Objective To investigate the cranial magnetic resonance imaging （MRI） features of patients with hepatolenticular degeneration （also known as Wilson disease，WD） and epilepsy， and to identify the neuroimaging risk factors for seizures in WD patients. Methods A total of 69 WD patients with epilepsy who were hospitalized in Affiliated Hospital of Neurology Institute， Anhui University of Chinese Medicine， from January 2018 to November 2025 were enrolled as study group， while 80 WD patients without seizures， matched for sex and age， during the same period of time were randomly selected as control group. Cranial MRI findings were compared between the two groups. Results There were 69 WD patients （43 male patients and 26 female patients） in the study group， with a mean age of （29.46±8.58） years at the time of attending the hospital， and all these patients had abnormal electroencephalogram （EEG） findings. There were no significant differences between the two groups in age of onset，disease duration， WD subtype， and serum copper. Cranial MRI showed that the putamen was the most common site of brain injury （47 patients， 68.1%）， followed by the frontal lobe （40 patients，58.0%） and the parietal lobe （31 patients，44.9%）， and there was a significantly higher probability of epilepsy in patients with abnormal lesions in the frontal， temporal， or parietal lobes （P<0.05）. Conclusion While the putamen is the most common site of brain injury in WD patients with epilepsy， frontal or temporal lobe injuries are neuroimaging risk factors for seizures in such patients.
Epilepsy, Frontal Lobe ; Putamen

The existing epilepsy seizure detection algorithms have problems such as overfitting and poor generalization ability due to high reliance on manual labeling of electroencephalogram's data and data imbalance between seizure and interictal periods. An unsupervised learning detection method for epileptic seizure that jointed graph attention network (GAT) and Transformer framework (GAT-T) was proposed. In this method, channel correlations were adaptively learned by GAT encoder. Temporal information was captured by one-dimensional convolution decoder. Combining outputs of the two mentioned above, predicted values for electroencephalogram were generated. The collective anomaly score was calculated and the detection threshold was determined. The results demonstrated that GAT-T achieved the average performance exceeding 90% (or 99%) with a 0.25 s (or 2 s) time segment length, which could effectively detect epileptic seizures. Moreover, the channel association probability matrix was expected to assist clinicians in the initial screening of the epileptogenic zone, and ablation experiments also reflected the significance of each module in GAT-T. This study may assist clinicians in making more accurate diagnostic and therapeutic decisions for epilepsy patients.
Humans ; Electroencephalography/methods* ; Epilepsy/physiopathology* ; Algorithms ; Seizures/physiopathology* ; Neural Networks, Computer ; Signal Processing, Computer-Assisted

Current epilepsy prediction methods are not effective in characterizing the multi-domain features of complex long-term electroencephalogram (EEG) data, leading to suboptimal prediction performance. Therefore, this paper proposes a novel multi-scale sparse adaptive convolutional network based on multi-head attention mechanism (MS-SACN-MM) model to effectively characterize the multi-domain features. The model first preprocesses the EEG data, constructs multiple convolutional layers to effectively avoid information overload, and uses a multi-layer perceptron and multi-head attention mechanism to focus the network on critical pre-seizure features. Then, it adopts a focal loss training strategy to alleviate class imbalance and enhance the model's robustness. Experimental results show that on the publicly created dataset (CHB-MIT) by MIT and Boston Children's Hospital, the MS-SACN-MM model achieves a maximum accuracy of 0.999 for seizure prediction 10 ~ 15 minutes in advance. This demonstrates good predictive performance and holds significant importance for early intervention and intelligent clinical management of epilepsy patients.
Humans ; Electroencephalography/methods* ; Epilepsy/physiopathology* ; Neural Networks, Computer ; Seizures/physiopathology* ; Signal Processing, Computer-Assisted ; Algorithms

OBJECTIVES: To explore the therapeutic mechanism of Chaihu Shugan (CHSG) Decoction for improving cognitive impairment in rats with epilepsy induced by lithium chloride and pilocarpine. METHODS: Male SD rat models of cognitive impairment model after epilepsy induced by intraperitoneal injection with lithium chloride and pilocarpine were randomly divided into 5 groups (n=12) for treatment with daily gavage of saline, donepezil (90 mg/kg), or CHSG Decoction at 2.5, 5.0, 10, 20 and 40 g/kg for 4 consecutive weeks, with 10 rats with intraperitoneal injection with saline as the blank control group. Morris water maze test was used to evaluate cognitive and behavioral changes of the rats after treatment. The mRNA and protein expressions of ASIC1, NR1, NR2A and NR2B in the hippocampus of rats were detected using RT-qPCR and Western blotting. RESULTS: Compared with those with saline treatment, the rat models treated with CHSG Decoction at 5 and 10 g/kg showed significantly shortened escape latency and prolonged stay in the target quadrant with increased number of platform crossings in Morris water maze test. CHSG Decoction treatment at the two doses significantly increased ASIC1, NR1, NR2A and NR2B protein expressions in the hippocampus of the rat models, and their mRNA expression levels were all increased significantly after the treatment at the doses above 2.5 g/kg. CONCLUSIONS CHSG Decoction can improve cognitive impairment in rats after epilepsy possibly by regulating the expression and channel activity of NMDAR protein and its subunit protein via upregulating ASIC1 to modulate neuronal excitability and synaptic plasticity in the hippocampus.
Animals ; Hippocampus/drug effects* ; Receptors, N-Methyl-D-Aspartate/metabolism* ; Acid Sensing Ion Channels/metabolism* ; Rats, Sprague-Dawley ; Male ; Rats ; Epilepsy/complications* ; Cognitive Dysfunction/drug therapy* ; Drugs, Chinese Herbal/therapeutic use* ; Up-Regulation ; Maze Learning

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

Epilepsy affects over 50 million people worldwide. Drug-resistant epilepsy (DRE) accounts for up to a third of these cases, and neuro-inflammation is thought to play a role in such cases. Despite being a long-debated issue in the field of DRE, the mechanisms underlying neuroinflammation have yet to be fully elucidated. The pro-inflammatory microenvironment within the brain tissue of people with DRE has been probed using single-cell multimodal transcriptomics. Evidence suggests that inflammatory cells and pro-inflammatory cytokines in the nervous system can lead to extensive biochemical changes, such as connexin hemichannel excitability and disruption of neurotransmitter homeostasis. The presence of inflammation may give rise to neuronal network abnormalities that suppress endogenous antiepileptic systems. We focus on the role of neuroinflammation and brain network anomalies in DRE from multiple perspectives to identify critical points for clinical application. We hope to provide an insightful overview to advance the quest for better DRE treatments.
Humans ; Drug Resistant Epilepsy/metabolism* ; Neuroinflammatory Diseases/immunology* ; Animals ; Brain/pathology* ; Nerve Net/pathology*

Mutations in ion channel genes have long been implicated in a spectrum of epilepsy syndromes. However, therapeutic decision-making is relatively complex for epilepsies associated with channelopathy. Therefore, in the present study, we used a patient-derived organoid model with a novel SCN2A mutation (p.E512K) to investigate the potential of utilizing such a model as a platform for preclinical testing of anti-seizure compounds. The electrophysiological properties of the variant Nav1.2 exhibited gain-of-function effects with increased current amplitude and premature activation. Immunofluorescence staining of patient-derived cortical organoids (COs) displayed normal neurodevelopment. Multielectrode array (MEA) recordings of patient-derived COs showed hyperexcitability with increased spiking and remarkable network bursts. Moreover, the application of patient-derived COs for preclinical drug testing using the MEA showed that they exhibit differential responses to various anti-seizure drugs and respond well to carbamazepine. Our results demonstrate that the individualized organoids have the potential to serve as a platform for preclinical pharmacological assessment.
Organoids/physiology* ; NAV1.2 Voltage-Gated Sodium Channel/genetics* ; Humans ; Anticonvulsants/pharmacology* ; Epilepsy/drug therapy* ; Mutation ; Cerebral Cortex/drug effects* ; Action Potentials/drug effects* ; Carbamazepine/pharmacology*

OBJECTIVE: To identify the optimal machine learning algorithm for predicting post-stroke epilepsy (PSE) within one year following acute stroke, establish a nomogram model based on this algorithm, and perform external validation to achieve accurate prediction of secondary epilepsy. METHODS: A total of 870 acute stroke patients admitted to the emergency department of Xiang'an Hospital of Xiamen University from June 2019 to June 2023 were enrolled for model development (model group). An external validation cohort of 435 acute stroke patients admitted to the Fifth Hospital of Xiamen during the same period was used to validate the machine learning algorithms and nomogram model. Patients were classified into control and epilepsy groups based on the development of PSE within one year. Clinical and laboratory data, including baseline characteristics, stroke location, vascular status, complications, hematologic parameters, and National Institutes of Health Stroke Scale (NIHSS) score, were collected for analysis. Nine machine learning algorithms such as logistic regression, CN2 rule induction, K-nearest neighbors, adaptive boosting, random forest, gradient boosting, support vector machine, naive Bayes, and neural network were applied to evaluate predictive performance. The area under the curve (AUC) of receiver operator characteristic curve (ROC curve) was used to identify the optimal algorithm. Logistic regression was used to screen risk factors for PSE, and the top 10 predictors were selected to construct the nomogram model. The predictive performance of the model was evaluated using the ROC curve in both the model and validation groups. RESULTS: Among the 870 patients in the model group, 29 developed PSE within one year. Among the nine algorithms tested, logistic regression demonstrated the best performance and generalizability, with an AUC of 0.923. Univariate logistic regression identified several risk factors for PSE, including platelet count, white blood cell count, red blood cell count, glycated hemoglobin (HbA1c), C-reactive protein (CRP), triglycerides, high-density lipoprotein (HDL), aspartate aminotransferase (AST), alanine aminotransferase (ALT), activated partial thromboplastin time (APTT), thrombin time, D-dimer, fibrinogen, creatine kinase (CK), creatine kinase-MB (CK-MB), lactate dehydrogenase (LDH), serum sodium, lactic acid, anion gap, NIHSS score, brain herniation, periventricular stroke, and carotid artery plaque. Further multivariate logistic regression analysis showed that white blood cell count, HDL, fibrinogen, lactic acid and brain herniation were independent risk factors [odds ratio (OR) were 1.837, 198.039, 47.025, 11.559, 70.722, respectively, all P < 0.05]. In the external validation group, univariate logistic regression analysis showed that platelet count, white blood cell count, CRP, triacylglycerol, APTT, D-dimer, fibrinogen, CK, CK-MB, LDH, NIHSS score, and cerebral herniation were risk factors for PSE one year after acute stroke. Further multiple logistic regression analysis showed that APTT and cerebral herniation were independent predictors (OR were 0.587 and 116.193, respectively, both P < 0.05). The nomogram model, constructed using 10 key variables-brain herniation, periventricular stroke, carotid artery plaque, white blood cell count, triglycerides, thrombin time, D-dimer, serum sodium, lactic acid, and NIHSS score-achieved an AUC of 0.908 in the model group and 0.864 in the external validation group. CONCLUSIONS The logistic regression-based prediction model for epilepsy one year after acute stroke, developed using machine learning algorithms, showed optimal predictive performance. The nomogram model based on the logistic regression-derived predictors showed strong discriminative power and was successfully validated externally, suggesting favorable clinical applicability and generalizability.
Humans ; Machine Learning ; Stroke/complications* ; Nomograms ; Epilepsy/etiology* ; Algorithms ; Male ; Female ; Logistic Models ; Middle Aged ; Aged ; Risk Factors ; Bayes Theorem

Psychogenic non-epileptic seizures are accompanied by motor,behavioral,sensory,and/or cognitive changes,with the clinical manifestations similar to epileptic seizures.This disease is easy to be misdiagnosed and neglected in clinical work.At present,most intervention measures still depend on the experience of clinicians.This article reviews the comorbidities of psychogenic non-epileptic seizures,including mental and cognitive disorders,somatic syndrome,sleep disorders,and epilepsy.This review aims to strengthen the precision of clinical treatment and management of patients with psychogenic non-epileptic seizures and provide more efficient individualized diagnosis and treatment programs for patients.
Humans ; Seizures/diagnosis* ; Comorbidity ; Epilepsy ; Sleep Wake Disorders ; Mental Disorders ; Psychophysiologic Disorders ; Cognition Disorders

OBJECTIVE: To analyze the disease patterns and acupoint selection characteristics of acupuncture for epilepsy in ancient acupuncture texts, providing references and ideas for clinical acupuncture treatment of epilepsy. METHODS: Texts from the Chinese Medical Classics (5th edition) regarding acupuncture for epilepsy are reviewed. The frequency of acupoints, meridian association, distribution, specific points, corresponding epilepsy subtypes, and needling techniques are statistically analyzed. The Apriori algorithm is used for association rule analysis, and a complex network analysis is conducted for high-frequency acupoints and their corresponding subtypes and treatments. RESULTS: A total of 205 acupuncture prescriptions are identified. Ancient texts favored differentiation-based treatments for epilepsy, primarily classified into epilepsy, wind epilepsy, and five epilepsy. Commonly used acupoints include Baihui (GV20), Jiuwei (CV15), Shenmen (HT7), Shenting (GV24), and Xinshu (BL15), with a focus on the acupoints of the governor vessel, the bladder meridian, and the conception vessel. The acupoints on the head, face are combined with the acupoints on the limbs, with skillful use of the five-shu points and intersection acupoints. The most frequent combinations are Shenmen (HT7)-Baihui (GV20), Shenting (GV24)-Baihui (GV20), and Xinshu (BL15)-Shenmen (HT7). Visual network analysis revealed that Baihui (GV20)-Shenting (GV24), Baihui (GV20)-Shenmen (HT7), and Baihui (GV20)-Zhaohai (KI6) are core acupoint combinations. Treatment mainly involved moxibustion or combined acupuncture and moxibustion. CONCLUSION The acupoint selection for epilepsy treatment in ancient texts is precise, frequently using Baihui (GV20), Jiuwei (CV15), Shenmen (HT7), Shenting (GV24), and Xinshu (BL15), etc., with emphasis on calming epilepsy, awakening the spirit, relaxing tendons, and nourishing the heart.
Acupuncture Points ; Humans ; Epilepsy/history* ; History, Ancient ; Acupuncture Therapy/history* ; Medicine in Literature/history* ; Meridians ; China