Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium.
Department of Neuroscience Imaging and Clinical Sciences, G. d'Annunzio University of Chieti-Pescara, Chieti, Italy.
Brain Topogr. 2024 May;37(3):461-474. doi: 10.1007/s10548-023-01008-0. Epub 2023 Oct 12.
Preterm neonates are at risk of long-term neurodevelopmental impairments due to disruption of natural brain development. Electroencephalography (EEG) analysis can provide insights into brain development of preterm neonates. This study aims to explore the use of microstate (MS) analysis to evaluate global brain dynamics changes during maturation in preterm neonates with normal neurodevelopmental outcome.The dataset included 135 EEGs obtained from 48 neonates at varying postmenstrual ages (26.4 to 47.7 weeks), divided into four age groups. For each recording we extracted a 5-minute epoch during quiet sleep (QS) and during non-quiet sleep (NQS), resulting in eight groups (4 age group x 2 sleep states). We compared MS maps and corresponding (map-specific) MS metrics across groups using group-level maps. Additionally, we investigated individual map metrics.Four group-level MS maps accounted for approximately 70% of the global variance and showed non-random syntax. MS topographies and transitions changed significantly when neonates reached 37 weeks. For both sleep states and all MS maps, MS duration decreased and occurrence increased with age. The same relationships were found using individual maps, showing strong correlations (Pearson coefficients up to 0.74) between individual map metrics and post-menstrual age. Moreover, the Hurst exponent of the individual MS sequence decreased with age.The observed changes in MS metrics with age might reflect the development of the preterm brain, which is characterized by formation of neural networks. Therefore, MS analysis is a promising tool for monitoring preterm neonatal brain maturation, while our study can serve as a valuable reference for investigating EEGs of neonates with abnormal neurodevelopmental outcomes.
早产儿由于大脑发育自然过程的中断，存在长期神经发育受损的风险。脑电图（EEG）分析可以提供有关早产儿大脑发育的深入了解。本研究旨在探索使用微状态（MS）分析来评估具有正常神经发育结局的早产儿在成熟过程中的全局脑动力学变化。该数据集包括 48 名早产儿在不同胎龄（26.4 至 47.7 周）时获得的 135 个 EEG，分为四个年龄组。对于每个记录，我们在安静睡眠（QS）和非安静睡眠（NQS）期间提取了 5 分钟的时段，共产生了 8 个组（4 个年龄组 x 2 个睡眠状态）。我们使用组水平图谱比较了组间的 MS 图谱和相应的（图谱特定）MS 指标。此外，我们还研究了个体图谱指标。四个组水平 MS 图谱解释了大约 70%的全局方差，并表现出非随机语法。当早产儿达到 37 周时，MS 地形图和转换发生了显著变化。对于两种睡眠状态和所有 MS 图谱，MS 持续时间随年龄的增加而减少，发生次数随年龄的增加而增加。个体图谱也存在相同的关系，个体图谱指标与胎龄之间存在很强的相关性（Pearson 系数高达 0.74）。此外，个体 MS 序列的赫斯特指数随年龄的增加而降低。随着年龄的增长，MS 指标的变化可能反映了早产儿大脑的发育，这一过程的特征是神经网络的形成。因此，MS 分析是监测早产儿大脑成熟的有前途的工具，而我们的研究可以为研究神经发育异常的新生儿 EEG 提供有价值的参考。
