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.
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.
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