Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 618-0288, Japan; Drug Discovery & Disease Research Laboratory, Shionogi & Co., Ltd., 3-1-1, Futaba-Cho, Toyonaka-shi, Osaka 561-0825, Japan.
Cognitive Mechanisms Laboratories, Advanced Telecommunications Research Institute International, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto 618-0288, Japan.
Neuroimage. 2022 Nov;263:119669. doi: 10.1016/j.neuroimage.2022.119669. Epub 2022 Oct 4.
In recent years, EEG microstate analysis has attracted much attention as a tool for characterizing the spatial and temporal dynamics of large-scale electrophysiological activities in the human brain. Canonical 4 states (classes A, B, C, and D) have been widely reported, and they have been pointed out for their relationships with cognitive functions and several psychiatric disorders such as schizophrenia, in particular, through their static parameters such as average duration, occurrence, coverage, and transition probability. However, the relationships between event-related microstate changes and their related cognitive functions, as is often analyzed in event-related potentials under time-locked frameworks, is still not well understood. Furthermore, not enough attention has been paid to the relationship between microstate dynamics and static characteristics. To clarify the relationships between the static microstate parameters and dynamic microstate changes, and between the dynamics and working memory (WM) function, we first examined the temporal profiles of the microstates during the N-back task. We found significant event-related microstate dynamics that differed predominantly with WM loads, which were not clearly observed in the static parameters. Furthermore, in the 2-back condition, patterns of state transitions from class A to C in the high- and low-performance groups showed prominent differences at 50-300 ms after stimulus onset. We also confirmed that the transition patterns of the specific time periods were able to predict the performance level (low or high) in the 2-back condition at a significant level, where a specific transition between microstates, namely from class A to C with specific polarity, contributed to the prediction robustly. Taken together, our findings indicate that event-related microstate dynamics at 50-300 ms after onset may be essential for WM function. This suggests that event-related microstate dynamics can reflect more highly-refined brain functions.
近年来，脑电图微状态分析作为一种描述人类大脑中大规模电生理活动的时空动力学的工具，引起了广泛关注。已广泛报道了典型的 4 种状态（A、B、C 和 D 类），并且已经指出它们与认知功能以及几种精神疾病（尤其是精神分裂症）有关，特别是通过它们的静态参数，如平均持续时间、出现率、覆盖率和转移概率。然而，事件相关微状态变化与其相关认知功能之间的关系，就像在时间锁定框架下的事件相关电位中经常分析的那样，仍然不太清楚。此外，对微状态动力学与其静态特征之间的关系还没有给予足够的重视。为了阐明静态微状态参数与动态微状态变化之间的关系，以及动态微状态与工作记忆（WM）功能之间的关系，我们首先研究了在 N-back 任务期间微状态的时间分布。我们发现了与 WM 负荷差异主要相关的显著事件相关微状态动力学，这些动力学在静态参数中没有被清楚地观察到。此外，在 2-back 条件下，高、低表现组中从 A 类到 C 类的状态转移模式在刺激后 50-300ms 表现出明显的差异。我们还证实，特定时间段的转移模式能够以显著的水平预测 2-back 条件下的表现水平（低或高），其中微状态之间的特定转移，即具有特定极性的从 A 类到 C 类的转移，对预测起到了稳健的作用。总之，我们的研究结果表明，刺激后 50-300ms 的事件相关微状态动力学可能是 WM 功能的关键。这表明事件相关微状态动力学可以反映更为精细的大脑功能。
