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.
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.
Epilepsy is a neurological disease with disordered brain network connectivity. It is important to analyze the brain network mechanism of epileptic seizure from the perspective of directed functional connectivity. In this paper, causal brain networks were constructed for different sub-bands of epileptic electroencephalogram (EEG) signals in interictal, preictal and ictal phases by directional transfer function method, and the information transmission pathway and dynamic change process of brain network under different conditions were analyzed. Finally, the dynamic changes of characteristic attributes of brain networks with different rhythms were analyzed. The results show that the topology of brain network changes from stochastic network to rule network during the three stage and the node connections of the whole brain network show a trend of gradual decline. The number of pathway connections between internal nodes of frontal, temporal and occipital regions increase. There are a lot of hub nodes with information outflow in the lesion region. The global efficiency in ictal stage of α, β and γ waves are significantly higher than in the interictal and the preictal stage. The clustering coefficients in preictal stage are higher than in the ictal stage and the clustering coefficients in ictal stage are higher than in the interictal stage. The clustering coefficients of frontal, temporal and parietal lobes are significantly increased. The results of this study indicate that the topological structure and characteristic properties of epileptic causal brain network can reflect the dynamic process of epileptic seizures. In the future, this study has important research value in the localization of epileptic focus and prediction of epileptic seizure.
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.
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.
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.
Aiming at the difference between the brain networks of children with attention deficit hyperactivity disorder (ADHD) and normal children in the task-executing state, this paper conducted a comparative study using the network features of the visual function area. Functional magnetic resonance imaging (fMRI) data of 23 children with ADHD ［age: (8.27 ± 2.77) years］ and 23 normal children ［age: (8.70 ± 2.58) years］ were obtained by the visual capture paradigm when the subjects were performing the guessing task. First, fMRI data were used to build a visual area brain function network. Then, the visual area brain function network characteristic indicators including degree distribution, average shortest path, network density, aggregation coefficient, intermediary, etc. were obtained and compared with the traditional whole brain network. Finally, support vector machines (SVM) and other classifiers in the machine learning algorithm were used to classify the feature indicators to distinguish ADHD children from normal children. In this study, visual brain function network features were used for classification, with a classification accuracy of up to 96%. Compared with the traditional method of constructing a whole brain network, the accuracy was improved by about 10%. The test results show that the use of visual area brain function network analysis can better distinguish ADHD children from normal children. This method has certain help to distinguish the brain network between ADHD children and normal children, and is helpful for the auxiliary diagnosis of ADHD children.
Electroencephalography (EEG) signals are strongly correlated with human emotions. The importance of nodes in the emotional brain network provides an effective means to analyze the emotional brain mechanism. In this paper, a new ranking method of node importance, weighted K-order propagation number method, was used to design and implement a classification algorithm for emotional brain networks. Firstly, based on DEAP emotional EEG data, a cross-sample entropy brain network was constructed, and the importance of nodes in positive and negative emotional brain networks was sorted to obtain the feature matrix under multi-threshold scales. Secondly, feature extraction and support vector machine (SVM) were used to classify emotion. The classification accuracy was 83.6%. The results show that it is effective to use the weighted K-order propagation number method to extract the importance characteristics of brain network nodes for emotion classification, which provides a new means for feature extraction and analysis of complex networks.
The aim of the present study was to investigate the alternations of brain functional networks at resting state in the schizophrenia (SCH) patients using voxel-wise degree centrality (DC) method. The resting-state functional magnetic resonance imaging (rfMRI) data were collected from 41 SCH patients and 41 matched healthy control subjects and then analyzed by voxel-wise DC method. The DC maps between the patient group and the control group were compared using by two sample t test. The correlation analysis was also performed between DC values and clinical symptom and illness duration in SCH group. Results showed that compared with the control group, SCH patients exhibited significantly decreased DC value in primary sensorimotor network, and increased DC value in executive control network. In addition, DC value of the regions with obvious differences between the two groups significantly correlated to Positive and Negative Syndrome Scale (PANSS) scores and illness duration of SCH patients. The study showed the abnormal functional integration in primary sensorimotor network and executive control network in SCH patients.
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.