Mental fatigue is the subjective state of people after excessive consumption of information resources. Its impact on cognitive activities is mainly manifested as decreased alertness, poor memory and inattention, which is highly related to the performance after impaired working memory. In this paper, the partial directional coherence method was used to calculate the coherence coefficient of scalp electroencephalogram (EEG) of each electrode. The analysis of brain network and its attribute parameters was used to explore the changes of information resource allocation of working memory under mental fatigue. Mental fatigue was quickly induced by the experimental paradigm of adaptive N-back working memory. Twenty-five healthy college students were randomly recruited as subjects, including 14 males and 11 females, aged from 20 to 27 years old, all right-handed. The behavioral data and resting scalp EEG data were collected simultaneously. The results showed that the main information transmission pathway of the brain changed under mental fatigue, mainly in the frontal lobe and parietal lobe. The significant changes in brain network parameters indicated that the information transmission path of the brain decreased and the efficiency of information transmission decreased significantly. In the causal flow of each electrode and the information flow of each brain region, the inflow of information resources in the frontal lobe decreased under mental fatigue. Although the parietal lobe region and occipital lobe region became the main functional connection areas in the fatigue state, the inflow of information resources in these two regions was still reduced as a whole. These results indicated that mental fatigue affected the information resources allocation of working memory, especially in the frontal and parietal regions which were closely related to working memory.
Selective attention promotes the perception of brain to outside world and coordinates the allocation of limited brain resources. It is a cognitive process which relies on the neural activities of attention-related brain network. As one of the important forms of brain activities, neural oscillations are closely related to selective attention. In recent years, the relationship between selective attention and neural oscillations has become a hot issue. The new method that using external rhythmic stimuli to influence neural oscillations, i.e., neural entrainment, provides a promising approach to investigate the relationship between selective attention and neural oscillations. Moreover, it provides a new method to diagnose and even to treat the attention dysfunction. This paper reviewed the research status on the relationship between selective attention and neural oscillations, and focused on the application prospects of neural entrainment in revealing this relationship and diagnosing, even treating the attention dysfunction.
Repetitive transcranial magnetic stimulation (rTMS) can influence the stimulated brain regions and other distal brain regions connecting to them. The purpose of the study is to investigate the effects of low-frequency rTMS over primary motor cortex on brain by analyzing the brain functional connectivity and coordination between brain regions. 10 healthy subjects were recruited. 1 Hz rTMS was used to stimulate primary motor cortex for 20 min. 1 min resting state electroencephalography (EEG) was collected before and after the stimulation respectively. By performing phase synchronization analysis between the EEG electrodes, the brain functional network and its properties were calculated. Signed-rank test was used for statistical analysis. The result demonstrated that the global phase synchronization in alpha frequency band was decreased significantly after low-frequency rTMS (P<0.05). The phase synchronization was down-regulated between motor cortex and ipsilateral frontal/parietal cortex, and also between contralateral parietal cortex and bilateral frontal cortex. The mean degree and global efficiency of brain functional networks in alpha frequency band were significantly decreased (P<0.05), and the mean shortest path length were significantly increased (P<0.05), which suggested the information transmission of the brain networks and its efficiency was reduced after low-frequency rTMS. This study verified the inhibition function of the low-frequency rTMS to brain activities, and demonstrated that low-frequency rTMS stimulation could affect both stimulating brain regions and distal brain regions connected to them. The findings in this study could be of guidance to clinical application of low-frequency rTMS.
Somatosensory vibration can stimulate somatosensory area of human body, and this stimulation is tranferred to somatosensory nerves, and influences the somatic cortex, which is on post-central gyrus and paracentral lobule posterior of cerebral cortex, so that it alters the functional status of brain. The aim of the present study was to investigate the neural mechanism of brain state induced by somatosensory vibration. Twelve subjects were involved in the 20 Hz vibration stimulation test. Linear and nonlinear methods, such as relative change of relative power (RRP), Lempel-Ziv complexity (LZC) and brain network based on cross mutual information (CMI), were applied to discuss the change of brain under somatosensory vibration stimulation. The experimental results showed the frequency following response (FFR) by RRP of spontaneous electroencephalogram (EEG) in 20 Hz vibration, and no obvious change by LZC. The information transmission among various cortical areas enhanced under 20 Hz vibration stimulation. Therefore, 20 Hz somatosensory vibration may be able to adjust the functional status of brain.
Background music has been increasingly affecting people’s lives. The research on the influence of background music on working memory has become a hot topic in brain science. In this paper, an improved electroencephalography (EEG) experiment based on n-back paradigm was designed. Fifteen university students without musical training were randomly selected to participate in the experiment, and their behavioral data and the EEG data were collected synchronously in order to explore the influence of different types of background music on spatial positioning cognition working memory. The exact low-resolution brain tomography algorithm (eLORETA) was applied to localize the EEG sources and the cross-correlation method was used to construct the cortical brain function networks based on the EEG source signals. Then the characteristics of the networks under different conditions were analyzed and compared to study the effects of background music on people’s working memory. The results showed that the difference of peak periods after stimulated by different types of background music were mainly distributed in the signals of occipital lobe and temporal lobe (P < 0.05). The analysis results showed that the brain connectivity under the condition with background music were stronger than those under the condition without music. The connectivities in the right occipital and temporal lobes under the condition of rock music were significantly higher than those under the condition of classical music. The node degrees, the betweenness centrality and the clustering coefficients under the condition without music were lower than those under the condition with background music. The node degrees and clustering coefficients under the condition of classical music were lower than those under the condition of rock music. It indicates that music stimulation increases the brain activity and has an impact on the working memory, and the effect of rock music is more remarkable than that of classical music. The behavioral data showed that the response accuracy in the state of no music, classical music and rock music were 86.09% ± 0.090%, 80.96% ± 0.960% and 79.36% ± 0.360%, respectively. We conclude that background music has a negative impact on the working memory, for it takes up the cognitive resources and reduces the cognitive ability of spatial location.
The directed functional connectivity in cerebral cortical is the key to understanding the pattern of the behavioral tissue. This process was studied to explore the directed functional network of rifle shooters at cerebral cortical rhythms from electroencephalogram (EEG) data, aiming to provide neurosciences basis for the future development of accelerating rifle skill learning method. The generalized orthogonalized partial directed coherence (gOPDC) algorithm was used to calculate the effective directed functional connectivity of the experts and novices in the pre-shot period. The results showed that the frontal, frontal-central, central, parietal and occipital regions were activated. Moreover, the more directed functional connections numbers in right hemispheres were observed compared to the left hemispheres. Furthermore, as compared to experts, novices had more activated regions, the stronger strength of connections and the lower value of the global efficiency during the pre-shot period. Those indirectly supported the conclusion that the novices needed to recruit more brain resources to accomplish tasks, which was consistent with " neural efficiency” hypothesis of the functional cerebral cortical in experts.
The aim of this study is to explore the effects of continuous theta-burst transcranial magnetic stimulation (cTBS) on functional brain network in emotion processing. Before and after the intervention of cTBS over left dorsolateral prefrontal cortex (DLPFC) of ten participants who were asked to perform the emotion gender recognition task, we recorded their scalp electroencephalograms (EEG). Then we used the phase synchronization of EEG to measure the connectivity between two nodes. We then calculated the network efficiency to describe the efficiency of information transmission in brain regions. Our research showed that after the intervention of cTBS and the stimulation of the emotion face picture, there was an obvious enhancement in the event-related spectral perturbation after stimuli onset in beta band in 100–300 ms. Under the stimulation of different emotion picture, the values of global phase synchronization for negative and neutral stimuli were enhanced compared to positive ones. And the increased small-worldness was found in emotional processing. In summary, based on the effect of activity change in the left DLPFC on emotion processing brain network, the emotional processing mechanism of brain networks were preliminary explored and it provided the reference for the research of emotion processing brain network in the future.
Objective To investigate the differences in the topology of functional brain networks between populations with good spatial navigation ability and those with poor spatial navigation ability. Methods From September 2020 to September 2021, 100 college students from PLA Army Border and Coastal Defense Academy were selected to test the spatial navigation ability. The 25 students with the highest spatial navigation ability were selected as the GN group, and the 25 with the lowest spatial navigation ability were selected as the PN group, and their resting-state functional MRI and 3D T1-weighted structural image data of the brain were collected. Graph theory analysis was applied to study the topology of the brain network, including global and local topological properties. Results The variations in the clustering coefficient, characteristic path length, and local efficiency between the GN and PN groups were not statistically significant within the threshold range (P>0.05). The brain functional connectivity networks of the GN and PN groups met the standardized clustering coefficient (γ)>1, the standardized characteristic path length (λ)≈1, and the small-world property (σ)>1, being consistent with small-world network property. The areas under curve (AUCs) for global efficiency (0.22±0.01 vs. 0.21±0.01), γ value (0.97±0.18 vs. 0.81±0.18) and σ value (0.75±0.13 vs. 0.64±0.13) of the GN group were higher than those of the PN group, and the differences were statistically significant (P<0.05); the between-group difference in AUC for λ value was not statistically significant (P>0.05). The results of the nodal level analysis showed that the AUCs for nodal clustering coefficients in the left superior frontal gyrus of orbital region (0.29±0.05 vs. 0.23±0.07), the right rectus gyrus (0.29±0.05 vs. 0.23±0.09), the middle left cingulate gyrus and its lateral surround (0.22±0.02 vs. 0.25±0.02), the left inferior occipital gyrus (0.32±0.05 vs. 0.35±0.05), the right cerebellar area 3 (0.24±0.04 vs. 0.26±0.03), and the right cerebellar area 9 (0.22±0.09 vs. 0.13±0.13) were statistically different between the two groups (P<0.05). The differences in AUCs for degree centrality and nodal efficiency between the two groups were not statistically significant (P>0.05). Conclusions Compared with people with good spatial navigation ability, the topological properties of the brains of the ones with poor spatial navigation ability still conformed to the small-world network properties, but the connectivity between brain regions reduces compared with the good spatial navigation ability group, with a tendency to convert to random networks and a reduced or increased nodal clustering coefficient in some brain regions. Differences in functional brain network connectivity exist among people with different spatial navigation abilities.
Cognitive impairment is one of the three primary symptoms of schizophrenic patients and shows important value in early detection and warning for high-risk individuals. To study the specifics of electroencephalogram (EEG) in patients with schizophrenia under the cognitive load, we collected EEG signals from 17 schizophrenic patients and 19 healthy controls, extracted signals of each band based on wavelet transform, calculated the characteristics of nonlinear dynamic and functional brain networks, and automatically classified the two groups of people by using a machine learning algorithm. Experimental results indicated that the correlation dimension and sample entropy showed significant differences in α, β, θ, and γ rhythm of the Fp1 and Fp2 electrodes between groups under the cognitive load. These results implied that the functional disruptions in the frontal lobe might be the important factors of cognitive impairments in schizophrenic patients. Further results of the automatic classification analysis indicated that the combination of nonlinear dynamics and functional brain network properties as the input characteristics of the classifier showed the best performance, with the accuracy of 76.77%, sensitivity of 72.09%, and specificity of 80.36%. The results of this study demonstrated that the combination of nonlinear dynamics and function brain network properties may be potential biomarkers for early screening and auxiliary diagnosis of schizophrenia.
With the wide application of virtual reality technology and the rapid popularization of virtual reality devices, the problem of brain fatigue caused by prolonged use has attracted wide attention. Sixteen healthy subjects were selected in this study. And electroencephalogram (EEG) signals were acquired synchronously while the subjects watch videos in similar types presented by traditional displayer and virtual reality separately. Two questionnaires were conducted by all subjects to evaluate the state of fatigue before and after the experiment. The mutual correlation method was selected to construct the mutual correlation brain network of EEG signals before and after watching videos in two modes. We also calculated the mutual correlation coefficient matrix and the mutual correlation binary matrix and compared the average of degree, clustering coefficient, path length, global efficiency and small world attribute during two experiments. The results showed that the subjects were easier to get fatigue by watching virtual reality video than watching video presented by traditional displayer in a certain period of time. By comparing the characteristic parameters of brain network before and after watching videos, it was found that the average degree value, the average clustering coefficient, the average global efficiency and the small world attribute decreases while the average path length value increased significantly. In addition, compared to traditional plane video, the characteristic parameters of brain network changed more greatly after watching the virtual reality video with a significant difference (P < 0.05). This study can provide theoretical basis and experimental reference for analyzing and evaluating brain fatigue induced by virtual reality visual experience.