Sahoo Satya S, Turner Matthew D, Wang Lei, Ambite Jose Luis, Appaji Abhishek, Rajasekar Arcot, Lander Howard M, Wang Yue, Turner Jessica A
Case Western Reserve University, Cleveland, OH, United States.
Department of Psychiatry and Behavioral Health, The Ohio State University Wexner Medical Center, Columbus, OH, United States.
Front Neuroinform. 2023 Jul 24;17:1216443. doi: 10.3389/fninf.2023.1216443. eCollection 2023.
Despite the efforts of the neuroscience community, there are many published neuroimaging studies with data that are still not or . Users face significant challenges in 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 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.
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.
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.
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.
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尚未公开或无法获取。由于缺乏诸如实验方案、研究仪器以及研究参与者详细信息等出处元数据，用户在管理神经影像学数据时面临重大挑战，而这些元数据对于数据重用也是必需的。为了实施神经影像学数据的FAIR准则，我们开发了一个迭代的本体工程过程，并使用它创建了NeuroBridge本体。NeuroBridge本体是一个可计算的出处术语模型，用于实施FAIR原则，并且与一项用本体术语注释全文文章的国际努力相结合，该本体使用户能够找到相关的神经影像学数据集。
基于我们之前在元数据建模方面的工作，并与一个代表性语料库的初始注释协同进行，我们对诊断术语（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、基于任务的等）、临床症状评估（阳性和阴性症状量表、酒精使用障碍识别测试）以及各种其他评估进行了建模。我们利用注释团队的反馈来识别缺失的元数据术语，并将其添加到NeuroBridge本体中，并且我们对本体进行了重构，以支持由另一组独立注释者对神经影像学文章语料库进行最终注释，以及NeuroBridge神经影像学数据集搜索门户的功能。
NeuroBridge本体由660个类、49个属性和3200条公理组成。该本体包括到现有本体的映射，使NeuroBridge本体能够与其他特定领域的术语系统互操作。使用该本体，我们注释了186篇描述参与者类型、扫描、临床和认知评估的神经影像学全文文章。
NeuroBridge本体是第一个可计算的元数据模型，它代表了近期精神分裂症和物质使用障碍研究中神经影像学研究可用的数据类型；它可以根据需要扩展以包括更详细的术语。这个元数据本体有望形成计算基础，以帮助研究人员使他们的数据符合FAIR标准，并支持用户进行可重复的神经影像学研究。
