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Extending NI-DM to share the results and provenance of a neuroimaging study: implementation within SPM and FSL.

Camille Maumet1*, Guillaume Flandin2, B N. Nichols3, Jason Steffener4, Karl Helmer5, Krzysztof J. Gorgolewski6, Tibor Auer7, Gully Burns8, Fariba Fana9, Jessica A. Turner10, Thomas E. Nichols1, 11, Satrajit S. Ghosh12, Jean-Baptiste Poline13 and David B. Keator9, 14
  • 1 University of Warwick, Warwick Manufacturing Group, United Kingdom
  • 2 UCL Institute of Neurology, Wellcome Trust Centre for Neuroimaging, United Kingdom
  • 3 University of Washington, Integrated Brain Imaging Center, United States
  • 4 Columbia University, Department of Neurology, United States
  • 5 Massachusetts General Hospital, Dept. of Radiology, United States
  • 6 Max Plank Institute for Human Cognitive and Brain Sciences, Germany
  • 7 MRC Cognition and Brain Sciences Unit, Methods Group, United Kingdom
  • 8 Information Sciences Institute, United States
  • 9 University of California, Dept. of Neurology, United States
  • 10 Georgia State University, Psychology and Neuroscience, United States
  • 11 University of Warwick, Dept. of Statistics, United Kingdom
  • 12 Massachusetts Institute of Technology, McGovern Institute for Brain Research, United States
  • 13 University of California at Berkeley, Helen Wills Neuroscience Institute, BIC, United States
  • 14 University of California, Dept. of Psychiatry and Human Behavior, Dept. of Computer Science, United States

Introduction
We propose a model for communicating functional brain imaging analyses results and associated provenance for enhancing data sharing, conducting meta-analyses, and for use by software and database developers. A typical neuroimaging study is divided in several steps including: data acquisition, pre-processing, statistical analysis and eventually publication. At each of these steps, new data is produced and a set of parameters, referred to as meta-data, must be recorded to facilitate reproducibility and meta-analyses [22]. The pre-processing and statistical analysis steps are usually performed inside a single analysis software (e.g. SPM [26] , FSL [6], AFNI [3]) or pipeline (e.g. Nipype [20], LONI pipeline [13], aa [1]). Further, a number of databases devoted to the storage of raw (ADNI [2], LORIS [14], XNAT [28], Shanoir [25], HID [11], COINS [5], IDA [29], etc.) and derived data (OpenFMRI [21], Neurovault [19], BrainMap [4], SumsDB [27], NeuroSynth [18]) have emerged and greatly encourage data sharing across the community [23].
However, in the absence of a common format to encode the meta-data, the communication between neuroimaging software is limited and databases are forced to query the user for or manually annotate missing meta-data (e.g. Neurovault, SumsDB, brainmap.org) or to use data mining approaches to automatically extract this information from the published papers (e.g. NeuroSynth, Brainspell).
In [7] and [12], we introduced the Neuroimaging Data Model (NI-DM), a domain-specific extension of the recently-approved W3C recommendation, PROV-DM [24]. Our work initially focused on the description of the dataset-experiment hierarchy [7,16] and provenance in Freesurfer [17]. Along with these models, a lexicon of DICOM terms was defined to capture the precise meaning of each entity [8, 9, 10]. Recently, we extended NI-DM to model the results of statistical parametric mapping studies, such as fMRI brain mapping results, and their provenance [15]. Here, we review our recent progress in implementing NI-DM to share the statistical results of a neuroimaging study in both FSL and SPM.

Methods
As presented in Fig. 1, this NI-DM extension focused on the final steps of a neuroimaging study including statistical estimation (computing the effects estimates and their standard errors) and inference on the statistical map (producing a thresholded map of regionally specific effects usually included in the result section of a neuroimaging study).
We defined a recommended minimal set of neuroimaging metadata to be reported for functional MRI analyses, by engaging experts in neuroimaging data analysis in a series of weekly video conferences and focused workshops. In conjunction with the software development team, for both SPM and FSL, we implemented a native export in NI-DM as part of the analysis software.

Results
Fig 2 provides an overview of the proposed NI-DM extension. This result provides a formal model of the statistical inference in brain imaging, and is therefore also a first attempt to provide a unified view of this activity across software.

Conclusion
Further work will extend the data model to AFNI and other image analysis software, and integrate with NeuroVault.org API. This will allow developers to submit NI-DM description of the inference along the statistical maps thus providing rich metadata crucial for performing accurate meta analyses. Having a standardised and community driven way of adding and accessing data will improve usability and utility of NeuroVault.org. This initial test bed will allow to evaluate and refine the NI-DM standard in a practical context.

Figure 1
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Figure 2
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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

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Keywords: neuroinformatics, Semantic Web, provenance, metadata, modelling, Neuroimaging, fMRI, MRI, data sharing

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

Presentation Type: Demo, to be considered for oral presentation

Topic: General neuroinformatics

Citation: Maumet C, Flandin G, Nichols BN, Steffener J, Helmer K, Gorgolewski KJ, Auer T, Burns G, Fana F, Turner JA, Nichols TE, Ghosh SS, Poline J and Keator DB (2014). Extending NI-DM to share the results and provenance of a neuroimaging study: implementation within SPM and FSL.. Front. Neuroinform. Conference Abstract: Neuroinformatics 2014. doi: 10.3389/conf.fninf.2014.18.00031

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

* Correspondence: Dr. Camille Maumet, University of Warwick, Warwick Manufacturing Group, Coventry, United Kingdom, contact@camillemaumet.com