Bona Jonathan, Kemp Aaron S, Cox Carli, Nolan Tracy S, Pillai Lakshmi, Das Aparna, Galvin James E, Larson-Prior Linda, Virmani Tuhin, Prior Fred
Department of Biomedical Informatics, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.
Neurocognitive Dynamics Laboratory, Psychiatric Research Institute, University of Arkansas for Medical Sciences (UAMS), Little Rock, AR, United States.
Front Artif Intell. 2022 Feb 10;4:649970. doi: 10.3389/frai.2021.649970. eCollection 2021.
Neuroimaging is among the most active research domains for the creation and management of open-access data repositories. Notably lacking from most data repositories are integrated capabilities for semantic representation. The Arkansas Imaging Enterprise System (ARIES) is a research data management system which features integrated capabilities to support semantic representations of multi-modal data from disparate sources (imaging, behavioral, or cognitive assessments), across common image-processing stages (preprocessing steps, segmentation schemes, analytic pipelines), as well as derived results (publishable findings). These unique capabilities ensure greater reproducibility of scientific findings across large-scale research projects. The current investigation was conducted with three collaborating teams who are using ARIES in a project focusing on neurodegeneration. Datasets included magnetic resonance imaging (MRI) data as well as non-imaging data obtained from a variety of assessments designed to measure neurocognitive functions (performance scores on neuropsychological tests). We integrate and manage these data with semantic representations based on axiomatically rich biomedical ontologies. These instantiate a knowledge graph that combines the data from the study cohorts into a shared semantic representation that explicitly accounts for relations among the entities that the data are about. This knowledge graph is stored in a triple-store database that supports reasoning over and querying these integrated data. Semantic integration of the non-imaging data using background information encoded in biomedical domain ontologies has served as a key feature-engineering step, allowing us to combine disparate data and apply analyses to explore associations, for instance, between hippocampal volumes and measures of cognitive functions derived from various assessment instruments.
神经影像学是开放获取数据存储库创建和管理方面最活跃的研究领域之一。大多数数据存储库明显缺乏语义表示的集成能力。阿肯色州成像企业系统（ARIES）是一个研究数据管理系统，其特点是具有集成能力，可支持来自不同来源（成像、行为或认知评估）、跨常见图像处理阶段（预处理步骤、分割方案、分析管道）以及派生结果（可发表的研究结果）的多模态数据的语义表示。这些独特的能力确保了大规模研究项目中科学发现的更高可重复性。当前的调查是与三个合作团队进行的，他们在一个专注于神经退行性变的项目中使用ARIES。数据集包括磁共振成像（MRI）数据以及从各种旨在测量神经认知功能的评估中获得的非成像数据（神经心理测试的表现分数）。我们基于公理丰富的生物医学本体，用语义表示对这些数据进行集成和管理。这些实例化了一个知识图谱，该图谱将研究队列中的数据组合成一个共享的语义表示，明确说明了数据所涉及实体之间的关系。这个知识图谱存储在一个三元组存储数据库中，该数据库支持对这些集成数据进行推理和查询。使用生物医学领域本体中编码的背景信息对非成像数据进行语义集成，已成为关键的特征工程步骤，使我们能够组合不同的数据并应用分析来探索关联，例如，海马体体积与来自各种评估工具的认知功能测量之间的关联。
