Imaging has emerged as one of the key tools used by biomedical investigators to further our understanding of human biology in health and disease. While the scale and scope of imaging research have exploded, informatics tools to support this research have not kept pace. The overarching aim of this proposal is to develop a comprehensive open source imaging informatics platform built to the highest engineering standards and using the most advanced software technologies available. This platform - XNAT 2.0 - will build on the existing XNAT platform, which is widely used by research institutions across the world and is a component of the National Institutes of Health-sponsored research informatics backbone that includes the Biomedical Informatics Research Network (BIRN) and Cancer Biomedical Informatics Grid (caBIG). The XNAT 2.0 platform will be implemented as service-oriented architecture (SOA), and a suite of services will be built on this architecture to support the core requirements of the imaging research enterprise. Software interfaces will also be designed and implemented in XNAT 2.0 to support the development of third party XNAT tools and components and to encourage developers of image processing and analysis tools to interoperate with XNAT. Finally, methods will be designed and implemented to deploy XNAT services on massively scalable computing systems. The resulting platform will improve the quality and scale of imaging research and will greatly facilitate multi-site collaboration and data sharing. Most importantly, it will provide researchers and their institutions with the necessary tools to maximize the potential of imaging to improve health and our understanding of human biology.
In vivo imaging is one of the key methods used by biomedical researchers to study human biology in health and disease. The imaging informatics platform described in this application will enable biomedical researchers to capture, analyze, and share imaging and related data. These capabilities address key bottlenecks in the pathway to discovering cures to complex diseases such as Alzheimer's disease, cancer, and heart disease.
|Gurney, Jenny; Olsen, Timothy; Flavin, John et al. (2017) The Washington University Central Neuroimaging Data Archive. Neuroimage 144:287-293|
|Mishra, Shruti; Gordon, Brian A; Su, Yi et al. (2017) AV-1451 PET imaging of tau pathology in preclinical Alzheimer disease: Defining a summary measure. Neuroimage 161:171-178|
|Dierker, Donna; Roland, Jarod L; Kamran, Mudassar et al. (2017) Resting-state Functional Magnetic Resonance Imaging in Presurgical Functional Mapping: Sensorimotor Localization. Neuroimaging Clin N Am 27:621-633|
|Goyal, Manu S; Vlassenko, Andrei G; Blazey, Tyler M et al. (2017) Loss of Brain Aerobic Glycolysis in Normal Human Aging. Cell Metab 26:353-360.e3|
|Natelson Love, Marissa; Clark, David G; Cochran, J Nicholas et al. (2017) Clinical, imaging, pathological, and biochemical characterization of a novel presenilin 1 mutation (N135Y) causing Alzheimer's disease. Neurobiol Aging 49:216.e7-216.e13|
|Babulal, Ganesh M; Ghoshal, Nupur; Head, Denise et al. (2016) Mood Changes in Cognitively Normal Older Adults are Linked to Alzheimer Disease Biomarker Levels. Am J Geriatr Psychiatry 24:1095-1104|
|Kogan, Alex; Alpert, Kathryn; Ambite, Jose Luis et al. (2016) Northwestern University schizophrenia data sharing for SchizConnect: A longitudinal dataset for large-scale integration. Neuroimage 124:1196-201|
|Brier, Matthew R; McCarthy, John E; Benzinger, Tammie L S et al. (2016) Local and distributed PiB accumulation associated with development of preclinical Alzheimer's disease. Neurobiol Aging 38:104-111|
|Glasser, Matthew F; Smith, Stephen M; Marcus, Daniel S et al. (2016) The Human Connectome Project's neuroimaging approach. Nat Neurosci 19:1175-87|
|Lee, Megan H; Miller-Thomas, Michelle M; Benzinger, Tammie L et al. (2016) Clinical Resting-state fMRI in the Preoperative Setting: Are We Ready for Prime Time? Top Magn Reson Imaging 25:11-8|
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