BOLD-Related EEG Signal Estimation Software

DESCRIPTION (provided by applicant): Blood-oxygen-level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is the dominant noninvasive modality for studying human brain functional localization in basic and clinical neurosciences. Although the three-dimensional spatial resolution of scalp-recorded electroencephalography (EEG) is ambiguous, its temporal resolution is roughly three orders of magnitude better than fMRI. Consequently, a key issue in imaging neuroscience is how to integrate EEG with fMRI. The absence of a reliable computational bridge linking EEG to fMRI is a critical barrier to an integrated spatiotemporal experimental investigation of human brain function. We propose to develop a data-driven approach to integration-detection and estimation of regional BOLD- related EEG (rBRE) signals-which is constrained by a simple (though extensible) functional model of neuroelectric-hemodynamic coupling. An rBRE signal is a spatially and temporally filtered EEG signal which, when transformed via thefunctional model, demonstrates statistically significant coupling strength and regional specificity. Concurrent EEG-fMRI datasets are used to tune spatial and temporal filters which maximize EEG-BOLD coupling based on a particular form of conditional mutual information. After detection, an rBRE signal may be estimated at the temporal resolution of EEG. After successful completion of Phase I, we will have implemented rBRE signal detection algorithms in prototype software, verified their correct implementation using quasi-realistic simulations, and studied the effects of initialization errors, SNR, and region size. In particular, we will have shown that it is feasible to detect regional BOLD-related EEG signals reliably in human data. PUBLIC HEALTH RELEVANCE: How to integrate EEG with fMRI data is an important issue faced by many cognitive, behavioral, and social neuroscientists who are practitioners of both modalities. If successful, the research and development efforts described in this proposal willposition SSI as a leading, innovative provider of software for integrated EEG-fMRI analysis. In addition to supporting concurrent EEG-fMRI capabilities, the developed software will be useful to neurophysiology labs which have access primarily to EEG apartfrom fMRI. Scientists working with us are interested in this software for the study of neurological, neurodevelopmental and psychiatric disorders, as well as basic brain research.