NeuroBridge: a prototype platform for discovery of the long-tail neuroimaging data

Abstract

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.

NeuroBridge ontology: computable provenance metadata to give the long tail of neuroimaging data a FAIR chance for secondary use

“…The query expression is automatically expanded using OWL reasoning to include relevant subclasses of a selected ontology term, and this expanded query expression is used to search for neuroimaging experimental studies that match the query constraints ( Hitzler et al, 2009 ). Please see our companion NeuroBridge paper in this Research Topic issue for more details of the platform ( Wang et al, Under Review ).…”

“…This reverse mapping from articles to ontologies is facilitated through the human-annotated stage where identification of relevant sentence structure is performed. These manually annotated examples are used to train machine learning (ML) model to identify similar mappings [described in our companion paper ( Wang et al, Under Review )].…”

“…The NeuroBridge platform aims to identify, index, and analyze provenance metadata information from neuroimaging articles available in the PubMed Central repository and map specific studies to user queries related to research hypotheses. The NeuroBridge platform with its multiple components and sources is described in more detail in a companion paper in this Research Topic ( Wang et al, Under Review ). To enable the modeling of computable metadata that underpins the data search platform, we developed the NeuroBridge ontology based on FAIR guidelines for the neuroimaging domain.…”

“…In this paper, we describe a novel iterative ontology engineering process that was developed and implemented to create the NeuroBridge ontology that supports: (1) Fine granularity annotation of full text articles describing neuroimaging studies; (2) Automated parsing and indexing of terms describing experimental design details of neuroimaging studies; and (3) Interactive user queries to locate experimental studies that match research terms ( Figure 1 ). The automated parsing and indexing of research papers as well as the interactive user queries require the development of machine learning algorithms and web application resources together with the NeuroBridge ontology, therefore, they are outside the scope of this paper and are described in the companion paper ( Wang et al, Under Review ). The rest of the paper is structured as follows: In the Section “2.…”