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 ~ 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.

新生儿一生中大部分时间都在睡觉。在睡眠期间，他们的大脑会经历其功能组织的快速变化。微状态分析允许通过将脑电图 (EEG) 不断变化的时空特征表示为一系列短暂的头皮拓扑图（微状态）来捕捉大脑功能组织中发生的快速动态变化。在这项研究中，我们将正在进行的新生儿脑电图建模为有限数量的微状态序列，并研究提取的微状态特征是否在 REM 和 NREM 睡眠（通常称为新生儿的活跃和安静睡眠状态——AS 和 QS）中发生了改变并取决于脑电图频带。使用改进版的 k-means 聚类算法分析了 60 名足月健康婴儿的 19 通道脑电图记录。结果表明，数据集中约 70% 的方差可以使用 7 个主要微状态模板来描述。在两种睡眠状态下，主要微状态的平均持续时间和平均发生率显着不同。微状态语法分析表明，表征 AS 和 QS 的微状态序列具有特定的非偶然结构，在两种睡眠状态中有所不同。特定频带中新生儿脑电图的微观状态分析表明，解释的方差明显依赖于频率。总体而言，我们的研究结果表明（1）新生儿脑电图的时空动态可以通过有限数量的微状态模板的非偶然序列来描述；(2) 这些微状态模板所描述的大脑动力学取决于频率；