OBJECTIVE: To approach the evaluation of relative α variant score monitored by bedside continuous electroencephalography and optic nerve sheath diameter (ONSD) evaluated by ultrasound in patients with ischemic hypoxic encephalopathy, and to observe the effect of neurofeedback training on brain function. METHODS: A prospective observational study was conducted. The patients admitted to the emergency and intensive care department of Shanghai Pudong New Area People's Hospital from January 2021 to December 2023, who meet the diagnostic criteria of ischemic hypoxic encephalopathy with the Glasgow coma score (GCS) ≤ 8 at admission receiving neurofeedback training were enrolled as the study object (observation group), and the patients without neurofeedback training and GCS score ≤ 8 at admission were enrolled as the controls (control group). Both groups received intravenous neurotrophic therapy combining ganglioside and cerebrolysin for 10 days as one course of treatment. On this basis, the observation group additionally received continuous neurofeedback training including visual feedback, auditory feedback, meditation and relaxation for 14 days. Bedside continuous electroencephalography was used for monitoring relative α variation score, and ultrasound was used to determine ONSD. The average power and slow wave power [expressed as delta-theta ratio (DTR)] of five channels in electroencephalography before and 14 days after neurofeedback training were examined. The differences in peripheral blood neutrophil/lymphocyte ratio (NLR), Hamilton depression scale (HAMD) score, National Institutes of Health stroke scale (NIHSS) score, plasma levels of 5-hydroxytryptamine (5-HT) and brain-derived neurotrophic factor (BDNF). RESULTS: A total of 60 patients were enrolled in the observation group and 50 patients in the control group finally. There was no significant difference in gender, age or course of disease between the two groups. The ONSD and relative α variant score in the observation group were significantly higher than those in the control group [ONDS (mm): 5.59±0.42 vs. 3.23±0.34, relative α variant score: 2.28±0.39 vs. 0.83±0.28, both P < 0.01]. After neurofeedback training for 14 days, the mean power and DTR in five channels of electroencephalography in the observation group were significantly lower than those before treatment [mean power (μV²/Hz): 95.35±3.61 vs. 102.58±4.23 in frontal pole 1 (Fp1), 38.56±4.73 vs. 46.13±2.36 in frontal 3 (F3), 34.33±5.87 vs. 51.71±4.65 in central 3 (C3), 58.37±4.45 vs. 62.95±3.22 in F7, 45.23±2.41 vs. 54.14±2.45 in temporal 3 (T3); DTR (μV²/Hz): 75.21±11.34 vs. 84.12±11.35 in ground electrode (GND), 72.31±21.67 vs. 88.23±10.25 in reference electrode (REF), 81.34±8.57 vs. 92.41±8.56 in F4, 71.25±5.42 vs. 87.23±5.64 in parietal 3 (P3), 70.12±5.88 vs. 85.67±6.12 in P4; all P < 0.05]. However, there was no significant difference in the mean power of five channels before and after treatment in the control group. There was no significant difference in the HAMD score or NIHSS score before treatment between the two groups. The above scores at 14 days after treatment were significantly lower than before, and the decrease was more significant in the observation group (HAMD score: 4.59±1.06 vs. 10.69±0.97, NIHSS score: 6.81±0.66 vs. 8.45±0.87, both P < 0.01). There was no significant difference in the plasma 5-HT, BDNF or peripheral blood NLR before treatment between the two groups. The above parameters at 14 days after treatment were improved as compared with before, and the levels in the observation group were superior to control group [5-HT (mg/L): 150.25±17.37 vs. 123.34±16.18, BDNF (mg/L): 19.37±2.35 vs. 12.48±2.18, NLR: 4.78±0.83 vs. 5.81±1.17, all P < 0.01]. CONCLUSIONS Both ONDS determined by ultrasound and relative α variation score monitored by electroencephalography changed significantly in the patients with ischemic hypoxic encephalopathy. Neurofeedback training can effectively improve brain function in patients with ischemic hypoxic encephalopathy.
Humans ; Electroencephalography ; Prospective Studies ; Neurofeedback ; Optic Nerve/diagnostic imaging* ; Ultrasonography ; Hypoxia-Ischemia, Brain/physiopathology* ; Male ; Female ; Middle Aged

OBJECTIVE: To determine whether the glutamatergic neurons in the paraventricular nucleus of the thalamus (PVT) is involved in the change of consciousness induced by propofol through a combination of behavioral and electroencephalography (EEG) recordings. METHODS: Healthy male VGluT2-IRES-Cre mice aged 8-12 weeks were used in this experiment. (1) The glutamatergic neurons in the PVT was selectively damaged, and its effect on propofol anesthesia induction and recovery times as well as the energy of EEG in different frequency bands were observed. (2) Optogenetics was utilized to selectively activate or inhibit glutamatergic neurons in the PVT to assess their influence on anesthesia induction and recovery times under propofol as well as the energy of EEG in different frequency bands. RESULTS: (1) Selective ablation of glutamatergic neurons in the PVT significantly delayed recovery from propofol anesthesia with statistical difference as compared with the control group (s: 409.43±117.49 vs. 273.71±51.52, P < 0.05), but had no significant effect on anesthesia induction time. During the recovery phase of propofol, selective ablation of glutamatergic neurons in the PVT exhibited higher α-wave (1-4 Hz) power and reduced β-wave (12-15 Hz) power as compared with the control group. (2) Optogenetic activation of glutamatergic neurons in the PVT significantly prolonged anesthesia induction time under propofol (s: 161.67±29.09 vs. 119.33±18.98, P < 0.05) while significantly shortening the recovery time from propofol anesthesia (s: 208.67±57.19 vs. 288.83±34.52, P < 0.05). During the induction phase of propofol, activation of glutamatergic neurons in PVT reduced α-wave and α-wave (8-12 Hz) power, while during the recovery phase, α-wave power significantly increased as compared with the control group. (3) Optogenetic inhibition of glutamatergic neurons in the PVT delayed recovery from propofol anesthesia (s: 403.50±129.06 vs. 252.83±45.31, P < 0.05), but had no significant effect on induction time. During both the induction phase and recovery phase of propofol, the optogenetic inhibition of glutamatergic neurons in the PVT exhibited increased α-wave power. CONCLUSION Glutamatergic neurons in the PVT are involved in the regulation of propofol anesthesia recovery process.
Animals ; Propofol/pharmacology* ; Mice ; Neurons/physiology* ; Male ; Electroencephalography ; Wakefulness ; Midline Thalamic Nuclei ; Optogenetics

Sepsis-associated encephalopathy (SAE) is a common complication of sepsis, referring to a diffuse brain dysfunction caused by sepsis in the absence of direct central nervous system (CNS) infection. SAE occurs in up to 70% of patients with sepsis. Globally, the annual incidence of sepsis ranges from 30.0 to 48.9 million cases, resulting in approximately 11 million deaths per year, which accounts for 20% of all global mortalities. SAE is identified as an independent risk factor contributing to the increased mortality rate among these patients. Early diagnosis of SAE and related cerebral protection interventions hold significant clinical importance. Currently, the main indicators of brain function for sepsis patients include Glasgow coma score (GCS), confusion assessment method for the intensive care unit (CAM-ICU), electroencephalogram (EEG), brain CT or magnetic resonance imaging (MRI) and other related imaging changes, which have the problems of low sensitivity, poor specificity, and non-objective evaluation of the results of the diagnosis of SAE. This article focuses on the latest progress in the pathogenesis of SAE and systematically reviews potential biomarkers related to the onset of SAE from multiple aspects, including inflammatory markers, endothelial and neuronal injury markers, and metabolic markers. This will provide new insights for the clinical diagnosis and treatment of SAE.
Humans ; Sepsis-Associated Encephalopathy/therapy* ; Biomarkers ; Sepsis/complications* ; Magnetic Resonance Imaging ; Electroencephalography ; Brain Diseases/etiology*

Electroencephalography (EEG) and magnetic resonance imaging (MRI), as neuroimaging technologies, provided objective and visualized technical tools for analyzing the brain effect mechanisms of acupuncture and moxibustion from the perspectives of brain structure, function, metabolism, and hemodynamics. The advancement of artificial intelligence (AI) algorithms can compensate for issues such as the large and scattered nature of neuroimaging data, inconsistent quality, and high heterogeneity of image information. The integration of AI with neuroimaging can facilitate individualized, intelligent, and precise prediction of acupuncture and moxibustion effects, enable intelligent classification of differential acupuncture responses, and identify brain activation patterns. This paper focuses on EEG and MRI, analyzing how machine learning and deep learning optimize multimodal neuroimaging data and their applications in the study of acupuncture and moxibustion brain effects mechanisms. Furthermore, it highlights current research gaps and limitations to provide insights for future studies on acupuncture brain effects mechanisms.
Humans ; Acupuncture Therapy ; Brain/physiology* ; Moxibustion ; Neuroimaging/methods* ; Artificial Intelligence ; Magnetic Resonance Imaging ; Electroencephalography

BACKGROUND: Visual input significantly influences cerebral activity related to locomotor navigation, although the underlying mechanism remains unclear. This study aimed to analyze the effects of chronic visual impairment and its rehabilitation on sensorimotor integration during level walking in patients with age-related cataract. METHODS: This prospective case series enrolled 14 female patients (68.4 ± 4.7 years) with age-related cataract, scheduled for consecutive cataract surgeries at the Department of Ophthalmology in Peking University Third Hospital from June 2019 to June 2020. Electroencephalogram (EEG) signals during level walking were recorded using a portable EEG system before and 4 weeks after visual restoration. Walking speed was assessed using the Footscan system. Spectral power of the theta and alpha bands was analyzed with repeated-measures analysis of variance, with Assignment (rest and walking), Phase (preoperative and postoperative), and Electrode sites (F3, Fz, F4, O1, and O2) as within-subject factors. RESULTS: Compared to the visual impairment state, theta band power significantly decreased after visual restoration (13.16 ± 1.58 μV 2vs. 23.65 ± 3.48 μV 2 , P = 0.018). Theta activity was notably reduced during walking (17.24 ± 2.43 μV 2vs. 37.86 ± 6.62 μV 2 , P = 0.017), while theta power at rest was not significantly different between the two phases (9.44 ± 1.24 μV 2vs. 9.08 ± 1.74 μV 2 , P = 0.864). Changes in walking speed were correlated with alterations in theta power at electrode sites of O1 ( r = -0.574, P = 0.032) and O2 ( r = -0.648, P = 0.012). Alpha band power remained stable during walking and was unaffected by visual status. CONCLUSIONS Chronic visual impairment from age-related cataract triggers enhanced cerebral activation of sensorimotor integration to compensate for visual decline during locomotion. This cerebral over-activation is effectively alleviated by visual restoration.
Humans ; Female ; Walking/physiology* ; Aged ; Electroencephalography/methods* ; Prospective Studies ; Middle Aged ; Cataract/physiopathology* ; Vision Disorders/physiopathology*

The hippocampus, a major component of the limbic system, is the most important region related to emotion regulation and memory processing. Cognitive impairment and depressive symptoms observed in Alzheimer's disease (AD) patients may be attributed to hippocampal damage caused by amyloid β-protein (Aβ). Our previous studies have demonstrated that a steroid sulfatase inhibitor DU-14 can enhance hippocampal synaptic plasticity and spatial memory abilities in a chronic AD murine model by counteracting the toxic effects of Aβ. However, limited experimental evidence exists regarding the efficacy of steroid sulfatase inhibitor on depressive symptoms in AD animal models. In this study, we investigated the effects of DU-14 on depressive symptoms and theta-band neuronal oscillations in rats with intrahippocampal injection of Aβ_1-42 using various behavioral tests such as sucrose preference test, tail suspension test, forced swimming test, and in vivo hippocampal local field potential (LFP) recording. The results demonstrated that, in comparison to the control group: (1) rats in the Aβ group exhibited a decrease in sucrose preference, indicating a loss of interest in pleasurable activities; (2) rats in the Aβ group displayed aggravated depressive-like behavior characterized by prolonged immobility time during tail suspension and forced swimming tests; (3) Aβ disrupted the induction of theta rhythm via tail pinch stimulation, and resulted in a significant reduction in peak power of theta rhythm. In contrast to the Aβ group, pretreatment with DU-14 resulted in: (1) a significant improvement in Aβ-induced anhedonia, as evidenced by increased sucrose preference; (2) significant alleviation of Aβ-induced despair and depressive-like behaviors, reflected by reduced immobility time during tail suspension and forced swimming tests; (3) successful mitigation of Aβ-mediated inhibition on bilateral hippocampal theta rhythm. These findings indicate that steroid sulfatase inhibitor DU-14 can counteract neurotoxicity induced by Aβ, and prevent Aβ-induced depressive-like behavior and suppression of theta rhythm.
Animals ; Amyloid beta-Peptides/toxicity* ; Rats ; Depression/physiopathology* ; Theta Rhythm/drug effects* ; Hippocampus/physiopathology* ; Male ; Rats, Sprague-Dawley ; Alzheimer Disease/physiopathology* ; Steryl-Sulfatase/antagonists & inhibitors* ; Peptide Fragments ; Behavior, Animal/drug effects*

This paper proposes a motor imagery recognition algorithm based on feature fusion and transfer adaptive boosting (TrAdaboost) to address the issue of low accuracy in motor imagery (MI) recognition across subjects, thereby increasing the reliability of MI-based brain-computer interfaces (BCI) for cross-individual use. Using the autoregressive model, power spectral density and discrete wavelet transform, time-frequency domain features of MI can be obtained, while the filter bank common spatial pattern is used to extract spatial domain features, and multi-scale dispersion entropy is employed to extract nonlinear features. The IV-2a dataset from the 4 ^th International BCI Competition was used for the binary classification task, with the pattern recognition model constructed by combining the improved TrAdaboost integrated learning algorithm with support vector machine (SVM), k nearest neighbor (KNN), and mind evolutionary algorithm-based back propagation (MEA-BP) neural network. The results show that the SVM-based TrAdaboost integrated learning algorithm has the best performance when 30% of the target domain instance data is migrated, with an average classification accuracy of 86.17%, a Kappa value of 0.723 3, and an AUC value of 0.849 8. These results suggest that the algorithm can be used to recognize MI signals across individuals, providing a new way to improve the generalization capability of BCI recognition models.
Brain-Computer Interfaces ; Humans ; Support Vector Machine ; Algorithms ; Neural Networks, Computer ; Imagination/physiology* ; Pattern Recognition, Automated/methods* ; Electroencephalography ; Wavelet Analysis

Emotion classification and recognition is a crucial area in emotional computing. Physiological signals, such as electroencephalogram (EEG), provide an accurate reflection of emotions and are difficult to disguise. However, emotion recognition still faces challenges in single-modal signal feature extraction and multi-modal signal integration. This study collected EEG, electromyogram (EMG), and electrodermal activity (EDA) signals from participants under three emotional states: happiness, sadness, and fear. A feature-weighted fusion method was applied for integrating the signals, and both support vector machine (SVM) and extreme learning machine (ELM) were used for classification. The results showed that the classification accuracy was highest when the fusion weights were set to EEG 0.7, EMG 0.15, and EDA 0.15, achieving accuracy rates of 80.19% and 82.48% for SVM and ELM, respectively. These rates represented an improvement of 5.81% and 2.95% compared to using EEG alone. This study offers methodological support for emotion classification and recognition using multi-modal physiological signals.
Humans ; Emotions/physiology* ; Electroencephalography ; Support Vector Machine ; Electromyography ; Signal Processing, Computer-Assisted ; Galvanic Skin Response/physiology* ; Machine Learning ; Male

Existing emotion recognition research is typically limited to static laboratory settings and has not fully handle the changes in emotional states in dynamic scenarios. To address this problem, this paper proposes a method for dynamic continuous emotion recognition based on electroencephalography (EEG) and eye movement signals. Firstly, an experimental paradigm was designed to cover six dynamic emotion transition scenarios including happy to calm, calm to happy, sad to calm, calm to sad, nervous to calm, and calm to nervous. EEG and eye movement data were collected simultaneously from 20 subjects to fill the gap in current multimodal dynamic continuous emotion datasets. In the valence-arousal two-dimensional space, emotion ratings for stimulus videos were performed every five seconds on a scale of 1 to 9, and dynamic continuous emotion labels were normalized. Subsequently, frequency band features were extracted from the preprocessed EEG and eye movement data. A cascade feature fusion approach was used to effectively combine EEG and eye movement features, generating an information-rich multimodal feature vector. This feature vector was input into four regression models including support vector regression with radial basis function kernel, decision tree, random forest, and K-nearest neighbors, to develop the dynamic continuous emotion recognition model. The results showed that the proposed method achieved the lowest mean square error for valence and arousal across the six dynamic continuous emotions. This approach can accurately recognize various emotion transitions in dynamic situations, offering higher accuracy and robustness compared to using either EEG or eye movement signals alone, making it well-suited for practical applications.
Humans ; Electroencephalography/methods* ; Emotions/physiology* ; Eye Movements/physiology* ; Signal Processing, Computer-Assisted ; Support Vector Machine ; Algorithms

Depression, a mental health disorder, has emerged as one of the significant challenges in the global public health domain. Investigating the pathogenesis of depression and accurately assessing the symptomatic changes are fundamental to formulating effective clinical diagnosis and treatment strategies. Utilizing non-invasive brain imaging technologies such as functional magnetic resonance imaging and scalp electroencephalography, existing studies have confirmed that the onset of depression is closely associated with abnormal neural activities and altered functional connectivity in multiple brain regions. Magnetoencephalography, unaffected by tissue conductivity and skull thickness, boasts high spatial resolution and signal-to-noise ratio, offering unique advantages and significant value in revealing the abnormal brain mechanisms and neural characteristics of depression. This review, starting from the rhythmic characteristics, nonlinear dynamic features, and connectivity characteristics of magnetoencephalography in depression patients, revisits the research progress on magnetoencephalography features related to depression, discusses current issues and future development trends, and provides insights for the study of pathophysiological mechanisms, as well as for clinical diagnosis and treatment of depression.
Humans ; Magnetoencephalography/methods* ; Brain/physiopathology* ; Depression/diagnosis* ; Electroencephalography ; Magnetic Resonance Imaging