Ontologies for neuroscience: What are they and what are they good for?

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Abstract
Current information technology practices in neuroscience make it difficult to understand the organization of the brain across spatial scales. Subcellular junctional connectivity, cytoarchitectural local connectivity, and long-range topographical connectivity are just a few of the relevant data domains that must be synthesized in order to make sense of the brain. However, due to the heterogeneity of the data produced within these domains, the landscape of multiscale neuroscience data is fragmented. A standard framework for neuroscience data is needed to bridge existing digital data resources and to help in the conceptual unification of the multiple disciplines of neuroscience. Using our efforts in building ontologies for neuroscience as an example, we examine the benefits and limits of ontologies as a solution for this data integration problem. We provide several examples of their application to problems of image annotation, content-based retrieval of structural data, and integration of data across scales and researchers.