NeuroBridge ontology: computable provenance metadata to give the long tail of neuroimaging data a FAIR chance for secondary use
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...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-07-01
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| Series: | Frontiers in Neuroinformatics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fninf.2023.1216443/full |
| _version_ | 1797773407722405888 |
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| author | Satya S. Sahoo Matthew D. Turner Lei Wang Jose Luis Ambite Abhishek Appaji Arcot Rajasekar Howard M. Lander Yue Wang Jessica A. Turner |
| author_facet | Satya S. Sahoo Matthew D. Turner Lei Wang Jose Luis Ambite Abhishek Appaji Arcot Rajasekar Howard M. Lander Yue Wang Jessica A. Turner |
| author_sort | Satya S. Sahoo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T22:06:02Z |
| format | Article |
| id | doaj.art-63f062c1b2dc44b1b34660971eab18b3 |
| institution | Directory Open Access Journal |
| issn | 1662-5196 |
| language | English |
| last_indexed | 2024-03-12T22:06:02Z |
| publishDate | 2023-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Neuroinformatics |
| spelling | doaj.art-63f062c1b2dc44b1b34660971eab18b32023-07-24T14:13:00ZengFrontiers Media S.A.Frontiers in Neuroinformatics1662-51962023-07-011710.3389/fninf.2023.12164431216443NeuroBridge ontology: computable provenance metadata to give the long tail of neuroimaging data a FAIR chance for secondary useSatya S. Sahoo0Matthew D. Turner1Lei Wang2Jose Luis Ambite3Abhishek Appaji4Arcot Rajasekar5Howard M. Lander6Yue Wang7Jessica A. Turner8Case Western Reserve University, Cleveland, OH, United StatesDepartment of Psychiatry and Behavioral Health, The Ohio State University Wexner Medical Center, Columbus, OH, United StatesDepartment of Psychiatry and Behavioral Health, The Ohio State University Wexner Medical Center, Columbus, OH, United StatesUniversity of Southern California, Los Angeles, CA, United StatesBMS College of Engineering, Bengaluru, IndiaUniversity of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesUniversity of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesUniversity of North Carolina at Chapel Hill, Chapel Hill, NC, United StatesDepartment of Psychiatry and Behavioral Health, The Ohio State University Wexner Medical Center, Columbus, OH, United StatesBackgroundDespite 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.https://www.frontiersin.org/articles/10.3389/fninf.2023.1216443/fullFAIR neuroimaging datacomputable provenance metadataNeuroBridge ontologyontology text annotationW3C PROV ontology |
| spellingShingle | Satya S. Sahoo Matthew D. Turner Lei Wang Jose Luis Ambite Abhishek Appaji Arcot Rajasekar Howard M. Lander Yue Wang Jessica A. Turner NeuroBridge ontology: computable provenance metadata to give the long tail of neuroimaging data a FAIR chance for secondary use Frontiers in Neuroinformatics FAIR neuroimaging data computable provenance metadata NeuroBridge ontology ontology text annotation W3C PROV ontology |
| title | NeuroBridge ontology: computable provenance metadata to give the long tail of neuroimaging data a FAIR chance for secondary use |
| title_full | NeuroBridge ontology: computable provenance metadata to give the long tail of neuroimaging data a FAIR chance for secondary use |
| title_fullStr | NeuroBridge ontology: computable provenance metadata to give the long tail of neuroimaging data a FAIR chance for secondary use |
| title_full_unstemmed | NeuroBridge ontology: computable provenance metadata to give the long tail of neuroimaging data a FAIR chance for secondary use |
| title_short | NeuroBridge ontology: computable provenance metadata to give the long tail of neuroimaging data a FAIR chance for secondary use |
| title_sort | neurobridge ontology computable provenance metadata to give the long tail of neuroimaging data a fair chance for secondary use |
| topic | FAIR neuroimaging data computable provenance metadata NeuroBridge ontology ontology text annotation W3C PROV ontology |
| url | https://www.frontiersin.org/articles/10.3389/fninf.2023.1216443/full |
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