Advertisement

Neuroinformatics

, Volume 11, Issue 4, pp 495–505 | Cite as

Neuroinformatics Database (NiDB) – A Modular, Portable Database for the Storage, Analysis, and Sharing of Neuroimaging Data

  • Gregory A. Book
  • Beth M. Anderson
  • Michael C. Stevens
  • David C. Glahn
  • Michal Assaf
  • Godfrey D. Pearlson
Original Article

Abstract

We present a modular, high performance, open-source database system that incorporates popular neuroimaging database features with novel peer-to-peer sharing, and a simple installation. An increasing number of imaging centers have created a massive amount of neuroimaging data since fMRI became popular more than 20 years ago, with much of that data unshared. The Neuroinformatics Database (NiDB) provides a stable platform to store and manipulate neuroimaging data and addresses several of the impediments to data sharing presented by the INCF Task Force on Neuroimaging Datasharing, including 1) motivation to share data, 2) technical issues, and 3) standards development. NiDB solves these problems by 1) minimizing PHI use, providing a cost effective simple locally stored platform, 2) storing and associating all data (including genome) with a subject and creating a peer-to-peer sharing model, and 3) defining a sample, normalized definition of a data storage structure that is used in NiDB. NiDB not only simplifies the local storage and analysis of neuroimaging data, but also enables simple sharing of raw data and analysis methods, which may encourage further sharing.

Keywords

Database Neuroinformatics Neuroimaging Pipeline 

Notes

Acknowledgments

Features and ideas for the Neuroinformatics Database were conceived by many individuals over the course of its development. In this way, the entire staff of the Olin Neuropsychiatry Research Center contributed to its development. Development of NiDB was supported by the National Institutes of Health (NIH) from the following grants: R37-MH43775 (NIMH), R01-AA016599 (NIAAA), RC1-AA019036-01 (NIAAA), P50-AA12870-11 (NIAAA), R01-MH077945 (NIMH), R01-MH080956-01 (NIMH), R01-MH081969 (NIMH), R01-MH082022 (NIMH), R03-DA027893 (NHLBI), R01-EB006841 (NIH/NIBIB), R44-MH075481-03A2 (NIMH), R01-AA015615-01 (NIAAA), R01-DA020709 (NIDA), RC1-MH089257 (NIH/NIMH), R01-MH074797-01 (NIMH).

References

  1. Cox, R. W. (1996). AFNI: software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162–173.PubMedCrossRefGoogle Scholar
  2. Das, S., et al. (2011). LORIS: a web-based data management system for multi-center studies. Frontiers in Neuroinformatics, 5, 37.PubMedGoogle Scholar
  3. Frackowiak, R. S. J. (1997). Human brain function (Vol. xiii, p. 528). San Diego: Academic.Google Scholar
  4. Gadde, S., et al. (2012). XCEDE: an extensible schema for biomedical data. Neuroinformatics, 10(1), 19–32.PubMedCrossRefGoogle Scholar
  5. Hähn, D., et al., (2012). Neuroimaging in the browser using the X toolkit, in neuroinformatics. Munich, Germany.Google Scholar
  6. Hall, D., et al. (2012). Sharing heterogeneous data: the national database for autism research. Neuroinformatics, 10(4), 331–339.PubMedCrossRefGoogle Scholar
  7. Jack, C. R., Jr., et al. (2008). The Alzheimer’s Disease Neuroimaging Initiative (ADNI): MRI methods. Journal of Magnetic Resonance Imaging, 27(4), 685–691.PubMedCrossRefGoogle Scholar
  8. Jenkinson, M., et al. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. NeuroImage, 17(2), 825–841.PubMedCrossRefGoogle Scholar
  9. Jenkinson, M., et al. (2012). Fsl. NeuroImage, 62(2), 782–790.PubMedCrossRefGoogle Scholar
  10. Keator, D. B., et al. (2008). A national human neuroimaging collaboratory enabled by the Biomedical Informatics Research Network (BIRN). IEEE Transactions on Information Technology in Biomedicine, 12(2), 162–172.PubMedCrossRefGoogle Scholar
  11. Marcus, D.S., Olsen, T., Ramaratnam, M., & Buckner, R.L. (2005). XNAT: A software framework for managing neuroimaging laboratory data. in organization for human brain mapping annual meeting. Toronto.Google Scholar
  12. Marcus, D. S., et al. (2007). The extensible neuroimaging archive toolkit: an informatics platform for managing, exploring, and sharing neuroimaging data. Neuroinformatics, 5(1), 11–34.PubMedGoogle Scholar
  13. McDonald, M. (2005). Analysis of the September 15, 2005 voter fraud report submitted to the New Jersey Attorney General. New York: NYU School of Law.Google Scholar
  14. Mueller, S. G., et al. (2005). Ways toward an early diagnosis in Alzheimer’s disease: the Alzheimer’s Disease Neuroimaging Initiative (ADNI). Alzheimer’s & Dementia, 1(1), 55–66.CrossRefGoogle Scholar
  15. Poline, J. B., et al. (2012). Data sharing in neuroimaging research. Frontiers in Neuroinformatics, 6, 9.PubMedCrossRefGoogle Scholar
  16. Purcell, S., et al. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. American Journal of Human Genetics, 81(3), 559–575.PubMedCrossRefGoogle Scholar
  17. Scott, A., et al. (2011). COINS: an innovative informatics and neuroimaging tool suite built for large heterogeneous datasets. Frontiers in Neuroinformatics, 5, 33.PubMedCrossRefGoogle Scholar
  18. Zeilinger, G. (2013). dcm4chee. Available from: http://www.dcm4che.org.

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Gregory A. Book
    • 1
  • Beth M. Anderson
    • 1
  • Michael C. Stevens
    • 1
    • 2
  • David C. Glahn
    • 1
    • 2
  • Michal Assaf
    • 1
    • 2
    • 3
  • Godfrey D. Pearlson
    • 1
    • 2
    • 3
  1. 1.Olin Neuropsychiatry Research CenterHartford HospitalHartfordUSA
  2. 2.Department of PsychiatryYale University School of MedicineNew HavenUSA
  3. 3.Department of NeurobiologyYale University School of MedicineNew HavenUSA

Personalised recommendations