Skip to main content

Thank you for visiting You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Commentary
  • Published:

Best practices in data analysis and sharing in neuroimaging using MRI



Given concerns about the reproducibility of scientific findings, neuroimaging must define best practices for data analysis, results reporting, and algorithm and data sharing to promote transparency, reliability and collaboration. We describe insights from developing a set of recommendations on behalf of the Organization for Human Brain Mapping and identify barriers that impede these practices, including how the discipline must change to fully exploit the potential of the world's neuroimaging data.

This is a preview of subscription content, access via your institution

Relevant articles

Open Access articles citing this article.

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout


  1. Ioannidis, J.P.A. PLoS Med. 2, e124 (2005).

    Article  Google Scholar 

  2. Open Science Collaboration. Science 349, aac4716 (2015).10.1126/science.aac4716

  3. Anonymous. Nature 515, 7 (2014).10.1038/515007b

  4. Nichols, T.E. et al. bioRxiv (2016).

    Google Scholar 

  5. Peng, R.D. Science 334, 1226–1227 (2011).

    Article  CAS  Google Scholar 

  6. Bennett, C.M. & Miller, M.B. Cogn. Affect. Behav. Neurosci. 13, 690–702 (2013).

    Article  Google Scholar 

  7. Schnack, H.G. et al. Hum. Brain Mapp. 31, 1967–1982 (2010).

    Article  Google Scholar 

  8. Noble, S. et al. Neuroimage 277, 88–100 (2017).

    Google Scholar 

  9. Boekel, W. et al. Cortex 66, 115–133 (2015).

    Article  Google Scholar 

  10. Kriegeskorte, N., Simmons, W.K., Bellgowan, P.S.F. & Baker, C.I. Nat. Neurosci. 12, 535–540 (2009).

    Article  CAS  Google Scholar 

  11. Poldrack, R.A. et al. Neuroimage 40, 409–414 (2008).

    Article  Google Scholar 

  12. Brakewood, B. & Poldrack, R.A. Neuroimage 82, 671–676 (2013).

    Article  Google Scholar 

  13. Waskom, M.L., Kumaran, D., Gordon, A.M., Rissman, J. & Wagner, A.D. J. Neurosci. 34, 10743–10755 (2014).

    Article  CAS  Google Scholar 

  14. Whitaker, K.J. et al. Proceedings of the National Academy of Sciences, 113, 9105–9110 (2016).

    Article  CAS  Google Scholar 

  15. Pernet, C.R. et al. Neuroimage 119, 164–174 (2015).

    Article  Google Scholar 

  16. Owens, B. Science 351, 329 (2016).

    Article  CAS  Google Scholar 

  17. Mennes, M., Biswal, B.B., Castellanos, F.X. & Milham, M.P. Neuroimage 82, 683–691 (2013).

    Article  Google Scholar 

  18. Gorgolewski, K.J. et al. Sci. Data 3, 160044 (2016).

    Article  Google Scholar 

  19. Bulik-Sullivan, B.K. et al. Nat. Genet. 47, 291–295 (2015).

    Article  CAS  Google Scholar 

  20. Salimi-Khorshidi, G., Smith, S.M., Keltner, J.R., Wager, T.D. & Nichols, T.E. Neuroimage 45, 810–823 (2009).

    Article  Google Scholar 

  21. Goble, C. IEEE Internet Comput. 18, 4–8 (2014).

    Article  Google Scholar 

  22. Glasser, M.F. et al. Nat. Neurosci. 19, 1175–1187 (2016).

    Article  Google Scholar 

  23. Abraham, A. et al. Neuroimage 147, 736–745 (2016).

    Article  Google Scholar 

  24. ISO. Statistics - Vocabulary and Symbols. Part 2: Applied Statistics. ISO 3534–2 (Second ed.) (ISO, 2006).

Download references


T.E.N. is supported by the Wellcome Trust (100309/Z/12/Z) and NIH (R01 NS075066-01A1, R01 EB015611-01). B.T.T.Y. is supported by Singapore MOE Tier 2 (MOE2014-T2-2-016), NUS (DPRT/944/09/14, R185000271720), NMRC (CBRG14nov007) and the NUS YIA. S.B.E. is supported by the Deutsche Forschungsgemeinschaft (DFG, EI 816/4-1, LA 3071/3-1; EI 816/6-1), the National Institute of Mental Health (R01-MH074457), the Helmholtz Portfolio Theme 'Supercomputing and Modeling for the Human Brain' and the European Union Seventh Framework Programme (FP7/2007-2013) under grant agreement no. 604102). M.H. was supported by funds from the German federal state of Saxony-Anhalt and the European Regional Development Fund (ERDF), project: Center for Behavioral Brain Sciences, and CRCNS BMBF/NSF (01GQ1411/1429999). N.K. was supported by the UK Medical Research Council and a European Research Council Starting Grant (261352). A.C.E., S.D. and T.G. are supported by the Irving Ludmer Family Foundation and the Ludmer Centre for Neuroinformatics and Mental Health. T.W. was supported by a ZonMw TOP grant (91211021). R.A.P. is supported by the Laura and John Arnold Foundation. M.P.M. is a Phyllis Green and Randolph Cowen Scholar and is supported in part by the NIH (U01 MH099059; R01 AG047596). J.-B.P. is supported by the NIBIB (P41EB019936) and by NIH–National Institute on Drug Abuse (U24DA038653), as well as by the Laura and John Arnold Foundation.

Author information

Authors and Affiliations


Corresponding author

Correspondence to Thomas E Nichols.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Table 1 (PDF 82 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nichols, T., Das, S., Eickhoff, S. et al. Best practices in data analysis and sharing in neuroimaging using MRI. Nat Neurosci 20, 299–303 (2017).

Download citation

  • Published:

  • Issue Date:

  • DOI:

This article is cited by


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing