suggest that widespread practice of WM tasks may lead to a permanent enhancement of resting-state frontoparietal connections. WM training may affect the functional connectivity between brain networks covered by the task. The central role of WM in human cognition, coupled with its limited capabilities, has led to current attempts to improve WM function through training. Based on the retention stage during online WM, the WM behavioral performance can be predicted by spectral entropy, and the fusion features composed of spectral entropy and Lempel-Ziv complexity can effectively classify working memory load. The ability to retain information in WM is the basis for maintaining a good cognitive state. From the perspective of information processing, memory consists of three stages: encoding, retention, and extraction. Working memory (WM) is a system that maintains information online in order to complete a task or goal and carries out the operation and processing of the retained information. 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. 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 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. 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). 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. At the same time, it was found that better cognitive performance of individuals indicated stronger small-world characteristics of resting-state WM networks.
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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. Previous studies have shown that different frequency band oscillations are associated with cognitive processing such as working memory (WM).