Somatic mosaicism in a fraction of brain cells causes neurodevelopmental disorders, including childhood intractable epilepsy. However, the threshold for somatic mosaicism leading to brain dysfunction is unknown. In this study, we induced various mosaic burdens in focal cortical dysplasia type II (FCD II) mice, featuring mTOR somatic mosaicism and spontaneous behavioral seizures. The mosaic burdens ranged from approximately 1,000 to 40,000 neurons expressing the mTOR mutant in the somatosensory (SSC) or medial prefrontal (PFC) cortex. Surprisingly, approximately 8,000 to 9,000 neurons expressing the MTOR mutant, which are extrapolated to constitute 0.08-0.09% of total cells or roughly 0.04% of variant allele frequency (VAF) in the mouse hemicortex, were sufficient to trigger epileptic seizures. The mutational burden was correlated with seizure frequency and onset, with a higher tendency for electrographic inter-ictal spikes and beta- and gamma-frequency oscillations in FCD II mice exceeding the threshold. Moreover, mutation-negative FCD II patients in deep sequencing of their bulky brain tissues revealed somatic mosaicism of the mTOR pathway genes as low as 0.07% in resected brain tissues through ultra-deep targeted sequencing (up to 20 million reads). Thus, our study suggests that extremely low levels of somatic mosaicism can contribute to brain dysfunction.

Epidural spinal cord stimulation (SCS) is indicated for the treatment of intractable pain and is widely used in clinical practice. In previous basic research, the therapeutic effects of SCS have been demonstrated for epileptic seizure. However, the mechanism has not yet been elucidated. In this study, we investigated the therapeutic effect of SCS and the influence of epileptic seizure. First, SCS in the cervical spine was performed. The rats were divided into four groups: control group and treatment groups with SCS conducted at 2, 50, and 300 Hz frequency. Two days later, convulsions were induced by the intraperitoneal administration of kainic acid, followed by video monitoring to assess seizures. We also evaluated glial cells in the hippocampus by fluorescent immunostaining, electroencephalogram measurements, and inflammatory cytokines such as C-C motif chemokine ligand 2 (CCL2) by quantitative real-time polymerase chain reaction. Seizure frequency and the number of glial cells were significantly lower in the 300 Hz group than in the control group. SCS at 300 Hz decreased gene expression level of CCL2, which induces monocyte migration. SCS has anti-seizure effects by inhibiting CCL2-mediated cascades. The suppression of CCL2 and glial cells may be associated with the suppression of epileptic seizure.

BACKGROUND: Over 60% of epilepsy patients globally are children, whose early diagnosis and treatment are critical for their development and can substantially reduce the disease\'s burden on both families and society. Numerous algorithms for automated epilepsy detection from EEGs have been proposed. Yet, the occurrence of epileptic seizures during an EEG exam cannot always be guaranteed in clinical practice. Models that exclusively use seizure EEGs for detection risk artificially enhanced performance metrics. Therefore, there is a pressing need for a universally applicable model that can perform automatic epilepsy detection in a variety of complex real-world scenarios.
METHODS: To address this problem, we have devised a novel technique employing a temporal convolutional neural network with self-attention (TCN-SA). Our model comprises two primary components: a TCN for extracting time-variant features from EEG signals, followed by a self-attention (SA) layer that assigns importance to these features. By focusing on key features, our model achieves heightened classification accuracy for epilepsy detection.
RESULTS: The efficacy of our model was validated on a pediatric epilepsy dataset we collected and on the Bonn dataset, attaining accuracies of 95.50% on our dataset, and 97.37% (A v. E), and 93.50% (B vs E), respectively. When compared with other deep learning architectures (temporal convolutional neural network, self-attention network, and standardized convolutional neural network) using the same datasets, our TCN-SA model demonstrated superior performance in the automated detection of epilepsy.
CONCLUSIONS: The proven effectiveness of the TCN-SA approach substantiates its potential as a valuable tool for the automated detection of epilepsy, offering significant benefits in diverse and complex real-world clinical settings.

BACKGROUND: Epilepsy is the most common neurological disorder among humans after headaches. According to the World Health Organization, approximately 50-65 million individuals were diagnosed with epilepsy throughout the world, and around two million new cases of epilepsy are added to this figure every year.
METHODS: Designed as descriptive and cross-sectional research, this study was performed on 132 elementary school teachers. Training on epilepsy and epileptic seizure was given to teachers. The pretest and posttest research data were collected with the face-to-face interview method. In this process, the epilepsy knowledge scale was used as well as a survey form that had questions designed to find out about teachers\' personal characteristics. The Statistical Package for Social Science 25.0 was utilized in the statistical analysis of research data. In the research, the statistical significance was identified if the p-value was below.05 (p < .05).
RESULTS: Of all teachers participating in the study, 59.1% were female, 90.2% were married, and 47.7% witnessed an epilepsy seizure before. The mean of teachers\' pretest epilepsy knowledge scores was 8.43 ± 4.31 points before the training while the mean of their posttest epilepsy knowledge scores was 12.65 ± 2.48 points after the training. The difference between the means of pretest and posttest scores was statistically significant (p = .000). After the training, there was a statistically significant increase in means of scores obtained by teachers from each item of the epilepsy knowledge scale (p < .05).
CONCLUSIONS: As there was a statistically significant improvement in levels of teachers\' knowledge about both epilepsy and epileptic seizure after the training, it is recommended that the training about the approach to epilepsy and epileptic seizure be given to all teachers, and additionally, including these topics in the course curricula of universities is recommended.

OBJECTIVE: Closure surgery of patent foramen ovale (PFO) has been found to effectively control cryptogenic stroke and migraine, but it is uncertain whether PFO closure could also alleviate epileptic seizures. This study aims to observe the therapeutic effect of PFO closure on epileptic seizures.
METHODS: Since July 11th, 2017, in the neurology department of West China Hospital, Sichuan University, Chengdu, we have been regularly monitoring patients with epilepsy who have undergone PFO closure. The patient\'s clinical information, such as frequency, duration, and severity of seizures, before and after surgery was recorded in detail as well as postoperative safety events.
RESULTS: Of the 31 epilepsy patients who confirmed PFO observed (27 cases were drug-resistant epilepsy, 87.10%), average age of surgery was 23.74 years, and 12 cases were female (38.71%). After one-year follow-up, 26 patients (83.87%) achieved remission of seizure frequency, and 22 of whom (70.97%) experienced a remission of more than 50%. Additionally, compared to before surgery, 22 cases (70.97%) reported a decrease in the average seizure duration, and 20 cases (64.52%) reported a reduction in seizure severity. In the seizure indicators of frequency, average duration and severity, significant differences were identified between preoperative and postoperative comparisons with all test p values were <0.05. Furthermore, no serious safety events were reported except for one patient who briefly reported chest pain, and all patients expressed effective PFO closure.
CONCLUSIONS: The PFO closure has been shown for the first time to result in a significant reduction in the frequency, duration, and severity of seizures. Patients with drug-resistant epilepsy and PFO with a large shunt are ideal candidates for undergoing PFO closure.
CONCLUSIONS: Since PFO closure was found to have a good therapeutic effect on cryptogenic stroke and migraine, it has become a credible complementary therapy for the treatment of neurological diseases, and drug-resistant epilepsy with PFO is expected to become the next target disease that PFO closure could significantly improve.

BACKGROUND: One of the mechanisms of epileptgenesis is impairment of inhibitory neural circuits. Several studies have compared neural changes among subtypes of gamma-aminobutyric acid-related (GABAergic) neurons after acquired epileptic seizure. However, it is unclear that GABAergic neural modifications that occur during acquisition process of epileptic seizure.
METHODS: Male rats were injected with pentylenetetrazole (PTZ kindling: n = 30) or saline (control: n = 15) every other day to observe the development of epileptic seizure stages. Two time points were identified: the point at which seizures were most difficult to induce, and the point at which seizures were most easy to induce. The expression of GABAergic neuron-related proteins in the hippocampus was immunohistochemically compared among GABAergic subtypes at each of these time points.
RESULTS: Bimodal changes in seizure stages were observed in response to PTZ kindling. The increase of seizure stage was transiently suppressed after 8 or 10 injections, and then progressed again by the 16th injection. Based on these results, we defined 10 injections as a short-term injection period during which seizures are less likely to occur, and 20 injections as a long-term injection period during which continuous seizures are likely to occur. The immunohistochemical analysis showed that hippocampal glutamic acid decarboxylase 65 (GAD65) expression was increased after short-term kindling but unchanged after long-term kindling. Increased GAD65 expression was limited to somatostatin-positive (SOM+) cells among several GABAergic subtypes. By contrast, GAD, GABA, GABAAR α1, GABABR1, and VGAT cells showed no change following short- or long-term PTZ kindling.
CONCLUSIONS: PTZ kindling induces bimodal changes in the epileptic seizure stage. Seizure stage is transiently suppressed after short-term PTZ injection with GAD65 upregulation in SOM+ cells. The seizure stage is progressed again after long-term PTZ injection with GAD65 reduction to baseline level.

Epileptic seizures have been widely considered as a complication of external or iatrogenic factors in schizophrenia. However, epidemiologic, neurodevelopmental and genetic data have changed regards on this topic considering the complexity of the bidirectional link between epilepsy and schizophrenia. We will examine these data constituting the pathophysiological aspects of this particular association and detail the particular impact of antipsychotics on the occurence of epileptic seizure in schizophrenia as well as the management strategies.

OBJECTIVE: We evaluated the on-scene time of emergency medical services (EMS) for cases where discrimination between acute stroke and epileptic seizures at the initial examination was difficult and identified factors linked to delays in such scenarios.
METHODS: A retrospective review of cases with suspected seizure using the EMS database of fire departments across six Japanese cities between 2016 and 2021 was conducted. Patient classification was based on transport codes. We defined cases with stroke-suspected seizure as those in whom epileptic seizure was difficult to differentiate from stroke and evaluated their EMS on-scene time compared to those with epileptic seizures.
RESULTS: Among 30,439 cases with any seizures, 292 cases of stroke-suspected seizure and 8,737 cases of epileptic seizure were included. EMS on-scene time in cases of stroke-suspected seizure was shorter than in those with epileptic seizure after propensity score matching (15.1±7.2 min vs. 17.0±9.0 min; p = 0.007). Factors associated with delays included transport during nighttime (odds ratio [OR], 1.73, 95 % confidence interval [CI] 1.02-2.93, p = 0.041) and transport during the 2020-2021 pandemic (OR, 1.77, 95 % CI 1.08-2.90, p = 0.022).
CONCLUSIONS: This study highlighted the difference between the characteristics in EMS for stroke and epileptic seizure by evaluating the response to cases with stroke-suspected seizure. Facilitating prompt and smooth transfers of such cases to an appropriate medical facility after admission could optimize the operation of specialized medical resources.

One of the epileptic patients\' challenges is to detect the time of seizures and the possibility of predicting. This research aims to provide an algorithm based on deep learning to detect and predict the time of seizure from one to two minutes before its occurrence. The proposed Convolutional Neural Network (CNN) can detect and predict the occurrence of focal epilepsy seizures through single-lead-ECG signal processing instead of using EEG signals. The structure of the proposed CNN for seizure detection and prediction is the same. Considering the requirements of a wearable system, after a few light pre-processing steps, the ECG signal can be used as input to the neural network without any manual feature extraction step. The desired neural network learns purposeful features according to the labelled ECG signals and then performs the classification of these signals. Training of 39-layer CNN for seizure detection and prediction has been done separately. The proposed method can detect seizures with an accuracy of 98.84% and predict them with an accuracy of 94.29%. With this approach, the ECG signal can be a promising indicator for the construction of portable systems for monitoring the status of epileptic patients.

[This corrects the article DOI: 10.3389/fendo.2023.1220957.].