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.
Epilepsy, as one of the most common brain network disorders in clinical practice, is characterized by recurrent, episodic, transient, and stereotyped clinical manifestations, and often presents with various comorbid conditions. Microstate analysis is an emerging EEG analysis technique that serves as a large-scale brain network analysis method based on the clustering of EEG topographic maps. It has been commonly used in brain network research for various neurological and psychiatric disorders. In recent years, EEG microstate analysis has also been gradually applied to the study of epileptic brain networks, potentially providing new insights into the mechanisms of epilepsy, seizure prediction, diagnosis and treatment, prognosis, and the assessment of comorbidities.
Exploring the functional network during the interaction between emotion and cognition is an important way to reveal the underlying neural connections in the brain. Sparse Bayesian network (SBN) has been used to analyze causal characteristics of brain regions and has gradually been applied to the research of brain network. In this study, we got theta band and alpha band from emotion electroencephalogram (EEG) of 22 subjects, constructed effective networks of different arousal, and analyzed measurements of complex network including degree, average clustering coefficient and characteristic path length. We found that: ① compared with EEG signal of low arousal, left middle temporal extensively interacted with other regions in high arousal, while right superior frontal interacted less; ② average clustering coefficient was higher in high arousal and characteristic path length was shorter in low arousal.
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.
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.
As a complex system, the topology of human’s brain network has an important effect on further study of brain’s structural and functional mechanism. Graph theory, a kind of sophisticated analytic strategies, is widely used for analyzing complex brain networks effectively and comparing difference of topological structure alteration in normal development and pathological condition. For the purpose of using this analysis methodology efficiently, it is necessary to develop graph-based visualization software. Thus, we developed VisConnectome, which displays analysis results of the brain network friendly and intuitively. It provides an original graphical user interface (GUI) including the tool window, tool bar and innovative double slider filter, brain region bar, runs in any Windows operating system and doesn’t rely on any platform such as Matlab. When importing the user-defined script file that initializes the brain network, VisConnectome abstracts the brain network to the ball-and-stick model and render it. VisConnectome allows a series of visual operations, such as identifying nodes and connection, modifying properties of nodes and connection such as color and size with the color palette and size double slider, imaging the brain regions, filtering the brain network according to its size property in a specific domain as simplification and blending with the brain surface as a context of the brain network. Through experiment and analysis, we conclude that VisConnectome is an effective visualization software with high speed and quality, which helps researchers to visualize and compare the structural and functional brain networks flexibly.
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.
Currently, about one-third of patients with anti-epilepsy drug or resective surgery continue to have sezure, the mechanism remin unknown. Up to date, the main target for presurgical evaluation is to determene the EZ and SOZ. Since the early nineties of the last century network theory was introduct into neurology, provide new insights into understanding the onset, propagation and termination. Focal seizure can impact the function of whole brain, but the abnormal pattern is differet to generalized seizure. Brain network is a conception of mathematics. According to the epilepsy, network node and hub are related to the treatment. Graphy theory and connectivity are main algorithms. Understanding the mechanism of epilepsy deeply, since study the theory of epilepsy network, can improve the planning of surgery, resection epileptogenesis zone, seizure onset zone and abnormal node of hub simultaneously, increase the effect of resectiv surgery and predict the surgery outcome. Eventually, develop new drugs for correct the abnormal network and increase the effect. Nowadays, there are many algorithms for the brain network. Cooperative study by the clinicans and biophysicists instituted standard and extensively applied algorithms is the precondition of widely used clinically.
Objective To investigate the pathological mechanism of epileptic comorbid sleep disorder by analyzing the changes of cerebral white matter diffusion tensor in patients with sleep disorder with negative magnetic resonance imaging (MRI) epilepsy based on the method of tract-based spatial statistics (TBSS). Methods MRI negative epilepsy patients comorbid sleep disorder who were epileptic patients treated l in China-Japan Union Hospital of Jilin University from January 2020 to December 2022 completed the Epworth sleepiness scale (ESS) and Pittsburgh sleep quality index (PSQI) tests, and those who complained of sleep disorder and PSQI index ≥11 were monitored by nighttime polysomnography (PSG) and those with objective sleep disorder confirmed by PSG were included in the epilepsy comorbid sleep disorder group. Healthy volunteers with matching gender, age, education were included in the health control group. Diffusion tensor image ( DTI) was collected for all subjects by using a 3.0T magnetic resonance scanner. Diffusion parameters were compared between the two groups using TBSS. Results This study included 36 epilepsy patients comorbid sleep disorder and 35 healthy volunteers. epilepsy patients comorbid sleep disorder showed significantly lower fraction anisotropy (FA) (P<0.05) and significantly higher mean diffusivity (MD) (P<0.05) than the health control group . Brain regions with statistical differences in FA reduction included middle peduncle of cerebellum, genu of corpus callosum, body of corpus callosum, splenium of corpus callosum, anterior corona radiata, external capsule and right posterior thalamic radiation.Brain regions with statistical differences in MD degradation included genu of corpus callosum, body of corpus callosum, anterior limb of internal capsule, anterior corona radiata, superior corona radiata, external capsule and right posterior limb of internal capsul. Conclusion Patients with epilepsy comorbidities with sleep disorders have widespread and symmetric white matter damage.The white matter damage is concentrated in the front of the brain.
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.