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Making big data open: data sharing in neuroimaging

Abstract

In the last decade, major advances have been made in the availability of shared neuroimaging data, such that there are more than 8,000 shared MRI (magnetic resonance imaging) data sets available online. Here we outline the state of data sharing for task-based functional MRI (fMRI) data, with a focus on various forms of data and their relative utility for subsequent analyses. We also discuss challenges to the future success of data sharing and highlight the ethical argument that data sharing may be necessary to maximize the contribution of human subjects.

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Figure 1: Data scales in neuroimaging.

References

  1. Van Essen, D.C. et al. The WU-Minn human connectome project: an overview. Neuroimage 80, 62–79 (2013).

    Article  PubMed  Google Scholar 

  2. Poline, J.-B. et al. Data sharing in neuroimaging research. Front. Neuroinform. 6, 9 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  3. Keator, D.B. et al. Towards structured sharing of raw and derived neuroimaging data across existing resources. Neuroimage 82, 647–661 (2013).

    CAS  PubMed  Article  Google Scholar 

  4. Mennes, M., Biswal, B.B., Castellanos, F.X. & Milham, M.P. Making data sharing work: The FCP/INDI experience. Neuroimage 82, 683–691 (2013).

    PubMed  Article  Google Scholar 

  5. Brakewood, B. & Poldrack, R.A. The ethics of secondary data analysis: considering the application of Belmont Principles to the sharing of neuroimaging data. Neuroimage 82, 671–676 (2013).

    PubMed  Article  Google Scholar 

  6. Posner, M.I., Petersen, S.E., Fox, P.T. & Raichle, M.E. Localization of cognitive operations in the human brain. Science 240, 1627–1631 (1988).

    CAS  PubMed  Article  Google Scholar 

  7. Poldrack, R.A. Mapping mental function to brain structure: how can cognitive neuroimaging succeed? Perspect. Psychol. Sci. 5, 753–761 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  8. Lloyd, D. Functional MRI and the study of human consciousness. J. Cogn. Neurosci. 14, 818–831 (2002).

    PubMed  Article  Google Scholar 

  9. Poldrack, R.A., Halchenko, Y.O. & Hanson, S.J. Decoding the large-scale structure of brain function by classifying mental states across individuals. Psychol. Sci. 20, 1364–1372 (2009).

    PubMed  Article  Google Scholar 

  10. Schwartz, Y., Thirion, B. & Varoquaux, G. Mapping paradigm ontologies to and from the brain. Adv. Neural. Inf. Process. Syst. 26, 1673–1681 (2013).

    Google Scholar 

  11. Poldrack, R.A. et al. Discovering relations between mind, brain, and mental disorders using topic mapping. PLoS Comput. Biol. 8, e1002707 (2012).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  12. Anonymous. Announcement: reducing our irreproducibility. Nature 496, 398 (2013).

  13. Anonymous. How science goes wrong. The Economist (19 October 2013).

  14. Button, K.S. et al. Power failure: why small sample size undermines the reliability of neuroscience. Nat. Rev. Neurosci. 14, 365–376 (2013).

    CAS  Article  PubMed  Google Scholar 

  15. Ioannidis, J.P.A. Why most published research findings are false. PLoS Med. 2, e124 (2005).

    PubMed  PubMed Central  Article  Google Scholar 

  16. David, S.P. et al. Potential reporting bias in FMRI studies of the brain. PLoS ONE 8, e70104 (2013).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  17. Medland, S.E., Jahanshad, N., Neale, B.M. & Thompson, P.M. Whole-genome analyses of whole-brain data: working within an expanded search space. Nat. Neurosci. 17, 791–800 (2014).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  18. Carp, J. On the plurality of (methodological) worlds: estimating the analytic flexibility of fMRI experiments. Front. Neurosci. 6, 149 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  19. Ioannidis, J.P.A. Why most discovered true associations are inflated. Epidemiology 19, 640–648 (2008).

    PubMed  Article  Google Scholar 

  20. Kriegeskorte, N., Lindquist, M.A., Nichols, T.E., Poldrack, R.A. & Vul, E. Everything you never wanted to know about circular analysis, but were afraid to ask. J. Cereb. Blood Flow Metab. 30, 1551–1557 (2010).

    PubMed  PubMed Central  Article  Google Scholar 

  21. Kriegeskorte, N., Simmons, W.K., Bellgowan, P.S.F. & Baker, C.I. Circular analysis in systems neuroscience: the dangers of double dipping. Nat. Neurosci. 12, 535–540 (2009).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  22. Vul, E., Harris, C., Winkielman, P. & Pashler, H. Puzzlingly high correlations in fMRI studies of emotion, personality, and social cognition. Perspect. Psychol. Sci. 4, 274–290 (2009).

    PubMed  Article  Google Scholar 

  23. Simmons, J.P., Nelson, L.D. & Simonsohn, U. False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol. Sci. 22, 1359–1366 (2011).

    PubMed  Article  Google Scholar 

  24. Wicherts, J.M., Bakker, M. & Molenaar, D. Willingness to share research data is related to the strength of the evidence and the quality of reporting of statistical results. PLoS ONE 6, e26828 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  25. Biswal, B.B. et al. Toward discovery science of human brain function. Proc. Natl. Acad. Sci. USA 107, 4734–4739 (2010).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  26. Tomasi, D. & Volkow, N.D. Functional connectivity density mapping. Proc. Natl. Acad. Sci. USA 107, 9885–9890 (2010).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  27. Rubinov, M. & Sporns, O. Weight-conserving characterization of complex functional brain networks. Neuroimage 56, 2068–2079 (2011).

    Article  PubMed  Google Scholar 

  28. Yan, C.-G., Craddock, R.C., Zuo, X.-N., Zang, Y.-F. & Milham, M.P. Standardizing the intrinsic brain: towards robust measurement of inter-individual variation in 1,000 functional connectomes. Neuroimage 80, 246–262 (2013).

    PubMed  Article  Google Scholar 

  29. Zuo, X.-N. et al. Network centrality in the human functional connectome. Cereb. Cortex 22, 1862–1875 (2012).

    PubMed  Article  Google Scholar 

  30. Yeo, B.T.T., Krienen, F.M., Chee, M.W.L. & Buckner, R.L. Estimates of segregation and overlap of functional connectivity networks in the human cerebral cortex. Neuroimage 88C, 212–227 (2013).

    Google Scholar 

  31. Zalesky, A., Fornito, A., Cocchi, L., Gollo, L.L. & Breakspear, M. Time-resolved resting-state brain networks. Proc. Natl. Acad. Sci. USA 111, 10341–10346 (2014).

    CAS  PubMed  Article  PubMed Central  Google Scholar 

  32. Webb, J.T., Ferguson, M.A., Nielsen, J.A. & Anderson, J.S. BOLD Granger causality reflects vascular anatomy. PLoS ONE 8, e84279 (2013).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  33. Salimi-Khorshidi, G., Smith, S.M., Keltner, J.R., Wager, T.D. & Nichols, T.E. Meta-analysis of neuroimaging data: a comparison of image-based and coordinate-based pooling of studies. Neuroimage 45, 810–823 (2009).

    PubMed  Article  Google Scholar 

  34. Laird, A.R., Lancaster, J.L. & Fox, P.T. BrainMap: the social evolution of a human brain mapping database. Neuroinformatics 3, 65–78 (2005).

    Article  PubMed  Google Scholar 

  35. Yarkoni, T., Poldrack, R.A., Nichols, T.E., Van Essen, D.C. & Wager, T.D. Large-scale automated synthesis of human functional neuroimaging data. Nat. Methods 8, 665–670 (2011).

    CAS  PubMed  PubMed Central  Article  Google Scholar 

  36. Van Horn, J.D. et al. The Functional Magnetic Resonance Imaging Data Center (fMRIDC): the challenges and rewards of large-scale databasing of neuroimaging studies. Phil. Trans. R. Soc. Lond. B 356, 1323–1339 (2001).

    CAS  Article  Google Scholar 

  37. Poldrack, R.A. et al. Toward open sharing of task-based fMRI data: the OpenfMRI project. Front. Neuroinform. 7, 12 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  38. Van Horn, J.D. & Gazzaniga, M.S. Why share data? lessons learned from the fMRIDC. Neuroimage 82, 677–682 (2013).

    PubMed  Article  Google Scholar 

  39. Marques, J.P. et al. MP2RAGE, a self bias-field corrected sequence for improved segmentation and T1-mapping at high field. Neuroimage 49, 1271–1281 (2010).

    PubMed  Article  Google Scholar 

  40. Hoge, R.D. Calibrated fMRI. Neuroimage 62, 930–937 (2012).

    PubMed  Article  Google Scholar 

  41. Gauthier, C.J., Desjardins-Crépeau, L., Madjar, C., Bherer, L. & Hoge, R.D. Absolute quantification of resting oxygen metabolism and metabolic reactivity during functional activation using QUO2 MRI. Neuroimage 63, 1353–1363 (2012).

    CAS  PubMed  Article  Google Scholar 

  42. Hurko, O. et al. The ADNI publication policy: commensurate recognition of critical contributors who are not authors. Neuroimage 59, 4196–4200 (2012).

    PubMed  Article  Google Scholar 

  43. Rohlfing, T. & Poline, J.-B. Why shared data should not be acknowledged on the author byline. Neuroimage 59, 4189–4195 (2012).

    CAS  PubMed  Article  Google Scholar 

  44. Gorgolewski, K.J., Margulies, D.S. & Milham, M.P. Making data sharing count: a publication-based solution. Front. Neurosci. 7, 9 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  45. Gorgolewski, K.J. et al. A test-retest fMRI dataset for motor, language and spatial attention functions. Gigascience 2, 6 (2013).

    PubMed  PubMed Central  Article  Google Scholar 

  46. Hanke, M. et al. A high-resolution 7-tesla fMRI dataset from complex natural stimulation with an audio movie. Sci. Data 1, 140003 (2014).

    PubMed  PubMed Central  Article  Google Scholar 

  47. Anonymous. Using someone else's data. practiCal fMRI: the nuts & bolts http://practicalfmri.blogspot.com/2014/02/using-someone-elses-data.html (2014).

  48. Meehl, P. Theory testing in psychology and physics: a methodological paradox. Philos. Sci. 34, 103–115 (1967).

    Article  Google Scholar 

  49. Monogan, J.E. III. A case for registering studies of political outcomes: an application in the 2010 house elections. Polit. Anal. 21, 21–37 (2013).

    Article  Google Scholar 

  50. HD-200 Consortium. The ADHD-200 consortium: a model to advance the translational potential of neuroimaging in clinical neuroscience. Front. Syst. Neurosci. 6, 62 (2012).

  51. Guyon, I., Hur, A.B., Gunn, S. & Dror, G. Result analysis of the NIPS 2003 feature selection challenge. Adv. Neural Inf. Process. Syst. 17, 545–552 (2004).

    Google Scholar 

  52. Marshall, E. Bermuda rules: community spirit, with teeth. Science 291, 1192 (2001).

    CAS  PubMed  Article  Google Scholar 

  53. Gardner, D. et al. The neuroscience information framework: a data and knowledge environment for neuroscience. Neuroinformatics 6, 149–160 (2008).

    PubMed  PubMed Central  Article  Google Scholar 

  54. Marenco, L., Ascoli, G.A., Martone, M.E., Shepherd, G.M. & Miller, P.L. The NIF LinkOut broker: a web resource to facilitate federated data integration using NCBI identifiers. Neuroinformatics 6, 219–227 (2008).

    PubMed  PubMed Central  Article  Google Scholar 

  55. Langille, M.G.I. & Eisen, J.A. BioTorrents: a file sharing service for scientific data. PLoS ONE 5, e10071 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  56. Gadde, S. et al. XCEDE: an extensible schema for biomedical data. Neuroinformatics 10, 19–32 (2012).

    PubMed  Article  Google Scholar 

  57. Turner, J.A. & Laird, A.R. The cognitive paradigm ontology: design and application. Neuroinformatics 10, 57–66 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

  58. Poldrack, R.A. et al. The cognitive atlas: toward a knowledge foundation for cognitive neuroscience. Front. Neuroinform. 5, 17 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  59. Marcus, D.S., Olsen, T.R., Ramaratnam, M. & Buckner, R.L. The extensible neuroimaging archive toolkit: an informatics platform for managing, exploring, and sharing neuroimaging data. Neuroinformatics 5, 11–34 (2007).

    PubMed  Article  Google Scholar 

  60. Van Horn, J.D. & Toga, A.W. Is it time to re-prioritize neuroimaging databases and digital repositories? Neuroimage 47, 1720–1734 (2009).

    PubMed  Article  Google Scholar 

  61. Das, S., Zijdenbos, A.P., Harlap, J., Vins, D. & Evans, A.C. LORIS: a web-based data management system for multi-center studies. Front. Neuroinform. 5, 37 (2011).

    PubMed  Google Scholar 

  62. Scott, A. et al. COINS: an innovative informatics and neuroimaging tool suite built for large heterogeneous datasets. Front. Neuroinform. 5, 33 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  63. Marcus, D.S. et al. Human connectome project informatics: quality control, database services, and data visualization. Neuroimage 80, 202–219 (2013).

    PubMed  Article  Google Scholar 

  64. Rex, D.E., Ma, J.Q. & Toga, A.W. The LONI pipeline processing environment. Neuroimage 19, 1033–1048 (2003).

    PubMed  Article  Google Scholar 

  65. Dinov, I. et al. Neuroimaging study designs, computational analyses and data provenance using the LONI pipeline. PLoS ONE 5, e13070 (2010).

    PubMed  PubMed Central  Article  CAS  Google Scholar 

  66. Gorgolewski, K. et al. Nipype: a flexible, lightweight and extensible neuroimaging data processing framework in Python. Front. Neuroinform. 5, 13 (2011).

    PubMed  PubMed Central  Article  Google Scholar 

  67. MacKenzie-Graham, A.J., Van Horn, J.D., Woods, R.P., Crawford, K.L. & Toga, A.W. Provenance in neuroimaging. Neuroimage 42, 178–195 (2008).

    PubMed  Article  Google Scholar 

  68. Bellec, P. et al. The pipeline system for octave and MATLAB (PSOM): a lightweight scripting framework and execution engine for scientific workflows. Front. Neuroinform. 6, 7 (2012).

    PubMed  PubMed Central  Article  Google Scholar 

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Acknowledgements

Thanks to T. Schonberg, D.S. Margulies and K. Heuer for comments on an early draft. Preparation of this paper was supported by the US National Science Foundation (OCI-1131441) and US National Institute of Drug Abuse (1R21DA034316-S1) to R.A.P. and Max Planck Society to K.J.G.

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Correspondence to Russell A Poldrack.

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Poldrack, R., Gorgolewski, K. Making big data open: data sharing in neuroimaging. Nat Neurosci 17, 1510–1517 (2014). https://doi.org/10.1038/nn.3818

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