Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Australia.
Department of Psychiatry, Central Clinical School, Monash University, Melbourne, Australia.
Hum Brain Mapp. 2023 Dec 15;44(18):6484-6498. doi: 10.1002/hbm.26525. Epub 2023 Oct 24.
Electroencephalographic (EEG) microstates can provide a unique window into the temporal dynamics of large-scale brain networks across brief (millisecond) timescales. Here, we analysed fundamental temporal features of microstates extracted from the broadband EEG signal in a large (N = 139) cohort of children spanning early-to-middle childhood (4-12 years of age). Linear regression models were used to examine if participants' age and biological sex could predict the temporal parameters GEV, duration, coverage, and occurrence, for five microstate classes (A-E) across both eyes-closed and eyes-open resting-state recordings. We further explored associations between these microstate parameters and posterior alpha power after removal of the 1/f-like aperiodic signal. The microstates obtained from our neurodevelopmental EEG recordings broadly replicated the four canonical microstate classes (A to D) frequently reported in adults, with the addition of the more recently established microstate class E. Biological sex served as a significant predictor in the regression models for four of the five microstate classes (A, C, D, and E). In addition, duration and occurrence for microstate E were both found to be positively associated with age for the eyes-open recordings, while the temporal parameters of microstates C and E both exhibited associations with alpha band spectral power. Together, these findings highlight the influence of age and sex on large-scale functional brain networks during early-to-middle childhood, extending understanding of neural dynamics across this important period for brain development.
脑电微状态可以提供一个独特的窗口，了解大脑网络在短暂（毫秒）时间尺度上的时间动态。在这里，我们分析了从宽带 EEG 信号中提取的微状态的基本时间特征，该信号来自跨越早期到中期儿童期（4-12 岁）的大型（N=139）儿童队列。线性回归模型用于研究参与者的年龄和生物性别是否可以预测五个微状态类（A-E）的时间参数 GEV、持续时间、覆盖度和出现率，这些参数适用于闭眼和睁眼静息状态记录。我们进一步探索了这些微状态参数与去除 1/f 样非周期性信号后后 alpha 功率之间的关联。从我们的神经发育 EEG 记录中获得的微状态广泛复制了在成年人中经常报告的四个典型微状态类（A 到 D），同时还增加了最近建立的微状态类 E。生物性别是五个微状态类（A、C、D 和 E）中的四个回归模型中的一个重要预测因素。此外，在睁眼记录中，微状态 E 的持续时间和出现率都与年龄呈正相关，而微状态 C 和 E 的时间参数都与 alpha 频带光谱功率有关。总之，这些发现强调了年龄和性别的影响，这些影响存在于儿童早期到中期的大脑大尺度功能网络中，扩展了对大脑发育这一重要时期神经动态的理解。
