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.

中文翻译：

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NeuroBridge 本体：可计算的来源元数据，为神经影像数据的长尾提供二次使用的公平机会

背景尽管神经科学界做出了努力，但仍有许多已发表的神经影像学研究的数据仍未得到证实。可找到的或者无障碍。用户面临重大挑战重用由于缺乏元数据来源而导致的神经影像数据，例如实验方案、研究仪器和研究参与者的详细信息，这也是互操作性。为了实施神经影像数据的 FAIR 指南，我们开发了迭代本体工程流程，并用它来创建 NeuroBridge 本体。NeuroBridge 本体是一个可计算的出处术语模型，用于实施公平原则，并与用本体术语注释全文文章的国际努力一起，该本体使用户能够定位相关的神经影像数据集。方法建立在我们之前在元数据建模方面的工作基础上，并在与代表性语料库的初始注释相一致，我们对诊断术语（例如精神分裂症、酒精使用障碍）、磁共振成像（MRI）扫描类型（T1 加权、基于任务等）、临床症状评估（PANSS 、审计）以及各种其他评估。我们利用注释团队的反馈来识别缺失的元数据术语，这些术语已添加到 NeuroBridge 本体中，并且我们重组了本体以支持第二组独立注释者对神经影像文章语料库的最终注释作为 NeuroBridge 神经影像数据集搜索门户的功能。结果 NeuroBridge 本体由 660 个类组成，具有 49 个属性和 3,200 个公理。该本体包括到现有本体的映射，使 NeuroBridge 本体能够与其他领域特定术语系统互操作。使用本体，我们注释了 186 篇神经影像全文文章，描述了参与者类型、扫描、临床和认知评估。 结论 NeuroBridge 本体是第一个可计算元数据模型，代表了最近精神分裂症和物质使用障碍神经影像研究中可用的数据类型研究; 它可以根据需要进行扩展以包含更细化的术语。该元数据本体预计将形成计算基础，帮助研究人员使其数据符合 FAIR 标准，并支持用户进行可重复的神经影像研究。