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.
The aim of this Core area is the development of technologies to aid in filling in the resolution gaps of critical importance in the large-scale study of brain tissue, from whole brain down to the subcellular level. These techniques are being employed extensively in support of structural studies on genetically engineered animal models of disease and are driving the creation of a multi-resolution and multiscale data exploration environment being created as part of the Biomedical Informatics Research Network (BIRN) project. They will also support projects involving correlated structural and functional imaging of relatively large structures like synaptic complexes and spiny dendrites. The large scale tissue mapping resources at NCMIR have reached a relatively mature form and during the previous funding period we employed these resources in a number of collaborative projects focused on a range of biological questions, including cancer, neurodegeneration, and neuronal regeneration. We have previously published an article in Neuroinformatics detailing the methods and tools developed at NCMIR during the past few years for acquiring, processing, and annotating large scale LM images (Price et al., 2006). These projects have made use of all three of our large scale imaging platforms. This effort has yielded a considerable amount of data for a wide and a significant number of datasets for annotation and incorporation in NCMIR's Cell Centered Database (CCDB;see Section 3.2 of this progress report). We also continue to make important improvements in instrumentation and software in order to further optimize the collection and processing of such large, information-rich datasets.
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