Neural ElectroMagnetic Ontologies: ERP Knowledge Representation & Integration

神经电磁本体：ERP 知识表示

• 批准号: 8069619
• 负责人: Dejing Dou
• 金额: $ 51.55万
• 依托单位: UNIVERSITY OF OREGON
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-05-01 至 2013-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8069619
• 关键词: Address; Area; Auditory; Automatic Data Processing; Brain; Brain imaging; Brodmann' s area; Calibration; Classification; Clinical; Clinical Research; Code; Cognitive; Collaborations; Communities; Comprehension; Computer software; Data; Data Analyses; Data Provenance; Data Set; Data Storage and Retrieval; Databases; Development; Electromagnetics; Environment; Equipment; Evaluation; Event-Related Potentials; Generations; Goals; Head; Health; Human; Internet; Knowledge; Label; Laboratories; Laboratory Research; Language; Lead; Location; Logic; Maps; Measures; Meta-Analysis; Metadata; Methods; Modeling; Nature; Neuropsychology; Neurosciences; Neurosciences Research; Online Systems; Ontology; Pattern; Procedures; Process; Reading; Research; Research Methodology; Research Personnel; Resolution; Resource Sharing; Rest; Scalp structure; Scanning; Semantics; Series; Simulate; Site; Solutions; Source; Stream; Surface; System; Techniques; Technology; Testing; To specify; Translations; Visual; Work; base; data acquisition; data integration; data management; data mining; data sharing; density; design; encephalography; evaluation/testing; information organization; interdisciplinary approach; interest; member; neurodevelopment; neuroinformatics; relating to nervous system; relational database; research study; sensor; spatiotemporal; success; task analysis; tool; tool development; web-accessible

Description (provided by applicant): Research in the design and implementation of "Neural ElectroMagnetic Ontologies" (NEMO) will address a critical need for tools to support representation, storage, and sharing of brain electromagnetic data. Electro- encephalography (EEG) and event-related potentials (ERP) are venerable techniques for cognitive and clinical research on human brain function. To realize their full potential, however, it will be necessary to address some long-standing challenges in comparing results across experiments and research laboratories. NEMO will address this need by providing ERP ontologies that can be used for meta-analysis of patterns across experiment contexts and research labs. Given the widespread use of EEG and ERP methods, and their clinical as well as research applications, development of such a system is both timely and significant. System design and implementation will rest on six specific aims. The first goal is to develop rigorous procedures for classification and labeling of electrophysiological patterns (event-related potentials, or ERPs) (Aim 1). The methods and tools that are developed initially for classification and labeling of surface (sensor- level) data will then be extended to support classification of data in source (anatomical) space (Aim 2). Next, we will represent the concepts that define ERP patterns as formal logics, or "ontologies," and will use those concepts to describe the ERP patterns. Relational databases will be modeled based on the ontologies to support high-level questions about the nature of ERP patterns and the relationships between patterns that are associated with different lab, experiment, and analysis contexts (Aim 3). The application domain for our project is reading and language. We have established a consortium of experts in this area who will contribute EEG and ERP data from experimental studies and will collaborate with us on the design and testing, and evaluation of the tools developed for this project. The practical scientific aim will be to conduct meta-analyses of ERP patterns in reading and language. In addition to re-analyses of existing cross-lab data, new experiment paradigms (adapted from the fBIRN project) will be carried out across research sites to calibrate data acquisition and preprocessing methods, and to test the robustness of patterns across different experiment contexts (Aim 4). Initially, we will develop a different ontology for each representational space (e.g., sensor and source space) and each analysis method. Then, we will capture the semantic mappings between different sets of patterns (different ontologies) using data mining (Aim 5). To support this work, we will develop an integrated tool environment for storage and management of EEG and ERP data and meta-data, measure generation and labeling, ontology development, and meta-analysis. This environment will be web-accessible so that partners will have shared access to the project data, analysis tools, ontologies, and meta-analysis results (Aim 6). At the end of this project, the ontologies, annotated database, tools, and technologies will be made available to the larger research community. PUBLIC HEALTH RELEVANCE The practical goal for the NEMO project is to build an ontology database to support data sharing and meta- analysis of EEG and ERP results. The ability to describe brain electrophysiological patterns from different research laboratories and different experiment contexts within a common framework will have immediate benefits for the neuroscience community, as well as long-term benefits for neuroscience research and for scientific areas with similar requirements for robust data representation and integration, and data and resources sharing.

描述(由申请人提供)：“神经电磁本体论”(NEMO)的设计和实现研究将解决对支持脑电磁数据的表示、存储和共享的工具的迫切需求。脑电(EEG)和事件相关电位(ERP)是人类脑功能认知和临床研究的重要手段。然而，为了充分发挥它们的潜力，有必要解决在实验和研究实验室之间比较结果方面的一些长期挑战。NEMO将通过提供可用于跨实验环境和研究实验室的模式元分析的ERP本体来满足这一需求。鉴于脑电和事件相关电位方法的广泛应用，以及它们在临床和研究中的应用，开发这样的系统是及时和重要的。系统的设计和实施将取决于六个具体目标。第一个目标是为电生理模式(事件相关电位或事件相关电位)制定严格的分类和标记程序(目标1)。最初为表面(传感器级)数据分类和标记而开发的方法和工具随后将扩展到支持源(解剖)空间的数据分类(目标2)。接下来，我们将表示将ERP模式定义为形式化逻辑或“本体”的概念，并将使用这些概念来描述ERP模式。将根据本体论对关系数据库进行建模，以支持有关ERP模式的性质以及与不同实验室、实验和分析环境相关联的模式之间的关系的高级问题(目标3)。我们项目的应用领域是阅读和语言。我们已经在这一领域建立了一个专家联盟，他们将从实验研究中贡献EEG和ERP数据，并将在为该项目开发的工具的设计、测试和评估方面与我们合作。实践性的科学目标将是对阅读和语言中的ERP模式进行元分析。除了对现有的跨实验室数据进行重新分析外，还将在各研究地点开展新的实验范式(改编自fBIRN项目)，以校准数据采集和预处理方法，并测试不同实验背景下模式的稳健性(目标4)。最初，我们将为每个表征空间(例如，传感器和源空间)和每种分析方法开发不同的本体。然后，我们将使用数据挖掘捕获不同模式集(不同本体)之间的语义映射(目标5)。为了支持这项工作，我们将开发一个集成的工具环境，用于存储和管理EEG和ERP数据以及元数据、测量生成和标记、本体开发和元分析。这一环境将可通过网络访问，以便合作伙伴共享访问项目数据、分析工具、本体和元分析结果(AIM 6)。在该项目结束时，将向更大的研究社区提供本体、带注释的数据库、工具和技术。公共卫生相关性NEMO项目的实际目标是建立一个本体论数据库，以支持EEG和ERP结果的数据共享和荟萃分析。在一个共同框架内描述来自不同研究实验室和不同实验背景的脑电生理模式的能力将立即为神经科学界带来好处，并为神经科学研究以及对稳健的数据表示和整合以及数据和资源共享具有类似要求的科学领域带来长期好处。