Bio-information College, Chongqing University of Posts and Telecommunications, Chongqing 400065, China.
Neural Plast. 2021 Mar 20;2021:6628021. doi: 10.1155/2021/6628021. eCollection 2021.
Previous studies have shown that different frequency band oscillations are associated with cognitive processing such as working memory (WM). Electroencephalogram (EEG) coherence and graph theory can be used to measure functional connections between different brain regions and information interaction between different clusters of neurons. At the same time, it was found that better cognitive performance of individuals indicated stronger small-world characteristics of resting-state WM networks. However, little is known about the neural synchronization of the retention stage during ongoing WM tasks (i.e., online WM) by training on the whole-brain network level. Therefore, combining EEG coherence and graph theory analysis, the present study examined the topological changes of WM networks before and after training based on the whole brain and constructed differential networks with different frequency band oscillations (i.e., theta, alpha, and beta). The results showed that after WM training, the subjects' WM networks had higher clustering coefficients and shorter optimal path lengths than before training during the retention period. Moreover, the increased synchronization of the frontal theta oscillations seemed to reflect the improved executive ability of WM and the more mature resource deployment; the enhanced alpha oscillatory synchronization in the frontoparietal and fronto-occipital regions may reflect the enhanced ability to suppress irrelevant information during the delay and pay attention to memory guidance; the enhanced beta oscillatory synchronization in the temporoparietal and frontoparietal regions may indicate active memory maintenance and preparation for memory-guided attention. The findings may add new evidence to understand the neural mechanisms of WM on the changes of network topological attributes in the task-related mode.
先前的研究表明，不同频段的振荡与认知处理有关，如工作记忆 (WM)。脑电图 (EEG) 相干性和图论可用于测量不同脑区之间的功能连接以及不同神经元簇之间的信息交互。同时，研究发现个体更好的认知表现表明静息状态 WM 网络具有更强的小世界特征。然而，在进行中的 WM 任务（即在线 WM）保留阶段，个体的全脑网络水平上的神经同步性训练知之甚少。因此，本研究结合 EEG 相干性和图论分析，基于全脑，检查了训练前后 WM 网络的拓扑变化，并构建了具有不同频段振荡（即 theta、alpha 和 beta）的差分网络。结果表明，在 WM 训练后，在保留期内，与训练前相比，受试者的 WM 网络在保留期内具有更高的聚类系数和更短的最优路径长度。此外，额叶 theta 振荡的同步性增加似乎反映了 WM 执行能力的提高和资源配置的更加成熟；额顶和额枕区 alpha 振荡同步性的增强可能反映了在延迟期间抑制无关信息和关注记忆引导的能力增强；颞顶和额顶区 beta 振荡同步性的增强可能表明主动进行记忆维持和为记忆引导注意力做准备。这些发现可能为理解 WM 的神经机制提供新的证据，即任务相关模式下网络拓扑属性的变化。
