Imagine being a treasure hunter, searching for the rarest and most precious gems hidden in the vast depths of the ocean. Just like those gems, there is a wealth of neuroimaging data waiting to be discovered in the depths of scientific literature. With the NeuroBridge prototype, scientists have developed a powerful platform that can uncover the long-tail neuroimaging data that often goes unnoticed. By using an ontology-based search and natural-language text-mining techniques, the NeuroBridge prototype allows researchers to identify papers relevant to their specific research question. Its unique architecture includes an extensible ontology for modeling study metadata and a document processor that uses pre-trained deep-learning models to efficiently analyze articles for ontological terms. With its interactive Query Builder, users can easily navigate through the NeuroBridge website and access valuable resources like PubMed abstracts and full-text articles. The potential for this prototype is vast, as ongoing work aims to validate its document processor with a larger corpus, expand the ontology to include detailed imaging data, and integrate XNAT-based neuroimaging databases to enhance data accessibility. Are you ready to dive into the world of neuroimaging data? Explore the NeuroBridge prototype and unlock a wealth of untapped knowledge!

IntroductionOpen science initiatives have enabled sharing of large amounts of already collected data. However, significant gaps remain regarding how to find appropriate data, including underutilized data that exist in the long tail of science. We demonstrate the NeuroBridge prototype and its ability to search PubMed Central full-text papers for information relevant to neuroimaging data collected from schizophrenia and addiction studies.MethodsThe NeuroBridge architecture contained the following components: (1) Extensible ontology for modeling study metadata: subject population, imaging techniques, and relevant behavioral, cognitive, or clinical data. Details are described in the companion paper in this special issue; (2) A natural-language based document processor that leveraged pre-trained deep-learning models on a small-sample document corpus to establish efficient representations for each article as a collection of machine-recognized ontological terms; (3) Integrated search using ontology-driven similarity to query PubMed Central and NeuroQuery, which provides fMRI activation maps along with PubMed source articles.ResultsThe NeuroBridge prototype contains a corpus of 356 papers from 2018 to 2021 describing schizophrenia and addiction neuroimaging studies, of which 186 were annotated with the NeuroBridge ontology. The search portal on the NeuroBridge website https://neurobridges.org/ provides an interactive Query Builder, where the user builds queries by selecting NeuroBridge ontology terms to preserve the ontology tree structure. For each return entry, links to the PubMed abstract as well as to the PMC full-text article, if available, are presented. For each of the returned articles, we provide a list of clinical assessments described in the Section “Methods” of the article. Articles returned from NeuroQuery based on the same search are also presented.ConclusionThe NeuroBridge prototype combines ontology-based search with natural-language text-mining approaches to demonstrate that papers relevant to a user’s research question can be identified. The NeuroBridge prototype takes a first step toward identifying potential neuroimaging data described in full-text papers. Toward the overall goal of discovering “enough data of the right kind,” ongoing work includes validating the document processor with a larger corpus, extending the ontology to include detailed imaging data, and extracting information regarding data availability from the returned publications and incorporating XNAT-based neuroimaging databases to enhance data accessibility.

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