Intelligent Queries over BIRN Data using the Foundational Model of Anatomy and a Distributed Query-Based Data Integration System

We demonstrate the usefulness of the Foundational Model of Anatomy (FMA) ontology in reconciling different neuroanatomical parcellation schemes in order to facilitate automatic annotation and “intelligent” querying and visualization over a large multisite fMRI study of schizophrenic versus normal controls

Enabling RadLex with the Foundational Model of Anatomy Ontology to Organize and Integrate Neuro-imaging Data

In this study we focused on empowering RadLex with an ontological framework and additional content derived from the Foundational Model of Anatomy Ontology1 thereby providing RadLex the facility to correlate the different standards used in annotating neuroradiological image data. The objective of this work is to promote data sharing, data harmonization and interoperability between disparate neuroradiological labeling systems