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.

OBJECTIVES: To explore the relationship between the Observer's Assessment of Alertness/Sedation (OAAS) score and the bispectral index (BIS) during propofol titration for general anesthesia induction and analyze the impact of BIS monitoring delay on anesthetic depth assessment. METHODS: This study was conducted among 90 patients (ASA class I-II) undergoing elective surgery under general anesthesia. For anesthesia induction, the patients received propofol titration at the rate of 0.5 mg·kg^-1·min^-1 till OAAS scores of 4, 3, 2, and 1 were reached. After achieving an OAAS score of 1, remifentanil (2 μg·kg⁻¹) and rocuronium (0.6 mg·kg⁻¹) were administered, and tracheal intubation was performed 2 min later. BIS values, mean arterial pressure (MAP), heart rate (HR), and propofol dosage at each OAAS score were recorded, and the correlation between OAAS scores and BIS values was analyzed. The diagnostic performance of BIS values for determining when the OAAS score reaches 1 was analyzed using ROC curve. RESULTS: All the patients successfully completed tracheal intubation. BIS values of the patients at each of the OAAS scores differed significantly (P<0.01), and the mean BIS value decreased by 4.08, 8.32, 5.43 and 5.24 as the OAAS score decreased from 5 to 4, from 4 to 3, from 3 to 2, and from 2 to 1, respectively. There was a significant correlation between the OAAS score and BIS values (ρ=0.775, P<0.001). The median BIS value for an OAAS score of 1 was 76, at which point 83.33% of the patients had BIS values exceeding 60. ROC curve analysis showed that for determining an OAAS score of 1, BIS value, at the optimal cutoff value of 84, had a sensitivity of 88.9%, a specificity of 73.3%, and an area under the curve of 0.842 (0.803-0.881). CONCLUSIONS OAAS score during induction of general anesthesia is strongly correlated with BIS value and is a highly sensitive and timely indicator to compensate for the delay in BIS monitoring.
Humans ; Propofol/administration & dosage* ; Male ; Female ; Middle Aged ; Anesthesia, General/methods* ; Adult ; Consciousness Monitors ; Aged ; Young Adult ; Monitoring, Intraoperative/methods* ; Electroencephalography

OBJECTIVES: To assess the regulatory effect of cannabidiol (CBD) on circadian rhythm sleep disorders following general anesthesia and explore its potential mechanism in a rat model of propofol-induced rhythm sleep disorder. METHODS: An electrode was embedded in the skull for cortical EEG recording in 24 male SD rats, which were randomized into control, propofol, CBD treatment, and diazepam treatment groups (n=6). Eight days later, a single dose of propofol (10 mg/kg) was injected via the tail vein with anesthesia maintenance for 3 h in the latter 3 groups, and daily treatment with saline, CBD or diazepam was administered via gavage; the control rats received only saline injection. A wireless system was used for collecting EEG, EMG, and body temperature data within 72 h after propofol injection. After data collection, blood samples and hypothalamic tissue samples were collected for determining serum levels of oxidative stress markers and hypothalamic expressions of the key clock proteins. RESULTS: Compared with the control rats, the rats with CBD treatment showed significantly increased sleep time at night (20:00-6:00), especially during the time period of 4:00-6:00 am. Compared with the rats in propofol group, which had prolonged SWS time and increased sleep episodes during 18:00-24:00 and sleep-wake transitions, the CBD-treated rats exhibited a significant reduction of SWS time and fewer SWS-to-active-awake transitions with increased SWS aspects and sleep-wake transitions at night (24:00-08:00). Diazepam treatment produced similar effect to CBD but with a weaker effect on sleep-wake transitions. Propofol caused significant changes in protein expressions and redox state, which were effectively reversed by CBD treatment. CONCLUSIONS CBD can improve sleep structure and circadian rhythm in rats with propofol-induced sleep disorder possibly by regulating hypothalamic expressions of the key circadian clock proteins, suggesting a new treatment option for perioperative sleep disorders.
Animals ; Rats, Sprague-Dawley ; Male ; Cannabidiol/therapeutic use* ; Rats ; Circadian Rhythm/drug effects* ; Propofol/adverse effects* ; Anesthesia, General/adverse effects* ; Sleep Wake Disorders/chemically induced* ; Hypothalamus/metabolism* ; Electroencephalography

OBJECTIVES: To investigate the regulatory role of astrocytes in the dorsomedial hypothalamus (DMH) during sevoflurane anesthesia emergence. METHODS: Forty-two male C57BL/6 mice were randomized into 6 groups (n=7) for assessing astrocyte activation in the dorsomedial hypothalamus (DMH) under sevoflurane anesthesia. Two groups of mice received microinjection of agfaABC1D promoter-driven AAV2 vector into the DMH for GCaMP6 overexpression, and the changes in astrocyte activity during sevoflurane or air inhalation were recorded using calcium imaging. For assessing optogenetic activation of astrocytes, another two groups of mice received microinjection of an optogenetic virus or a control vector into the DMH with optic fiber implantation, and sevoflurane anesthesia emergence was compared using behavioral experiments. In the remaining two groups, electroencephalogram (EEG) recording during sevoflurane anesthesia emergence was conducted after injection of the hChR2-expressing and control vectors. Anesthesia induction and recovery were assessed by observing the righting reflex. EEG data were recorded under 2.0% sevoflurane to calculate the burst suppression ratio (BSR) and under 1.5% sevoflurane for power spectrum analysis. Immunofluorescence staining was performed to visualize the colocalization of GFAP-positive astrocytes with viral protein signals. RESULTS: Astrocyte activity in the DMH decreased progressively as sevoflurane concentration increased. During 2.0% sevoflurane anesthesia, the mice injected with the ChR2-expressing virus exhibited a significantly shortened wake-up time (P<0.05), and optogenetic activation of the DMH astrocytes led to a marked reduction in BSR (P<0.001). Under 1.5% sevoflurane anesthesia, optogenetic activation resulted in a significant increase in EEG gamma power and a significant decrease in delta power in ChR2 group (P<0.01). CONCLUSIONS Optogenetic activation of DMH astrocytes facilitates sevoflurane anesthesia emergence but does not significantly influence anesthesia induction. These findings offer new insights into the mechanisms underlying anesthesia emergence and may provide a potential target for accelerating postoperative recovery and managing anesthesia-related complications.
Animals ; Astrocytes/physiology* ; Sevoflurane ; Mice, Inbred C57BL ; Mice ; Male ; Electroencephalography ; Anesthetics, Inhalation/pharmacology* ; Hypothalamus/cytology* ; Anesthesia Recovery Period ; Methyl Ethers/pharmacology*

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