ObjectiveTo analyze the effect of mitochondrial ultrastructural changes caused by morphine toxicity on abnormal discharge of cat cerebral cortex, and to explore the possible mechanism of brain function damage caused by morphine dependence.MethodsTwelve domestic cats were divided into control group (3 cats) and morphine exposed group (9 cats) according to the method of random number table. After the model was successfully established by the method of dose increasing, the changes of mitochondrial ultrastructure of cortical neurons were observed under the electron microscope.ResultsElectroencephalogram (EEG) monitoring in morphine exposed group showed that the cortical EEG was widely abnormal, physiological waves were reduced, and abnormal discharges were frequent. And the electron microscopy showed that the number, morphology, internal membrane structure and the inclusion body in the matrix of neurons changed in various aspects. The EEG and electron microscopy of the control group were normal.ConclusionMorphine can damage neurons in the cerebral cortex and lead to abnormal discharge, which is closely related to the ultrastructural changes of neuron mitochondria. The toxicity of morphine mitochondria can be the initial mechanism of energy metabolism dysfunction of brain cells and eventually lead to the disorder of brain electrophysiological function.
Attention level evaluation refers to the evaluation of people's attention level through observation or experimental testing, and its research results have great application value in education and teaching, intelligent driving, medical health and other fields. With its objective reliability and security, electroencephalogram signals have become one of the most important technical means to analyze and express attention level. At present, there is little review literature that comprehensively summarize the application of electroencephalogram signals in the field of attention evaluation. To this end, this paper first summarizes the research progress on attention evaluation; then the important methods for electroencephalogram attention evaluation are analyzed, including data preprocessing, feature extraction and selection, attention evaluation methods, etc.; finally, the shortcomings of the current development in the field of electroencephalogram attention evaluation are discussed, and the future development trend is prospected, to provide research references for researchers in related fields.
Motor imagery is often used in the fields of sports training and neurorehabilitation for its advantages of being highly targeted, easy to learn, and requiring no special equipment, and has become a major research paradigm in cognitive neuroscience. Transcranial direct current stimulation (tDCS), an emerging neuromodulation technique, modulates cortical excitability, which in turn affects functions such as locomotion. However, it is unclear whether tDCS has a positive effect on motor imagery task states. In this paper, 16 young healthy subjects were included, and the electroencephalogram (EEG) signals and near-infrared spectrum (NIRS) signals of the subjects were collected when they were performing motor imagery tasks before and after receiving tDCS, and the changes in multiscale sample entropy (MSE) and haemoglobin concentration were calculated and analyzed during the different tasks. The results found that MSE of task-related brain regions increased, oxygenated haemoglobin concentration increased, and total haemoglobin concentration rose after tDCS stimulation, indicating that tDCS increased the activation of task-related brain regions and had a positive effect on motor imagery. This study may provide some reference value for the clinical study of tDCS combined with motor imagery.
As an emerging non-invasive brain stimulation technique, transcranial direct current stimulation (tDCS) has received increasing attention in the field of stroke disease rehabilitation. However, its efficacy needs to be further studied. The tDCS has three stimulation modes: bipolar-stimulation mode, anode-stimulation mode and cathode-stimulation mode. Nineteen stroke patients were included in this research (10 with left-hemisphere lesion and 9 with right). Resting electroencephalogram (EEG) signals were collected from subjects before and after bipolar-stimulation, anodal-stimulation, cathodal-stimulation, and pseudo-stimulation, with pseudo-stimulation serving as the control group. The changes of multi-scale intrinsic fuzzy entropy (MIFE) of EEG signals before and after stimulation were compared. The results revealed that MIFE was significantly greater in the frontal and central regions after bipolar-stimulation (P < 0.05), in the left central region after anodal-stimulation (P < 0.05), and in the frontal and right central regions after cathodal-stimulation (P < 0.05) in patients with left-hemisphere lesions. MIFE was significantly greater in the frontal, central and parieto-occipital joint regions after bipolar-stimulation (P < 0.05), in the left frontal and right central regions after anodal- stimulation (P < 0.05), and in the central and right occipital regions after cathodal-stimulation (P < 0.05) in patients with right-hemisphere lesions. However, the difference before and after pseudo-stimulation was not statistically significant (P > 0.05). The results of this paper showed that the bipolar stimulation pattern affected the largest range of brain areas, and it might provide a reference for the clinical study of rehabilitation after stroke.
There are two modes to display panoramic movies in virtual reality (VR) environment: non-stereoscopic mode (2D) and stereoscopic mode (3D). It has not been fully studied whether there are differences in the activation effect between these two continuous display modes on emotional arousal and what characteristics of the related neural activity are. In this paper, we designed a cognitive psychology experiment in order to compare the effects of VR-2D and VR-3D on emotional arousal by analyzing synchronously collected scalp electroencephalogram signals. We used support vector machine (SVM) to verify the neurophysiological differences between the two modes in VR environment. The results showed that compared with VR-2D films, VR-3D films evoked significantly higher electroencephalogram (EEG) power (mainly reflected in α and β activities). The significantly improved β wave power in VR-3D mode showed that 3D vision brought more intense cortical activity, which might lead to higher arousal. At the same time, the more intense α activity in the occipital region of the brain also suggested that VR-3D films might cause higher visual fatigue. By the means of neurocinematics, this paper demonstrates that EEG activity can well reflect the effects of different vision modes on the characteristics of the viewers’ neural activities. The current study provides theoretical support not only for the future exploration of the image language under the VR perspective, but for future VR film shooting methods and human emotion research.
To accurately capture and effectively integrate the spatiotemporal features of electroencephalogram (EEG) signals for the purpose of improving the accuracy of EEG-based emotion recognition, this paper proposes a new method combining independent component analysis-recurrence plot with an improved EfficientNet version 2 (EfficientNetV2). First, independent component analysis is used to extract independent components containing spatial information from key channels of the EEG signals. These components are then converted into two-dimensional images using recurrence plot to better extract emotional features from the temporal information. Finally, the two-dimensional images are input into an improved EfficientNetV2, which incorporates a global attention mechanism and a triplet attention mechanism, and the emotion classification is output by the fully connected layer. To validate the effectiveness of the proposed method, this study conducts comparative experiments, channel selection experiments and ablation experiments based on the Shanghai Jiao Tong University Emotion Electroencephalogram Dataset (SEED). The results demonstrate that the average recognition accuracy of our method is 96.77%, which is significantly superior to existing methods, offering a novel perspective for research on EEG-based emotion recognition.
Gelastic seizure (GS) is a type of epilepsy characterized primarily by inappropriate bursts of laughter, with or without other epileptic events. Based on the timing of symptoms, the presence of emotional changes, and disturbances of consciousness, GS is classified into simple and complex types. The generation of laughter involves two major neural pathways: the emotional pathway and the volitional pathway. The neural network involved in GS includes structures such as the frontal lobe, insula, cingulate gyrus, temporal lobe, and brainstem.The most common cause of GS is a hypothalamic hamartoma, and stereotactic electroencephalography can record discharges from the lesion itself. Surgical removal of the hypothalamic hamartoma can result in immediate cessation of GS in the majority of patients, while some may experience partial improvement with persistent epileptic-like discharges detectable on scalp electroencephalography (EEG). Early surgical intervention may improve prognosis.In cases of non-hypothalamic origin of GS with no apparent imaging abnormalities, focal discharges are often observed on EEG and these cases respond well to antiepileptic drugs. Conversely, patients with structural abnormalities suggested by imaging studies tend to have multifocal discharges and a poorer response to medication. In a small subset of medically refractory non-hypothalamic GS, surgical intervention can effectively control symptoms.This article provides a comprehensive review of the etiology, neural networks involved, EEG characteristics, and treatment options for GS, with the goal of improving understanding of this relatively rare type of epileptic seizure.
The electroencephalogram (EEG) signal is a general reflection of the neurophysiological activity of the brain, which has the advantages of being safe, efficient, real-time and dynamic. With the development and advancement of machine learning research, automatic diagnosis of Alzheimer’s diseases based on deep learning is becoming a research hotspot. Started from feedforward neural networks, this paper compared and analysed the structural properties of neural network models such as recurrent neural networks, convolutional neural networks and deep belief networks and their performance in the diagnosis of Alzheimer’s disease. It also discussed the possible challenges and research trends of this research in the future, expecting to provide a valuable reference for the clinical application of neural networks in the EEG diagnosis of Alzheimer’s disease.
The causes of mental disorders are complex, and early recognition and early intervention are recognized as effective way to avoid irreversible brain damage over time. The existing computer-aided recognition methods mostly focus on multimodal data fusion, ignoring the asynchronous acquisition problem of multimodal data. For this reason, this paper proposes a framework of mental disorder recognition based on visibility graph (VG) to solve the problem of asynchronous data acquisition. First, time series electroencephalograms (EEG) data are mapped to spatial visibility graph. Then, an improved auto regressive model is used to accurately calculate the temporal EEG data features, and reasonably select the spatial metric features by analyzing the spatiotemporal mapping relationship. Finally, on the basis of spatiotemporal information complementarity, different contribution coefficients are assigned to each spatiotemporal feature and to explore the maximum potential of feature so as to make decisions. The results of controlled experiments show that the method in this paper can effectively improve the recognition accuracy of mental disorders. Taking Alzheimer's disease and depression as examples, the highest recognition rates are 93.73% and 90.35%, respectively. In summary, the results of this paper provide an effective computer-aided tool for rapid clinical diagnosis of mental disorders.
Purpose To analyze the clinical characteristicsand prognostic factors of Status epilepticus (SE) in children. Methods The clinical data of 33 children with SE treated in Jinan Central Hospital Affiliated of Shandong University from January 2014 to June 2021 were collected, and their clinical characteristics were analyzed. Then, according to Glasgow prognosis scale, the children were divided into good prognosis group (n=20) and poor prognosis group (n=13). The age of first attack, duration of attack, type of attack and SE classification, EEG, cranial imaging and etiology were used to analyze the influencing factors of SE prognosis. Results 75.7% were 0 ~ 6 years old in the age of first attack, and 29 cases of convulsive status epilepticus accounted for 87.9% in the classification of seizure types. There were significant differences in age of first attack, duration of attack, EEG, history of mental retardation and etiology between the two groups (P<0.05); Logistic regression analysis showed that the age of first attack, duration of attack, history of mental retardation and EEG were independent factors affecting the prognosis. Conclusion Low age, especially ≤ 6 years old, is the high incidence of SE in children at first attack. Most children are symptomatic and have obvious incentives. Convulsive SE is the main type of SE in children. The age of first onset, duration of epilepsy, history of mental retardation, and EEG can affect the prognosis of SE.