A Human Electrophysiology, Associated Anatomic Data and Integrated Tool Resource

描述(申请人提供)：目前的技术允许以高时间分辨率记录256个或更多头皮部位的脑电和/或磁活动，以及并发的行为和其他心理生理时间序列，同时在一些脑外科手术和外科手术规划过程中常规地获取密集的人类颅内数据。对象解剖磁共振(MR)、计算机断层扫描(CT)和/或扩散张量(DT)头部图像也可用。标准的分析方法只提取了这些数据中包含的关于人脑动态的丰富信息的一小部分。我们建议UCSD Swartz计算神经科学中心(EEGLAB软件环境开发项目的总部)、UCSD生物系统研究中心(生物医学信息学研究网络(BIRN)协调中心)和其他六个人类电生理学研究社区的领导人合作，开发一个公共的“人类电生理学、相关解剖数据和集成工具(HeadIT)资源”。这一框架将建立在BIRN数据仓库框架(www.nBirn.net/bdr)的基础上，从而扩大其范围和能力。HeadIT资源将共享现有的、高质量、有充分记录的数据集，使其得以存档保存，并继续向公众开放，以利用日益强大的分析工具进行重新分析和荟萃分析。最初，HeadIT储存库扩展了BIRN数据储存库内的基础数据库，将包含由SCCN和其他机构贡献的丰富的人类电生理数据集合，并以物理方式分布在专注于七个研究领域的中心托管的存储节点上：癫痫、神经康复、注意力、磁记录、儿童发育、神经信息学和多模式成像。HeadIT资源将包括一个软件设施，用于访问和分析EEGLAB(sccn.ucsd.edu/eeglab)和其他广泛使用的基于MatLab的电生理工具环境中的存储库数据。EEGLAB将扩展为包括一个基础工具集，用于在多个已存档的HeadIT研究中执行元分析。我们将为提供的HeadIT数据制定最低限度的信息标准和质量保证测试，这是一个用于交互数据可视化的设施，并将通过指定的正在进行的研究合作来测试和验证HeadIT资源的可操作性，这些合作将作为工具和数据框架开发和测试的初始用户社区。公共卫生相关性：拟议的“人类电生理学、相关解剖数据和综合工具(HeadIT)资源”将允许使用免费可用的分析工具重新分析免费获得的大脑活动记录以及相关的行为和生理测量。这将允许对任何单一研究中看不到的模式进行大型多研究荟萃分析，重新分析以验证现有数据中以前发表的结论，并将连续更先进的工具应用于在广泛的临床和基础研究领域收集的复杂和昂贵的数据。

DESCRIPTION (provided by applicant): Current technology allows recording of brain electrical and/or magnetic activity from 256 or more scalp sites with high temporal resolution, plus concurrent behavioral and other psychophysiological time series, while dense human intracranial data are routinely acquired during some brain surgery and surgery planning procedures. Subject anatomic magnetic resonance (MR), computerized tomography (CT), and/or diffusion tensor (DT) head images may also be available. Standard analysis approaches extract only a small part of the rich information about human brain dynamics contained in these data. We propose a collaboration between the UCSD Swartz Center for Computational Neuroscience (home to the EEGLAB software environment development project), the UCSD Center for Research in Biological Systems (home to the Biomedical Informatics Research Network (BIRN) coordinating center), and leaders in six other human electrophysiological research communities to develop a public 'A Human Electrophysiology, Associated Anatomic Data and Integrated Tool (HeadIT) resource'. This framework will be built on the BIRN Data Repository framework (www.nbirn.net/bdr), thereby expanding its scope and capabilities. The HeadIT resource will share existing, high- quality, well-documented data sets, allowing their archival preservation and continued public availability for re-analysis and meta-analysis with increasingly powerful analysis tools. Initially, the HeadIT repository, extending a foundational database within the BIRN Data Repository will contain a rich collection of human electrophysiological data contributed by SCCN and others and physically distributed across storage nodes hosted by centers focused on seven research fields: epilepsy, neurorehabilitation, attention, magnetic recording, child development, neuroinformatics, and multimodal imaging. The HeadIT resource will include a software facility for accessing and analyzing repository data in the EEGLAB (sccn.ucsd.edu/eeglab) and other widely-used Matlab-based electrophysiological tool environments. EEGLAB will be extended to include a foundational tool set for performing meta-analyses across more than one archived HeadIT study. We will develop minimal information standards and quality assurance tests for contributed HeadIT data, a facility for interactive data visualization, and will test and validate the operability of the HeadIT resource via named ongoing research collaborations that will serve as the initial user community for tool and data framework development and testing. PUBLIC HEALTH RELEVANCE: The proposed 'A Human Electrophysiology, Associated Anatomic Data and Integrated Tool (HeadIT) Resource' will allow re-analysis of freely available recordings of brain activity and associated behavioral and physiologic measures using freely available analysis tools. This will allow large multi-study meta-analyses for patterns not visible in any single study, re-analyses to validate previously published conclusions from existing data, and application of successively more advanced tools to complex and costly data collected in a wide range of clinical and basic research areas.