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.
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.
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.
ObjectiveTo explore the effect mechanism of rehabilitation therapy post incomplete spinal cord injury (ISCI) in the view of graph theoretic analysis of the whole brain regions by the means of resting state functional magnetic resonance imaging (rs-fMRI).MethodsPatients with ISCI admitted to the Department of Rehabilitation Medicine of Tsinghua University Affiliated Beijing Tsinghua Changgung Hospital from January 2017 to June 2020 were selected and healthy subjects recruited in the same period were also selected. The patients were given comprehensive rehabilitation treatment for 2 weeks, including physical therapy, functional electrical stimulation treadwheel, walking training, etc. Healthy subjects and patients before and after treatment course were examined by rs-fMRI. While patients were assessed using the motor and sensory scores of American Spinal Injury Association (ASIA), muscle tone assessment, pain assessment, Walking Index for spinal cord injury (WISCI) as well as the Spinal Cord Independence Measure (SCIM).ResultsA total of 23 ISCI patients and 22 healthy subjects were included. After 2 weeks of treatment, ASIA lower limb motor function scores (P<0.001), ASIA sensory scores (P=0.005), Visual Analogue Scale (VAS) (P=0.027), WISCI (P=0.005) and SCIM (P<0.001) scores of the patients were significantly improved compared with before treatment. Before treatment, compared with healthy subjects, ISCI patients had lower betweenness centrality (BC) in the brain regions of R38, lower local efficiency (LE) in L21, L22, L39, L41, L42, L44, L48, R22, R39 and lower weighted degree (WD) in L22, L39, L41, L44, L48, R22, R39. After treatment, compared with the healthy subjects, the BC of R5, R6, R7, AR111 and AL112 of ISCI patients increased and in R6 and AR94, the clustering coefficient increased. The LE and WD of L21 and R21 in ISCI patients after treatment were higher than those before treatment.ConclusionsThe functional analysis of the whole brain network based on graph analysis can sensitively reflect the changes of brain network remodeling in patients with ISCI. Spinal cord injury can cause the decline of graph theoretical attributes of the auditory center-related brain regions. After rehabilitation treatment, the sensorimotor cortex, auditory center and extravertebral brain regions information transmitting ability in the whole brain network are improved, suggesting that rehabilitation training may participate in brain function remodeling by activating the sensorimotor center and non-motor center-related brain regions.
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.
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.
The deep learning-based automatic detection of epilepsy electroencephalogram (EEG), which can avoid the artificial influence, has attracted much attention, and its effectiveness mainly depends on the deep neural network model. In this paper, an attention-based multi-scale residual network (AMSRN) was proposed in consideration of the multiscale, spatio-temporal characteristics of epilepsy EEG and the information flow among channels, and it was combined with multiscale principal component analysis (MSPCA) to realize the automatic epilepsy detection. Firstly, MSPCA was used for noise reduction and feature enhancement of original epilepsy EEG. Then, we designed the structure and parameters of AMSRN. Among them, the attention module (AM), multiscale convolutional module (MCM), spatio-temporal feature extraction module (STFEM) and classification module (CM) were applied successively to signal reexpression with attention weighted mechanism as well as extraction, fusion and classification for multiscale and spatio-temporal features. Based on the Children’s Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) public dataset, the AMSRN model achieved good results in sensitivity (98.56%), F1 score (98.35%), accuracy (98.41%) and precision (98.43%). The results show that AMSRN can make good use of brain network information flow caused by seizures to enhance the difference among channels, and effectively capture the multiscale and spatio-temporal features of EEG to improve the performance of epilepsy detection.
White matter lesion (WML) of presumed vascular origin is one of the common imaging manifestations of cerebral small vessel diseases, which is the main reason of cognitive impairment and even vascular dementia in the elderly. However, there is a lack of early and effective diagnostic methods currently. In recent years, studies of diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rs-fMRI) have shown that cognitive impairment in patients with WMLs is associated with disrupted white matter microstructural and brain network connectivity. Therefore, it’s speculated that DTI and rs-fMRI can be effective in early imaging diagnosis of WMLs-related cognitive impairment. This article reviews the role and significance of DTI and rs-fMRI in WMLs-related cognitive impairment.
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.