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

Abstract

BackgroundDespite 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.MethodsBuilding 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.ResultsThe 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.ConclusionThe 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.

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

“…These extensions will require similarly significant human effort including manually labeling a training set of papers with the ontology terms and careful review and curation of this work. See the companion paper in this issue for more detail of the labeling methods ( Sahoo et al, 2023 ). As the system grows, the current iteration of the system supports this human labeling process by providing draft labels, and the entity-recognition, entity-linking, 2-stage natural language model will be retrained to complete the extension.…”

“…Full details of the ontology and its development process are described in the companion paper in this special issue ( Sahoo et al, 2023 ). The NeuroBridge ontology was developed in the metadata framework called the S3 model that classified provenance metadata related to research studies into the categories of study instrument , study data , and study method ( Sahoo et al, 2019 ), which extended the World Wide Web Consortium (W3C) PROV specification to represent provenance metadata for the biomedical domain.…”

“…Using the ontology, we annotated 186 papers from our document corpus on the participant types, scanning, clinical and cognitive assessments. See the companion paper in this special issue for a more thorough presentation of the ontology and annotations ( Sahoo et al, 2023 ), including the class hierarchy representing various diagnoses and assessment scales. The latest version of the NeuroBridge ontology is available on GitHub (NeuroBridge) (see text footnote 4) and will be made available on BioPortal soon.…”

“…Our long-term goal is to bridge the research question with data and scientific workflow, thereby significantly speeding up the cycle of hypothesis-based research. In the companion paper in this special issue, we describe the NeuroBridge ontology ( Sahoo et al, 2023 ). In this paper, we report the NeuroBridge prototype platform that focused on neuroimaging studies of schizophrenia and addiction disorders as application domains.…”