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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