Currently in the neuroimaging community (and elsewhere in neuroscience), researchers employ a variety of deferent procedures and atlases to parcellate and label brain regions that are of interest in their work. The result is that many dierent labels are used to indicate the same spatial region, and in some cases, the same label is used to indicate dierent regions. This well-known nomenclature problem, which has the negative consequence of making cross-study comparison especially dicult, has previously been addressed by developing semantic mappings between synonymous and/or hierarchically-related region labels. How- ever, the spatial relationships between regions as delineated by dierent atlases can be quite complex, are poorly understood, and are not adequately captured by such semantic mappings. We have developed a new approach to this atlas concordance problem based on analyzing the spatial relationships between various brain parcellations. By applying our metrics to dierent parcellation schemes now in use, we found that the overall concordance between partitions is rather poor, which suggests the need for a \meta-atlas" or systematic procedures for mapping between dierent atlases. Through this grant proposal, we wish to expand upon the tools and methods which we have developed for this purpose, and make them available to the neuroscience community. The rest specic aim will be to make the atlas comparison and meta-analysis tools available online through an interactive, customizable website. The second aim is for algorithmic innovations to enhance the concordance analysis of brain parcellations and nomenclatures. This will include the incorporation of additional atlases and functionality as well as further theoretical developments of overall atlas concordance measures. The third aim is integration with BIRN and caBIG infrastructures. This will include mappings from the brain region labels used in the analyzed atlases to the BIRNLex ontology, additional web services within the BIRN Atlas Interoperability Framework, and adding appropriate functionality to the neuroimage processing pipelines on the BIRN GRID. Successful completion of our project will enhance data integration and meta-analysis of neuroimaging data sets, and broadly impact both basic and clinical research in neurology and neuropsychiatry. PUBLIC HEALTH RELEVANCE: Functional brain imaging techniques have revolutionized basic and clinical neuroscience, but there are some signicant challenges - particularly, a multiplicity of brain atlases and nomenclature schemes that make cross-study comparison and meta analysis dicult. We have developed a framework to deal with this atlas concordance problem, including quantitative measures and software tools. The proposed research will make these tools available to the general neuroimaging community, expand and improve the preliminary analysis and will integrate with the BIRN and caBIG infrastructures.