Dynamic functional connectivity from rest to sleep

ESWAR DAMARAJU; ENZO TAGLIAZUCCHI; HELMUT LAUFS; VINCE CALHOUN

Honolulu

Congreso; Organization for Human Brain Mapping; 2015

Several recent studies demonstrated the dynamic nature of resting state functionalconnectivity (Hutchinson et al. 2013, Calhoun et al., 2014 ). One recent approach is the use ofgroup ICA combined with joint estimation of the precision matrix using a tapered slidingwindow (Allen et al 2012). Although this method can capture reliable recurrent patterns ofdynamic functional connectivity, the estimation can be poor using shorter window lengths.Here we use simultaneous EEG-resting functional imaging data collected continuously over50 minutes while the subjects transitioned between wakefulness and sleep stages (labeled viaEEG-based sleep scoring) and assessed the ability of sliding-window based dynamic functionalnetwork connectivity (dFNC) measures to track the changes across these dierent wakefulnessstates.