This subproject is one of many research subprojects utilizing the resources provided by a Center grant funded by NIH/NCRR. The subproject and investigator (PI) may have received primary funding from another NIH source, and thus could be represented in other CRISP entries. The institution listed is for the Center, which is not necessarily the institution for the investigator. Recent advances in imaging technologies have provided human brain mapping researchers with awide repertoire of experimental approaches to elucidate brain structure and function at a variety of spatial scales. Over the entire neuroscience community, a vast amount of human brain mapping data is being gathered. However, lack of suitable a information management system keeps the value and scientific impact of this data from reaching its full potential, as it is explored almost exclusively by the lab of origin using data and resources which are locally accessible. We envision a worldwide information management network capable of increasing the creativity and productivity of neuroscience investigators, as they use shared human brain mapping data to generate and test ideas that were unanticipated by the data's originators. To achieve this vision, we propose to design an open source information management system that enables the exploration, analysis, and controlled sharing of structural magnetic resonance imaging (MRI) data. This system will function flexibly in multiple modes: as an international repository and data exploration platform for the neuroscience community, as part of a peer-to-peer sharing network, or as a lightweight stand-alone laboratory information management system. We expect that increased creativity and productivity will derive from the ability to: 1) access a large number of MRI datasets that sample a broad demographic spectrum;2) query by example (e.g. """"""""retrieve volumes which look like this example"""""""") and over specific brain region attributes (e.g., volume) to generate and test neuroscientific hypotheses;3) create, visualize, and manipulate average brain representations tailored to a demographic population of interest;and 4) rely on a common set of basic terms and sharing principles to faciliate communication in peer relationships. The system's design will entail the development of innovative informatics techniques in the areas of data modeling, query languages, and systems architecture. It will be scalable in size, extensible in capabilities, flexible in data sharing policy options, and compatible in format with other neuroinformatic systems.
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