The Scourge of Neuroanatomical Nomenclature: A Rational Strategy
Maryann E. Martone (University of California, San Diego), Fahim T. Imam (University of California, San Diego), Stephen D. Larson (University of California, San Diego)
One of the core aims of the Neuroscience Information Framework (NIF; http://neuinfo.org) is to establish an interoperable semantic framework for searching and integrating data across diverse systems. As for neuroanatomy, NIF and other projects, e.g., NeuroNames , BAMS, have established the means to translate among different available nomenclatures. The NIF project, in collaboration with the INCF Program on the Ontologies of Neural Structures (PONS), has established the NeuroLex (http://neurolex.org), a semantic wiki for developing a knowledgebase around the core concepts of neuroscience.
As part of the NeuroLex, we have defined a standard reference vocabulary for mammalian neuroanatomical structures, based on the classical structure hierarchy of the NeuroNames and common terminology found in neuroanatomy textbooks. As verified by text mining of neuroanatomical names , these terms tend to be common across species. We have also defined “parcellation schemes”- delineations made on a particular species by a specific author in the context of an atlas or a paper - that reference these core structures. In order to relate brain regions and different parcels, we have defined the ‘overlap’ property in NeuroLex. The term ‘overlap’ in NeuroLex implies some degree of spatial co-localization, although it does not, at this point, specify the degree.
We have worked with the PONS group to implement a reasonable strategy for defining brain structures and relating them with different parcellations that is useful for implementation within information systems like the NIF. This poster presentation will depict the overall NIF strategy in details.
 D. M. Bowden, E. Song, et al.: NeuroNames: An Ontology for the BrainInfo Portal to Neuroscience on the Web. Neuroinformatics 10 (1): 97-114, 2012
 L. French, P. Pavlidis: Using text mining to link journal articles to neuroanatomical databases. J Comp Neurol. 2011, doi: 10.1002/cne.23012