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

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*

Neural activities differentiating bodies versus non-body stimuli have been identified in the occipitotemporal cortex of both humans and nonhuman primates. However, the neural mechanisms of coding the similarity of different individuals' bodies of the same species to support their categorical representations remain unclear. Using electroencephalography (EEG) and magnetoencephalography (MEG), we investigated the temporal and spatial characteristics of neural processes shared by different individual body silhouettes of the same species by quantifying the repetition suppression of neural responses to human and animal (chimpanzee, dog, and bird) body silhouettes showing different postures. Our EEG results revealed significant repetition suppression of the amplitudes of early frontal/central activity at 180-220 ms (P2) and late occipitoparietal activity at 220-320 ms (P270) in response to animal (but not human) body silhouettes of the same species. Our MEG results further localized the repetition suppression effect related to animal body silhouettes in the left supramarginal gyrus and left frontal cortex at 200-440 ms after stimulus onset. Our findings suggest two neural processes that are involved in spontaneous categorical representations of animal body silhouettes as a cognitive basis of human-animal interactions.
Humans ; Animals ; Male ; Electroencephalography ; Magnetoencephalography ; Female ; Young Adult ; Adult ; Pattern Recognition, Visual/physiology* ; Brain Mapping ; Photic Stimulation ; Brain/physiology* ; Dogs

Rhythm, as a prominent characteristic of auditory experiences such as speech and music, is known to facilitate attention, yet its contribution to working memory (WM) remains unclear. Here, human participants temporarily retained a 12-tone sequence presented rhythmically or arrhythmically in WM and performed a pitch change-detection task. Behaviorally, while having comparable accuracy, rhythmic tone sequences showed a faster response time and lower response boundaries in decision-making. Electroencephalographic recordings revealed that rhythmic sequences elicited enhanced non-phase-locked beta-band (16 Hz-33 Hz) and theta-band (3 Hz-5 Hz) neural oscillations during sensory encoding and WM retention periods, respectively. Importantly, the two-stage neural signatures were correlated with each other and contributed to behavior. As beta-band and theta-band oscillations denote the engagement of motor systems and WM maintenance, respectively, our findings imply that rhythm facilitates auditory WM through intricate oscillation-based interactions between the motor and auditory systems that facilitate predictive attention to auditory sequences.
Humans ; Memory, Short-Term/physiology* ; Male ; Beta Rhythm/physiology* ; Female ; Theta Rhythm/physiology* ; Young Adult ; Auditory Perception/physiology* ; Adult ; Electroencephalography ; Acoustic Stimulation ; Reaction Time/physiology* ; Brain/physiology* ; Attention/physiology*

General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators or behavioral responses, fall short of accurately capturing the nuanced states of unconsciousness. This study introduces a machine learning-based approach to decode anesthesia depth, leveraging EEG data across different anesthesia states induced by propofol and esketamine in rats. Our findings demonstrate the model's robust predictive accuracy, underscored by a novel intra-subject dataset partitioning and a 5-fold cross-validation method. The research diverges from conventional monitoring by utilizing anesthetic infusion rates as objective indicators of anesthesia states, highlighting distinct EEG patterns and enhancing prediction accuracy. Moreover, the model's ability to generalize across individuals suggests its potential for broad clinical application, distinguishing between anesthetic agents and their depths. Despite relying on rat EEG data, which poses questions about real-world applicability, our approach marks a significant advance in anesthesia monitoring.
Animals ; Machine Learning ; Electroencephalography/methods* ; Ketamine/administration & dosage* ; Rats ; Male ; Propofol/administration & dosage* ; Rats, Sprague-Dawley ; Anesthesia, General/methods* ; Brain/physiology* ; Intraoperative Neurophysiological Monitoring/methods*

Depression is increasingly prevalent among adolescents and can profoundly impact their lives. However, the early detection of depression is often hindered by the time-consuming diagnostic process and the absence of objective biomarkers. In this study, we propose a novel approach for depression detection based on an affective brain-computer interface (aBCI) and the resting-state electroencephalogram (EEG). By fusing EEG features associated with both emotional and resting states, our method captures comprehensive depression-related information. The final depression detection model, derived through decision fusion with multiple independent models, further enhances detection efficacy. Our experiments involved 40 adolescents with depression and 40 matched controls. The proposed model achieved an accuracy of 86.54% on cross-validation and 88.20% on the independent test set, demonstrating the efficiency of multimodal fusion. In addition, further analysis revealed distinct brain activity patterns between the two groups across different modalities. These findings hold promise for new directions in depression detection and intervention.
Humans ; Male ; Female ; Adolescent ; Case-Control Studies ; Depression/diagnosis* ; Early Diagnosis ; Rest ; Electroencephalography/methods* ; Brain-Computer Interfaces ; Models, Psychological ; Reproducibility of Results ; Affect/physiology* ; Photic Stimulation/methods* ; Video Recording ; Brain/physiopathology*

Closed-loop neuromodulation, especially using the phase of the electroencephalography (EEG) rhythm to assess the real-time brain state and optimize the brain stimulation process, is becoming a hot research topic. Because the EEG signal is non-stationary, the commonly used EEG phase-based prediction methods have large variances, which may reduce the accuracy of the phase prediction. In this study, we proposed a machine learning-based EEG phase prediction network, which we call EEG phase prediction network (EPN), to capture the overall rhythm distribution pattern of subjects and map the instantaneous phase directly from the narrow-band EEG data. We verified the performance of EPN on pre-recorded data, simulated EEG data, and a real-time experiment. Compared with widely used state-of-the-art models (optimized multi-layer filter architecture, auto-regress, and educated temporal prediction), EPN achieved the lowest variance and the greatest accuracy. Thus, the EPN model will provide broader applications for EEG phase-based closed-loop neuromodulation.
Humans ; Electroencephalography/methods* ; Brain/physiology* ; Machine Learning ; Signal Processing, Computer-Assisted ; Male ; Adult ; Neural Networks, Computer ; Brain Waves/physiology*

Approximately 30%-40% of epilepsy patients do not respond well to adequate anti-seizure medications (ASMs), a condition known as pharmacoresistant epilepsy. The management of pharmacoresistant epilepsy remains an intractable issue in the clinic. Its early prediction is important for prevention and diagnosis. However, it still lacks effective predictors and approaches. Here, a classical model of pharmacoresistant temporal lobe epilepsy (TLE) was established to screen pharmacoresistant and pharmaco-responsive individuals by applying phenytoin to amygdaloid-kindled rats. Ictal electroencephalograms (EEGs) recorded before phenytoin treatment were analyzed. Based on ictal EEGs from pharmacoresistant and pharmaco-responsive rats, a convolutional neural network predictive model was constructed to predict pharmacoresistance, and achieved 78% prediction accuracy. We further found the ictal EEGs from pharmacoresistant rats have a lower gamma-band power, which was verified in seizure EEGs from pharmacoresistant TLE patients. Prospectively, therapies targeting the subiculum in those predicted as "pharmacoresistant" individual rats significantly reduced the subsequent occurrence of pharmacoresistance. These results demonstrate a new methodology to predict whether TLE individuals become resistant to ASMs in a classic pharmacoresistant TLE model. This may be of translational importance for the precise management of pharmacoresistant TLE.
Epilepsy, Temporal Lobe/diagnosis* ; Animals ; Drug Resistant Epilepsy/drug therapy* ; Electroencephalography/methods* ; Rats ; Anticonvulsants/pharmacology* ; Neural Networks, Computer ; Male ; Humans ; Phenytoin/pharmacology* ; Adult ; Disease Models, Animal ; Female ; Rats, Sprague-Dawley ; Young Adult ; Convolutional Neural Networks