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

Public Deposited
Analytics

Downloadable Content

Download PDF
Request Version for Screen Reader
Creator
  • Wang, Lei
    • Other Affiliation: Ohio State University Wexner Medical Center
  • Ambite, José Luis
    • Other Affiliation: University of Southern California
  • Appaji, Abhishek
    • Other Affiliation: BMS College of Engineering
  • Bijsterbosch, Janine
    • Other Affiliation: Washington University in St. Louis
  • Dockes, Jerome
    • Other Affiliation: McGill University
  • Herrick, Rick
    • Other Affiliation: Washington University in St. Louis
  • Kogan, Alex
    • Other Affiliation: Ohio State University Wexner Medical Center
  • Lander, Howard
    • Affiliation: Renaissance Computing Institute
  • Marcus, Daniel
    • Other Affiliation: Washington University in St. Louis
  • Moore, Stephen M.
    • Other Affiliation: Washington University in St. Louis
  • Poline, Jean-Baptiste
    • Other Affiliation: McGill University
  • Rajasekar, Arcot
    • Affiliation: Renaissance Computing Institute
  • Sahoo, Satya S.
    • Other Affiliation: Case Western Reserve University
  • Turner, Matthew D.
    • Other Affiliation: The Ohio State University Wexner Medical Center
  • Wang, Xiaochen
    • Other Affiliation: Pennsylvania State University
  • Wang, Yue
    • Affiliation: School of Information and Library Science
  • Turner, Jessica A.
    • Other Affiliation: The Ohio State University Wexner Medical Center
Abstract
  • Introduction Open 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. Methods The 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. Results The 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. Conclusion The 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.
Date of publication
Keyword
DOI
Identifier
Resource type
  • Article
License
  • Attribution 4.0 International
Journal title
  • Frontiers in Neuroinformatics
Journal volume
  • 24
Journal issue
  • 17
Page start
  • 13242
Language
  • English
Version
  • Publisher
ISSN
  • 1662-5196
Publisher
  • Frontiers Media SA

Relations

Parents:

This work has no parents.

In Collection:

Items

Thumbnail Title Date Uploaded Visibility Actions
file details fninf-17-1215261.pdf 2023-09-06 Public Download