Event Abstract

Standardizing Metadata in Brain Imaging

  • 1 University of California, Irvine, Psychiatry and Human Behavior, United States
  • 2 University of California, Berkley, United States
  • 3 SRI International, United States
  • 4 Massachusetts Institute of Technology, McGovern Institute for Brain Research, United States
  • 5 University of Warwick, United Kingdom
  • 6 Stanford University, Psychology, United States
  • 7 University of Cambridge, United Kingdom
  • 8 Child Mind Institute, United States
  • 9 National Institute of Mental Health, Scientific and Statistics Computing Core, United States
  • 10 UCL Institute of Neurology, Wellcome Trust Centre for Neuroimaging, United Kingdom
  • 11 Dartmouth College, Psychology and Brain Sciences, United States
  • 12 Otto-von-Guericke University, Institute of Psychology II, Germany
  • 13 University of Massachusetts, Psychiatry, United States
  • 14 Massachusetts General Hospital, Radiology, United States
  • 15 University of Oxford, United Kingdom
  • 16 Sage Bionetworks, United States
  • 17 International Neuroinformatics Coordinating Facility, Sweden
  • 18 Washington University School of Medicine, Radiology, United States
  • 19 Max Planck Institute, Germany
  • 20 University Nice de Sophia-Antipolis, France
  • 21 University of Toronto, Canada
  • 22 Columbia University, neurology, United States
  • 23 Georgia State University, Psychology and Neuroscience, United States
  • 24 University of Southern California, Keck School of Medicine, United States
  • 25 Montreal Neurological Institute, Canada

Introduction: In neuroimaging open data sharing is not a common practice [1]. While publishing a paper in many disciplines requires that data be made public, in human brain imaging there is no general agreement that data should be shared, and there is a lack of community standard for data sharing. However, the neuroimaging community increasingly recognizes that sharing raw and processed data is critical for reproducible research, enabling meta-analyses and allowing for serendipitous discoveries. In light of this challenge, the Neuroimaging and Data Sharing Task Force (NIDASH-TF) formed by the International Neuroinformatics Coordinating Facility’s (INCF) Program on Standards for Data Sharing [10] supports the development of standards and tools that will have a community-wide impact on the prevalence of neuroimaging data sharing. In this abstract we report on work to facilitate the sharing of neuroimaging metadata and analysis results. Methods: The Neuroimaging Data Model Working Group (NIDM WG), a sub-group formed to design a metadata model for neuroimaging, holds weekly calls with participating members from the international community and organizes INCF-hosted yearly meetings. The NIDASH-TF wiki [10] is the primary resource for disseminating information and contains weekly minutes, publications, and links to products. NIDASH code is available in the GitHub repository (github.com/incf-nidash). The Google Group incf-datasharing [11] hosts an email list on data sharing issues, reaching out to a wider community. The NIDASH-TF meets several times a year to review progress on projects (eg [16]) that will make data sharing easier and fruitful for the scientific community. Results & Discussion: The NIDM WG has developed DICOM [6,7] and neuroimaging [2,7] terminologies, and the Neuroimaging Data Model (NIDM) [2,5]. NIDM is a neuroimaging-specific extension of the PROV Data Model (PROV-DM; [18]) to facilitate sharing of semantically meaningful neuroimaging provenance and derived data. Using these tools, we have developed novel applications to demonstrate federating data across relational databases and spreadsheets [4], visualizing FreeSurfer segmentations [12] across a large cohort [3], and modeling SPM and FSL statistical results [8], and have started to model results from AFNI. Further, we have developed detailed specifications of the core NIDM standard and “object models”, specifying the recommended minimal set of entities, agents, and activities to describe datasets, workflows, and derived data. The SPM and FSL statistical analysis object model specifications [14] and examples are available online [15]. Under the auspices of NIDASH, C. Gorgolewski and colleagues have also developed a website for sharing raw statistical maps (NeuroVault.org) which uses NIDM [9]. The INCF task force meetings have encouraged adoption of these resources in various outside projects. We are linking this work with projects that are providing and hosting data, developing lexicons, and generating derived data for different purposes (e.g. data mining). The group includes developers and is in close contact with projects that plan to use these resources, or may do so in the future (e.g., Neurosynth, Neurovault, Brainspell), as well as with developers of integration platforms (e.g. NeuroDebian). Recently, we have worked with R. Poldrack and colleagues on the new version of the OpenfMRI specifications and will be describing this standard in the NIDM-experiment model [17]. Conclusions: The immediate goals of the NIDASH NIDM working group are to 1) refine existing terminologies and object models, 2) continue working with software developers to incorporate NIDM into their software, 3) create similar models for related tools such as multivariate models so that common aspects across software packages can be identified, and 4) facilitate broad and expanded use of the NIDM standard for data querying and data exchange, fostering applications such as meta-analyses. Standardization within communities are always challenging. The task force has adopted cultural practices of open source software development to carry out the specification of standards for brain imaging data sharing.


We would like to acknowledge the work of all the INCF task force members as well as of many other colleagues who have helped the task force. We are particularly indebted to Mathew Abrams, Linda Lanyon, Roman Valls Guimera and Sean Hill for their support at the INCF. Further we acknowledge the long-standing support of DDWG activities by the BIRN coordinating center (NIH 1 U24 RR025736-01), and the Wellcome Trust for support of CM & TEN.


[1] Poline J.B., Breeze J., Ghosh S., Gorgolewski K., Halchenko Y., Hanke M., Haselgrove C., Helmer K., Keator D.B., Marcus D., Poldrack R., Schwartz Y., Ashburner A., Kennedy D. Data sharing in neuroimaging research. Frontiers in Neuroinformatics. 2012; 6:9.

[2] Keator D.B., Helmer K., Steffener J., Turner J.A., Van Erp T.G.M., Gadde S., Ashish N., Burns G.A., Nichols B.N. Towards structured sharing of raw and derived neuroimaging data across existing resources. Neuroimage. 2013 Nov 15;82:647-61

[3] Nichols B.N., Stoner R., Keator D.B., Turner J., Helmer K.G., Ashish N., Steffener J., Grabowski T.J., Ghosh S. There’s an app for that: a semantic data provenance framework for reproducible brain imaging. Abstract and poster presentation at Organization of Human Brain Mapping, Seattle, WA. 2013.

[4] Nichols B.N., Steffener J., Haselgrove C., Keator D.B., Stoner R., Poline J.B., Ghosh S. Mapping Neuroimaging Resources into the NIDASH Data Model for Federated Information Retrieval. Abstract and poster presentation at Neuroinformatics 2013, Stockholm, Sweden. 2013.

[5] Ghosh S., Nichols B. N., Gadde S., Steffener J., Keator D. XCEDE-DM: A neuroimaging extension to the W3C provenance data model. Abstract and poster presentation at Neuro-Informatics Congress. Munich, Germany 2012.

[6] K.G. Helmer, S. Ghosh, B.N. Nichols, D. Keator, T. Nichols, J. Turner. Poster presentation at the International Neuroinformatics Coordinating Facility Neuroscience 2012, Munich, Germany,

[7] K.G. Helmer, S. Ghosh, D. Keator, C. Maumet, B.N. Nichols, T. Nichols, J.B. Poline, J. Steffener, J. Turner, W. Wong, M. Martone. The Addition of Neuroimaging Acquisition, Processing and Analysis Terms to Neurolex. Accepted abstract to Organization of Human Brain Mapping, Hamburg, Germany. 2014.

[8] C. Maumet, T. Nichols, B.N. Nichols, G. Flandin, J. Turner, K.G. Helmer,J. Steffener, J.B. Poline, S. Ghosh, D. Keator. Standardized reporting of neuroimaging results with NIDM in SPM, FSL and AFNI. Submitted abstract to Organization of Human Brain Mapping, Honolulu, Hawaii. 2015.

[9] Gorgolewski, K.J., Varoquaux, G., Rivera, G., Schwarz, Y., Ghosh, S.S., Maumet, C., Sochat, V.V., Nichols, T.E., Poldrack, R.A., Poline, J.-B., et al. (2015). NeuroVault.org: a web-based repository for collecting and sharing unthresholded statistical maps of the human brain. Frontiers in Neuroinformatics 9.

[10] http://www.incf.org/core/programs/datasharing

[11] wiki.incf.org/mediawiki/index.php/Neuroimaging_Task_Force

[12] http://groups.google.com/d/forum/incf-datasharing

[13] surfer.nmr.mgh.harvard.edu

[14] http://nidm.nidash.org

[15] https://github.com/incf-nidash/nidm/tree/master/nidm/nidm-results

[16] http://datasharing.incf.org/ni/One_Click_Prototype

[17] https://openfmri.org/ and http://fcon_1000.projects.nitrc.org/

[18] http://www.w3.org/TR/prov-dm/

Keywords: NIDM, Neuroimaging, neuroinformatics, provenance, metadata

Conference: Neuroinformatics 2015, Cairns, Australia, 20 Aug - 22 Aug, 2015.

Presentation Type: Poster, to be considered for oral presentation

Topic: General neuroinformatics

Citation: Keator DB, Poline J, Nichols BN, Ghosh SS, Maumet C, Gorgolewski KJ, Auer T, Craddock C, Chen G, Flandin G, Halchenko YO, Hanke M, Haselgrove C, Helmer K, Jenkinson M, Klein A, Lanyon L, Marcus D, Margulies D, Michel F, Nichols TE, Poldrack RA, Reynolds R, Saad Z, Schmah T, Steffener J, Turner JA, Van Horn JD, Das S and Kennedy DN (2015). Standardizing Metadata in Brain Imaging. Front. Neurosci. Conference Abstract: Neuroinformatics 2015. doi: 10.3389/conf.fnins.2015.91.00004

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Received: 20 Apr 2015; Published Online: 05 Aug 2015.

* Correspondence: Mr. David B Keator, University of California, Irvine, Psychiatry and Human Behavior, Irvine, CA, 92697, United States, dbkeator@uci.edu