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Accurate early prediction of epileptic seizures can provide timely treatment for patients. Previous studies have mainly focused on a single temporal or spatial dimension, making it difficult to take both relationships into account. Therefore, the effective properties of electroencephalograms (EEGs) may not be fully evaluated. To solve this problem, we propose a spatiotemporal graph attention network (STGAT) based on synchronization. The spatial and functional connectivity information between EEG channels was extracted by using the phase locking values (PLVs) first, which allowed multichannel EEG signals to be modeled as graph signals. Afterward, the STGAT model was used to dynamically learn the temporal correlation properties of EEG sequences and explore the spatial topological structure information of multiple channels. Experimental results demonstrated that the STGAT model was able to obtain spatiotemporal correlations and achieve good results on two benchmark datasets. The accuracy, specificity and sensitivity were 98.74%, 99.21% and 98.87%, respectively, on the CHB-MIT dataset. Moreover, all evaluation indices of the private dataset had reached more than 98.8%, with the area under the curve (AUC) reaching 99.96%. The proposed method is superior or comparable to the state-of-the-art models. Extensive experiments demonstrate that our end-to-end automatic seizure prediction model can be extended to design clinical assistant decision systems.

Importance: Selection of antiseizure medications (ASMs) for epilepsy remains largely a trial-and-error approach. Under this approach, many patients have to endure sequential trials of ineffective treatments until the "right drugs" are prescribed., Objective: To develop and validate a deep learning model using readily available clinical information to predict treatment success with the first ASM for individual patients., Design, Setting, and Participants: This cohort study developed and validated a prognostic model. Patients were treated between 1982 and 2020. All patients were followed up for a minimum of 1 year or until failure of the first ASM. A total of 2404 adults with epilepsy newly treated at specialist clinics in Scotland, Malaysia, Australia, and China between 1982 and 2020 were considered for inclusion, of whom 606 (25.2%) were excluded from the final cohort because of missing information in 1 or more variables., Exposures: One of 7 antiseizure medications., Main Outcomes and Measures: With the use of the transformer model architecture on 16 clinical factors and ASM information, this cohort study first pooled all cohorts for model training and testing. The model was trained again using the largest cohort and externally validated on the other 4 cohorts. The area under the receiver operating characteristic curve (AUROC), weighted balanced accuracy, sensitivity, and specificity of the model were all assessed for predicting treatment success based on the optimal probability cutoff. Treatment success was defined as complete seizure freedom for the first year of treatment while taking the first ASM. Performance of the transformer model was compared with other machine learning models., Results: The final pooled cohort included 1798 adults (54.5% female; median age, 34 years [IQR, 24-50 years]). The transformer model that was trained using the pooled cohort had an AUROC of 0.65 (95% CI, 0.63-0.67) and a weighted balanced accuracy of 0.62 (95% CI, 0.60-0.64) on the test set. The model that was trained using the largest cohort only had AUROCs ranging from 0.52 to 0.60 and a weighted balanced accuracy ranging from 0.51 to 0.62 in the external validation cohorts. Number of pretreatment seizures, presence of psychiatric disorders, electroencephalography, and brain imaging findings were the most important clinical variables for predicted outcomes in both models. The transformer model that was developed using the pooled cohort outperformed 2 of the 5 other models tested in terms of AUROC., Conclusions and Relevance: In this cohort study, a deep learning model showed the feasibility of personalized prediction of response to ASMs based on clinical information. With improvement of performance, such as by incorporating genetic and imaging data, this model may potentially assist clinicians in selecting the right drug at the first trial.

Background: To minimize drug-related toxicity and monitor dosing regimens, an ultra-sensitive, simple and high-throughput analytical method for therapeutic drug monitoring is required. A novel LC-MS/MS bioassay of levetiracetam, lamotrigine and 10-hydroxycarbazepine in human plasma was established. The analytes were separated on a Hypersil GOLD™ C 18 column under a 2.5 min isocratic elution after one-step protein precipitation. MS detection was performed under electrospray ionization positive-mode fitted with selected reaction monitoring. The validated ranges were 0.1-20 μg/ml for LTG, 0.3-60 μg/ml for 10-hydroxycarbazepine and levetiracetam. The intra- and inter-batches of precision and accuracy was within ±15%. The novel method met all other criteria., Conclusion: This method can be used to monitor drug concentrations and decision-making in epileptic patients., (© 2022 John Wiley & Sons Ltd.)

Background: Epilepsy was defined as an abnormal brain network model disease in the latest definition. From a microscopic perspective, it is also particularly important to observe the Mutual Information (MI) of the whole brain network based on different lead positions., Methods: In this study, we selected EEG data from representative temporal lobe and frontal lobe epilepsy patients. Based on Phase Space Reconstruction and the calculation of MI indicator, we used Complex Network technology to construct a dynamic brain network function model of epilepsy seizure. At the same time, about the analysis of our network, we described the index changes and propagation paths of epilepsy discharge in different periods, and spatially monitors the seizure change process based on the analysis of the parameter characteristics of the complex network., Results: Our model portrayed the functional synergy between the various regions of the brain and the state transition during the seizure process. We also characterized the EEG synchronous propagation path and core nodes during seizures. The results shown the full node change path and the distribution of important indicators during the seizure process, which makes the state change of the seizure process more clearly., Conclusion: In this study, we have demonstrated that synchronization-based brain networks change with time and space. The EEG synchronous propagation path and core nodes during epileptic seizures can provide a reference for finding the focus area., (© 2021. The Author(s).)

Background: Epilepsy is one of the diseases of the nervous system, which has a large population in the world. Traditional diagnosis methods mostly depended on the professional neurologists' reading of the electroencephalogram (EEG), which was time-consuming, inefficient, and subjective. In recent years, automatic epilepsy diagnosis of EEG by deep learning had attracted more and more attention. But the potential of deep neural networks in seizure detection had not been fully developed., Methods: In this article, we used a one-dimensional convolutional neural network (1-D CNN) to replace the residual network architecture's traditional convolutional neural network (CNN). Moreover, we combined the Independent recurrent neural network (indRNN) and CNN to form a new residual network architecture-independent convolutional recurrent neural network (RCNN). Our model can achieve an automatic diagnosis of epilepsy EEG. Firstly, the important features of EEG were learned by using the residual network architecture of 1-D CNN. Then the relationship between the sequences were learned by using the recurrent neural network. Finally, the model outputted the classification results., Results: On the small sample data sets of Bonn University, our method was superior to the baseline methods and achieved 100% classification accuracy, 100% classification specificity. For the noisy real-world data, our method also exhibited powerful performance., Conclusion: The model we proposed can quickly and accurately identify the different periods of EEG in an ideal condition and the real-world condition. The model can provide automatic detection capabilities for clinical epilepsy EEG detection. We hoped to provide a positive significance for the prediction of epileptic seizures EEG., (© 2021. The Author(s).)

Increased expression of some matrix metalloproteinases (MMPs) is closely associated with epilepsy. However, factors that promote their expression have not been clarified. Long noncoding RNAs (lncRNAs) play crucial roles in the development of human diseases, including various cancers, but its potential function in temporal lobe epilepsy (TLE) has remained unexplored. In this study, we showed that hippocampal and serum ILF3-AS1 levels are higher in TLE patients than in matched controls. Interleukin (IL)-1β and tumor necrosis factor (TNF)-α induced ILF3-AS1 expression in astrocytes, while ectopic expression of ILF3-AS1 enhanced IL-6 and TNF-α expression. Ectopic ILF3-AS1 in astrocytes also increased expression of MMP2, MMP3, MMP9 and MMP14, but suppressed expression of miR-212. Consistent with that finding, miR-212 levels were lower in the hippocampus and serum of TLE patients than their controls. This suggests that ILF3-AS1 promotes expression of inflammatory cytokines and MMPs by targeting miR-212 and that ILF3-AS1 plays a crucial role in the development of TLE.

Objective: The authors sought to perform a preliminary assessment of the safety and effectiveness of stereotactic laser interstitial thermal therapy (LITT) for patients with cerebral cavernous malformation (CCM)-related epilepsy., Methods: The authors retrospectively analyzed 6 patients with CCM-related epilepsy who underwent LITT. Pre-, intra-, and postoperative brain MRI studies were used to characterize preoperative CCM volume, ablation volume, and postablation hemosiderin volume. Clinical outcomes were assessed postoperatively during clinic follow-up visits or phone interviews., Results: LITT was performed in 7 CCMs in 6 patients. Two patients had familial CCM disease with multifocal lesions. Four treated CCMs were extratemporal, and 3 were in or near the visual pathways. The median follow-up was 25 (range 12-39) months. Five of 6 (83%) patients achieved seizure freedom (Engel I classification), of whom 4 (67%) were Engel IA and 1 was Engel IC after a single seizure on postoperative day 4. The remaining patient had rare seizures (Engel II). One patient had a nondisabling visual field deficit. There were no hemorrhagic complications. All patients were discharged within 24 hours postablation. MRI 3-11 months after ablation demonstrated expected focal necrosis and trace hemosiderin-related T2 hypointensity measuring 9%-44% (median 24%) of the original lesion volume, with significant (p = 0.04) volume reduction., Conclusions: LITT is a minimally invasive option for treating CCM-related epilepsy with seizure outcomes comparable to those achieved with open lesionectomy. The precision of LITT allows for the obliteration of eloquent, deep, small, and multifocal lesions with low complication rates, minimal postoperative discomfort, and short hospital stays. In this study the feasibility and benefits of this method were demonstrated in 2 patients with multifocal lesions.

Valproic acid (VPA) is used as one of the first-line antiepileptic drugs to control seizure in epilepsy patients. However, one third of patients do not respond to VPA. This study is to investigate the influence of single nucleotide polymorphisms (SNPs) in multidrug transporters on VPA responses in Han Chinese epilepsy patients on VPA monotherapy. Twelve SNPs involved in VPA transport pathways, including ABCC2, ABCC4, ABCG2, MCT1, MCT2 and OATP2B1 were genotyped in 153 Han Chinese epilepsy patients. We found that among all the patients, MCT1 rs60844753 CC carriers have higher incidence of VPA-resistance than CG carriers (P = 0.05), and in subgroup of generalized seizure, ABCC2 rs3740066 CC carriers had higher frequency of VPA resistance than TC + TT carriers (P = 0.03). Although other SNPs were not correlated with VPA resistance, significant ethnic difference was found in minor allele frequency of these SNPs, indicating that the influence of these SNPs on VPA efficacy should be broadly investigated in other ethnic populations. This study provides nominal evidence that SNPs of genes involved in the transport of VPA contribute to interpatient variation in VPA response. Although the associations were abolished after Bonferroni correction, the results provide an incentive for further research in sufficiently large samples., (Copyright © 2019 The Japanese Society for the Study of Xenobiotics. Published by Elsevier Ltd. All rights reserved.)

Background: Characterizing the synchronous changes of epileptic seizures in different stages between different regions is profound to understand the transmission pathways of epileptic brain network and epileptogenic foci. There is currently no adequate quantitative calculation method for describing the propagation pathways of electroencephalogram (EEG) signals in the brain network from the short and long term. The goal of this study is to explore the innovative method to locate epileptic foci, mapping synchronization in the brain networks based on EEG., Methods: Mutual information was used to analyze the short-term synchronization in the full electrodes; while nonlinear dynamics quantifies the statistical independencies in the long -term among all electrodes. Then graph theory based on the complex network was employed to construct a dynamic brain network for epilepsy patients when they were awake, asleep and in seizure, analyzing the changing topology indexes., Results: Epileptic network achieved a high degree of nonlinear synchronization compared to awake time. and the main path of epileptiform activity was revealed by searching core nodes. The core nodes of the brain network were in connection with the onset zone. Seizures always happened with a high degree of distribution., Conclusions: This study indicated the path of EEG synchronous propagation in seizures, and core nodes could locate the epileptic foci accurately in some epileptic patients.

Background: Automated seizure detection from clinical EEG data can reduce the diagnosis time and facilitate targeting treatment for epileptic patients. However, current detection approaches mainly rely on limited features manually designed by domain experts, which are inflexible for the detection of a variety of patterns in a large amount of patients' EEG data. Moreover, conventional machine learning algorithms for seizure detection cannot accommodate multi-channel Electroencephalogram (EEG) data effectively, which contains both temporal and spatial information. Recently, deep learning technology has been widely applied to perform image processing tasks, which could learns useful features from data and process multi-channel data automatically. To provide an effective system for automatic seizure detection, we proposed a new three-dimensional (3D) convolutional neural network (CNN) structure, whose inputs are multi-channel EEG signals., Methods: EEG data of 13 patients were collected from one center hospital, which has already been inspected by experts. To represent EEG data in CNN, firstly time series of each channel of EEG data was converted into the two-dimensional image. Then all channel images were combined into 3D images according to the mutual correlation intensity between different electrodes. Finally, a CNN was constructed using 3D kernels to predict different stages of EEG data, including inter-ictal, pre-ictal, and ictal stages. The system performance was evaluated and compared with the traditional feature-based classifier and the two-dimensional (2D) deep learning method., Results: It demonstrated that multi-channel EEG data could provide more information for increasing the specificity and sensitivity in cpmparison result between the single and multi-channel. And the 3D CNN based on multi-channel outperformed the 2D CNN and traditional signal processing methods with an accuracy of more than 90%, an sensitivity of 88.90% and an specificity of 93.78%., Conclusions: This is the first effort to apply 3D CNN in detecting seizures from EEG. It provides a new way of learning patterns simultaneously from multi-channel EEG signals, and demonstrates that deep neural networks in combination with 3D kernels can establish an effective system for seizure detection.

Objective: We aimed to investigate the effect of a code stroke system on the development of poststroke epilepsy., Methods: We retrospectively analyzed consecutive patients treated with IV thrombolysis under or outside the code stroke system between 2003 and 2012. Patients were followed up for at least 2 years or until death. Factors with p < 0.1 in univariate comparisons were selected for multivariable logistic and Cox regression., Results: A total of 409 patients met the eligibility criteria. Their median age at stroke onset was 75 years (interquartile range 64-83 years); 220 (53.8%) were male. The median follow-up duration was 1,074 days (interquartile range 119-1,671 days). Thirty-two patients (7.8%) had poststroke seizures during follow-up, comprising 7 (1.7%) with acute symptomatic seizures and 25 (6.1%) with late-onset seizures. Twenty-six patients (6.4%) fulfilled the definition of poststroke epilepsy. Three hundred eighteen patients (77.8%) were treated with the code stroke system while 91 (22.2%) were not. After adjustment for age and stroke etiology, use of the code stroke system was associated with decreased odds of poststroke epilepsy (odds ratio = 0.36, 95% confidence interval 0.14-0.87, p = 0.024). Cox regression showed lower adjusted hazard rates for poststroke epilepsy within 5 years for patients managed under the code stroke system (hazard ratio = 0.60, 95% confidence interval 0.47-0.79, p < 0.001)., Conclusion: The code stroke system was associated with reduced odds and instantaneous risk of poststroke epilepsy. Further studies are required to identify the contribution of the individual components and mechanisms against epileptogenesis after stroke., Classification of Evidence: This study provides Class III evidence that for people with acute ischemic stroke, implementation of a code stroke system reduces the risk of poststroke epilepsy., (© 2018 American Academy of Neurology.)

Valproic acid(VPA) is a classic drug that used to treat epilepsy in monotherapy or combination with other anticonvulsant drugs such as lamotrigine (LTG). Although it was reported that VPA could increase lamotrigine trough concentration in clinical practice, there was no report about the effect of LTG on the trough concentration of VPA and its main metabolites, such as 4-ene-VPA, 3-OH-VPA, 4-OH-VPA, 3-keto-VPA, 2-PGA which are related to the therapeutic and toxic effects of VPA. In this study, a simple and rapid method for the simultaneous determination of VPA and its five metabolites in human plasma using HPLC-MS/MS was developed for the first time. Benzoic acid was used as an internal standard (IS). Separation was performed on a Hypersil GOLD C 18 column by isocratic elution using acetonitrile: 10mM ammonium acetate solution (90:10, v/v) as mobile phase at a flow rate of 0.3mL/min. A triple quadrupole mass spectrometer operating in the negative ion-switching, electron spray ionization mode with selection reaction monitoring (SRM) was employed to determine transitions of m/z 143.183→143.183, 157.033→113.165, 173.017→129.074, 159.058→101.082, 140.870→140. 870, 159.049→123.076, 121.035→77.136 for VPA, 2-PGA, 3-keto-VPA, 3-OH-VPA, 4-ene-VPA, 4-OH-VPA and IS, respectively. The method also afforded satisfactory results in terms of sensitivity, specificity, precision (intra- and inter-batch), accuracy, recovery, matrix effect and stability. This method was successfully applied to evaluate the effect of LTG on the trough concentration of VPA, 2-PGA, 3-keto-VPA, 3-OH-VPA, 4-ene-VPA, 4-OH-VPA in Chinese epilepsy patients. The result showed that there was no significant difference in the concentration of VPA and its five metabolites between patients in VPA monotherapy and patients in therapy combining VPA with LTG., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Background: Valproate (VPA) as a first-line antiepileptic drug is useful for the most types of epileptic seizure treatment. Previous studies observed that VPA influenced one-carbon metabolism (OCM), consequently, DNA methylation. However, other individual genetic variations, as well as VPA, modify DNA methylation., Objective: In this study, we investigated associations between genetic variations in OCM and leukocyte DNA methylation in VPA-treated patients with epilepsy., Methods: This was a cross-sectional study of 101 epileptic patients who underwent VPA monotherapy and 68 healthy controls. All subjects were measured OCM-related nutrients (folate, homocysteine and vitamin B12), and DNA methylation of specific regions were analyzed. Furthermore, we examined the associations between genetic variations in OCM and DNA methylation levels in epileptic patients., Results: VPA-treated patients with epilepsy exhibited both higher serum homocysteine and vitaminB12 levels and lower folate levels relative to controls (P = 0.018, P = 0.003, P < 0.001 respectively), the methylation level of the MTHFR amplicon was significantly lower in the VPA group compared with those in the controls (P = 0.043). VPA-treated epileptic patients carrying the T-allele of methylenetetrahydrofolate reductase (MTHFR) c.677C>T showed higher serum Hcy levels than those observed in the 677CC group (P < 0.01). Epileptic patients who carried G-allele of methionine synthase (MTR) c.2756A>G showed significantly lower MTHFR amplicon methylation levels compared to carriers of the wild-type MTR 2756AA genotype (P = 0.028)., Conclusion: Our study provided evidence that the MTR c.2756A>G polymorphism is associated with MTHFR amplicon hypomethylation in VPA-treated patients with epilepsy., (Copyright © 2017 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.)

Introduction: It has been suggested that uncontrolled epilepsy might not necessarily equate to drug resistance when antiepileptic drugs (AEDs) are used at relatively low doses, a practice frequently observed in rural areas of China., Objective: To assess the clinical benefits of further drug manipulation in this situation, we prospectively followed up the outcomes of patients with uncontrolled epilepsy while taking relatively low doses of AEDs., Methods: The study included patients aged 16 years and older with uncontrolled epilepsy and who were receiving at least one AED at a dosage below 50% of the World Health Organization (WHO) defined daily dose (DDD) (50% DDD) (Group 1). Patients with drug-resistant epilepsy were included for comparison (Group 2). Both groups were followed-up for at least 2 years. Seizure outcomes after further drug manipulations were recorded at the last follow-up., Results: A total of 197 patients (55.3% male) were included in Group 1 and 32 (46.9% male) in Group 2; their mean duration of follow-up was 28.85 ± 1.90 and 30.91 ± 2.04 months, respectively. At the last follow-up, 16.8% (33/197) of patients in Group 1 had become seizure-free compared with none in Group 2 (p < 0.001). Seventeen of 93 (18.3%) patients in Group 1 became seizure free after increasing the dosage of baseline AED(s) alone. Only 5.5% (3/55) of patients who had failed to respond to an AED at ≥50% DDD at baseline became seizure free compared with 21.1% (30/142) who did not have such a history (p = 0.001). The number of AEDs taken at a dosage below 50% DDD at baseline was not associated with seizure outcome., Conclusions: Uncontrolled epilepsy could become controlled in a substantial proportion of patients by dose increase alone, particularly if there is no history of drug failure at ≥50% DDD.

Lamotrigine (LTG) is commonly used to control seizure in epilepsy patients and with referenced therapeutic windows in clinical practice. This study is to identify and characterize the function of genetic variants that influence the trough concentrations of LTG in epilepsy patients following monotherapy regimen (37.5-250 mg/d). Twelve single nucleotide polymorphisms (SNPs) involved in LTG metabolism and transport pathways, including UGT2B7, ABCB1, ABCG2, NR1I2 and HNF4α were genotyped in 140 Chinese epilepsy patients. Steady-state trough concentration of LTG was measured by a high-performance liquid chromatography method. Polymorphisms in ABCG2 rs2231142, rs3114020, HNF4α rs2071197 and ABCB1 rs1128503 were found to be associated with LTG CDR (concentration/dose normalized by body weight). In addition, multiple linear regression analysis revealed that ABCG2 rs2231142 had a remarkable effect on LTG concentrations which is stated to be 4.8% of the variability of LTG and may also help to interpret ethnic difference in LTG pharmacokinetics. Our findings provided new insights that SNPs of genes involved in the transport of LTG contribute to interpatient variation in LTG pharmacokinetics. Future studies are necessary to determine whether these SNPs can be used to provide LTG dosing guidance and influence seizure control and adverse reaction of LTG., (Copyright © 2015. Published by Elsevier Ltd.)

Purpose: The aim of this study was to compare the serum levels of one-carbon metabolism (OCM) nutrients (e.g., folate, homocysteine and vitamin B12) and peripheral blood DNA methylation in epileptic patients under treatment with antiepileptic drugs (AEDs) and in healthy controls., Methods: In this cross-sectional study, 60 patients with epilepsy who were receiving valproate (VPA) (n = 30) or lamotrigine (LTG) (n = 30) monotherapy were enrolled. Thirty age and sex matched healthy subjects served as the controls. Serum concentrations of OCM nutrients and peripheral blood DNA methylation status were measured., Results: Compared to the control group, the VPA group had higher serum levels of homocysteine (p<0.05). No difference in homocysteine concentration was observed in the LTG group. Patients receiving VPA or LTG had significantly lower serum folate levels in comparison with controls (p<0.001). The level of methylation of long interspersed nucleotide element-1 (LINE-1) in peripheral blood was not significantly different between the AED monotherapy group and healthy controls. A difference in the methylation levels of methylenetetrahydrofolate reductase (MTHFR) amplicon was observed between AED-treated patients with epilepsy and controls (p<0.01). A positive correlation between serum folate levels and peripheral blood MTHFR amplicon methylation status was also observed (r = 0.25, p = 0.023)., Conclusion: Our findings suggest that the effects of AED monotherapy on OCM may induce specific regions of DNA hypomethylation.

Background: Weight gain is the most frequent adverse effect of valproic acid (VPA) treatment, resulting in poor compliance and many endocrine disturbances. Similarities in the weight change of monozygotic twins receiving VPA strongly suggests that genetic factors are involved in this effect. However, few studies have been conducted to identify the relevant genetic polymorphisms. Additionally, the causal relationship between the VPA concentration and weight gain has been controversial. Thus, we investigated the effects of single nucleotide polymorphisms (SNPs) in several appetite stimulation and energy homeostasis genes and the steady state plasma concentrations (Css) of VPA on the occurrence of weight gain in patients., Methods: A total of 212 epilepsy patients receiving VPA were enrolled. Nineteen SNPs in 11 genes were detected using the Sequenom MassArray iPlex platform, and VPA Css was determined by high-performance liquid chromatography (HPLC)., Results: After 6 months of treatment, 20.28% of patients were found to gain a significant amount of weight (weight gained ≥7%). Three SNPs in the leptin receptor (LEPR), ankyrin repeat kinase domain containing 1 (ANKK1), and α catalytic subunit of adenosine monophosphate-activated protein kinase (AMPK) showed significant associations with VPA-induced weight gain (p < 0.001, p = 0.017 and p = 0.020, respectively). After Bonferroni correction for multiple tests, the genotypic association of LEPR rs1137101, the allelic association of LEPR rs1137101, and ANKK1 rs1800497 with weight gain remained significant. However, the VPA Css in patents who gained weight were not significantly different from those who did not gain weight (p = 0.121)., Conclusions: LEPR and ANKK1 genetic polymorphisms may have value in predicting VPA-induced weight gain., (© The Author 2015. Published by Oxford University Press on behalf of CINP.)

Objective: To determine whether valproate (VPA) monotherapy influences homocysteine metabolism in patients with epilepsy., Design: Systematic review and meta-analysis., Data Sources: We searched all articles in English through PubMed, Web of Science and EMBASE published up to August 2013 concerning the homocysteine levels in VPA monotherapeutic patients with epilepsy., Participants: VPA-treated patients with epilepsy (n=266) and matched healthy controls (n=489)., Outcome Measures: Heterogeneity between studies was assessed using I(2) statistics. Pooled standardised mean difference (SMD) and 95% CIs were calculated using a random effect model., Results: A total of eight eligible studies were enrolled in our meta-analysis. We compared the plasma levels of homocysteine in VPA-treated patients with epilepsy and healthy controls. There was significant heterogeneity in the estimates according to the I(2) test (I(2)=65.6%, p=0.005). Plasma homocysteine levels in VPA-treated patients with epilepsy were significantly higher than in healthy controls under a random effect model. (SMD, 0.62; 95% CI 0.32 to 0.92). Further subgroup analyses suggested that no significant differences were present when grouped by ethnicity and age, but the risk of heterogeneity in the West Asian group (I(2)=47.4%, p=0.107) was diminished when compared with that of the overall group (I(2)=65.6%, p=0.005)., Conclusions: Our meta-analysis indicates that VPA monotherapy is associated with the increase in plasma homocysteine levels in patients with epilepsy. Whether this association is influenced by ethnicity needs further research., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.)

Electroencephalogram (EEG) is the primary tool in investigation of the brain science. It is necessary to carry out a deepgoing study into the characteristics and information hidden in EEGs to meet the needs of the clinical research. In this paper, we present a wavelet-nonlinear dynamic methodology for analysis of nonlinear characteristic of EEGs and delta, theta, alpha, and beta sub-bands. We therefore studied the effectiveness of correlation dimension (CD), largest Lyapunov exponen, and approximate entropy (ApEn) in differentiation between the interictal EEG and ictal EEG based on statistical significance of the differences. The results showed that the nonlinear dynamic char acteristic of EEG and EEG subbands could be used as effective identification statistics in detecting seizures.

EEG recordings contain valuable physiological and pathological information in the process of seizure. The dynamic changes of brain electrical activity provide foundation and possibility for research and development of automatic detection system about epilepsy. In this paper, a nonlinear dynamic method is presented for analysis of the nonlinear dynamic characteristics of EEGs and delta, theta, alpha, and beta sub-bands of EEGs based on wavelet transform. The extracted feature is used as the input vector of a support vector machine (SVM) to construct classifiers. The results showed that the classification accuracy of SVM classifier based on nonlinear dynamic characteristics to classify the EEG into interictal EEGs and ictal EEGs reached 90% or higher. The support vector machine has good generalization in detecting the epilepsy EEG signals as a nonlinear classifier.

The intracellular concentration of chloride ([Cl(-)]i) determines the strength and polarity of GABA neurotransmission. STE20/SPS1-related proline/alanine-rich kinase (SPAK) is known as an indirect regulator of [Cl(-)]i for its activation of Na-K-2 Cl(-)co-transporters (NKCC) and inhibition of K-Cl(-)co-transporters (KCC) in many organs. NKCC1 or KCC2 expression changes have been demonstrated previously in the hippocampal neurons of mice with pilocarpine-induced status epilepticus (PISE). However, it remains unclear whether SPAK modulates [Cl(-)]i via NKCC1 or KCC2 in the brain. Also, there are no data clearly characterizing SPAK expression in cortical or hippocampal neurons or confirming an association between SPAK and epilepsy. In the present study, we examined SPAK expression and co-expression with NKCC1 and KCC2 in the hippocampal neurons of mice with PISE, and we investigated alterations in SPAK expression in the hippocampus of such mice. Significant increases in SPAK mRNA and protein levels were detected during various stages of PISE in the PISE mice in comparison to levels in age-matched sham (control) and blank treatment (control) mice. SPAK and NKCC1 expression increased in vitro, while KCC2 was down-regulated in hippocampal neurons following hypoxic conditioning. However, SPAK overexpression did not influence the expression levels of NKCC1 or KCC2. Using co-immunoprecipitation, we determined that the intensity of interaction between SPAK and NKCC1 and between SPAK and KCC2 increased markedly after oxygen-deprivation, whereas SPAK overexpression strengthened the relationships. The [Cl(-)]i of hippocampal neurons changed in a corresponding manner under the different conditions. Our data suggests that SPAK is involved in the plasticity of GABA signaling function in acquired epilepsy via adjustment of [Cl(-)]i in hippocampal neurons.

NKCC1 and KCC2 are encoded by slc12 gene family and involved in the maintenance of intracellular chloride concentration which may be associated with epileptogenesis. In this study, we aimed to investigate the long-term expression profiles of NKCC1 and KCC2 in CA1 region in the mice model of lithium-pilocarpine induced status epilepticus (PISE) and their relationship with epileptogenesis. We found NKCC1 mRNA and proteins were up-regulated at 1 d, 14 d and 45 d after pilocarpine injection, while KCC2 was down-regulated. According to obtained results, there were some expressional changes of NKCC1 and KCC2. Deregulation of their expression may break the balance of intracellular and extracellular chloride concentration which contributes to the mechanism of hyperexcitability leading to seizures. Also it may provide new drug targets for development of new antiepileptic medicine.

Background: Epilepsy was defined as an abnormal brain network model disease in the latest definition. From a microscopic perspective, it is also particularly important to observe the Mutual Information (MI) of the whole brain network based on different lead positions.Methods: In this study, we selected EEG data from representative temporal lobe and frontal lobe epilepsy patients. Based on Phase Space Reconstruction and the calculation of MI indicator, we used Complex Network technology to construct a dynamic brain network function model of epilepsy seizure. At the same time, about the analysis of our network, we described the index changes and propagation paths of epilepsy discharge in different periods, and spatially monitors the seizure change process based on the analysis of the parameter characteristics of the complex network.Results: Our model portrayed the functional synergy between the various regions of the brain and the state transition during the seizure process. We also characterized the EEG synchronous propagation path and core nodes during seizures. The results shown the full node change path and the distribution of important indicators during the seizure process, which makes the state change of the seizure process more clearly.Conclusion: In this study, we have demonstrated that synchronization-based brain networks change with time and space. The EEG synchronous propagation path and core nodes during epileptic seizures can provide a reference for finding the focus area. [ABSTRACT FROM AUTHOR]

Background: Epilepsy is one of the diseases of the nervous system, which has a large population in the world. Traditional diagnosis methods mostly depended on the professional neurologists' reading of the electroencephalogram (EEG), which was time-consuming, inefficient, and subjective. In recent years, automatic epilepsy diagnosis of EEG by deep learning had attracted more and more attention. But the potential of deep neural networks in seizure detection had not been fully developed.Methods: In this article, we used a one-dimensional convolutional neural network (1-D CNN) to replace the residual network architecture's traditional convolutional neural network (CNN). Moreover, we combined the Independent recurrent neural network (indRNN) and CNN to form a new residual network architecture-independent convolutional recurrent neural network (RCNN). Our model can achieve an automatic diagnosis of epilepsy EEG. Firstly, the important features of EEG were learned by using the residual network architecture of 1-D CNN. Then the relationship between the sequences were learned by using the recurrent neural network. Finally, the model outputted the classification results.Results: On the small sample data sets of Bonn University, our method was superior to the baseline methods and achieved 100% classification accuracy, 100% classification specificity. For the noisy real-world data, our method also exhibited powerful performance.Conclusion: The model we proposed can quickly and accurately identify the different periods of EEG in an ideal condition and the real-world condition. The model can provide automatic detection capabilities for clinical epilepsy EEG detection. We hoped to provide a positive significance for the prediction of epileptic seizures EEG. [ABSTRACT FROM AUTHOR]

Introduction: Cerebral cortical ischemia is a risk factor for post-stroke seizures. However, the optimal imaging method is unclear. We investigated CT perfusion (CTP) in detecting cortical ischemia and its correlation with post-stroke seizures compared with non-contrast CT (NCCT). Methods: We included patients with acute ischemic stroke admitted to the Royal Melbourne Hospital between 2009 and 2014. Post-stroke seizure information was collected. Cortical involvement was determined on acute NCCT and CTP ( T, cerebral blood volume [CBV], and cerebral blood flow [CBF]). The association between cortical involvement detected by different imaging modalities and post-stroke seizures was examined. Results: Three-hundred fifty-two patients were included for analysis. Fifty-nine percent were male, and median age was 73 years (inter-quartile range 61-82). Follow-up was available for 96 %; median follow-up duration was 377 days (inter-quartile range 91-1018 days). Thirteen patients had post-stroke seizures (3.9 %). Cortical involvement was significantly associated with post-stroke seizures across all modalities. CBV had the highest hazard ratio (11.3, 95 % confidence interval (CI) 1.1-41.2), followed by NCCT (5.3, 95 % CI 1.5-18.0) and CBF (4.2, 95 % CI 1.1-15.2). Sensitivity was highest for T (100 %), followed by CBV and CBF (both 76.9 %) and NCCT (63.6 %). Specificity was highest for CBV (77.8 %), then NCCT (75.6 %), CBF (54.0 %), and T (29.1 %). Receiver-operating characteristic area under the curve was significantly different between imaging modalities ( p < 0.001), CBV 0.77, NCCT 0.70, CBF 0.65, and T 0.65. Conclusion: CTP may improve sensitivity and specificity of cortical involvement for post-stroke seizures compared to NCCT. [ABSTRACT FROM AUTHOR]

Objective In this study on the analysis of EEG signals for seizure prediction, we used a combination of statistically relevant theory and nonlinear dynamics to maximize the sensitivity of nonlinear analysis and improved prediction accuracy (PA) and effectiveness. Methods First, a physiological reference range of approximate entropy (ApEn) was set up based on normal EEG data. Second, using the concept of global optimization, all EEG electrodes were used in the study regardless of the location of epileptic foci, and the five-electrode group with the strongest synchronization discharge was employed as the optimal electrode group for the next prediction. We set a warning signal when the ApEn values of the data were below the reference range in five electrodes at the same time. Results From the overall 142.7-h EEG signal containing 37 seizures from nine epileptics, our PA was 94.59%, the false prediction rate was 0.084/h, and the mean prediction time was 26.64 min. Conclusion Combining statistically relevant theory and nonlinear dynamics can significantly improve the sensitivity of the nonlinear analysis in seizure prediction. Significance This method may provide a theoretical foundation for the development of a clinical real-time warning system for patients with partial epilepsy. [ABSTRACT FROM AUTHOR]

Abstract: Voltage-gated sodium channels (VGSC) are important determinants of neuronal excitability which are implicated in the pathogenesis of epilepsy. Ankyrin-G contributes to the distribution and regulation of VGSC. Here we investigated the alterations of the two α-subunits SCN8A and SCN1A and their adapter ankyrin-G in the hippocampal cornu ammonis 1 (CA1) of rats after pilocarpine induced status epilepticus (PISE), compared to the sham-control group (C1) and blank-control group (C2). Significant increase of SCN8A mRNA (41.08% increase compared to C1, P <0.001; 30.88% increase compared to C2, P =0.011) was detected 60 days after PISE. At D1 SCN8A mRNA reduced but no significant changes were detected when compared to controls (one-way ANOVA, F =1.232, P =0.276). After measuring the optical density of Western blot, we detected significant differences between the levels of SCN8A protein in different groups but no difference between the protein levels of SCN1A at D1 and D60 after pilocarpine treatment compared to the control. At D60 the relative copies of ankyrin-G mRNA on internal control β-actin in PISE group increased significantly compared to C1 and C2 (one-way ANOVA, F =16.537, P <0.001). Significantly increase of ankyrin-G immunoreactivity in Western blot from the PISE group 1 day and 60 days after PISE was observed, compared to the controls (one-way ANOVA, F =24.255 at D1, P <0.001; F =29.280 at D60, P <0.001). After analyzing the double-stained cells counting, we detected significant differences between the numbers of SCN8A+/ankyrin-G+ immunoreactive cells in different groups in acute and chronic period following PISE (two way-ANOVA, F group =37.905, P <0.001; F day =45.310, P <0.001). The data revealed that both SCN8A and ankyrin-G increased significantly in the CA1 subfield of the rat hippocampus 60 days following pilocarpine induced status epilepticus and co-localized with each other. [Copyright &y& Elsevier]

• The sensitivity of small sharp spikes (SSS) for epilepsy was 18.6 % and the specificity was 97.5 %. • The incidence of SSS was strongly correlated with the frequency of interictal epileptiform discharges (IEDs). • SSS are closely co-lateralized with IEDs in patients with temporal lobe epilepsy (TLE). • SSS can be epileptiform EEG markers in patients with TLE. To determine the incidence and clinical significance of small sharp spikes (SSS) in the patient population of the adult Epilepsy Monitoring Unit (EMU). This is a retrospective study of EEG data and medical records from consecutive patients who underwent video-EEG recording in the adult EMU from March 2013 to February 2019. SSS, interictal epileptiform discharges (IEDs), and ictal patterns were identified. Of the 909 patients reviewed, SSS were observed in110 (12.1 %) patients. Epilepsy was present in 101 of the 110 (91.8 %) patients with SSS and in 441 of the 799 (55.2 %) patients without SSS. The incidence of epilepsy was significantly higher in patients with SSS than in those without SSS (OR = 9.1, 95 % CI: 4.5–18.3, P < 0.01). The sensitivity of SSS for epilepsy was 18.6 % and the specificity was 97.5 %. The incidence of SSS was strongly correlated with the frequency of IEDs (OR 1.89; 95 %CI: 1.60–2.24, P < 0.01). When both present, SSS and IEDs were co-lateralized in the same hemisphere. There is a statistically significant association between SSS and focal epilepsy. SSS have similar clinical implications to IEDs in the lateralization and localization of temporal lobe seizures. SSS can be an epileptiform EEG pattern for temporal lobe epilepsy. [ABSTRACT FROM AUTHOR]

• This paper presents an effective method to preprocess the original EEG data into suitable shape to preserve the temporal and spatial information as much as possible. • A feasible method, long-term recurrent convolutional network (LRCN), was proposed to create epileptic seizure prediction model, which could classify epileptic stages and set an early warning in advance. • The new network structure and instructive settings in the network were proposed. • The post-processing strategy was proposed to predict the seizures in different time scales. The segments-evaluation and events-evaluation were processed to evaluate the performance. • Different methods were made comparisons using the same data in this paper. overall, the LRCN network offers an about 5–9% increase in sensitivity and specificity. A seizure prediction system can detect seizures prior to their occurrence and allow clinicians to provide timely treatment for patients with epilepsy. Research on seizure prediction has progressed from signal processing analyses to machine learning. However, most prediction methods are hand-engineered and have high computational complexity, increasing the difficulty of obtaining real-time predictions. Some forecasting and early warning methods have achieved good results in the short term but have low applicability in practical situations over the long term. First, electroencephalogram (EEG) time series were converted into two-dimensional images for multichannel fusion. A feasible method, a long-term recurrent convolutional network (LRCN), was proposed to create a spatiotemporal deep learning model for predicting epileptic seizures. The convolutional network block was used to automatically extract deep features from the data. The long short-term memory (LSTM) block was incorporated into learning a time sequence for identifying the preictal segments. New network settings and a postprocessing strategy were proposed in the seizure prediction model. The deep seizure prediction model achieved an accuracy of 93.40%, prediction sensitivity of 91.88% and specificity of 86.13% in segment-based evaluations. For the event-based evaluations, 164 seizures were predicted. The proposed method provides high sensitivity and a low false prediction rate (FPR) of 0.04 F P/h. We employed different methods, including the LRCN, deep learning and traditional machine learning methods, and compared them using the same data in this paper. Overall, the LRCN offers approximately 5–9% increased sensitivity and specificity. This study describes the LRCN network for analyzing EEG data to predict epileptic seizures, thereby enabling the implementation of an early warning system that detects epileptic seizures before they occur in clinical applications. [ABSTRACT FROM AUTHOR]