ArticleFrontiers in neuroinformatics2023
NeuroBridge ontology: computable provenance metadata to give the long tail of neuroimaging data a FAIR chance for secondary use.
Article in Frontiers in neuroinformatics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Reconciling global collaboration and data sovereignty in neuroscience.Nature neuroscience · 2026Review
- Large language models can extract metadata for annotation of human neuroimaging publications.Frontiers in neuroinformatics · 2025Article
- Towards building a trustworthy pipeline integrating Neuroscience Gateway and Open Science Chain.Database : the journal of biological databases and curation · 2024Article
- NeuroBridge: a prototype platform for discovery of the long-tail neuroimaging data.Frontiers in neuroinformatics · 2023Article
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9 authors.
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Abstract
Background: Despite the efforts of the neuroscience community, there are many published neuroimaging studies with data that are still not Methods: Building on our previous work in metadata modeling, and in concert with an initial annotation of a representative corpus, we modeled diagnosis terms (e.g., schizophrenia, alcohol usage disorder), magnetic resonance imaging (MRI) scan types (T1-weighted, task-based, etc.), clinical symptom assessments (PANSS, AUDIT), and a variety of other assessments. We used the feedback of the annotation team to identify missing metadata terms, which were added to the NeuroBridge ontology, and we restructured the ontology to support both the final annotation of the corpus of neuroimaging articles by a second, independent set of annotators, as well as the functionalities of the NeuroBridge search portal for neuroimaging datasets. Results: The NeuroBridge ontology consists of 660 classes with 49 properties with 3,200 axioms. The ontology includes mappings to existing ontologies, enabling the NeuroBridge ontology to be interoperable with other domain specific terminological systems. Using the ontology, we annotated 186 neuroimaging full-text articles describing the participant types, scanning, clinical and cognitive assessments. Conclusion: The NeuroBridge ontology is the first computable metadata model that represents the types of data available in recent neuroimaging studies in schizophrenia and substance use disorders research; it can be extended to include more granular terms as needed. This metadata ontology is expected to form the computational foundation to help both investigators to make their data FAIR compliant and support users to conduct reproducible neuroimaging research.
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