Enabling Data Storage and Availability of Multimodal Neuroimaging Studies—A NoSQL Based Solution

  • Filipe Fernandes
  • Paulo MarquesEmail author
  • Ricardo Magalhães
  • Nuno Sousa
  • Victor Alves
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 445)


Multimodal neuroimaging studies are of major interest in the clinical and research setting, enabling the combined study of the structure and function of the human brain. However, the amount of procedures applied, associated with the production of large volumes of data creates obstacles to the organization, maintenance and sharing of neuroimaging data. Taking this into account, we developed a NoSQL based solution that automates the process of organizing and sharing neuroimaging data. This system is composed by an application, which recognizes the files to be stored through the use of a standardized nomenclature of the files generated in the processing workflows. Additionally, the system is distributed in order to store data as documents enabling users to upload and retrieve files to/from the system in different locations. The prototype enhances the research process, through the simplification and reduction of the time spent organizing and sharing information.


MRI NoSQL Storage MongoDB Multimodal neuroimaging 


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  1. 1.
    Steen, R. G., Mull, C., Mcclure, R., Hamer, R. M., Lieberman, J. A. (2006). Brain volume in first-episode schizophrenia. The British Journal of Psychiatry , 510-518.Google Scholar
  2. 2.
    Sylvie Goldman, Liam M. Brien, Pauline A. Filipek, Isabelle Rapin, Martha R. Herbert, Motor stereotypies and volumetric brain alterations in children with Autistic Disorder, Research in Autism Spectrum Disorders, Volume 7, Issue 1, January 2013, Pages 82-92.Google Scholar
  3. 3.
    Filippi CG, Edgar MA, Ulug AM, et al. Appearance of meningiomas on diffusion-weighted images: correlating diffusion constants with histopathologic findings. AJNR Am J Neuroradiol 2001.Google Scholar
  4. 4.
    Rex, D. E., Ma, J. Q., Toga, A. W. (2003). The LONI Pipeline Processing Environment. NeuroImage, 19(3), 1033-1048.Google Scholar
  5. 5.
    Marques P, Soares JM, Alves V and Sousa N (2013) BrainCAT a tool for automated and combined functional magnetic resonance imaging and diffusion tensor imaging brain connectivity analysis. Front. Hum. Neurosci. 7:794.Google Scholar
  6. 6.
    Dale, A.M., Fischl, B., Sereno, M.I., 1999. Cortical surface-based analysis. I. Segmentation and surface reconstruction. Neuroimage 9, 179-194.Google Scholar
  7. 7.
    Gregory A. Book, Michael C. Stevens, Michal Assaf, David C. Glahn, Godfrey D. Pearlson, Neuroimaging data sharing on the neuroinformatics database platform, NeuroImage, Available online 16 April 2015, ISSN 1053-8119.Google Scholar
  8. 8.
    Andrew T. Reid, Danilo Bzdok, Sarah Genon, et al. ANIMA: A data-sharing initiative for neuroimaging meta-analyses, NeuroImage, Available online 29 July 2015, ISSN 1053- 8119.Google Scholar
  9. 9.
    Marcus, D., Olsen, T., Ramaratnam, M. and Buckner, R. (2007). The extensible neuroimaging archive toolkit. Neuroinform, 5(1), pp.11-33.Google Scholar
  10. 10.
    Magalhães, Ricardo, et al. “The Impact of Normalization and Segmentation on Resting-State Brain Networks.” Brain connectivity 5.3 (2015): 166-176.Google Scholar
  11. 11.
    Magalhães, Ricardo, et al. “Construction of Functional Brain Connectivity Networks.” Distributed Computing and Artificial Intelligence, 12th International Conference. Springer International Publishing, 2015.Google Scholar
  12. 12.
    Soares, José M., et al. “A hitchhiker’s guide to diffusion tensor imaging.”Frontiers in neuroscience 7 (2013).Google Scholar
  13. 13.
    J. Akeret, L. Gamper, A. Amara, A. Refregier, HOPE: A Python just-in-time compiler for astrophysical computations, Astronomy and Computing, Volume 10, April 2015, Pages 1-8Google Scholar
  14. 14.
    Moniruzzaman, Akhter Hossain (2013). NoSQL Database: New Era of Databases for Big data Analytics - Classification, Characteristics and Comparison. International Journal of Database Theory and Application Vol. 6, No. 4.Google Scholar
  15. 15.
    Hecht, R., Jablonski, S. (2011, December). NoSQL evaluation: A use case oriented survey. In Cloud and Service Computing (CSC), 2011 International Conference on (pp. 336-341). IEEE.Google Scholar
  16. 16.
    Han, J., Haihong, E., Le, G., Du, J.: Survey on NoSQL database. 6th Int. Conf. Pervasive Comput. Appl. 363–366 (2011).Google Scholar
  17. 17.
    Teng, C., Mitchell, J., Walker, C.: A medical image archive solution in the cloud. 2010 IEEE Int. Conf. Softw. Eng. Serv. Sci. 431–434 (2010).Google Scholar
  18. 18.
    Costa, C.M., Silva, A., Oliveira, J.L., Ribeiro, V.G., Ribeiro, J.: Himage PACS: A New Approach to Storage, Integration and Distribution of Cardiologic Images. In: Ratib, O.M. and Huang, H.K. (eds.) Medical Imaging 2004. pp. 277–287. International Society for Optics and Photonics (2004).Google Scholar
  19. 19., (2014). GAAIN Architecture. [online] Available at: [Accessed 8 Oct. 2015].

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Filipe Fernandes
    • 1
  • Paulo Marques
    • 2
    • 3
    Email author
  • Ricardo Magalhães
    • 2
    • 3
  • Nuno Sousa
    • 2
    • 3
  • Victor Alves
    • 1
  1. 1.Department of InformaticsUniversity of MinhoBragaPortugal
  2. 2.Life and Health Sciences Research Institute (ICVS), School of Health SciencesUniversity of MinhoBragaPortugal
  3. 3.ICVS/3Bs—PT Government Associate LaboratoryBraga/GuimarãesPortugal

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