SchizConnect: Virtual Data Integration in Neuroimaging

  • Jose Luis Ambite
  • Marcelo Tallis
  • Kathryn Alpert
  • David B. Keator
  • Margaret King
  • Drew Landis
  • George Konstantinidis
  • Vince D. Calhoun
  • Steven G. Potkin
  • Jessica A. Turner
  • Lei Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9162)

Abstract

In many scientific domains, including neuroimaging studies, there is a need to obtain increasingly larger cohorts to achieve the desired statistical power for discovery. However, the economics of imaging studies make it unlikely that any single study or consortia can achieve the desired sample sizes. What is needed is an architecture that can easily incorporate additional studies as they become available. We present such architecture based on a virtual data integration approach, where data remains at the original sources, and is retrieved and harmonized in response to user queries. This is in contrast to approaches that move the data to a central warehouse. We implemented our approach in the SchizConnect system that integrates data from three neuroimaging consortia on Schizophrenia: FBIRN’s Human Imaging Database (HID), MRN’s Collaborative Imaging and Neuroinformatics System (COINS), and the NUSDAST project at XNAT Central. A portal providing harmonized access to these sources is publicly deployed at schizconnect.org.

Keywords

Data integration Neuroimaging Mediation Schema mappings 

References

  1. 1.
    Turner, J.A.: The rise of large-scale imaging studies in psychiatry. GigaScience 3, 29 (2014)CrossRefGoogle Scholar
  2. 2.
    Glover, G.H., et al.: Function biomedical informatics research network recommendations for prospective multicenter functional MRI studies. J. Magn. Reson. Imaging JMRI 36, 39–54 (2012)MathSciNetCrossRefGoogle Scholar
  3. 3.
    King, M.D., Wood, D., Miller, B., Kelly, R., Landis, D., Courtney, W., Wang, R., Turner, J.A., Calhoun, V.D.: Automated collection of imaging and phenotypic data to centralized and distributed data repositories. Front. Neuroinform. 8, 60 (2014)CrossRefGoogle Scholar
  4. 4.
    Thompson, P.M., et al.: The ENIGMA consortium: large-scale collaborative analyses of neuroimaging and genetic data. Brain Imaging Behav. 8, 153–182 (2014)Google Scholar
  5. 5.
    Keator, D.B., et al.: A national human neuroimaging collaboratory enabled by the biomedical informatics research network (BIRN). IEEE Trans. Inf. Technol. Biomed. 12, 162–172 (2008)CrossRefGoogle Scholar
  6. 6.
    Hall, D., Huerta, M.F., McAuliffe, M.J., Farber, G.K.: Sharing heterogeneous data: the national database for autism research. Neuroinformatics 10, 331–339 (2012)CrossRefGoogle Scholar
  7. 7.
    Wang, L., et al.: Northwestern University Schizophrenia Data and Software Tool (NUSDAST). Frontiers in Neuroinformatics 7, 25 (2013)Google Scholar
  8. 8.
    Marcus, D.S., Olsen, T., Ramaratnam, M., Buckner, M.L.: The extensible neuroimaging archive toolkit (XNAT): An informatics platform for managing, exploring, and sharing neuroimaging data. Neuroinformatics 5, 11–34 (2005)Google Scholar
  9. 9.
    Scott, A., Courtney, W., Wood, D., de la Garza, R., Lane, S., King, M., Wang, R., Roberts, J., Turner, J.A., Calhoun, V.D.: COINS: An innovative informatics and neuroimaging tool suite built for large heterogeneous datasets. Front. Neuroinform. 5, 33 (2011)CrossRefGoogle Scholar
  10. 10.
    Ashish, N., Ambite, J.L., Muslea, M., Turner, J.: Neuroscience Data Integration through Mediation: An (F)BIRN Case Study. Front. Neuroinform. 4, 118 (2010)CrossRefGoogle Scholar
  11. 11.
    Doan, A., Halevy, A., Ives, Z.: Principles of Data Integration. Morgan Kauffman, Waltham (2012)Google Scholar
  12. 12.
    Fagin, R., Kolaitis, P.G., Miller, R.J., Popa, L.: Data Exchange: Semantics and query answering. In: Calvanese, D., Lenzerini, M., Motwani, R. (eds.) ICDT 2003. LNCS, vol. 2572, pp. 207–224. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  13. 13.
    Konstantinidis, G., Ambite, J.: Scalable query rewriting: a graph-based approach. In: SIGMOD Conference, pp. 97–108. ACM (2011)Google Scholar
  14. 14.
    Grant, A., Antonioletti, M., Hume, A.C., Krause, A., Dobrzelecki, B., Jackson, M.J., Parsons, M., Atkinson, M.P., Theocharopoulos, E.: OGSA-DAI: Middleware for data integration: selected applications. In: Fourth IEEE International Conference on eScience (2008)Google Scholar
  15. 15.
    The Globus Project (1997). http://www.globus.org
  16. 16.
    Turner, et al.: Terminology development towards harmonizing multiple clinical neuroimaging research repositories. In: Proceedings of DILS 2015 (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Jose Luis Ambite
    • 1
  • Marcelo Tallis
    • 1
  • Kathryn Alpert
    • 2
  • David B. Keator
    • 3
  • Margaret King
    • 4
  • Drew Landis
    • 4
  • George Konstantinidis
    • 1
  • Vince D. Calhoun
    • 4
    • 5
  • Steven G. Potkin
    • 3
  • Jessica A. Turner
    • 4
    • 6
  • Lei Wang
    • 2
  1. 1.University of Southern CaliforniaLos AngelesUSA
  2. 2.Northwestern UniversityChicagoUSA
  3. 3.University of CaliforniaIrvineUSA
  4. 4.Mind Research NetworkAlbuquerqueUSA
  5. 5.University of New MexicoAlbuquerqueUSA
  6. 6.Georgia State UniversityAtlantaUSA

Personalised recommendations