Characterization of the Functional Dynamics in the Neonatal Brain during REM and NREM Sleep States by means of Microstate Analysis

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Khazaei , M , Raeisi , K , Croce , P , Tamburro , G , Tokariev , A , Vanhatalo , S , Zappasodi , F & Comani , S 2021 , ' Characterization of the Functional Dynamics in the Neonatal Brain during REM and NREM Sleep States by means of Microstate Analysis ' , Brain Topography , vol. 34 , no. 5 , pp. 555-567 . https://doi.org/10.1007/s10548-021-00861-1

Title: Characterization of the Functional Dynamics in the Neonatal Brain during REM and NREM Sleep States by means of Microstate Analysis
Author: Khazaei, Mohammad; Raeisi, Khadijeh; Croce, Pierpaolo; Tamburro, Gabriella; Tokariev, Anton; Vanhatalo, Sampsa; Zappasodi, Filippo; Comani, Silvia
Contributor: University of Helsinki, HUSLAB
University of Helsinki, HUS Medical Imaging Center
Date: 2021-09
Language: eng
Number of pages: 13
Belongs to series: Brain Topography
ISSN: 0896-0267
URI: http://hdl.handle.net/10138/334475
Abstract: Neonates spend most of their life sleeping. During sleep, their brain experiences fast changes in its functional organization. Microstate analysis permits to capture the rapid dynamical changes occurring in the functional organization of the brain by representing the changing spatio-temporal features of the electroencephalogram (EEG) as a sequence of short-lasting scalp topographies-the microstates. In this study, we modeled the ongoing neonatal EEG into sequences of a limited number of microstates and investigated whether the extracted microstate features are altered in REM and NREM sleep (usually known as active and quiet sleep states-AS and QS-in the newborn) and depend on the EEG frequency band. 19-channel EEG recordings from 60 full-term healthy infants were analyzed using a modified version of the k-means clustering algorithm. The results show that similar to 70% of the variance in the datasets can be described using 7 dominant microstate templates. The mean duration and mean occurrence of the dominant microstates were significantly different in the two sleep states. Microstate syntax analysis demonstrated that the microstate sequences characterizing AS and QS had specific non-casual structures that differed in the two sleep states. Microstate analysis of the neonatal EEG in specific frequency bands showed a clear dependence of the explained variance on frequency. Overall, our findings demonstrate that (1) the spatio-temporal dynamics of the neonatal EEG can be described by non-casual sequences of a limited number of microstate templates; (2) the brain dynamics described by these microstate templates depends on frequency; (3) the features of the microstate sequences can well differentiate the physiological conditions characterizing AS and QS.
Subject: Neonatal EEG
EEG microstate analysis
Sleep states
Neonatal brain dynamics
TEMPORAL CORRELATIONS
EEG
PRETERM
CONNECTIVITY
WAKEFULNESS
NETWORKS
SCHIZOPHRENIA
TOPOGRAPHY
3112 Neurosciences
3124 Neurology and psychiatry
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