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The Human Connectome Project: A data acquisition perspective
Abstract
The Human Connectome Project (HCP) is an ambitious 5-year effort to characterize brain connectivity and function and their variability in healthy adults. This review summarizes the data acquisition plans being implemented by a consortium of HCP investigators who will study a population of 1200 subjects (twins and their non-twin siblings) using multiple imaging modalities along with extensive behavioral and genetic data. The imaging modalities will include diffusion imaging (dMRI), resting-state fMRI (R-fMRI), task-evoked fMRI (T-fMRI), T1- and T2-weighted MRI for structural and myelin mapping, plus combined magnetoencephalography and electroencephalography (MEG/EEG). Given the importance of obtaining the best possible data quality, we discuss the efforts underway during the first two years of the grant (Phase I) to refine and optimize many aspects of HCP data acquisition, including a new 7T scanner, a customized 3T scanner, and improved MR pulse sequences.
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Cites
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
Oostenveld, R., Fries, P., Maris, E., & Schoffelen, J.-M. (2011) · Computational Intelligence and Neuroscience · doi:10.1155/2011/156869 · Cited by 10,323
Self-citationPower, J. D., Barnes, K. A., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2011.10.018 · Cited by 7,429
Self-citationSENSE: Sensitivity encoding for fast MRI
Pruessmann, K. P., Weiger, M., Scheidegger, M. B., & Boesiger, P. (1999) · Magnetic Resonance in Medicine · doi:10.1002/(sici)1522-2594(199911)42:5<952::aid-mrm16>3.0.co;2-s · Cited by 6,375
Generalized autocalibrating partially parallel acquisitions (GRAPPA)
Griswold, M. A., Jakob, P. M., Heidemann, R. M., Nittka, M., Jellús̆, V., Wang, J., Kiefer, B., & Haase, A. (2002) · Magnetic Resonance in Medicine · doi:10.1002/mrm.10171 · Cited by 5,311
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-citationFriston, K. J., Harrison, L., & Penny, W. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00202-7 · Cited by 4,583
Methodology for Genetic Studies of Twins and Families
Neale, M. C., & Cardon, L. R. (1992) · doi:10.1007/978-94-015-8018-2 · Cited by 4,005
Probabilistic diffusion tractography with multiple fibre orientations: What can we gain?
Behrens, T. E. J., Berg, H. J., Jbabdi, S., Rushworth, M. F. S., & Woolrich, M. W. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2006.09.018 · Cited by 3,471
Self-citationAndersson, J. L. R., Skare, S., & Ashburner, J. (2003) · NeuroImage · doi:10.1016/s1053-8119(03)00336-7 · Cited by 3,469
Research works printed but not linked: 1
- Marcus, 2011, The Human Connectome Project informatics platform, Frontiers in Neuroscience 5, 4
Cited by
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-citationThe Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository
Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., et al. (2013) · Journal of Digital Imaging · doi:10.1007/s10278-013-9622-7 · Cited by 4,599
Self-citationfMRIPrep: A robust preprocessing pipeline for functional MRI
Esteban, O., Markiewicz, C. J., Blair, R. W., Moodie, C. A., Isik, A. I., Erramuzpe, A., Kent, J. D., Goncalves, M., DuPre, E., Snyder, M., et al. (2019) · Nature Methods · doi:10.1038/s41592-018-0235-4 · Cited by 3,905
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
Function in the human connectome: task-fMRI and individual differences in behavior
Barch, D. M., Burgess, G. C., Harms, M. P., Petersen, S. E., Schlaggar, B. L., Corbetta, M., Glasser, M. F., Curtiss, S., Dixit, S., Feldt, C., et al. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.05.033 · Cited by 1,529
Self-citationTractography-based connectomes are dominated by false-positive connections
Maier-Hein, K. H., Neher, P., Houde, J.-C., Côté, M.-A., Garyfallidis, E., Zhong, J., Chamberland, M., Yeh, F.-C., Lin, Y.-C., Ji, Q., et al. (2016) · doi:10.1101/084137 · Cited by 1,376
Magnetoencephalography for Brain Electrophysiology and Imaging
Baillet, S. (2017) · Nature Neuroscience · doi:10.1038/nn.4504 · Cited by 992
The conception of the ABCD study: From substance use to a broad NIH collaboration
Volkow, N. D., Koob, G. F., Croyle, R. T., Bianchi, D. W., Gordon, J. A., Koroshetz, W. J., Pérez-Stable, E. J., Riley, W. T., Bloch, M. H., Conway, K., et al. (2018) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2017.10.002 · Cited by 970
MRIQC: Advancing the automatic prediction of image quality in MRI from unseen sites
Esteban, O., Birman, D., Schaer, M., Koyejo, O. O., Poldrack, R. A., & Gorgolewski, K. J. (2017) · PLOS ONE · doi:10.1371/journal.pone.0184661 · Cited by 920
A Weighted and Directed Interareal Connectivity Matrix for Macaque Cerebral Cortex
Markov, N. T., Ercsey-Ravasz, M. M., Ribeiro Gomes, A. R., Lamy, C., Magrou, L., Vezoli, J., Misery, P., Falchier, A., Quilodran, R., Gariel, M. A., et al. (2014) · Cerebral Cortex · doi:10.1093/cercor/bhs270 · Cited by 857
Thomas, C., Ye, F. Q., Irfanoglu, M. O., Modi, P., Saleem, K. S., Leopold, D. A., & Pierpaoli, C. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1405672111 · Cited by 725
Graph analysis of the human connectome: Promise, progress, and pitfalls
Fornito, A., Zalesky, A., & Breakspear, M. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.087 · Cited by 743
Open science challenges, benefits and tips in early career and beyond
Allen, C., & Mehler, D. M. A. (2019) · PLOS Biology · doi:10.1371/journal.pbio.3000246 · Cited by 608
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
Parcellating cortical functional networks in individuals
Wang, D., Buckner, R. L., Fox, M. D., Holt, D. J., Holmes, A. J., Stoecklein, S., Langs, G., Pan, R., Qian, T., Li, K., et al. (2015) · Nature Neuroscience · doi:10.1038/nn.4164 · Cited by 527
Reveley, C., Seth, A. K., Pierpaoli, C., Silva, A. C., Yu, D., Saunders, R. C., Leopold, D. A., & Ye, F. Q. (2015) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1418198112 · Cited by 417
Stimulation mapping of white matter tracts to study brain functional connectivity.
Duffau, H. (2015) · Nature Reviews Neurology · doi:10.1038/nrneurol.2015.51 · Cited by 437
Deoni, S. C. L., Dean, D. C., O'Muircheartaigh, J., Dirks, H., & Jerskey, B. A. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.07.037 · Cited by 390
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
Psychological processing in chronic pain: a neural systems approach
Simons, L. E., Elman, I., & Borsook, D. (2014) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2013.12.006 · Cited by 359
Mathematical framework for large-scale brain network modeling in The Virtual Brain
Sanz-Leon, P., Knock, S. A., Spiegler, A., & Jirsa, V. K. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.01.002 · Cited by 356
The ABCD study: understanding the development of risk for mental and physical health outcomes
Karcher, N. R., & Barch, D. M. (2021) · Neuropsychopharmacology · doi:10.1038/s41386-020-0736-6 · Cited by 351
Self-citationMulti-compartment microscopic diffusion imaging
Kaden, E., Kelm, N. D., Carson, R. P., Does, M. D., & Alexander, D. C. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.06.002 · Cited by 345
van den Heuvel, M. P., Bullmore, E. T., & Sporns, O. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.03.001 · Cited by 328
Psychometric Properties of the Penn Computerized Neurocognitive Battery
Moore, T. M., Reise, S. P., Gur, R. E., Hakonarson, H., & Gur, R. C. (2015) · Neuropsychology · doi:10.1037/neu0000093 · Cited by 325
Analysis of task-based functional MRI data preprocessed with fMRIPrep
Esteban, O., Ciric, R., Finc, K., Blair, R. W., Markiewicz, C. J., Moodie, C. A., Kent, J. D., Goncalves, M., DuPre, E., Gomez, D. E. P., et al. (2020) · Nature Protocols · doi:10.1038/s41596-020-0327-3 · Cited by 315
Clinical 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
Generative models of the human connectome
Betzel, R. F., Avena-Koenigsberger, A., Goñi, J., He, Y., de Reus, M. A., Griffa, A., Vértes, P. E., Mišic, B., Thiran, J.-P., Hagmann, P., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.09.041 · Cited by 303
Consciousness in humans and non-human animals: recent advances and future directions
Boly, M., Seth, A. K., Wilke, M., Ingmundson, P., Baars, B., Laureys, S., Edelman, D. B., & Tsuchiya, N. (2013) · Frontiers in Psychology · doi:10.3389/fpsyg.2013.00625 · Cited by 291
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 ENIGMA Toolbox: multiscale neural contextualization of multisite neuroimaging datasets
Larivière, S., Paquola, C., Park, B.-Y., Royer, J., Wang, Y., Benkarim, O., Vos de Wael, R., Valk, S. L., Thomopoulos, S. I., Kirschner, M., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01186-4 · Cited by 269
Evaluation and statistical inference for human connectomes
Pestilli, F., Yeatman, J. D., Rokem, A., Kay, K. N., & Wandell, B. A. (2014) · Nature Methods · doi:10.1038/nmeth.3098 · Cited by 259
Diffusion tensor imaging for understanding brain development in early life
Qiu, A., Mori, S., & Miller, M. I. (2015) · Annual Review of Psychology · doi:10.1146/annurev-psych-010814-015340 · Cited by 238
Neurite imaging reveals microstructural variations in human cerebral cortical gray matter
Fukutomi, H., Glasser, M. F., Zhang, H., Autio, J. A., Coalson, T. S., Okada, T., Togashi, K., Van Essen, D. C., & Hayashi, T. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2018.02.017 · Cited by 234
Self-citationRoalf, D. R., Quarmley, M., Elliott, M. A., Satterthwaite, T. D., Vandekar, S. N., Ruparel, K., Gennatas, E. D., Calkins, M. E., Moore, T. M., Hopson, R., et al. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2015.10.068 · Cited by 199
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
Cui, H., Dai, W., Zhu, Y., Kan, X., Gu, A. A. C., Lukemire, J., Zhan, L., He, L., Guo, Y., & Yang, C. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2022.3218745 · Cited by 218
Kochunov, P., Jahanshad, N., Marcus, D., Winkler, A., Sprooten, E., Nichols, T. E., Wright, S. N., Hong, L. E., Patel, B., Behrens, T., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.02.050 · Cited by 215
Self-citationMapping Connectivity Damage in the Case of Phineas Gage
Van Horn, J. D., Irimia, A., Torgerson, C. M., Chambers, M. C., Kikinis, R., & Toga, A. W. (2012) · PLoS ONE · doi:10.1371/journal.pone.0037454 · Cited by 196
White, T., Blok, E., & Calhoun, V. D. (2022) · Human Brain Mapping · doi:10.1002/hbm.25120 · Cited by 176
“It is possible for researchers to obtain some study data, that is, Human Connectome Project data, via an encrypted hard drive that is mailed.”
BackgroundInfluentialOpportunities of connectomic neuromodulation
Horn, A., & Fox, M. D. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117180 · Cited by 187
Networks of task co-activations
Laird, A. R., Eickhoff, S. B., Rottschy, C., Bzdok, D., Ray, K. L., & Fox, P. T. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.073 · Cited by 168
Yeh, F.-C., Vettel, J. M., Singh, A., Poczos, B., Grafton, S. T., Erickson, K. I., Tseng, W.-Y. I., & Verstynen, T. D. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005203 · Cited by 151
Three distinct sets of connector hubs integrate human brain function
Gordon, E. M., Lynch, C. J., Gratton, C., Laumann, T. O., Gilmore, A. W., Greene, D. J., Ortega, M., Nguyen, A. L., Schlaggar, B. L., Petersen, S. E., et al. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.07.050 · Cited by 149
Self-citationMicrostructure informed tractography: pitfalls and open challenges
Daducci, A., Dal Palú, A., Descoteaux, M., & Thiran, J.-P. (2016) · Frontiers in Neuroscience · doi:10.3389/fnins.2016.00247 · Cited by 138
Mangin, J.-F., Fillard, P., Cointepas, Y., Le Bihan, D., Frouin, V., & Poupon, C. (2013) · NeuroImage · doi:10.1016/j.neuroimage.2013.04.009 · Cited by 121
Jbabdi, S., Sotiropoulos, S. N., & Behrens, T. E. (2013) · Current Opinion in Neurobiology · doi:10.1016/j.conb.2012.12.004 · Cited by 107
Self-citationThe sensitivity of diffusion MRI to microstructural properties and experimental factors
Afzali, M., Pieciak, T., Newman, S., Garyfallidis, E., Özarslan, E., Cheng, H., & Jones, D. K. (2021) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2020.108951 · Cited by 117
An afferent white matter pathway from the pulvinar to the amygdala facilitates fear recognition
McFadyen, J., Mattingley, J. B., & Garrido, M. I. (2019) · eLife · doi:10.7554/elife.40766 · Cited by 115
Dynamic causal brain circuits during working memory and their functional controllability
Cai, W., Ryali, S., Pasumarthy, R., Talasila, V., & Menon, V. (2021) · Nature Communications · doi:10.1038/s41467-021-23509-x · Cited by 105
Serotonin regulation of behavior via large-scale neuromodulation of serotonin receptor networks
Salvan, P., Fonseca, M., Winkler, A. M., Beauchamp, A., Lerch, J. P., & Johansen-Berg, H. (2023) · Nature Neuroscience · doi:10.1038/s41593-022-01213-3 · Cited by 101
Diffusion-MRI in neurodegenerative disorders
Goveas, J., O’Dwyer, L., Mascalchi, M., Cosottini, M., Diciotti, S., De Santis, S., Passamonti, L., Tessa, C., Toschi, N., & Giannelli, M. (2015) · Magnetic Resonance Imaging · doi:10.1016/j.mri.2015.04.006 · Cited by 104
Toward understanding the impact of trauma on the early developing human brain
Thomason, M. E., & Marusak, H. A. (2017) · Neuroscience · doi:10.1016/j.neuroscience.2016.02.022 · Cited by 101
Dysregulated oscillatory connectivity in the visual system in autism spectrum disorder
Seymour, R. A., Rippon, G., Gooding-Williams, G., Schoffelen, J. M., & Kessler, K. (2019) · Brain · doi:10.1093/brain/awz214 · Cited by 84
Quasi-periodic patterns contribute to functional connectivity in the brain
Abbas, A., Belloy, M., Kashyap, A., Billings, J., Nezafati, M., Schumacher, E. H., & Keilholz, S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.01.076 · Cited by 81
Behavior-related gene regulatory networks: A new level of organization in the brain
Sinha, S., Jones, B. M., Traniello, I. M., Bukhari, S. A., Halfon, M. S., Hofmann, H. A., Huang, S., Katz, P. S., Keagy, J., Lynch, V. J., et al. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1921625117 · Cited by 85
Shared Predisposition in the Association Between Cannabis Use and Subcortical Brain Structure
Pagliaccio, D., Barch, D. M., Bogdan, R., Wood, P. K., Lynskey, M. T., Heath, A. C., & Agrawal, A. (2015) · JAMA Psychiatry · doi:10.1001/jamapsychiatry.2015.1054 · Cited by 77
Self-citationLarge-Scale Functional Brain Organization
Menon, V. (2015) · Brain Mapping · doi:10.1016/b978-0-12-397025-1.00024-5 · Cited by 76
Opportunities and Challenges for Psychiatry in the Connectomic Era
Fornito, A., Bullmore, E. T., & Zalesky, A. (2017) · Biological Psychiatry: Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2016.08.003 · Cited by 74
Sinke, M. R. T., Otte, W. M., Christiaens, D., Schmitt, O., Leemans, A., van der Toorn, A., Sarabdjitsingh, R. A., Joëls, M., & Dijkhuizen, R. M. (2018) · Brain Structure and Function · doi:10.1007/s00429-018-1628-y · Cited by 69
Fiber tractography using machine learning
Neher, P. F., Côté, M.-A., Houde, J.-C., Descoteaux, M., & Maier-Hein, K. H. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.07.028 · Cited by 68
Altered topology of the functional speech production network in non-fluent/agrammatic variant of PPA
Mandelli, M. L., Welch, A. E., Vilaplana, E., Watson, C., Battistella, G., Brown, J. A., Possin, K. L., Hubbard, H. I., Miller, Z. A., Henry, M. L., et al. (2018) · Cortex · doi:10.1016/j.cortex.2018.08.002 · Cited by 67
Neuromarkers for Mental Disorders: Harnessing Population Neuroscience
Jollans, L., & Whelan, R. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00242 · Cited by 56
Technical Note: MRQy — An open‐source tool for quality control of MR imaging data
Sadri, A. R., Janowczyk, A., Zhou, R., Verma, R., Beig, N., Antunes, J., Madabhushi, A., Tiwari, P., & Viswanath, S. E. (2020) · Medical Physics · doi:10.1002/mp.14593 · Cited by 63
Fiber length profiling: a novel approach to structural brain organization
Bajada, C. J., Schreiber, J., & Caspers, S. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2018.10.070 · Cited by 55
Siddiqi, S. H., Kandala, S., Hacker, C. D., Bouchard, H., Leuthardt, E. C., Corbetta, M., Morey, R. A., & Brody, D. L. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.abn0441 · Cited by 50
Self-citationRNA-Seq Reveals Novel Transcriptional Reorganization in Human Alcoholic Brain
Farris, S. P., & Dayne Mayfield, R. (2014) · International Review of Neurobiology · doi:10.1016/b978-0-12-801105-8.00011-4 · Cited by 49
Chen, H., Liu, T., Zhao, Y., Zhang, T., Li, Y., Li, M., Zhang, H., Kuang, H., Guo, L., Tsien, J. Z., et al. (2015) · NeuroImage · doi:10.1016/j.neuroimage.2015.04.050 · Cited by 46
Molecular neuroanatomy: a generation of progress
Pollock, J. D., Wu, D.-Y., & Satterlee, J. S. (2014) · Trends in Neurosciences · doi:10.1016/j.tins.2013.11.001 · Cited by 36
Confound Removal and Normalization in Practice: A Neuroimaging Based Sex Prediction Case Study
More, S., Eickhoff, S. B., Caspers, J., & Patil, K. R. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-67670-4_1 · Cited by 37
Wada, A., Tsuruta, K., Irie, R., Kamagata, K., Maekawa, T., Fujita, S., Koshino, S., Kumamaru, K., Suzuki, M., Nakanishi, A., et al. (2019) · Magnetic Resonance in Medical Sciences · doi:10.2463/mrms.mp.2018-0091 · Cited by 37
Deep reinforcement learning and its applications in medical imaging and radiation therapy: a survey
Xu, L., Zhu, S., & Wen, N. (2022) · Physics in Medicine & Biology · doi:10.1088/1361-6560/ac9cb3 · Cited by 35
Human habit neural circuitry may be perturbed in eating disorders
Wang, A. R., Kuijper, F. M., Barbosa, D. A. N., Hagan, K. E., Lee, E., Tong, E., Choi, E. Y., McNab, J. A., Bohon, C., & Halpern, C. H. (2023) · Science Translational Medicine · doi:10.1126/scitranslmed.abo4919 · Cited by 27
Luo, Q., Zhang, L., Huang, C.-C., Zheng, Y., Kanen, J. W., Zhao, Q., Yao, Y., Quinlan, E. B., Jia, T., Banaschewski, T., et al. (2020) · BMC Medicine · doi:10.1186/s12916-020-01743-2 · Cited by 26
Diffusion MRI tractography filtering techniques change the topology of structural connectomes
Frigo, M., Deslauriers-Gauthier, S., Parker, D., Aziz Ould Ismail, A., John Kim, J., Verma, R., & Deriche, R. (2020) · Journal of Neural Engineering · doi:10.1088/1741-2552/abc29b · Cited by 19
Deep attention super-resolution of brain magnetic resonance images acquired under clinical protocols
Li, B. M., Castorina, L. V., Valdés Hernández, M. D. C., Clancy, U., Wiseman, S. J., Sakka, E., Storkey, A. J., Jaime Garcia, D., Cheng, Y., Doubal, F., et al. (2022) · Frontiers in Computational Neuroscience · doi:10.3389/fncom.2022.887633 · Cited by 17
He, T., Kong, R., Holmes, A. J., Nguyen, M., Sabuncu, M. R., Eickhoff, S. B., Bzdok, D., Feng, J., & Yeo, B. T. T. (2018) · doi:10.1101/473603 · Cited by 16
Sulpizio, V., Strappini, F., Fattori, P., Galati, G., Galletti, C., Pecchinenda, A., & Pitzalis, S. (2022) · Brain Structure and Function · doi:10.1007/s00429-022-02549-z · Cited by 12
Girisha, M. N., Badiger, V. P., & Pattar, S. (2022) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-022-00353-7 · Cited by 4
Camastra, C., Pelagi, A., Quattrone, A., & Sarica, A. (2025) · Lecture Notes in Computer Science · doi:10.1007/978-3-031-82487-6_17 · Cited by 1
Camastra, C., Pelagi, A., Quattrone, A., & Sarica, A. (2026) · Brain Sciences · doi:10.3390/brainsci16040405 · Cited by 1
Metadata
- Authors
- Van Essen, D. C., Ugurbil, K., Auerbach, E., Barch, D., Behrens, T. E. J., Bucholz, R., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., Della Penna, S., Feinberg, D., Glasser, M. F., Harel, N., Heath, A. C., Larson-Prior, L., Marcus, D., Michalareas, G., Moeller, S., Oostenveld, R., Petersen, S. E., Prior, F., Schlaggar, B. L., Smith, S. M., Snyder, A. Z., Xu, J., & Yacoub, E.
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2012-10
- Volume
- 62
- Issue
- 4
- Pages
- 2222-2231
- ISSN
- 1053-8119
- PMC
- PMC3606888
- Cited by
- 2,680
- References
- 65
- Language
- en
- Licence
- www.elsevier.com/tdm/userlicense/1.0/ (tdm)
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 ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
