Brain and Behavior: we want you to share your data.

We at Brain and Behavior are happy, for one, that data sharing is now here

Ontologies for Neuroscience: What are they and What are they Good for?

Current information technology practices in neuroscience make it difficult to understand the organization of the brain across spatial scales. Subcellular junctional connectivity, cytoarchitectural local connectivity, and long-range topographical connectivity are just a few of the relevant data domains that must be synthesized in order to make sense of the brain. However, due to the heterogeneity of the data produced within these domains, the landscape of multiscale neuroscience data is fragmented. A standard framework for neuroscience data is needed to bridge existing digital data resources and to help in the conceptual unification of the multiple disciplines of neuroscience. Using our efforts in building ontologies for neuroscience as an example, we examine the benefits and limits of ontologies as a solution for this data integration problem. We provide several examples of their application to problems of image annotation, content-based retrieval of structural data, and integration of data across scales and researchers

Issues in the Design of a Pilot Concept-Based Query Interface for the Neuroinformatics Information Framework

This paper describes a pilot query interface that has been constructed to help us explore a "concept-based" approach for searching the Neuroscience Information Framework (NIF). The query interface is concept-based in the sense that the search terms submitted through the interface are selected from a standardized vocabulary of terms (concepts) that are structured in the form of an ontology. The NIF contains three primary resources: the NIF Resource Registry, the NIF Document Archive, and the NIF Database Mediator. These NIF resources are very different in their nature and therefore pose challenges when designing a single interface from which searches can be automatically launched against all three resources simultaneously. The paper first discusses briefly several background issues involving the use of standardized biomedical vocabularies in biomedical information retrieval, and then presents a detailed example that illustrates how the pilot concept-based query interface operates. The paper concludes by discussing certain lessons learned in the development of the current version of the interface

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

Development and use of Ontologies Inside the Neuroscience Information Framework: A Practical Approach

An initiative of the NIH Blueprint for neuroscience research, the Neuroscience Information Framework (NIF) project advances neuroscience by enabling discovery and access to public research data and tools worldwide through an open source, semantically enhanced search portal. One of the critical components for the overall NIF system, the NIF Standardized Ontologies (NIFSTD), provides an extensive collection of standard neuroscience concepts along with their synonyms and relationships. The knowledge models defined in the NIFSTD ontologies enable an effective concept-based search over heterogeneous types of web-accessible information entities in NIF’s production system. NIFSTD covers major domains in neuroscience, including diseases, brain anatomy, cell types, sub-cellular anatomy, small molecules, techniques, and resource descriptors. Since the first production release in 2008, NIF has grown significantly in content and functionality, particularly with respect to the ontologies and ontology-based services that drive the NIF system. We present here on the structure, design principles, community engagement, and the current state of NIFSTD ontologies

A Formal Ontology of Subcellular Neuroanatomy

The complexity of the nervous system requires high-resolution microscopy to resolve the detailed 3D structure of nerve cells and supracellular domains. The analysis of such imaging data to extract cellular surfaces and cell components often requires the combination of expert human knowledge with carefully engineered software tools. In an effort to make better tools to assist humans in this endeavor, create a more accessible and permanent record of their data, and to aid the process of constructing complex and detailed computational models, we have created a core of formalized knowledge about the structure of the nervous system and have integrated that core into several software applications. In this paper, we describe the structure and content of a formal ontology whose scope is the subcellular anatomy of the nervous system (SAO), covering nerve cells, their parts, and interactions between these parts. Many applications of this ontology to image annotation, content-based retrieval of structural data, and integration of shared data across scales and researchers are also described

Interdisciplinary perspectives on the development, integration and application of cognitive ontologies

We discuss recent progress in the development of cognitive ontologies and summarize three challenges in the coordinated development and application of these resources. Challenge 1 is to adopt a standardized definition for cognitive processes. We describe three possibilities and recommend one that is consistent with the standard view in cognitive and biomedical sciences. Challenge 2 is harmonization. Gaps and conflicts in representation must be resolved so that these resources can be combined for mark-up and interpretation of multi-modal data. Finally, Challenge 3 is to test the utility of these resources for large-scale annotation of data, search and query, and knowledge discovery and integration. As term definitions are tested and revised, harmonization should enable coordinated updates across ontologies. However, the true test of these definitions will be in their community-wide adoption which will test whether they support valid inferences about psychological and neuroscientific data