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Abstract:
Multichannel records of the spontaneous brain electrical activity show that EEG topography remains quasi-stable for short time periods (≈80–120ms). These periods of quasi-stability are often referred to as “EEG microstates” [1]. Changes in temporal characteristics of microstates
(duration, occurrence, coverage and transition probabilities) are associated with psychiatric disorders as well as mental states [2]. In this study we investigated the association of the microstate parameters with vigilance level. We analysed resting-state, eyes-closed recordings from 19 healthy male subjects of simultaneous 3 Tesla fMRI and 64-channel EEG. Four microstate classes were identified using EEGLAB plugin for microstates analysis by Thomas Koenig (www.thomaskoenig.ch/index. php/software/). EEG data were epoched into 1.8s epochs. We calculated duration,
occurrence, coverage and transition probabilities of the four microstate classes for each epoch, resulting in the time courses of the microstate parameters. Vigilance time series were calculated as root mean square amplitude in the alpha band (7-13 Hz) divided by the rms amplitudes in the delta and theta bands (1-7 Hz) at each epoch. For each subject correlation between vigilance time series and time courses of the microstate parameters were calculated. We used one-sample t-test on the Fisher z-transformed correlation coefficients to test for the group level association (Bonferroni corrected). We also used support vector machine regression to predict the vigilance time series based on the parameters of the microstates. We found that the time course of duration (t=3.61, p=0.008) and contribution (t=2.95, p=0.034) of microstate class C is positively associated while the time course of occurrence
(t=-4.43, p=0.001) and contribution (t=-4.00, p=0.003) of microstate class A is negatively associated with vigilance time series. Finally, we show, that microstate parameters can be used for the prediction of the individual vigilance level (r=0.38, p<0.001).
