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.

Keywords: Data model; Database; Neuroimaging; Provenance; Web services; XCEDE.

Figure 1

NI-DM can bridge information dimensions across Project, Workflow, and Derived Data. The “nidm” and “fs” namespaces are used to reference terms or annotations specific to NI-DM or the FreeSurfer analysis package, respectively. Explicit relationships link components together blurring the line between project information and processing workflows.

Figure 2

PROV-DM Core Structures are 1) entity - a physical, digital, conceptual, or other kind of thing with some fixed aspects, and can be real or imaginary., 2) activity - something that occurs over a period of time and acts upon or with entities; it may include consuming, processing, transforming, modifying, relocating, using, or generating entities., 3) agent -something that bears some form of responsibility for an activity taking place, for the existence of an entity, or for another agent's activity, and the relationships a) wasDerivedBy, b) used, c) wasGeneratedBy, d) wasInformedBy, e) wasAssociatedWith, f) actedOnBehalfOf, g) wasAttributedTo. (figure adopted from http://www.w3.org/TR/prov-dm/ ).

Figure 3

Provenance graph of the NI-DM example in section 3.2.2.1. Entities are represented by rectangles and activities as ellipses. Associations are indicated with edge labels. Text has been color coded to indicate which data source (hid, xnat) the item is associated with.

Figure 4

Provenance graph of a slice timing correction process on functional MRI data using the batch processing capabilities of SPM8. The provenance XML file is created using automated scripts run in Matlab under the SPM8 software. The graph is created to visualize the relationships between input entities and parameters entities (black edges) and the output entities (blue edges) with the activity entity represented by an ellipse.