Neuroanatomical Domain of the Foundational Model of Anatomy Ontology

Background: 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. Description: 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. Conclusions: 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

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

Challenges in Reconciling Different Views of Neuroanatomy in a Reference Ontology of Anatomy

A fundamental requirement for integrating neuroscience data is a well-structured ontology that can incorporate, accommodate and reconcile different neuroanatomical views. Here we describe the challenges in creating such ontology, and, because of its principled design, illustrate the potential of the Foundational Model of Anatomy to be that ontology

An exercise on developing an ontology-epistemology about schizophrenia and neuroanatomy

This paper describes preliminary ideas on formalizing some concepts of neuroanatomy into ontological and epistemological terms. We envisage the application of this ontology on the assimilation of facts about medical knowledge about neuroimages from schizophrenic patients

Application of Neuroanatomical Ontologies for Neuroimaging Data Annotation

The annotation of functional neuroimaging results for data sharing and re-use is particularly challenging, due to the diversity of terminologies of neuroanatomical structures and cortical parcellation schemes. To address this challenge, we extended the Foundational Model of Anatomy Ontology (FMA) to include cytoarchitectural, Brodmann area labels, and a morphological cortical labeling scheme (e.g., the part of Brodmann area 6 in the left precentral gyrus). This representation was also used to augment the neuroanatomical axis of RadLex, the ontology for clinical imaging. The resulting neuroanatomical ontology contains explicit relationships indicating which brain regions are “part of” which other regions, across cytoarchitectural and morphological labeling schemas. We annotated a large functional neuroimaging dataset with terms from the ontology and applied a reasoning engine to analyze this dataset in conjunction with the ontology, and achieved successful inferences from the most specific level (e.g., how many subjects showed activation in a subpart of the middle frontal gyrus) to more general (how many activations were found in areas connected via a known white matter tract?). In summary, we have produced a neuroanatomical ontology that harmonizes several different terminologies of neuroanatomical structures and cortical parcellation schemes. This neuroanatomical ontology is publicly available as a view of FMA at the Bioportal website1. The ontological encoding of anatomic knowledge can be exploited by computer reasoning engines to make inferences about neuroanatomical relationships described in imaging datasets using different terminologies. This approach could ultimately enable knowledge discovery from large, distributed fMRI studies or medical record mining

Computational neuroanatomy: ontology-based representation of neural components and connectivity

Background: A critical challenge in neuroscience is organizing, managing, and accessing the explosion in neuroscientific knowledge, particularly anatomic knowledge. We believe that explicit knowledge-based approaches to make neuroscientific knowledge computationally accessible will be helpful in tackling this challenge and will enable a variety of applications exploiting this knowledge, such as surgical planning. Results: We developed ontology-based models of neuroanatomy to enable symbolic lookup, logical inference and mathematical modeling of neural systems. We built a prototype model of the motor system that integrates descriptive anatomic and qualitative functional neuroanatomical knowledge. In addition to modeling normal neuroanatomy, our approach provides an explicit representation of abnormal neural connectivity in disease states, such as common movement disorders. The ontology-based representation encodes both structural and functional aspects of neuroanatomy. The ontology-based models can be evaluated computationally, enabling development of automated computer reasoning applications. Conclusion: Neuroanatomical knowledge can be represented in machine-accessible format using ontologies. Computational neuroanatomical approaches such as described in this work could become a key tool in translational informatics, leading to decision support applications that inform and guide surgical planning and personalized care for neurological disease in the future

BAMS Neuroanatomical Ontology: Design and Implementation

We describe in this paper the structure and main features of a domain specific ontology for neuroscience, the BAMS Neuroanatomical Ontology. The ontology includes a complete set of concepts that describe the parts of the rat nervous system, a growing set of concepts that describe neuron populations identified in different brain regions, and relationships between concepts. The ontology is linked with a complex representation of structural and physiological variables used to classify neurons, which is encoded in BAMS. BAMS Neuroanatomical Ontology is accessible on the web and includes an interface that allows browsing terms, viewing criteria for classification, and accessing associated information

Problems and Solutions with Integrating Terminologies into Evolving Knowledge Bases

We have merged two established anatomical terminologies with an evolving ontology of biological structure: the Foundational Model of Anatomy. We describe the problems we have encountered and the solutions we have developed. We believe that both the problems and solutions generalize to the integration of any legacy terminology with a disciplined ontology within the same domain