Towards structured sharing of raw and derived neuroimaging data across existing resources

Abstract

Data sharing efforts increasingly contribute to the acceleration of scientific discovery. Neuroimaging data is accumulating in distributed domain-specific databases and there is currently no integrated access mechanism nor an accepted format for the critically important meta-data that is necessary for making use of the combined, available neuroimaging data. In this manuscript, we present work from the Derived Data Working Group, an open-access group sponsored by the Biomedical Informatics Research Network (BIRN) and the International Neuroimaging Coordinating Facility (INCF) focused on practical tools for distributed access to neuroimaging data. The working group develops models and tools facilitating the structured interchange of neuroimaging meta-data and is making progress towards a unified set of tools for such data and meta-data exchange. We report on the key components required for integrated access to raw and derived neuroimaging data as well as associated meta-data and provenance across neuroimaging resources. The components include (1) a structured terminology that provides semantic context to data, (2) a formal data model for neuroimaging with robust tracking of data provenance, (3) a web service-based application programming interface (API) that provides a consistent mechanism to access and query the data model, and (4) a provenance library that can be used for the extraction of provenance data by image analysts and imaging software developers. We believe that the framework and set of tools outlined in this manuscript have great potential for solving many of the issues the neuroimaging community faces when sharing raw and derived neuroimaging data across the various existing database systems for the purpose of accelerating scientific discovery.

Sharing brain mapping statistical results with the neuroimaging data model

“…As more and more data become available online and as standardisation effort like the RII develops, we believe that these technologies will become more widespread. RDF was chosen as a basis for NIDM for the expressivity of its graph-based structures, the possibility to form intricate queries across datasets 37 , as well as for the extensibility of the created data models and the possibility to interconnect across knowledge domains (cf. ref.…”

“…The NeuroImaging Data Model (NIDM) 37,38 was created to expand upon the initial development of XCEDE, introducing a domain-specific extension of PROV using semantic web technologies and the Resource Description Framework (RDF). The goal of NIDM is to provide a complete description of provenance for neuroimaging studies, from raw data to the final results including all the steps in-between.…”

Developing an Integrated Image Bank and Metadata for Large-scale Research in Cerebrovascular Disease: Our Experience from the Stroke Image Bank Project

“…Similarly, an ontology-based approach was also employed in the NeuroLOG (Gibaud et al, 2011) as well as NeuroWeb (Colombo et al, 2010) projects. A hybrid approach has also been proposed by Keator et al (2013), where an ontology-based resource, NeuroLex (Larson and Martone, 2013), is combined with information obtained from other resources such as the Human Imaging Database 15 and XNAT. 16 None of these were suitable for stroke, thereby suggesting that lack of ontology for a given specialized domain raises significant neuroimaging data integration challenges (Smith et al, 2015).…”

“…The importance of this information has been emphasized (Keator et al, 2013) and documented as one of the guiding principles of data sharing best practices (Nichols et al, 2016).…”

Neuroinformatics Software Applications Supporting Electronic Data Capture, Management, and Sharing for the Neuroimaging Community

“…After all entries requested for the data release are locked, the data is provided to the members of the NCANDA consortium via a set of comma-separated-value (CSV) files exported from REDCap with corresponding data dictionaries for each data element. Plans for sharing the data with the broader research community include technology to facilitate interoperability with neuroinformatics resources, such as the Neuroimaging Data Model (NIDM) standard for data exchange (Keator et al, 2013), the Cognitive Atlas ontology (Poldrack et al, 2011) for data annotation, and the Neuroimaging Informatics Resource Technology Clearinghouse Image Repository (NITRC) (Kennedy et al, 2015) and OpenfMRI (Poldrack et al, 2013) data repositories. The resulting organization of this data set would then align with the approach proposed by the Research Domain Criteria (RDoC) Initiative (Insel et al, 2010), where data can be explored at different levels of analysis (e.g., from circuit-level to family environment) and by broad domains of function (e.g., cognitive systems or working memory).…”