Federated Informatics Research Environment (FIRE) is an integrated suite of tools to put your data on a GridFTP set of servers, link it to a database which is automatically federated, to be able to retrieve it from multiple sites, with the data wrapped in XCEDE (XML) wrappers. It’s built based on functional and structural neuroimaging, but if you have other data types, you don’t have to start from scratch figuring everything out. These tools are built to generalize to other formats and study designs.

See the FIRE Primer for more information on the suite of tools, both current and coming.

The Upload scripts work in concert with the GridFTP servers, XCEDE, and the standardized data hierarchies to put data onto the GridFTP servers and link it to the appropriate database.

Organizing and locating information, both clinical metadata and imaging information, in a distributed data environment can be challenging. Because each site in the collaboratory maintains its own linked data storage, it is very important to develop standards for how and where the images are stored, metadata descriptions of what the images are, and how to find them.

To address these requirements the BIRN has implemented standardized data storage hierarchies on GridFTP servers. The data storage hierarchy was designed to be amenable to automated processing scripts and robust enough to account for differences in storage requirements across BIRN consortia. Not only is the physical organization of files maintained, but so are XML-based descriptors of what the files mean, which aids in data analysis.

The goals of the data upload scripts are:

• getting imaging information into the standardized data storage hierarchies,
• creating XML-based metadata about the data acquisition,
• converting scanner-specific, proprietary data formats into standard “useable” formats, and
• linking data locations within the distributed file system into a site’s human imaging database (HID)

Once the data has been “uploaded” using these scripts, any site in the collaboratory can locate the datasets by querying their local site’s HID database for particular imaging parameters, protocol specifications, or clinical metadata and downloading the images for further analyses. Using this decentralized approach, the BIRN has put the sites in control of their data while still maintaining shareability and access to the information.

Status: New upload scripts working with the HID and GridFTP are scheduled to be available by the end of 2010. The previous versions which worked with the SRB are available here for archival purposes.

The FBIRN agar phantom is used regularly for Quality Assurance, to assess scanner stability as originally reported in:

Friedman L, Glover GH (2006) Report on a Multicenter fMRI Quality Assurance Protocol. Journal of Magnetic Resonance in Imaging, 23:827-839

A single QA dataset consists of 200 regular EPI images collected with a flip angle of 77 degrees, and another 100 images with a flip angle of 10 degrees. The full information about the QA protocol is available here: https://xwiki.nbirn.org:8443/xwiki/bin/view/Function-BIRN/FBIRN_Best_Practices .

Data collected on FBIRN scanners since 2007 will be available soon. Please check back later.

The BIRN_ID generator can be used to assign a unique ID to subjects in any type of experiment. Subjects in experiments, whether human or animal, are often assigned random numbers as identifiers. When data are being shared and compiled across studies, these numbers must be unique. The BIRN_ID Generator can be used to assign a unique identifying 12-digit number for a subject.

The BIRN_ID Generator package generates unique HIPAA-compliant IDs, a BIRN_ID, from scratch or from already-assigned local IDs.  It is a Java application that runs on any platform.  It uses a cryptographically-strong pseudo-random number generating algorithm, SHA1PRNG, provided by Sun, and a one-way MD5 hash.

The BIRN_ID Generator also can find a BIRN_ID for a given local ID when the link table and local ID are provided.  The one-way hash ensures that the user cannot use a BIRN_ID alone to find a local ID and identify a subject.

The birn-id generator can be checked out with this command:

svn co https://www.nitrc.org/svn/hid/utilities/trunk/utilities/birn-id_gen

BIRN_ID GENERATOR README

Prerequisites

Java JDK (or JRE) 1.3.1 or higher needs to be installed on your system

Installation

• Put the BIRN_ID Generator package in a selected installation directory
• Create a new environment variable, BIRNIDGEN_HOME, pointing to the installation directory

Usage

• To generate BIRNIDs:$BIRNIDGEN_HOME/bin/birnid_gen.sh -create -p <prefix> -l <linkTableFilename> [-cf <clinicalIdsFilename> | -c <clinicalId>]where <linkTableFilename> is a text file to store MD5 one-way hashes of clinicalIDs, <clinicalIdsFilename> is a file that contains a list of clinicalIDs (each line is a clinicalID) and <clinicalId> is a single clinicalID.

• To find the BIRNID from a clinicalID:$BIRNIDGEN_HOME/bin/birnid_gen.sh -find -c <clinicalId> -l <linkTableFilename>where <clinicalId> is a single clinicalID and <linkTableFilename> is a text file to store MD5 one-way hashes of clinicalIDs.

Background Information on the Random Number Generation

SHA1PRNG: The name of the pseudo-random number generation (PRNG) algorithm supplied by the SUN provider. This implementation follows the IEEE P1363 standard, Appendix G.7: “Expansion of source bits”, and uses SHA-1 as the foundation of the PRNG. It computes the SHA-1 hash over a true-random seed value concatenated with a 64-bit counter which is incremented by 1 for each operation. From the 160-bit SHA-1 output, only 64 bits are used.

SHA-1 is the Secure Hash Algorithm, as defined in Secure Hash Standard, NIST FIPS 180-1.

SHA1PRNG is a cryptographically strong pseudo-random number generator (PRNG). A cryptographically strong pseudo-random number minimally complies with the statistical random number generator tests specified in FIPS 140-2, Security Requirements for Cryptographic Modules, section 4.9.1. Additionally, SecureRandom must produce non-deterministic output and therefore it is required that the seed material be unpredictable and that output of SecureRandom be cryptographically strong sequences as described in RFC 1750: Randomness Recommendations for Security.

The FBIRN multi-site dataset of subjects with schizophrenia and controls includes functional MRI images, behavioral data, demographic, and clinical assessments on 253 subjects from around the US. Subjects were recruited locally, evaluated with a standardized clinical assessment battery, and scanned twice at each of the 9 participating FBIRN sites. A standard FBIRN protocol was used for image acquisition with controlled variation at different sites and included a sensorimotor task (SM), an Auditory Oddball task (AO), a Serial Item Recognition Paradigm (SIRP), and a breathhold task (BH).

To access this data: http://fbirnbdr.nbirn.net:8080/BDR/

This dataset includes five healthy subjects imaged twice at each of ten FBIRN MRI scanners on successive days. Functional and structural imaging, behavioral, and demographic data are available from 100 scanning sessions on these subjects.

To access this data: http://fbirnbdr.nbirn.net:8080/BDR/

This dataset includes ten healthy subjects scanned 3 times with 3 conditions: eyes open, eyes closed, and fixating in addition to two anatomical scans (T1 and T2).

To access this data: http://fbirnbdr.nbirn.net:8080/BDR/

This dataset includes seventeen healthy subjects with four resting state fixation scans plus one T1 scan and one T2 scan. The data were collected as part of a study on the behavioral effects of spontaneous BOLD fluctuations.

To access this data: http://fbirnbdr.nbirn.net:8080/BDR/

XCEDE is an extensible schema designed to store scientific data and meta data from a variety of sources and formats.  XCEDE has its origins in various XML schemas developed for collaborative neuroinformatics projects, and was developed to enable the transfer and storage of several types of data including (but not limited to) clinical, demographic, behavioral, physiological and image data.  Typical sources and targets for XCEDE data transfer include Web services, databases, data browsers, and data analysis tools.

Example of how a single subject may have MRI data, clinical data, multiple visits, or participate in multiple projects. XCEDE includes a standardized method for identifying these datasets.

The core XCEDE schema supports several major extensible components:

• A flexible experimental hierarchy: As illustrated in the above, the XCEDE experiment hierarchy consists of several levels (project, subject, visit, study, episode and acquisition) representing divisions of experiment data at various granularities. Elements at each level contain level-specific “info” elements, whose schema types may be derived to store experiment-specific or data modality-specific metadata. The linking mechanism between levels is flexible enough to support the omission of levels if the schema user finds them unnecessary.
• Experimental protocols: Specifications for order, number and time of experimental components (e.g. assessments within a clinical interview, scans within a scanning session, visits within a project) are supported using a generic protocol element.
• Event data: XCEDE has elements to support time-interval-annotated metadata such as behavioral stimulus/response data, per-timepoint QA metrics, etc.
• Data provenance: The complete history of a processing stream can be stored and propagated using provenance elements provided by the XCEDE schema.
• Plus: application-specific file and analysis lists (catalogs), interfaces to binary data, arbitrary user and application annotations, derived data, and more.

To access this tool: http://www.nitrc.org/projects/fbirn/

The Human Imaging Database (HID) is an extensible database management system developed to handle the increasingly large and diverse datasets collected as part of the MBIRN and FBIRN collaboratories and throughout clinical imaging communities at large.

Citations:

Keator, D.B.; Grethe, J.S.; Marcus, D.; Ozyurt, B.; Gadde, S.; Murphy, S.; Pieper, S.; Greve, D.;Notestine, R.; Bockholt, H.J.; Papadopoulos, P.; Function BIRN; Morphometry BIRN; BIRN Coordinating Center. (2008) A National Human Neuroimaging Collaboratory Enabled By The Biomedical Informatics Research Network (BIRN). IEEE Transactions on Information Technology in Biomedicine, Special Bio-Grid edition, 12 (2), 162-172.

David B. Keator, Dingying Wei, Syam Gadde, Jeremy Bockholt, Jeffrey S. Grethe, Daniel Marcus, Nicole Aucoin, Ibrahim B. Ozyurt. (2009). Derived Data Storage and Exchange Workflow for Large-Scale Neuroimaging Analyses on the BIRN Grid. Frontiers in Neuroscience, Manuscript in press.

To access this tool: http://www.nitrc.org/projects/hid/

The latest versions of HID web app and corresponding schema and scripts are only available via the NITRC subversion source control system. Its wiki is at http://www.nitrc.org/plugins/mwiki/index.php/hid:MainPage

If you want to get them from NITRC SVN, you must first register with NITRC at http://www.nitrc.org/account/register.php

HID web app release 2.2beta with GridFTP/GSI support is in NITRC Subversion Source Control System:

svn co https://www.nitrc.org/svn/hid/clinical/tags/release-2.2beta

For setup (there are new sections for GridFTP download mechanism usage preparation) and administration changes, please follow the README.html.

For web app configuration changes, please read https://www.nitrc.org/svn/hid/release-2.2beta/clinical/doc/tutorial.pdf .

The corresponding db schema scripts are in SVN:

svn co https://www.nitrc.org/svn/hid/schema/tags/release-2.2