The dynamic properties of a brain network during working memory based on the algorithm of cross-frequency coupling

Zhang, Wei; Guo, Lei; Liu, Dongzhao; Xu, Guizhi

doi:10.1007/s11571-019-09562-9

Cited by 26 publications

(17 citation statements)

References 41 publications

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“…However, an enormous amount of evidence based on fMRI and EEG investigations have suggested that the biological brain function network has a scale-free property and/or small-world property [28,29]. In our previous work, Zhang et al found that the rat brain network has the small-world property and that correct working memory storage can increase the connection of the network and efficiency of information transmission [30]. Based on the research results on the biological brain network, an ANN with complex network topology has been studied.…”

Section: Introductionmentioning

confidence: 98%

Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise

Guo

Kan

et al. 2020

PLoS ONE

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With the continuous improvement of automation and informatization, the electromagnetic environment has become increasingly complex. Traditional protection methods for electronic systems are facing with serious challenges. Biological nervous system has the self-adaptive advantages under the regulation of the nervous system. It is necessary to explore a new thought on electromagnetic protection by drawing from the self-adaptive advantage of the biological nervous system. In this study, the scale-free spiking neural network (SFSNN) is constructed, in which the Izhikevich neuron model is employed as a node, and the synaptic plasticity model including excitatory and inhibitory synapses is employed as an edge. Under white Gaussian noise, the noise suppression abilities of the SFSNNs with the high average clustering coefficient (ACC) and the SFSNNs with the low ACC are studied comparatively. The noise suppression mechanism of the SFSNN is explored. The experiment results demonstrate that the following. (1) The SFSNN has a certain degree of noise suppression ability, and the SFSNNs with the high ACC have higher noise suppression performance than the SFSNNs with the low ACC. (2) The neural information processing of the SFSNN is the linkage effect of dynamic changes in neuron firing, synaptic weight and topological characteristics. (3) The synaptic plasticity is the intrinsic factor of the noise suppression ability of the SFSNN.

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Section: Introductionmentioning

confidence: 98%

Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise

Guo

Kan

et al. 2020

PLoS ONE

Self Cite

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“…In the rat hippocampus, PAC can be observed in the region between theta and gamma, and the feature is also present in the human hippocampus and cortex ( 14 ). It has been shown that PAC can be used for studies of working memory and also for labeling seizures ( 15 , 16 ). As research on CFC has evolved, it has been used to identify brain states and task states in diseases such as epilepsy, Alzheimer's disease ( 17 ), and Parkinson's disease ( 18 ).…”

Section: Introductionmentioning

confidence: 99%

“…Multi-channel CFC can be correlated to understand how information is transferred between different brain regions. Such studies are currently focused on memory cognition, and it has been observed in networks constructed using CFC that storage in working memory increases the efficiency of network connectivity and information transfer ( 16 ). Moreover, the size of the cross-regional PAC (xPAC) predicted success in memory encoding, which may be a mechanism that represents a coordinated relationship between brain regions ( 23 ).…”

Section: Introductionmentioning

confidence: 99%

Epileptogenic Zone Location of Temporal Lobe Epilepsy by Cross-Frequency Coupling Analysis

Liu

Han

et al. 2021

Front. Neurol.

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Epilepsy is a chronic brain disease with dysfunctional brain networks, and electroencephalography (EEG) is an important tool for epileptogenic zone (EZ) identification, with rich information about frequencies. Different frequency oscillations have different contributions to brain function, and cross-frequency coupling (CFC) has been found to exist within brain regions. Cross-channel and inter-channel analysis should be both focused because they help to analyze how epilepsy networks change and also localize the EZ. In this paper, we analyzed long-term stereo-electroencephalography (SEEG) data from 17 patients with temporal lobe epilepsy. Single-channel and cross-channel CFC features were combined to establish functional brain networks, and the network characteristics under different periods and the localization of EZ were analyzed. It was observed that theta–gamma phase amplitude coupling (PAC) within the electrodes in the seizure region increased during the ictal (p < 0.05). Theta–gamma and delta–gamma PAC of cross-channel were enhanced in the early and mid-late ictal, respectively. It was also found that there was a strong cross-frequency coupling state between channels of EZ in the functional network during the ictal, along with a more regular network than interictal. The accuracy rate of EZ localization was 82.4%. Overall, the combination of single-channel and multi-channel cross-band coupling analysis can help identify seizures and localize EZ for temporal lobe epilepsy. Rhythmic coupling reveals a relationship between the functional network and the seizure status of epilepsy.

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“…Based on the local field potentials, Zhang et al analyzed the dynamic characteristics of brain networks during WM. Experimental results showed that the cross-frequency coupling between theta and gamma increased with learning days, which played an important role in the WM [ 15 ]. That is, low-frequency neural oscillations can support information processing and interaction among a large range of neural clusters [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2021

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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.

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The dynamic properties of a brain network during working memory based on the algorithm of cross-frequency coupling

Cited by 26 publications

References 41 publications

Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise

Noise suppression ability and its mechanism analysis of scale-free spiking neural network under white Gaussian noise

Epileptogenic Zone Location of Temporal Lobe Epilepsy by Cross-Frequency Coupling Analysis

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

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