08494 · Cited by 8,336
Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging
Fox, M. D., & Raichle, M. E. (2007) · Nature Reviews Neuroscience · doi:10.1038/nrn2201 · Cited by 6,700
Correspondence of the brain's functional architecture during activation and rest
Smith, S. M., Fox, P. T., Miller, K. L., Glahn, D. C., Fox, P. M., Mackay, C. E., Filippini, N., Watkins, K. E., Toro, R., Laird, A. R., et al. (2009) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0905267106 · Cited by 5,150
Self-citationConsistent resting-state networks across healthy subjects
Damoiseaux, J. S., Rombouts, S. A. R. B., Barkhof, F., Scheltens, P., Stam, C. J., Smith, S. M., & Beckmann, C. F. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0601417103 · Cited by 4,326
Self-citationMapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173
Investigations into resting-state connectivity using independent component analysis
Beckmann, C. F., DeLuca, M., Devlin, J. T., & Smith, S. M. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1634 · Cited by 3,398
Self-citationSearching for a baseline: Functional imaging and the resting human brain
Gusnard, D. A., & Raichle, M. E. (2001) · Nature Reviews Neuroscience · doi:10.1038/35094500 · Cited by 3,371
The Human Connectome: A Structural Description of the Human Brain
Sporns, O., Tononi, G., & Kötter, R. (2005) · PLoS Computational Biology · doi:10.1371/journal.pcbi.0010042 · Cited by 3,260
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Thomas Yeo, B. T., Krienen, F. M., Sepulcre, J., Sabuncu, M. R., Lashkari, D., Hollinshead, M., Roffman, J. L., Smoller, J. W., Zöllei, L., Polimeni, J. R., et al. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00338.2011 · Cited by 9,182
Self-citationThe organization of the human cerebellum estimated by intrinsic functional connectivity
Buckner, R. L., Krienen, F. M., Castellanos, A., Diaz, J. C., & Yeo, B. T. T. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00339.2011 · Cited by 7,304
Self-citationConn : A Functional Connectivity Toolbox for Correlated and Anticorrelated Brain Networks
Whitfield-Gabrieli, S., & Nieto-Castanon, A. (2012) · Brain Connectivity · doi:10.1089/brain.2012.0073 · Cited by 4,885
Self-citationBrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics
Xia, M., Wang, J., & He, Y. (2013) · PLoS ONE · doi:10.1371/journal.pone.0068910 · Cited by 4,106
Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates
Eklund, A., Nichols, T. E., & Knutsson, H. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602413113 · Cited by 3,487
Di Martino, A., Yan, C.-G., Li, Q., Denio, E., Castellanos, F. X., Alaerts, K., Anderson, J. S., Assaf, M., Bookheimer, S. Y., Dapretto, M., et al. (2014) · Molecular Psychiatry · doi:10.1038/mp.2013.78 · Cited by 2,780
Self-citationDysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications
Goldstein, R. Z., & Volkow, N. D. (2011) · Nature Reviews Neuroscience · doi:10.1038/nrn3119 · Cited by 2,749
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
Self-citationPrediction of individual brain maturity using fMRI
Dosenbach, N. U. F., Nardos, B., Cohen, A. L., Fair, D. A., Power, J. D., Church, J. A., Nelson, S. M., Wig, G. S., Vogel, A. C., Lessov-Schlaggar, C. N., et al. (2010) · Science · doi:10.1126/science.1194144 · Cited by 2,183
Self-citationICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data
Pruim, R. H. R., Mennes, M., van Rooij, D., Llera, A., Buitelaar, J. K., & Beckmann, C. F. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.064 · Cited by 1,684
Self-citationIntrinsic and task-evoked network architectures of the human brain
Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S., & Petersen, S. E. (2014) · Neuron · doi:10.1016/j.neuron.2014.05.014 · Cited by 1,564
Self-citationGeneration and evaluation of a cortical area parcellation from resting-state correlations
Gordon, E. M., Laumann, T. O., Adeyemo, B., Huckins, J. F., Kelley, W. M., & Petersen, S. E. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhu239 · Cited by 1,463
Self-citationA whole brain fMRI atlas generated via spatially constrained spectral clustering
Craddock, R. C., James, G. A., Holtzheimer, P. E., Hu, X. P., & Mayberg, H. S. (2012) · Human Brain Mapping · doi:10.1002/hbm.21333 · Cited by 1,636
Self-citationScanning the horizon: towards transparent and reproducible neuroimaging research
Poldrack, R. A., Baker, C. I., Durnez, J., Gorgolewski, K. J., Matthews, P. M., Munafò, M. R., Nichols, T. E., Poline, J.-B., Vul, E., & Yarkoni, T. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.167 · Cited by 1,410
Yan, C.-G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R. C., Di Martino, A., Li, Q., Zuo, X.-N., Castellanos, F. X., & Milham, M. P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.03.004 · Cited by 1,461
Self-citationSatterthwaite, T. D., Wolf, D. H., Loughead, J., Ruparel, K., Elliott, M. A., Hakonarson, H., Gur, R. C., & Gur, R. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.063 · Cited by 1,125
Time for united action on depression: a Lancet–World Psychiatric Association Commission
Herrman, H., Patel, V., Kieling, C., Berk, M., Buchweitz, C., Cuijpers, P., Furukawa, T. A., Kessler, R. C., Kohrt, B. A., Maj, M., et al. (2022) · The Lancet · doi:10.1016/s0140-6736(21)02141-3 · Cited by 1,104
Self-citationThe role of default network deactivation in cognition and disease
Anticevic, A., Cole, M. W., Murray, J. D., Corlett, P. R., Wang, X.-J., & Krystal, J. H. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.10.008 · Cited by 978
Kapur, S., Phillips, A. G., & Insel, T. R. (2012) · Molecular Psychiatry · doi:10.1038/mp.2012.105 · Cited by 980
Behavioral interpretations of intrinsic connectivity networks
Laird, A. R., Fox, P. M., Eickhoff, S. B., Turner, J. A., Ray, K. L., McKay, D. R., Glahn, D. C., Beckmann, C. F., Smith, S. M., & Fox, P. T. (2011) · Journal of Cognitive Neuroscience · doi:10.1162/jocn_a_00077 · Cited by 1,023
Self-citationClinical epidemiology of Alzheimer’s disease: assessing sex and gender differences
Mielke, M., Vemuri, P., & Rocca, W. (2014) · Clinical Epidemiology · doi:10.2147/clep.s37929 · Cited by 1,021
Single subject prediction of brain disorders in neuroimaging: Promises and pitfalls
Arbabshirani, M. R., Plis, S., Sui, J., & Calhoun, V. D. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.02.079 · Cited by 912
Functional connectomics from resting-state fMRI
Smith, S. M., Vidaurre, D., Beckmann, C. F., Glasser, M. F., Jenkinson, M., Miller, K. L., Nichols, T. E., Robinson, E. C., Salimi-Khorshidi, G., Woolrich, M. W., et al. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.016 · Cited by 952
Self-citationSaad, Z. S., Gotts, S. J., Murphy, K., Chen, G., Jo, H. J., Martin, A., & Cox, R. W. (2012) · Brain Connectivity · doi:10.1089/brain.2012.0080 · Cited by 859
Cross-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
“In functional imaging, resting-state fMRI acquisition are a promising source of very large data, via post-hoc aggregation (Biswal et al., 2010; Thompson et al., 2014; Di Martino et al., 2014) or large concerted efforts (Miller et al.”
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
“We successfully replicated the results from the 1 000 Functional Connectomes dataset (an entirely separate dataset)— that is, we found female > male connectivity within the default mode network and some evidence for male>female connectivity in sensorimotor and visual cortices (Biswal et al. 2010).”
MethodResultLarge-scale brain systems in ADHD: beyond the prefrontal–striatal model
Castellanos, F. X., & Proal, E. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2011.11.007 · Cited by 831
Self-citationTime-resolved resting-state brain networks
Zalesky, A., Fornito, A., Cocchi, L., Gollo, L. L., & Breakspear, M. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1400181111 · Cited by 753
Hallquist, M. N., Hwang, K., & Luna, B. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.116 · Cited by 753
The organization of the human striatum estimated by intrinsic functional connectivity
Choi, E. Y., Yeo, B. T. T., & Buckner, R. L. (2012) · Journal of Neurophysiology · doi:10.1152/jn.00270.2012 · Cited by 746
Self-citationThe development of human functional brain networks
Power, J. D., Fair, D. A., Schlaggar, B. L., & Petersen, S. E. (2010) · Neuron · doi:10.1016/j.neuron.2010.08.017 · Cited by 751
Self-citationZeng, L.-L., Shen, H., Liu, L., Wang, L., Li, B., Fang, P., Zhou, Z., Li, Y., & Hu, D. (2012) · Brain · doi:10.1093/brain/aws059 · Cited by 748
Self-citationNociception, pain, negative moods, and behavior selection
Baliki, M. N., & Apkarian, A. V. (2015) · Neuron · doi:10.1016/j.neuron.2015.06.005 · Cited by 731
Why are autism spectrum conditions more prevalent in males?
Baron-Cohen, S., Lombardo, M. V., Auyeung, B., Ashwin, E., Chakrabarti, B., & Knickmeyer, R. (2011) · PLoS Biology · doi:10.1371/journal.pbio.1001081 · Cited by 754
Spear, L. P. (2013) · Journal of Adolescent Health · doi:10.1016/j.jadohealth.2012.05.006 · Cited by 744
Schizophrenia, neuroimaging and connectomics
Fornito, A., Zalesky, A., Pantelis, C., & Bullmore, E. T. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.12.090 · Cited by 716
Lindquist, K. A., Satpute, A. B., Wager, T. D., Weber, J., & Barrett, L. F. (2016) · Cerebral Cortex · doi:10.1093/cercor/bhv001 · Cited by 713
Sex beyond the genitalia: the human brain mosaic
Joel, D., Berman, Z., Tavor, I., Wexler, N., Gaber, O., Stein, Y., Shefi, N., Pool, J., Urchs, S., Margulies, D. S., et al. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1509654112 · Cited by 684
Self-citationPrecision psychiatry: a neural circuit taxonomy for depression and anxiety
Williams, L. M. (2016) · The Lancet Psychiatry · doi:10.1016/s2215-0366(15)00579-9 · Cited by 641
Resting-state fMRI confounds and cleanup
Murphy, K., Birn, R. M., & Bandettini, P. A. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.001 · Cited by 666
Tractography: Where Do We Go from Here?
Jbabdi, S., & Johansen-Berg, H. (2011) · Brain Connectivity · doi:10.1089/brain.2011.0033 · Cited by 614
Aging and functional brain networks
Tomasi, D., & Volkow, N. D. (2012) · Molecular Psychiatry · doi:10.1038/mp.2011.81 · Cited by 578
Neuroimaging of the Philadelphia Neurodevelopmental Cohort
Satterthwaite, T. D., Elliott, M. A., Ruparel, K., Loughead, J., Prabhakaran, K., Calkins, M. E., Hopson, R., Jackson, C., Keefe, J., Riley, M., et al. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.07.064 · Cited by 504
Short-term meditation induces white matter changes in the anterior cingulate
Tang, Y.-Y., Lu, Q., Geng, X., Stein, E. A., Yang, Y., & Posner, M. I. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1011043107 · Cited by 514
Evaluation of ICA-AROMA and alternative strategies for motion artifact removal in resting state fMRI
Pruim, R. H. R., Mennes, M., Buitelaar, J. K., & Beckmann, C. F. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.063 · Cited by 487
Self-citationTopological organization of the human brain functional connectome across the lifespan
Cao, M., Wang, J.-H., Dai, Z.-J., Cao, X.-Y., Jiang, L.-L., Fan, F.-M., Song, X.-W., Xia, M.-R., Shu, N., Dong, Q., et al. (2014) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2013.11.004 · Cited by 440
Self-citationFair, D. A., Nigg, J. T., Iyer, S., Bathula, D., Mills, K. L., Dosenbach, N. U. F., Schlaggar, B. L., Mennes, M., Gutman, D., Bangaru, S., et al. (2013) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2012.00080 · Cited by 463
Self-citationDecreased interhemispheric functional connectivity in autism
Anderson, J. S., Druzgal, T. J., Froehlich, A., DuBray, M. B., Lange, N., Alexander, A. L., Abildskov, T., Nielsen, J. A., Cariello, A. N., Cooperrider, J. R., et al. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq190 · Cited by 438
Witte, A. V., Kerti, L., Margulies, D. S., & Floel, A. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.0385-14.2014 · Cited by 455
Self-citationImaging human connectomes at the macroscale
Craddock, R. C., Jbabdi, S., Yan, C.-G., Vogelstein, J. T., Castellanos, F. X., Di Martino, A., Kelly, C., Heberlein, K., Colcombe, S., & Milham, M. P. (2013) · Nature Methods · doi:10.1038/nmeth.2482 · Cited by 419
Self-citationAlexopoulos, G. S., Hoptman, M. J., Kanellopoulos, D., Murphy, C. F., Lim, K. O., & Gunning, F. M. (2012) · Journal of Affective Disorders · doi:10.1016/j.jad.2011.12.002 · Cited by 437
Self-citationNetwork-level structural covariance in the developing brain
Zielinski, B. A., Gennatas, E. D., Zhou, J., & Seeley, W. W. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1003109107 · Cited by 405
Networks of anatomical covariance
Evans, A. C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.054 · Cited by 429
Wang, J.-H., Zuo, X.-N., Gohel, S., Milham, M. P., Biswal, B. B., & He, Y. (2011) · PLoS ONE · doi:10.1371/journal.pone.0021976 · Cited by 416
Self-citationHahn, A., Stein, P., Windischberger, C., Weissenbacher, A., Spindelegger, C., Moser, E., Kasper, S., & Lanzenberger, R. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.064 · Cited by 385
Self-citationPreller, K. H., Burt, J. B., Ji, J. L., Schleifer, C. H., Adkinson, B. D., Stämpfli, P., Seifritz, E., Repovs, G., Krystal, J. H., Murray, J. D., et al. (2018) · eLife · doi:10.7554/elife.35082 · Cited by 390
Multisite functional connectivity MRI classification of autism: ABIDE results
Nielsen, J. A., Zielinski, B. A., Fletcher, P. T., Alexander, A. L., Lange, N., Bigler, E. D., Lainhart, J. E., & Anderson, J. S. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00599 · Cited by 377
Exploring the network dynamics underlying brain activity during rest
Cabral, J., Kringelbach, M. L., & Deco, G. (2014) · Progress in Neurobiology · doi:10.1016/j.pneurobio.2013.12.005 · Cited by 351
The future of fMRI connectivity
Smith, S. M. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.022 · Cited by 355
Self-citationA convergent functional architecture of the insula emerges across imaging modalities
Kelly, C., Toro, R., Di Martino, A., Cox, C. L., Bellec, P., Castellanos, F. X., & Milham, M. P. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.03.021 · Cited by 374
Self-citationAge-related changes in the default mode network are more advanced in Alzheimer disease
Jones, D. T., Machulda, M. M., Vemuri, P., McDade, E. M., Zeng, G., Senjem, M. L., Gunter, J. L., Przybelski, S. A., Avula, R. T., Knopman, D. S., et al. (2011) · Neurology · doi:10.1212/wnl.0b013e318233b33d · Cited by 370
Di Martino, A., Zuo, X.-N., Kelly, C., Grzadzinski, R., Mennes, M., Schvarcz, A., Rodman, J., Lord, C., Castellanos, F. X., & Milham, M. P. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.02.011 · Cited by 345
Self-citationStudying both sexes: a guiding principle for biomedicine
Clayton, J. A. (2016) · The FASEB Journal · doi:10.1096/fj.15-279554 · Cited by 358
Uddin, L. Q. (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00021 · Cited by 343
Chen, X., Lu, B., & Yan, C. (2018) · Human Brain Mapping · doi:10.1002/hbm.23843 · Cited by 340
Lehmann, M., Ghosh, P. M., Madison, C., Laforce, R., Corbetta-Rastelli, C., Weiner, M. W., Greicius, M. D., Seeley, W. W., Gorno-Tempini, M. L., Rosen, H. J., et al. (2013) · Brain · doi:10.1093/brain/aws327 · Cited by 325
Measuring macroscopic brain connections in vivo
Jbabdi, S., Sotiropoulos, S. N., Haber, S. N., Van Essen, D. C., & Behrens, T. E. (2015) · Nature Neuroscience · doi:10.1038/nn.4134 · Cited by 334
Satterthwaite, T. D., Connolly, J. J., Ruparel, K., Calkins, M. E., Jackson, C., Elliott, M. A., Roalf, D. R., Hopson, R., Prabhakaran, K., Behr, M., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.03.056 · Cited by 335
“As previously emphasized (Biswal et al., 2010; Gorgolewski et al., 2013; Mennes et al., 2012; Milham, 2012; Nooner et al., 2012; Bis et al., 2012; Stein et al., 2012), large publicly available datasets are a prerequisite for the collaboration necessary to gain traction towards understanding complex…”
Cox, C. L., Uddin, L. Q., Di Martino, A., Castellanos, F. X., Milham, M. P., & Kelly, C. (2012) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsr051 · Cited by 333
Self-citationClinical applications of the functional connectome
Castellanos, F. X., Di Martino, A., Craddock, R. C., Mehta, A. D., & Milham, M. P. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.083 · Cited by 312
Self-citationConnectivity-based parcellation of the human orbitofrontal cortex
Kahnt, T., Chang, L. J., Park, S. Q., Heinzle, J., & Haynes, J.-D. (2012) · The Journal of Neuroscience · doi:10.1523/jneurosci.0257-12.2012 · Cited by 299
Friston, K. J., Li, B., Daunizeau, J., & Stephan, K. E. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2010.12.039 · Cited by 283
Yang, C.-C., Barrós-Loscertales, A., Pinazo, D., Ventura-Campos, N., Borchardt, V., Bustamante, J.-C., Rodríguez-Pujadas, A., Fuentes-Claramonte, P., Balaguer, R., Ávila, C., et al. (2016) · Neural Plasticity · doi:10.1155/2016/9504642 · Cited by 284
“Resting state fMRI widely used seed-based correlation analysis between regions of interest (ROIs) by investigating interregional functional connectivity to reveal highly consistent patterns of functional connectivity across regions [32].”
MethodSelf-citationDiagnostic neuroimaging across diseases
Klöppel, S., Abdulkadir, A., Jack, C. R., Koutsouleris, N., Mourão-Miranda, J., & Vemuri, P. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.11.002 · Cited by 279
Kragel, P. A., Kano, M., Van Oudenhove, L., Ly, H. G., Dupont, P., Rubio, A., Delon-Martin, C., Bonaz, B. L., Manuck, S. B., Gianaros, P. J., et al. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0051-7 · Cited by 272
Satterthwaite, T. D., Wolf, D. H., Ruparel, K., Erus, G., Elliott, M. A., Eickhoff, S. B., Gennatas, E. D., Jackson, C., Prabhakaran, K., Smith, A., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.06.045 · Cited by 253
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Falk, E. B., Hyde, L. W., Mitchell, C., Faul, J., Gonzalez, R., Heitzeg, M. M., Keating, D. P., Langa, K. M., Martz, M. E., Maslowsky, J., et al. (2013) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1310134110 · Cited by 265
Self-citationAnticevic, A., Hu, S., Zhang, S., Savic, A., Billingslea, E., Wasylink, S., Repovs, G., Cole, M. W., Bednarski, S., Krystal, J. H., et al. (2014) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.10.021 · Cited by 268
Anticevic, A., Gancsos, M., Murray, J. D., Repovs, G., Driesen, N. R., Ennis, D. J., Niciu, M. J., Morgan, P. T., Surti, T. S., Bloch, M. H., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1208494109 · Cited by 243
Seghier, M. L., Fagan, E., & Price, C. J. (2010) · The Journal of Neuroscience · doi:10.1523/jneurosci.3377-10.2010 · Cited by 245
Messé, A., Rudrauf, D., Benali, H., & Marrelec, G. (2014) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1003530 · Cited by 215
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Haneef, Z., Lenartowicz, A., Yeh, H. J., Levin, H. S., Engel, J., & Stern, J. M. (2014) · Epilepsia · doi:10.1111/epi.12476 · Cited by 235
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Varoquaux, G., & Craddock, R. C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.007 · Cited by 233
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Rane, P., Cochran, D., Hodge, S. M., Haselgrove, C., Kennedy, D. N., & Frazier, J. A. (2015) · Harvard Review of Psychiatry · doi:10.1097/hrp.0000000000000072 · Cited by 234
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Rutherford, S., Fraza, C., Dinga, R., Kia, S. M., Wolfers, T., Zabihi, M., Berthet, P., Worker, A., Verdi, S., Andrews, D., et al. (2022) · eLife · doi:10.7554/elife.72904 · Cited by 214
Self-citationSexual dimorphism in the human brain: evidence from neuroimaging
Sacher, J., Neumann, J., Okon-Singer, H., Gotowiec, S., & Villringer, A. (2013) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2012.06.007 · Cited by 221
Self-citationfMRI clustering and false-positive rates
Cox, R. W., Chen, G., Glen, D. R., Reynolds, R. C., & Taylor, P. A. (2017) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1614961114 · Cited by 219
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Furman, D. J., Hamilton, J. P., & Gotlib, I. H. (2011) · Biology of Mood & Anxiety Disorders · doi:10.1186/2045-5380-1-11 · Cited by 208
Liston, C., Cohen, M. M., Teslovich, T., Levenson, D., & Casey, B. J. (2011) · Biological Psychiatry · doi:10.1016/j.biopsych.2011.03.022 · Cited by 216
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Cauda, F., Palermo, S., Costa, T., Torta, R., Duca, S., Vercelli, U., Geminiani, G., & Torta, D. M. E. (2014) · NeuroImage: Clinical · doi:10.1016/j.nicl.2014.04.007 · Cited by 212
Alaerts, K., Swinnen, S. P., & Wenderoth, N. (2016) · Social Cognitive and Affective Neuroscience · doi:10.1093/scan/nsw027 · Cited by 193
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Hoeft, F., Gabrieli, J. D. E., Whitfield-Gabrieli, S., Haas, B. W., Bammer, R., Menon, V., & Spiegel, D. (2012) · Archives of General Psychiatry · doi:10.1001/archgenpsychiatry.2011.2190 · Cited by 203
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García‐García, I., Jurado, M. Á., Garolera, M., Segura, B., Sala‐Llonch, R., Marqués‐Iturria, I., Pueyo, R., Sender‐Palacios, M. J., Vernet‐Vernet, M., Narberhaus, A., et al. (2013) · Human Brain Mapping · doi:10.1002/hbm.22104 · Cited by 200
Tyszka, J. M., Kennedy, D. P., Paul, L. K., & Adolphs, R. (2014) · Cerebral Cortex · doi:10.1093/cercor/bht040 · Cited by 187
Functional brain connectivity at rest changes after working memory training
Jolles, D. D., van Buchem, M. A., Crone, E. A., & Rombouts, S. A. R. B. (2013) · Human Brain Mapping · doi:10.1002/hbm.21444 · Cited by 183
Self-citationWhite, T., Blok, E., & Calhoun, V. D. (2022) · Human Brain Mapping · doi:10.1002/hbm.25120 · Cited by 176
“Examples include the Autism Brain Imaging Data Exchange (ABIDE-I and II)(Di Martino et al., 2014, 2017), 1,000 Functional Connectomes (Biswal et al., 2010), Consortium on Reliability and Reproducibility (Zuo et al., 2014), the REST-meta-MDD consortium (Yan et al., 2019) and the Healthy Brains Consortium (O'Connor et a…”
BackgroundFlorin, E., & Baillet, S. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.01.054 · Cited by 184
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Yarkoni, T., Poldrack, R. A., Van Essen, D. C., & Wager, T. D. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.08.004 · Cited by 174
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Jolles, D. D., van Buchem, M. A., Crone, E. A., & Rombouts, S. A. R. B. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq104 · Cited by 181
Anticevic, A., Hu, X., Xiao, Y., Hu, J., Li, F., Bi, F., Cole, M. W., Savic, A., Yang, G. J., Repovs, G., et al. (2015) · The Journal of Neuroscience · doi:10.1523/jneurosci.2310-14.2015 · Cited by 178
Metadata
- Authors
- Biswal, B. B., Mennes, M., Zuo, X.-N., Gohel, S., Kelly, C., Smith, S. M., Beckmann, C. F., Adelstein, J. S., Buckner, R. L., Colcombe, S., Dogonowski, A.-M., Ernst, M., Fair, D., Hampson, M., Hoptman, M. J., Hyde, J. S., Kiviniemi, V. J., Kötter, R., Li, S.-J., Lin, C.-P., Lowe, M. J., Mackay, C., Madden, D. J., Madsen, K. H., Margulies, D. S., Mayberg, H. S., McMahon, K., Monk, C. S., Mostofsky, S. H., Nagel, B. J., Pekar, J. J., Peltier, S. J., Petersen, S. E., Riedl, V., Rombouts, S. A. R. B., Rypma, B., Schlaggar, B. L., Schmidt, S., Seidler, R. D., Siegle, G. J., Sorg, C., Teng, G.-J., Veijola, J., Villringer, A., Walter, M., Wang, L., Weng, X.-C., Whitfield-Gabrieli, S., Williamson, P., Windischberger, C., Zang, Y.-F., Zhang, H.-Y., Castellanos, F. X., & Milham, M. P.
- Affiliations
- Rutgers, The State University of New Jersey (US)
- NYU Langone Health (US)
- FMRIB Centre, Oxford University, Oxford OX3 9DU, UK;
- University of Oxford (GB)
- Howard Hughes Medical Institute (US)
- Bangor University (GB)
- Hvidovre Hospital (DK)
- Mood and Anxiety Disorders Program, National Institute of Mental Health/National Institutes of Health, Department of Health and Human Services, Bethesda, MD 20892;
- Oregon Health & Science University (US)
- Yale University (US)
- Nathan Kline Institute for Psychiatric Research (US)
- Medical College of Wisconsin (US)
- Oulu University Hospital (FI)
- Radboud University Nijmegen (NL)
- National Yang Ming University (TW)
- Cleveland Clinic (US)
- Duke Medical Center (US)
- Max Planck Institute for Human Cognitive and Brain Sciences (DE)
- Emory University (US)
- The University of Queensland (AU)
- University of Michigan (US)
- Kennedy Krieger Institute (US)
- Washington University in St. Louis (US)
- TUM Klinikum (DE)
- Leiden University (NL)
- The University of Texas at Dallas (US)
- Charité – Universitätsmedizin Berlin (DE)
- University of Pittsburgh (US)
- Zhongda Hospital Southeast University (CN)
- University of Oulu (FI)
- Otto-von-Guericke-Universität Magdeburg (DE)
- Chinese Academy of Sciences (CN)
- Department of Brain and Cognitive Sciences, Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Boston, MA 02139;
- Western University (CA)
- Medical University of Vienna (AT)
- Beijing Normal University (CN)
- ORCID
- Biswal, Bharat B.
- Mennes, Maarten
- Zuo, Xi-Nian
- Gohel, Suril
- Kelly, Clare
- Beckmann, Christian F.
- Buckner, Randy L.
- Colcombe, Stan
- Fair, Damien
- Hampson, Michelle
- Hoptman, Matthew J.
- Hyde, James S.
- Kiviniemi, Vesa J.
- Lin, Ching-Po
- Lowe, Mark J.
- Mackay, Clare
- Madden, David J.
- Madsen, Kristoffer H.
- Margulies, Daniel S.
- Mayberg, Helen S.
- McMahon, Katie
- Monk, Christopher S.
- Mostofsky, Stewart H.
- Nagel, Bonnie J.
- Pekar, James J.
- Peltier, Scott J.
- Petersen, Steven E.
- Riedl, Valentin
- Rombouts, Serge A. R. B.
- Rypma, Bart
- Schlaggar, Bradley L.
- Schmidt, Sein
- Seidler, Rachael D.
- Siegle, Greg J.
- Sorg, Christian
- Teng, Gao-Jun
- Villringer, Arno
- Walter, Martin
- Wang, Lihong
- Windischberger, Christian
- Zang, Yu-Feng
- Castellanos, F. Xavier
- Milham, Michael P.
- Venue
- Proceedings of the National Academy of Sciences
- Publisher
- National Academy of Sciences
- Published
- 2010-03-09
- Volume
- 107
- Issue
- 10
- Pages
- 4734-4739
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- PMC
- PMC2842060
- OpenAlex
- W2008607322
- Cited by
- 2,970
- References
- 52
- Citations by year
- 2026: 72 · 2025: 117 · 2024: 115 · 2023: 127 · 2022: 163 · 2021: 205 · 2020: 255 · 2019: 187 · 2018: 182 · 2017: 219 · 2016: 205 · 2015: 246 · 2014: 239 · 2013: 236 · 2012: 200
- FWCI
- 123.01
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Proceedings of the National Academy of Sciences (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Data Archiving and Networked Services (DANS) (submittedVersion)
- Institutional Repository of Institute of Psychology, Chinese Academy of Sciences (Institute of Psychology, Chinese Academy of Sciences) (submittedVersion)
- Leiden Repository (Leiden University) (submittedVersion)
- Max Planck Digital Library (submittedVersion)
- mediaTUM – the media and publications repository of the Technical University Munich (Technical University Munich) (submittedVersion)
- MPG.PuRe (Max Planck Society) (submittedVersion)
- Radboud Repository (Radboud University) (submittedVersion)
- Funders
- Howard Hughes Medical Institute
- National Institutes of Health (UL1TR000448)
- National Institute of Mental Health (K02MH082998, R01MH083246, R01MH064783, R01MH081218)
- National Institute on Drug Abuse (R01DA016979, R03DA024775)
- National Institute of Neurological Disorders and Stroke (R01NS049176)
- National Institute of Biomedical Imaging and Bioengineering (N02-EB-6-4281)
- National Center for Advancing Translational Sciences
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- resting-state fMRI, functional connectivity, functional connectome, interindividual variability, brain-behavior relationship, brain development
- MeSH terms
- Adolescent, Adult, Age Factors, Aged, Algorithms, Analysis of Variance, Brain, Brain Mapping, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Neural Pathways, Sex Factors, Young Adult
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