NeuroNames, the nomenclature component of the software called BrainInfo, is not really an ontology. It is a collection of about 15000 terms, 2000 of which are acronyms. It allows a complete description of the primate brain in terms of about 900 structures (approximately 580 primary volumetric structures, 130 superstructures and 180 superficial structures). These terms form a hierarchy defined by a “volumetric substructure” relationship. Each term denoting a volumetric structure is also related to a set of terms that denote the “superficial features” of the structure. Although not a part of NeuroNames per se, each term has an implicit mapping to a primate brain atlas region—however, it is unclear that this mapping from the term to coordinates can be automatically recovered at this time.

A team led by Doug Bowden of School of Medicine and Washington National Primate Research Center, University of Washington, Seattle.

Primary citation:

Bowden DM, Martin RF (1995) NeuroNames brain hierarchy. Neuroimage 2:63–83.

A tree, where a term may occasionally have a species specification attribute and edges between the term-nodes, has one of the two relations mentioned above.

NeuroNames is primarily used as a term service of BrainInfo where one can search for the immediate neighborhood of a term and can map a term to an atlas region for the Macaque. The term-set from NeuroNames has been incorporated in the UMLS.

A. Within a Test Bed: Doug Bowden has provided a mapping from the NeuroNames terms to a set of terms applicable for the Mouse. This would help to standardize the anatomical term-set used in Mouse BIRN. The mapping can be used to find both volumetric sub (-super) structures and superficial anatomic features in the mouse.

B. Across Test-Beds: It is reasonable to assume that the mouse-to-primate-to-human mapping will also allow one to define mappings from mouse anatomical terms to human anatomical terms. This will enable us to ask cross-species queries to relate, for example, spots of lesions/changes in volumes/changes in surfaces etc. in humans to protein distributions in mouse models.