Research on Differential Brain Networks before and after WM Training under Different Frequency Band Oscillations

Abstract

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.

Aberrant Whole-Brain Resting-State Functional Connectivity Architecture in Obsessive-Compulsive Disorder: An EEG Study

“…The resting state usually refers to an eyes-closed (EC) and/or eyes-open (EO) state in EEG, which is a measure of the background or tonic level of brain activity [15] [55]. In the current study, it was further investigated the alteration of brain functional networks from EC to EO states in OCD patients, which was rarely reported in previous studies with EEG.…”

“…After eliminating the influence of various artifacts, the 30s artifact-free EEG data (15000 sample points) were selected from each participant and divided into five segments. Each segment of the EEGs was decomposed into the conventional EEG frequency bands, including theta (4-7 Hz), alpha (8)(9)(10)(11)(12), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (31-48 Hz) bands for calculating PLVs. To further improve the reliability of the current results, we took the average PLV of the five segments in each frequency band and applied them in the subsequent analysis of brain functional networks.…”

“…Few studies have looked at the topological organization of the whole-brain in OCD patients in different resting states. Furthermore, fMRI with low temporal resolutions could not reveal the sub-second time precision required by the neural mechanism of integrating and coordinating processing between different regions, such as modulating interactions among neurons and regulating information communication [14] [15].…”

Reorganization of Brain Functional Network during Task Switching before and after Mental Fatigue

“…The sparsity method was used to keep the connections whose connection strength was greater than the threshold as valid connections. It could ensure that the evaluation of network features would not be biased by different numbers of connections and possible low-weight false connections [ 33 , 34 ]. In this study, weighted networks, in which the values of the edges in the weighted network are the PLI values, were constructed with a sparsity of 15–30% and an increment of 1% for each step.…”

Effect of θ–γ neural oscillation stimulation in hippocampal CA3 area on spatial cognition ability in rats

“…Compared with the general avoidance response rats, the days to reach the standard, the training number, the correct response time and the error reaction number in simulated stimulus avoidance response rats were significantly reduced, but the correct response rate was significantly increased (all P<0.01); the θ-γ neural oscillations PAC in the hippocampal CA3 region in the simulated stimulus avoidance response rats (3)(4)(5)(38)(39)(40)(41)(42)(44)(45)(46)(47)(48)(5)(6)(7)(44)(45)(46)(47)(48)(54)(55)(56)(57)(58) were significantly higher than that in the general avoidance response rats (all P<0.05). Meanwhile, the protein expressions of NR2B and PSD-95 in hippocampal tissues were significantly increased (both P<0.05) in simulated stimulus avoidance response rats.…”