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. DESCRIPTION (provided by applicant): Technological advances in imaging have revolutionized the biomedical investigation of illness. The tremendous potential that this methodology brings to advancing diagnostic and prognostic capabilities and in treatment of illnesses has as yet remained largely an unfulfilled promise. This potential has been limited by a number of technological impediments that could be in large part overcome by the availability of a federated imaging database and the attendant infrastructure. Specifically, the ability to conduct clinical imaging studies across multiple sites, to analyze imaging data with the most powerful software regardless of development site, and to test new hypotheses on large collections of subjects with well characterized image and clinical data would have a demonstrable and positive impact on progress in this field. The Morphometry BIRN (mBIRN), established in October 2001, has made substantial progress in the development of this national infrastructure to develop a data and computational network based on a federated data acquisition and database across seven sites in the service of facilitating multi-site neuroanatomic analysis. Standardized structural MRI image acquisition protocols have been developed and implemented that demonstrably reduce initial sources of inter-site variance. Data structure, transmission, storage and querying aspects of the federated database have been implemented. In this continuation of the mBIRN efforts, we propose three broad areas of work: 1) continuing structural MRI acquisition optimization, calibration and validation to include T2 and DTI; 2) translation of site specific state-of-the-art image analysis, visualization and machine learning technologies to work in the federated, multi-site BIRN environment; and 3) extension of data management and database query capabilities to include additional imaging modalities, clinical disorders and individualized human genetic covariates. These broad areas of work will come together in through key collaborations that will ensure utilization promotion by facilitating data entry into the federated database and creation of database incentive functionality. Our participating sites include MGH (PI), BWH, UCI, Duke, UCLA, UCSD, John Hopkins, and newly added Washington University and MIT. We have made a concerted effort to bridge the gap that can exist between biomedical and computational sciences by recruiting to our group leaders in both of these domains. Our efforts will be coordinated with those of the entire BIRN consortium in order to insure that acquisition and database functionality, and application-based disorder queries are interoperable across sites and designed to advance the capabilities to further knowledge and understanding of health and disease. Specifically, we propose the following Projects, with their associated specific aims: Project 1 Standardize and calibrate the acquisition of high-resolution structural MRI data to facilitate precise, quantitative, platform independent, multi-site evaluation of normal and pathological structural imaging data at multiple field strengths. 1.1 Develop methods to improve structural Tr and FSE-based PD, T2-weighted, and FLAIR weighted MRI acquisition protocols that maximize image quality, improve sensitivity, reduce noise and enable quantitative analysis of healthy and diseased tissue across sites and instruments. 1.2 Extend these methods to the acquisition of diffusion sensitive imaging, to improve diffusion MRI protocols and correction methods that minimize variability across sites while optimizing image quality and sensitivity for reconstructing fiber tracts and detecting abnormalities. Project 2 Continue to develop, integrate and deploy a suite of freely available software to enable scientific investigation of the morphological bases of function and dysfunction through increasingly sophisticated image analysis on increasingly large subject populations acquired at multiple research sites. 2.1 Adapt and apply automated and semi-automated tools to segment subcortical structures, delineate the cortex, and parcellate cortical functional and anatomical regions from a range of input image protocols by drawing on expertise and existing software of the participating institutions. 2.2 Adapt and apply shape-based morphometric tools to investigate clinical and control populations: continue to develop interoperability between segmentation and shape analysis tools through standardized data representation. 2.3 Integrate Diffusion Tensor Imaging (DTI), anisotropy measurements, white matter (WM) atlases, and automated tractography techniques into the BIRN morphometry analysis infrastructure. 2.4 Provide an integrated visualization tool to support detailed investigation of morphometry and other data types. 2.5 Develop a visualization-based query tool to facilitate knowledge discovery and development of scientific explanations. 2.6 Adapt and apply machine-learning techniques to identify statistically related subpopulations of study subjects based on biomedical images. Project 3 Create an infrastructure that will ensure efficient data management, reliable processing and dynamic access to imaging, behavioral, clinical and genetic data. 3.1 Continue to develop and deploy an extensible database, that can be adapted to fulfill a local site''s needs and that interoperates with the federated BIRN database infrastructure 3.2 Extend the BIRN infrastructure based on the capabilities and needs of the mBIRN collaborators to incorporate T2, FLAIR, and diffusion image data and genetic information. 3.3 Develop, test, and validate automated graphical protocols for data integration, pre- and postprocessing and display.
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