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Abstract #0386

Reconstructing Resting State Brain Networks from High-resolution EEG

Han Yuan 1 , Lei Ding 1,2 , Min Zhu 2 , and Jerzy Bodurka 1,3

1 Laureate Institute for Brain Research, Tulsa, OK, United States, 2 School of Electrical and Computer Engineering, University of Oklahoma, Norman, OK, United States, 3 College of Engineering, University of Oklahoma, Norman, OK, United States

We developed a method to reconstruct the resting state networks (RSNs) from high-resolution EEG data. We combined electrophysiological source imaging and independent component analysis to obtain cortical distributions of eight RSNs from temporal independent EEG microstates. We further compared both spatial and temporal similarities of EEG-derived RSNs and BOLD-fMRI-derived RSNs from simultaneously acquired data. We found a high spatial similarity and temporal correlations among all eight RSNs independently identified from multimodal data. Results demonstrate the intrinsic connection between fast neuronal activity and slow hemodynamics fluctuation, and also show the utility of EEG in studying resting brain networks.

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