It is now possible to continuously record the brain's electromagnetic field from sixty or more scalp locations and concurrently to record brain blood flow or oxygenation level changes at tens of thousands of mm3 sized brain volume elements. Methods are needed to analyze this wealth of data and to separate out machine noise and physiological artifacts to examine functionally independent brain processing systems.
The aims of the proposal are threefold: (1) To test the feasibility of concurrent functional magnetic resonance imaging (fMRI) and high density encephalographic (EEG) recording during performance of simple and complex cognitive tasks; (2) To test applications of Independent Component Analysis (ICA) to concurrent fMRI and EEG data, first separately, by comparing the resulting fMRI source distributions with EEG source distributions derived from the ICA results using currently available EEG source localization approaches, and then jointly, by directly comparing the time courses of changes in fMRI activation and in the EEG frequency power spectrum; and (3) To develop, test, document and distribute a software toolbox, based on the widely-used MATLAB signal processing environment, for carrying out the analyses we envision and visualizing the results. The tools, which will be written for the widely available MATLAB environment, will allow researchers to decompose concurrently or separately recorded EEG and fMRI data into spatially and/or temporally independent components, and to evaluate their relationships to concurrent measures of task performance or other physiology and behavior. The analysis of electrophysiological and functional imaging data in both time and time/frequency domains will allow researchers to determine and evaluate relationships between ongoing or event-related changes in electrophysiology, hemodynamics and behavior and perform exploratory and hypothesis drive analyses on human brain data from both basic and clinical studies of human brain and cognitive function.
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