Objective To investigate the differences in emotional processing characteristics and electroencephalography （EEG） power values in patients with Parkinson disease （PD）. Methods A total of 24 PD patients were enrolled as subjects， and 30 healthy individuals were enrolled as control group. With the use of the EPIE experimental paradigm， SAM questionnaire was used to determine the scores of emotional valence and arousal， and EEG was used for real-time monitoring of cortical EEG signals. The two groups were compared in terms of the differences in valence/arousal and EEG power values under different emotions and their correlation. Results The PD group had significantly higher BAI and BDI scores than the control group[BAI（16.92±3.83）vs（11.62±3.65），t=4.521，P<0.05；BDI（22.69±2.30）vs（14.17±4.06），t=7.981，P<0.05]. In the negative mood， there were significant differences in valence/arousal between the two groups （t=4.505，-7.705，bothP<0.05）. There were significant differences between the two groups in power values at Fp1，Fp2，F7，F3，F4，T3，T4， and T5（t=-4.12，-12.43，5.76，-2.90，-4.72，-5.34，-5.81，-2.65，all P<0.05）. In the negative mood， for the control group， valence score was correlated with Fp1 （r=-0.837， P<0.01）， Fp2 （r=-0.920， P<0.01），F4（r=-0.604，P=0.008），P3（r=-0.658，P=0.003），and P4（r=-0.546，P=0.019）， and arousal score was correlated with Fp1（r=0.887， P<0.01）， Fp2 （r=0.958， P=0.003），F4（r=0.683，P=0.003），P3 （r=0.721， P=0.003），and P4 （r=0.610，P=0.007）； for the PD group， valence score was correlated with Fp2（r=-0.490，P=0.015） and F7（r=-0.564，P=0.004）， and arousal score was correlated with Fp2 （r=0.440， P=0.031） and F7（r=0.853，P<0.01）. Conclusion Patients with PD have negative emotional processing abnormalities associated with right PFC and left lateral FL.
Electroencephalography

Valacyclovir-associated neurotoxicity (VAN) is a recognized adverse effect in elderly patients with renal impairment but remains underdiagnosed due to its nonspecific presentation and overlap with acute neurologic emergencies. We report a 78-year-old Filipino female with end-stage renal disease on maintenance hemodialysis who developed acute disorientation, agitation, vivid visual hallucinations and generalized weakness shortly after initiation of valacyclovir for herpes zoster. Given the abrupt onset of neuropsychiatric symptoms, viral encephalitis was initially considered. Magnetic resonance imaging of the brain showed no evidence of acute infarction or encephalitis, while electroencephalography demonstrated diffuse generalized slowing consistent with an encephalopathic process. Review of the medication history revealed valacyclovir dosing that exceeded recommendations for patients with end-stage renal disease. Valacyclovir was discontinued and emergent hemodialysis was initiated resulting in marked improvement in sensorium after the second session and complete resolution of symptoms after the third. This case shows VAN as an important diagnostic mimic of acute encephalopathy in elderly patients with renal failure and emphasizes the critical role of early medication review in preventing unnecessary investigations and enabling prompt, reversible management.

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*

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*

Objective To investigate the electroencephalographic （EEG） manifestations of children with mitochondrial encephalomyopathy with lactic acidosis and stroke-like episodes （MELAS）， to explore the EEG characteristics of such patients and their correlation with neuroimaging findings， and to provide a reference for the early clinical diagnosis of MELAS. Methods A retrospective analysis was performed for the EEG and neuroimaging data of 11 children who were admitted to Department of Pediatric Neurology， The First Hospital of Jilin University， from January 2016 to December 2024 and were diagnosed with MELAS by genetic testing. Results There were varying degrees of EEG background abnormalities in the children with MELAS. Interictal epileptiform discharges predominantly involved the posterior head region. Seizures mainly manifested as focal seizures， frequently originating from the posterior head regions. Serial EEG and neuroimaging examinations showed changes in lesion sites over time， with consistent localization of lesions in more than half of the cases based on the two examinations， predominantly involving the posterior head regions. Conclusion In clinical practice， most children with MELAS have the initial manifestation of seizures， and the possibility of MELAS should be considered in case of the characteristic changes in EEG and imaging examinations， so as to provide help for early identification and diagnosis.
Electroencephalography

This article aims to improve the understanding of Lance-Adams syndrome （LAS） in clinical practice. By analyzing the clinical data of a patient diagnosed with LAS in 2021 and conducting a literature review， this article elaborates on the clinical symptoms， pathogenesis， diagnostic methods， and treatment regimens of LAS. LAS is a rare central nervous system disorder characterized by action myoclonus after cerebral hypoxia. The etiology of LAS is mainly associated with cerebral hypoxia， and cardiopulmonary resuscitation and asphyxia are the most common predisposing factors for LAS. Characteristic manifestations on electroencephalography have an important value in diagnosis. Clinicians should enhance their ability to identify LAS through typical clinical symptoms and electroencephalography in the early stage. This article also points out that there are still many challenges in the treatment of LAS， which requires further research and exploration. This article has an important reference value in improving the understanding， diagnosis， and treatment of LAS.
Electroencephalography

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