Neural ElectroMagnetic Ontologies: ERP Knowledge Representation & Integration

描述（由申请人提供）：“神经电磁本体”（NEMO）的设计和实现研究将解决对工具的关键需求，以支持大脑电磁数据的表示、存储和共享。脑电图（EEG）和事件相关电位（ERP）是人类大脑功能认知和临床研究的重要技术。然而，为了充分发挥它们的潜力，在比较实验和研究实验室的结果方面，有必要解决一些长期存在的挑战。NEMO将通过提供ERP本体来满足这一需求，该本体可用于跨实验环境和研究实验室的模式元分析。鉴于脑电图和ERP方法的广泛使用，以及它们在临床和研究中的应用，开发这样一个系统是及时和有意义的。系统的设计和实施将基于六个具体目标。第一个目标是制定严格的程序来分类和标记电生理模式（事件相关电位，或erp）（目标1）。最初为表面（传感器级）数据的分类和标记而开发的方法和工具将扩展到支持源（解剖）空间的数据分类（目标2）。接下来，我们将把定义ERP模式的概念表示为形式化逻辑或“本体论”，并将使用这些概念来描述ERP模式。关系数据库将基于本体论建模，以支持有关ERP模式的本质以及与不同实验室、实验和分析上下文相关的模式之间关系的高级问题（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提供脑电图和ERP数据，并将与我们合作设计、测试和评估为这个项目开发的工具。实际的科学目标将是对阅读和语言中的ERP模式进行元分析。除了重新分析现有的跨实验室数据，新的实验范式（改编自fBIRN项目）将跨研究地点进行，以校准数据采集和预处理方法，并测试模式在不同实验背景下的稳健性（目标4）。最初，我们将为每个表示空间（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（目标5）捕获不同模式集（不同本体）之间的语义映射。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数据和元数据，测量生成和标记，本体开发和元分析。这个环境可以通过网络访问，这样合作伙伴就可以共享项目数据、分析工具、本体和元分析结果（目标6）。在这个项目结束时，本体、带注释的数据库、工具和技术将提供给更大的研究社区。NEMO项目的实际目标是建立一个本体数据库，以支持脑电图和ERP结果的数据共享和元分析。在一个共同的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对神经科学社区产生直接的好处，对神经科学研究和对稳健数据表示和集成、数据和资源共享有类似要求的科学领域也有长期的好处。

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.