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

Lei Wang; José Luis Ambite; Abhishek Appaji; Janine Bijsterbosch; Jerome Dockes; Rick Herrick; Alex Kogan; Howard Lander; Daniel Marcus; Stephen M Moore; Jean-Baptiste Poline; Arcot Rajasekar; Satya S Sahoo; Matthew D Turner; Xiaochen Wang; Yue Wang; Jessica A Turner

doi:10.3389/fninf.2023.1215261

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

Front Neuroinform. 2023 Aug 31:17:1215261. doi: 10.3389/fninf.2023.1215261. eCollection 2023.

Authors

Lei Wang¹, José Luis Ambite², Abhishek Appaji³, Janine Bijsterbosch⁴, Jerome Dockes⁵, Rick Herrick⁴, Alex Kogan¹, Howard Lander⁶, Daniel Marcus⁴, Stephen M Moore⁴, Jean-Baptiste Poline⁵, Arcot Rajasekar^{6

7}, Satya S Sahoo⁸, Matthew D Turner¹, Xiaochen Wang⁹, Yue Wang⁷, Jessica A Turner¹

Affiliations

¹ Psychiatry and Behavioral Health Department, The Ohio State University Wexner Medical Center, Columbus, OH, United States.
² Information Sciences Institute and Computer Science, University of Southern California, Los Angeles, CA, United States.
³ Department of Medical Electronics Engineering, BMS College of Engineering, Bangalore, India.
⁴ Department of Radiology, Washington University in St. Louis, St. Louis, MO, United States.
⁵ Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada.
⁶ Renaissance Computing Institute, Chapel Hill, NC, United States.
⁷ School of Information and Library Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.
⁸ Department of Population and Quantitative Health Sciences, Case Western Reserve University, Cleveland, OH, United States.
⁹ College of Information Sciences and Technology, Pennsylvania State University, State College, PA, United States.

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.

Keywords: MRI; addiction; experimental design; metadata; ontology; schizophrenia; text-mining.

Grants and funding

The efforts described in this manuscript are funded by NIDA grant R01 DA053028 “CRCNS:NeuroBridge: Connecting big data for reproducible clinical neuroscience,” the NSF Office of Cyberinfrastructure OCI-1247652, OCI-1247602, and OCI-1247663 grants, “BIGDATA: Mid-Scale: ESCE: DCM: Collaborative Research: DataBridge–A Sociometric System for Long Tail Science Data Collections,” and by the NSF IIS Division of Information and Intelligent Systems grant number #1649397 “EAGER: DBfN: DataBridge for Neuroscience: A Novel Way of Discovery for Neuroscience Data,” NIMH grant U01 MH097435 “SchizConnect: Large-Scale Schizophrenia Neuroimaging Data Mediation and Federation,” NSF grant 1636893 SP0037646 “BD Spokes: SPOKE: MIDWEST: Collaborative: Advanced Computational Neuroscience Network (ACNN).” J-BP and JD were partially funded by the Michael J. Fox Foundation (LivingPark), the National Institutes of Health (NIH) NIH-NIBIB P41 EB019936 (ReproNim) NIH-NIMH R01 MH083320 (CANDIShare) and NIH RF1 MH120021 (NIDM), the National Institute Of Mental Health of the NIH under Award Number R01MH096906 (Neurosynth), as well as the Canada First Research Excellence Fund, awarded to McGill University for the Healthy Brains for Healthy Lives initiative and the Brain Canada Foundation with support from Health Canada. This project has been made possible by the Brain Canada Foundation, through the Canada Brain Research Fund, with the financial support of Health Canada and the McConnell Brain Imaging Centre.