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Abstract #1016

Validating the detection of slow BOLD changes with multi-echo fMRI denoised data using simultaneous EEG

Jennifer Evans1, Silvina Horovitz2, Peter Bandettini3, Carlos Zarate4, and Prantik Kundu5

1ETPB/NIMH, NIH, Bethesda, MD, United States, 2NINDS, NIH, Bethesda, MD, United States, 3NIMH, NIH, Bethesda, MD, United States, 4NIH, NIH, Bethesda, MD, United States, 5Mount Sinai, New York, NY, United States

In this study we use simultaneous electroencephalography (EEG) and multi-echo functional magnetic resonance imaging (ME-fMRI) to demonstrate the ability of ME-ICA denoising to resolve slow changes without need for baseline models. We use a visual flickering checkerboard with varying contrast to elicit a response measurable by fMRI and also EEG. We find that the ME-denoised data improves the fMRI timeseries correlation with the ideal task without removing the task signature that is shown to exist in the EEG data.

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