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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