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BIPS: A Framework for Curating and Executing Brain Imaging Pipelines

Filed under:

Satrajit Ghosh (Massachusetts Institute of Technology), Anisha Keshavan (Massachusetts Institute of Technology), John Salvatore (Massachusetts Institute of Technology), Arno Klein (Columbia University)

Reproducible science requires publicly accessible data, analysis tools and scripts and the computational environment to repeat analyses. However, in brain imaging only a tiny fraction of publications include the associated data and analysis scripts. When available, such scripts often require specialized environments to execute. Shared data typically do not contain any provenance information and shared results and outputs of analysis are not in an electronic form that allows efficient querying in a database context. Only some of the existing neuroimaging database systems capture some of these systems but are not necessarily coupled with the analysis tools (however, see LONI and IDA/XNAT). For the vast majority of users with familiarity with FSL, SPM, AFNI and other brain imaging analysis software, there is no easily available route to store the information associated with their analyses into these databases. As such the time and resource expenditure necessary to curate the analyses simply outweighs the possible benefits to be gathered by sharing the data [1].

Brain Imaging Processing Services (BIPS), an opensource framework [2], was developed with the explicit aim of making electronic data capture easy by providing access to reusable tools and environments and providing tool-chains that allow users to execute analyses. The currently available tool-chains focus on dicom conversion, analyzing structural, "resting state" functional and diffusion data and providing quality assurance reports. At each stage of any analysis, provenance is captured and stored in a queriable database. The quality assurance scripts provide metrics in the context of other subjects and studies stored in the database. Every workflow in BIPS is associated with a unique identifier and once accepted into the package will not change. Much like version control systems, a modification to a workflow creates a new “commit” or workflow with its own unique identifier. The metadata associated with a workflow enables querying and configuring workflows. The framework and associated web services are being built to conform to the XCEDE data model.

This work was conducted with the Neuroimaging Task Force of the INCF Program on Standards for Datasharing and the Gabrieli Lab in McGovern Institute for Brain Research at MIT.


1. Poline et al., (2012) Data sharing in neuroimaging research. Front. Neuroinform. doi: 10.3389/fninf.2012.00009
Preferred presentation format: Poster
Topic: Neuroimaging

Filed under:
Jean-Baptiste Poline
Jean-Baptiste Poline says:
May 08, 2012 09:12 AM
This may be a very significant step towards reproducible research
Andrew Davison
Andrew Davison says:
May 11, 2012 02:36 PM
I'm very interested to see different approaches to provenance tracking.