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

Satya S Sahoo; Matthew D Turner; Lei Wang; Jose Luis Ambite; Abhishek Appaji; Arcot Rajasekar; Howard M Lander; Yue Wang; Jessica A Turner

doi:10.3389/fninf.2023.1216443

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

Front Neuroinform. 2023 Jul 24:17:1216443. doi: 10.3389/fninf.2023.1216443. eCollection 2023.

Authors

Satya S Sahoo¹, Matthew D Turner², Lei Wang², Jose Luis Ambite³, Abhishek Appaji⁴, Arcot Rajasekar⁵, Howard M Lander⁵, Yue Wang⁵, Jessica A Turner²

Affiliations

¹ Case Western Reserve University, Cleveland, OH, United States.
² Department of Psychiatry and Behavioral Health, The Ohio State University Wexner Medical Center, Columbus, OH, United States.
³ University of Southern California, Los Angeles, CA, United States.
⁴ BMS College of Engineering, Bengaluru, India.
⁵ University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.

Abstract

Background: Despite the efforts of the neuroscience community, there are many published neuroimaging studies with data that are still not findable or accessible. Users face significant challenges in reusing neuroimaging data due to the lack of provenance metadata, such as experimental protocols, study instruments, and details about the study participants, which is also required for interoperability. To implement the FAIR guidelines for neuroimaging data, we have developed an iterative ontology engineering process and used it to create the NeuroBridge ontology. The NeuroBridge ontology is a computable model of provenance terms to implement FAIR principles and together with an international effort to annotate full text articles with ontology terms, the ontology enables users to locate relevant neuroimaging datasets.

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.

Keywords: FAIR neuroimaging data; NeuroBridge ontology; W3C PROV ontology; computable provenance metadata; ontology text annotation.

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).”