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

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

Epileptic seizures and the interictal epileptiform discharges both have similar waveforms. And a method to effectively extract features that can be used to distinguish seizures is of crucial importance both in theory and clinical practice. We constructed state transfer networks by using visibility graphlet at multiple sampling intervals and analyzed network features. We found that the characteristics waveforms in ictal periods were more robust with various sampling intervals, and those feature network structures did not change easily in the range of the smaller sampling intervals. Inversely, the feature network structures of interictal epileptiform discharges were stable in range of relatively larger sampling intervals. Furthermore, the feature nodes in networks during ictal periods showed long-term correlation along the process, and played an important role in regulating system behavior. For stereo-electroencephalography at around 500 Hz, the greatest difference between ictal and the interictal epileptiform occurred at the sampling interval around 0.032 s. In conclusion, this study effectively reveals the correlation between the features of pathological changes in brain system and the multiple sampling intervals, which holds potential application value in clinical diagnosis for identifying, classifying, and predicting epilepsy.
Humans ; Electroencephalography/methods* ; Epilepsy/diagnosis* ; Signal Processing, Computer-Assisted ; Brain/physiopathology* ; Seizures/diagnosis* ; Algorithms

With the high incidence of neurological diseases such as stroke and mental illness, rehabilitation treatments for neurological disorders have received widespread attention. Electroencephalography (EEG) technology, despite its excellent temporal resolution, has historically been limited in application due to its insufficient spatial resolution, and is mainly confined to preoperative assessment, intraoperative monitoring, and epilepsy detection. However, traditional constraints of EEG technology are being overcome with the popularization of EEG technology with high-density over 64-lead, the application of innovative analysis techniques and the integration of multimodal techniques, which are significantly broadening its applications in clinical settings. These advancements have not only reinforced the irreplaceable role of EEG technology in neurorehabilitation assessment, but also expanded its therapeutic potential through its combined use with technologies such as transcranial magnetic stimulation, transcranial electrical stimulation and brain-computer interfaces. This article reviewed the applications, advancements, and future prospects of EEG technology in neurorehabilitation assessment and treatment. Advancements in technology and interdisciplinary collaboration are expected to drive new applications and innovations in EEG technology within the neurorehabilitation field, providing patients with more precise and personalized rehabilitation strategies.
Humans ; Electroencephalography/methods* ; Brain-Computer Interfaces ; Neurological Rehabilitation/methods* ; Transcranial Magnetic Stimulation ; Transcranial Direct Current Stimulation ; Nervous System Diseases/diagnosis* ; Epilepsy/diagnosis*

Humans ; Diagnosis, Differential ; Long QT Syndrome/genetics* ; Syncope/diagnosis* ; Epilepsy/diagnosis* ; Electrocardiography

Uncovering the alterations of neural interactions within the brain during epilepsy is important for the clinical diagnosis and treatment. Previous studies have shown that the phase-amplitude coupling (PAC) can be used as a potential biomarker for locating epileptic zones and characterizing the transition of epileptic phases. However, in contrast to the θ-γ coupling widely investigated in epilepsy, few studies have paid attention to the β-γ coupling, as well as its potential applications. In the current study, we use the modulation index (MI) to calculate the scalp electroencephalography (EEG)-based β-γ coupling and investigate the corresponding changes during different epileptic phases. The results show that the β-γ coupling of each brain region changes with the evolution of epilepsy, and in several brain regions, the β-γ coupling decreases during the ictal period but increases in the post-ictal period, where the differences are statistically significant. Moreover, the alterations of β-γ coupling between different brain regions can also be observed, and the strength of β-γ coupling increases in the post-ictal period, where the differences are also significant. Taken together, these findings not only contribute to understanding neural interactions within the brain during the evolution of epilepsy, but also provide a new insight into the clinical treatment.
Humans ; Scalp ; Epilepsy/diagnosis* ; Brain ; Electroencephalography

There are some limitations in the localization of epileptogenic zone commonly used by human eyes to identify abnormal discharges of intracranial electroencephalography in epilepsy. However, at present, the accuracy of the localization of epileptogenic zone by extracting intracranial electroencephalography features needs to be further improved. As a new method using dynamic network model, neural fragility has potential application value in the localization of epileptogenic zone. In this paper, the neural fragility analysis method was used to analyze the stereoelectroencephalography signals of 35 seizures in 20 patients, and then the epileptogenic zone electrodes were classified using the random forest model, and the classification results were compared with the time-frequency characteristics of six different frequency bands extracted by short-time Fourier transform. The results showed that the area under curve (AUC) of epileptic focus electrodes based on time-frequency analysis was 0.870 (delta) to 0.956 (high gamma), and its classification accuracy increased with the increase of frequency band, while the AUC by using neural fragility could reach 0.957. After fusing the neural fragility and the time-frequency characteristics of the γ and high γ band, the AUC could be further increased to 0.969, which was improved on the original basis. This paper verifies the effectiveness of neural fragility in identifying epileptogenic zone, and provides a theoretical reference for its further clinical application.
Humans ; Electroencephalography/methods* ; Epilepsy/diagnosis* ; Seizures ; Stereotaxic Techniques

Accurate source localization of the epileptogenic zone (EZ) is the primary condition of surgical removal of EZ. The traditional localization results based on three-dimensional ball model or standard head model may cause errors. This study intended to localize the EZ by using the patient-specific head model and multi-dipole algorithms using spikes during sleep. Then the current density distribution on the cortex was computed and used to construct the phase transfer entropy functional connectivity network between different brain areas to obtain the localization of EZ. The experiment result showed that our improved methods could reach the accuracy of 89.27% and the number of implanted electrodes could be reduced by (19.34 ± 7.15)%. This work can not only improve the accuracy of EZ localization, but also reduce the additional injury and potential risk caused by preoperative examination and surgical operation, and provide a more intuitive and effective reference for neurosurgeons to make surgical plans.
Humans ; Scalp ; Brain Mapping/methods* ; Epilepsy/diagnosis* ; Electroencephalography/methods* ; Brain

OBJECTIVE: To explore the clinical and genetic characteristics of two children with Williams-Beuren syndrome (WBS). METHODS: Two children who had presented at the Department of Pediatrics, General Hospital of Ningxia Medical University respectively on January 26 and March 18, 2021 were selected as the study subjects. Clinical data and results of genetic testing of the two patients were analyzed. RESULTS: Both children had featured developmental delay, characteristic facies and cardiovascular malformation. Child 1 also had subclinical hypothyroidism, whilst child 2 had occurrence of epilepsy. Genetic testing revealed that child 1 has harbored a 1.54 Mb deletion in the 7q11.23 region, whilst child 2 has a 1.53 Mb deletion in the same region, in addition with a c.158G>A variant of the ATP1A1 gene and a c.12181A>G variant of the KMT2C gene. Based on the guidelines from the American College of Medical Genetics and Genomics, the c.158G>A and c.12181A>G variants were rated as variants of unknown significance (PM1+PM2_Supporting+PP2+PP3；PM2_Supporting). CONCLUSION Both children had characteristic features of WBS, for which deletions of the 7q11.23 region may be accountable. For children manifesting developmental delay, facial dysmorphism and cardiovascular malformations, the diagnosis of WBS should be suspected, and genetic testing should be recommended to confirm the diagnosis.
Child ; Humans ; Williams Syndrome/diagnosis* ; Genetic Testing ; Facies ; Epilepsy/genetics* ; Chromosomes, Human, Pair 7/genetics* ; Chromosome Deletion

Objective: To summarize the clinical characteristics of epileptic seizure associated with neurofibromatosis type 1 (NF1). Methods: From January 2017 to July 2023 at Children's Hospital Capital Institute of Pediatrics, medical records of patients with both NF1 and epileptic seizure were reviewed in this case series study. The clinical characteristics, treatment and prognosis were analyzed retrospectively. Results: A total of 15 patients(12 boys and 3 girls) were collected. Café-au-lait macules were observed in all 15 patients. There were 6 patients with neurodevelopmental disorders and the main manifestations were intellectual disability or developmental delay. The age at the first epileptic seizure was 2.5 (1.2, 5.5) years. There were various seizure types, including generalized tonic-clonic seizures in 8 patients, focal motor seizures in 6 patients, epileptic spasm in 4 patients, tonic seizures in 1 patient, absence in 1 patient, generalized myoclonic seizure in 1 patient and focal to bilateral tonic-clonic seizure in 1 patient. Among 14 patients whose brain magnetic resonance imaging results were available, there were abnormal signals in corpus callosum, basal ganglia, thalamus or cerebellum in 6 patients, dilated ventricles of different degrees in 3 patients, blurred gray and white matter boundary in 2 patients, agenesis of corpus callosum in 1 patient and no obvious abnormalities in the other patients. Among 13 epilepsy patients, 8 were seizure-free with 1 or 2 antiseizure medications(ASM), 1 with drug resistant epilepsy was seizure-free after left temporal lobectomy, and the other 4 patients who have received 2 to 9 ASM had persistent seizures. One patient with complex febrile convulsion achieved seizure freedom after oral administration of diazepam on demand. One patient had only 1 unprovoked epileptic seizure and did not have another seizure without taking any ASM. Conclusions: The first epileptic seizure in NF1 patients usually occurs in infancy and early childhood, with the main seizure type of generalized tonic-clonic seizure and focal motor seizure. Some patients have intellectual disability or developmental delay. Most epilepsy patients achieve seizure freedom with ASM.
Male ; Female ; Humans ; Child, Preschool ; Child ; Neurofibromatosis 1/diagnosis* ; Retrospective Studies ; Intellectual Disability ; Electroencephalography ; Epilepsy/etiology* ; Seizures/etiology*