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Abstract #0359

Identification of Anti-Correlated Resting-State Networks Using Simultaneous EEG-FMRI and Independent Components Analysis

Chi Wah Wong1, Valur Olafsson2, Hongjian He2, Tom Liu2

1Radiology, University of California - San Diego, La Jolla,, CA, United States; 2Radiology, University of California - San Diego, La Jolla, CA, United States

It has been shown with resting-state fMRI that the Default Mode Network (DMN) is anti-correlated with the Task Positive Network (TPN). In this study, we used simultaneous EEG-fMRI to investigate the relationship of the EEG alpha power time course with the resting-state BOLD signals in these anti-correlated networks. We found that the relation between the EEG alpha power and BOLD fMRI signals in these networks is stronger when using independent components (as determined with Independent Components Analysis) as compared to the use of the global alpha power.