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Neuroanatomical domain of the foundational model of anatomy ontology

B Nolan Nichols1*, Jose LV Mejino1, Landon T Detwiler1, Trond T Nilsen1, Maryann E Martone2, Jessica A Turner3, Daniel L Rubin4 and James F Brinkley1

Author Affiliations

1 University of Washington, Seattle, WA, USA

2 University of California San Diego, San Diego, CA, USA

3 Mind Research Network, Albuquerque, NM, USA

4 Stanford University, Stanford, CA, USA

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Journal of Biomedical Semantics 2014, 5:1  doi:10.1186/2041-1480-5-1

Published: 8 January 2014



The diverse set of human brain structure and function analysis methods represents a difficult challenge for reconciling multiple views of neuroanatomical organization. While different views of organization are expected and valid, no widely adopted approach exists to harmonize different brain labeling protocols and terminologies. Our approach uses the natural organizing framework provided by anatomical structure to correlate terminologies commonly used in neuroimaging.


The Foundational Model of Anatomy (FMA) Ontology provides a semantic framework for representing the anatomical entities and relationships that constitute the phenotypic organization of the human body. In this paper we describe recent enhancements to the neuroanatomical content of the FMA that models cytoarchitectural and morphological regions of the cerebral cortex, as well as white matter structure and connectivity. This modeling effort is driven by the need to correlate and reconcile the terms used in neuroanatomical labeling protocols. By providing an ontological framework that harmonizes multiple views of neuroanatomical organization, the FMA provides developers with reusable and computable knowledge for a range of biomedical applications.


A requirement for facilitating the integration of basic and clinical neuroscience data from diverse sources is a well-structured ontology that can incorporate, organize, and associate neuroanatomical data. We applied the ontological framework of the FMA to align the vocabularies used by several human brain atlases, and to encode emerging knowledge about structural connectivity in the brain. We highlighted several use cases of these extensions, including ontology reuse, neuroimaging data annotation, and organizing 3D brain models.

Data integration; Neuroanatomy; Neuroscience; Ontology; Brain atlas; Neuroinformatics; Information retrieval; mri