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An open science resource for establishing reliability and reproducibility in functional connectomics
Open accessAbstract
Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.
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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-citationFischl, B. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.01.021 · Cited by 9,526
The 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-citationFunctional connectivity in the resting brain: a network analysis of the default mode hypothesis
Greicius, M. D., Krasnow, B., Reiss, A. L., & Menon, V. (2003) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0135058100 · Cited by 6,358
Conn : 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
BrainNet 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
Self-citationMethods to detect, characterize, and remove motion artifact in resting state fMRI
Power, J. D., Mitra, A., Laumann, T. O., Snyder, A. Z., Schlaggar, B. L., & Petersen, S. E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2013.08.048 · Cited by 3,748
DPARSF: a MATLAB toolbox for “pipeline” data analysis of resting-state fMRI
Yan (2010) · Frontiers in System Neuroscience · doi:10.3389/fnsys.2010.00013 · Cited by 3,475
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
A method for making group inferences from functional MRI data using independent component analysis
Calhoun, V. D., Adali, T., Pearlson, G. D., & Pekar, J. J. (2001) · Human Brain Mapping · doi:10.1002/hbm.1048 · Cited by 2,994
Self-citation
Cited by
Scanning 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
Self-citationCiric, R., Wolf, D. H., Power, J. D., Roalf, D. R., Baum, G. L., Ruparel, K., Shinohara, R. T., Elliott, M. A., Eickhoff, S. B., Davatzikos, C., et al. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2017.03.020 · Cited by 1,042
Enhancing studies of the connectome in autism using the autism brain imaging data exchange II
Di Martino, A., O’Connor, D., Chen, B., Alaerts, K., Anderson, J. S., Assaf, M., Balsters, J. H., Baxter, L., Beggiato, A., Bernaerts, S., et al. (2017) · Scientific Data · doi:10.1038/sdata.2017.10 · Cited by 710
Self-citationMindboggling morphometry of human brains
Klein, A., Ghosh, S. S., Bao, F. S., Giard, J., Häme, Y., Stavsky, E., Lee, N., Rossa, B., Reuter, M., Chaibub Neto, E., et al. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005350 · Cited by 710
Kong, R., Li, J., Orban, C., Sabuncu, M. R., Liu, H., Schaefer, A., Sun, N., Zuo, X.-N., Holmes, A. J., Eickhoff, S. B., et al. (2019) · Cerebral Cortex · doi:10.1093/cercor/bhy123 · Cited by 593
Self-citationNoble, S., Scheinost, D., & Constable, R. T. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116157 · Cited by 580
Holmes, A. J., Hollinshead, M. O., O’Keefe, T. M., Petrov, V. I., Fariello, G. R., Wald, L. L., Fischl, B., Rosen, B. R., Mair, R. W., Roffman, J. L., et al. (2015) · Scientific Data · doi:10.1038/sdata.2015.31 · Cited by 444
Self-citationLi, J., Chen, J., Tang, Y., Wang, C., Landman, B. A., & Zhou, S. K. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102762 · Cited by 389
Self-citationChen, X., Lu, B., & Yan, C. (2018) · Human Brain Mapping · doi:10.1002/hbm.23843 · Cited by 340
Self-citationConsistency-based thresholding of the human connectome
Roberts, J. A., Perry, A., Roberts, G., Mitchell, P. B., & Breakspear, M. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.09.053 · Cited by 189
White, 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…”
Self-citationWang, Y., Wang, J., Jia, Y., Zhong, S., Zhong, M., Sun, Y., Niu, M., Zhao, L., Zhao, L., Pan, J., et al. (2017) · Translational Psychiatry · doi:10.1038/tp.2017.117 · Cited by 133
Motion artifact in studies of functional connectivity: Characteristics and mitigation strategies
Satterthwaite, T. D., Ciric, R., Roalf, D. R., Davatzikos, C., Bassett, D. S., & Wolf, D. H. (2019) · Human Brain Mapping · doi:10.1002/hbm.23665 · Cited by 137
Detect and correct bias in multi-site neuroimaging datasets
Wachinger, C., Rieckmann, A., & Pölsterl, S. (2021) · Medical Image Analysis · doi:10.1016/j.media.2020.101879 · Cited by 115
Progress toward openness, transparency, and reproducibility in cognitive neuroscience
Gilmore, R. O., Diaz, M. T., Wyble, B. A., & Yarkoni, T. (2017) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.13325 · Cited by 95
Multicenter stability of resting state fMRI in the detection of Alzheimer’s disease and amnestic MCI
Teipel, S. J., Wohlert, A., Metzger, C., Grimmer, T., Sorg, C., Ewers, M., Meisenzahl, E., Klöppel, S., Borchardt, V., Grothe, M. J., et al. (2017) · NeuroImage: Clinical · doi:10.1016/j.nicl.2017.01.018 · Cited by 62
Adhikari, B. M., Jahanshad, N., Shukla, D., Turner, J., Grotegerd, D., Dannlowski, U., Kugel, H., Engelen, J., Dietsche, B., Krug, A., et al. (2019) · Brain Imaging and Behavior · doi:10.1007/s11682-018-9941-x · Cited by 53
Self-citationFusing Mobile Phone Sensing and Brain Imaging to Assess Depression in College Students
Huckins, J. F., daSilva, A. W., Wang, R., Wang, W., Hedlund, E. L., Murphy, E. I., Lopez, R. B., Rogers, C., Holtzheimer, P. E., Kelley, W. M., et al. (2019) · Frontiers in Neuroscience · doi:10.3389/fnins.2019.00248 · Cited by 51
Self-citationFunctional connectivity subtypes associate robustly with ASD diagnosis
Urchs, S. G., Tam, A., Orban, P., Moreau, C., Benhajali, Y., Nguyen, H. D., Evans, A. C., & Bellec, P. (2022) · eLife · doi:10.7554/elife.56257 · Cited by 37
Self-citationRen, J., An, N., Lin, C., Zhang, Y., Sun, Z., Zhang, W., Li, S., Guo, N., Cui, W., Hu, Q., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02599-1 · Cited by 29
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Metadata
- Authors
- Zuo, X.-N., Anderson, J. S., Bellec, P., Birn, R. M., Biswal, B. B., Blautzik, J., Breitner, J. C. S., Buckner, R. L., Calhoun, V. D., Castellanos, F. X., Chen, A., Chen, B., Chen, J., Chen, X., Colcombe, S. J., Courtney, W., Craddock, R. C., Di Martino, A., Dong, H.-M., Fu, X., Gong, Q., Gorgolewski, K. J., Han, Y., He, Y., He, Y., Ho, E., Holmes, A., Hou, X.-H., Huckins, J., Jiang, T., Jiang, Y., Kelley, W., Kelly, C., King, M., LaConte, S. M., Lainhart, J. E., Lei, X., Li, H.-J., Li, K., Li, K., Lin, Q., Liu, D., Liu, J., Liu, X., Liu, Y., Lu, G., Lu, J., Luna, B., Luo, J., Lurie, D., Mao, Y., Margulies, D. S., Mayer, A. R., Meindl, T., Meyerand, M. E., Nan, W., Nielsen, J. A., O’Connor, D., Paulsen, D., Prabhakaran, V., Qi, Z., Qiu, J., Shao, C., Shehzad, Z., Tang, W., Villringer, A., Wang, H., Wang, K., Wei, D., Wei, G.-X., Weng, X.-C., Wu, X., Xu, T., Yang, N., Yang, Z., Zang, Y.-F., Zhang, L., Zhang, Q., Zhang, Z., Zhang, Z., Zhao, K., Zhen, Z., Zhou, Y., Zhu, X.-T., & Milham, M. P.
- Venue
- Scientific Data
- Published
- 2014-12-09
- Volume
- 1
- Issue
- 1
- Pages
- 140049-140049
- ISSN
- 2052-4463 (ISSN-L)
- OpenAlex
- W2093745477
- Cited by
- 445
- References
- 20
- Type
- data paper
- Language
- en
- Open access
- gold
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