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Developing and using the data models for neuroimaging: the NIDASH Working Group

David B. Keator1, 2, 3*, Satrajit S. Ghosh4, Camille Maumet5, Guillaume Flandin6, B N. Nichols7, Thomas E. Nichols5, 8, Gully A. Burns9, Ruediger Bruehl10, Cameron Craddock11, Blaise Federick12, Krzysztof Gorgolewski13, Yaroslav O. Halchenko14, Michael Hanke15, Christian Haselgrove16, Karl Helmer17, Arno Klein18, Daniel Marcus19, Michael Milham11, Franck Michel20, Russell Poldrack21, Jason Steffener22, Yannick Schwartz23, Richard M. Stoner24, Jessica A. Turner25, David N. Kennedy16 and Jean-Baptiste Poline26
  • 1 University of California, Irvine, Psychiatry and Human Behavior, United States
  • 2 University of California, Irvine, Computer Science, United States
  • 3 University of California, Irvine, Neurology, United States
  • 4 Massachusetts Institute of Technology, United States
  • 5 University of Warwick, United Kingdom
  • 6 UCL Institute of Neurology, United Kingdom
  • 7 University of Washington, United States
  • 8 University of Warwick, Statistics, United Kingdom
  • 9 University of Southern California., United States
  • 10 Physikalisch-Technische Bundesanstalt, Germany
  • 11 Child Mind Institute, United States
  • 12 McLean Hospital, Psychiatry, United States
  • 13 Max Planck Institute for Human Cognitive and Brain Sciences, Germany
  • 14 Dartmouth College, United States
  • 15 Otto-von-Guericke-University, Germany
  • 16 University of Massachusetts Medical School, Department of Psychiatry, United States
  • 17 Massachusetts General Hospital, United States
  • 18 Sage Bionetworks, United States
  • 19 Washington University, United States
  • 20 Centre National de la Recherche Scientifique, France
  • 21 University of Texas, United States
  • 22 Columbia University, United States
  • 23 Neurospin CEA, France
  • 24 University of California, Neurosciences, United States
  • 25 Georgia State University, United States
  • 26 University of California, Berkeley, United States

Introduction:
In neuroimaging, data sharing remains an exception [1]. While publishing a paper in many disciplines requires that data be made public, in human brain imaging there is no community standard for data sharing. However, the neuroimaging community increasingly recognizes sharing raw and processed data is critical for reproducible research, enabling meta-analyses and allowing for serendipitous discoveries.
In light of this challenge, two working groups focused on this mission joined to support the development of standards and tools that will have a community-wide impact on the prevalence of neuroimaging data data sharing:
(1) the Biomedical Informatics Research Network Derived Data Working Group (BIRN-DDWG) [10], and
(2) the Neuroimaging Task Force formed by the International Neuroinformatics Coordinating Facility’s (INCF) Program on Standards for Data Sharing [9]. We report here on their common work to facilitate the sharing of many aspects of neuroimaging data and analyses.

Methods:
The NIDASH Data Model Working Group (NI-DMWG) is composed of members of the BIRN-DDWG and the INCF Neuroimaging TF (www.incf.org). It holds weekly calls with participating members from the international community as well as several INCF-hosted yearly meetings. The TF wiki [11] is the primary resource for disseminating information and contains weekly minutes, publications, and links to products. NIDASH code is available on the “NI-” GitHub repository (github.com/ni-). The Google Group incf-datasharing [12] 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 NI-DMWG has developed DICOM [6,7] and neuroimaging [2,7] terminologies, and the NIDASH Data Model (NI-DM) [2,5]. NI-DM is a neuroimaging-specific extension of the PROV Data Model (PROV-DM; [11]) 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 [13] across a large cohort [3], and modeling SPM statistical results [8]. Further, we have begun development of detailed specifications of the core NI-DM standard and “object models” specifying the recommended minimal set of entities, agents, and activities to describe datasets, workflows, and derived data. A first version of the SPM statistical analysis object model specification [14] and examples [15] are available online. We have also developed a website for sharing raw statistical maps (NeuroVault.org) which will use NI-DM.
The INCF-TF 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).

Conclusions:
The immediate goals of the NIDASH DM working group are to 1) refine existing terminologies and object models, 2) continue working with software developers to incorporate NI-DM into their software, 3) create similar models for related tools (e.g., FSL, AFNI) so that common aspects across software packages can be identified, and 4) facilitate broad and expanded use of the NI-DM standard for data querying and data exchange, fostering applications such as meta-analyses.

Acknowledgements

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.

References

[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,
2012.

[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. Extending NI-DM to share the results and provenance of a neuroimaging study: an example with SPM. Submitted abstract to Organization of Human Brain Mapping, Hamburg, Germany. 2014.

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

[10] https://wiki.birncommunity.org/display/FBIRN/Derived+Data+Working+Group.

[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/ni-/ni-dm/tree/master/examples/spm

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

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

Keywords: NI-DM, neuroinformatics, datamodel, NIDASH, provenance

Conference: Neuroinformatics 2014, Leiden, Netherlands, 25 Aug - 27 Aug, 2014.

Presentation Type: Poster, not to be considered for oral presentation

Topic: General neuroinformatics

Citation: Keator DB, Ghosh SS, Maumet C, Flandin G, Nichols BN, Nichols TE, Burns GA, Bruehl R, Craddock C, Federick B, Gorgolewski K, Halchenko YO, Hanke M, Haselgrove C, Helmer K, Klein A, Marcus D, Milham M, Michel F, Poldrack R, Steffener J, Schwartz Y, Stoner RM, Turner JA, Kennedy DN and Poline J (2014). Developing and using the data models for neuroimaging: the NIDASH Working Group. Front. Neuroinform. Conference Abstract: Neuroinformatics 2014. doi: 10.3389/conf.fninf.2014.18.00030

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Received: 05 Apr 2014; Published Online: 04 Jun 2014.

* Correspondence: Mr. David B Keator, University of California, Irvine, Psychiatry and Human Behavior, Irvine, CA, 92679, United States, dbkeator@uci.edu