Efficacy and safety of herbal medicine combined with acupuncture in pediatric epilepsy treatment: A meta-analysis of randomized controlled trials.

OBJECTIVE: To evaluate the efficacy and safety of herbal medicine and acupuncture combination for pediatric epilepsy treatment. METHODS: Databases were searched from their interception until October 2023 to identify randomized controlled trials focusing on the therapeutic efficacy of herbal medicine-acupuncture combination (intervention group) for pediatric epilepsy. The primary outcome was the risk of treatment failure, whereas the secondary outcomes included the risk of post-treatment electroencephalogram (EEG) abnormalities and adverse events. Subgroup analyses were conducted based on the type of herbal compound formulas. Meta-regression analysis was conducted to examine the influence of patient demographics and clinical history on the therapeutic efficacy of herbal medicine-acupuncture combination for pediatric epilepsy. To assess the cumulative evidence, trial sequential analysis (TSA) was performed. RESULTS: The analysis included 10 trials involving a total of 882 pediatric patients. Meta-analysis revealed that the intervention group had a lower risk of treatment failure than the control group (risk ratio [RR] = 0.3, 95% confidence interval [CI]: 0.19-0.47, P<0.00001, I2 = 0%, 10 trials). Subgroup analyses showed that therapeutic efficacy was consistent among the different herbal compound formulas. Meta-regression analysis revealed that the efficacy of the treatments did not significantly vary with patient age, male sex, and duration of seizure history. TSA suggested that herbal medicine-acupuncture combination exerted a robust and conclusive effect on seizure treatment. Although the combined used of herbal medicine and acupuncture was not associated with a lower risk of post-treatment EEG abnormalities (RR = 0.82, 95%CI:0.6-1.11, P = 0.2, 3 trials), the risk of adverse events was reduced (RR = 0.27, 95%CI:0.18-0.41, P<0.00001, 4 trials). CONCLUSION: The meta-analysis suggested that combined use of herbal medicine and acupuncture is a promising and safe clinical approach for pediatric epilepsy treatment. Further large-scale studies are necessary to conclusively determine the efficacy and safety of herbal medicine and acupuncture in pediatric epilepsy treatment.

Diagnostic biomarker discovery from brain EEG data using LSTM, reservoir-SNN, and NeuCube methods in a pilot study comparing epilepsy and migraine.

The study introduces a new online spike encoding algorithm for spiking neural networks (SNN) and suggests new methods for learning and identifying diagnostic biomarkers using three prominent deep learning neural network models: deep BiLSTM, reservoir SNN, and NeuCube. EEG data from datasets related to epilepsy, migraine, and healthy subjects are employed. Results reveal that BiLSTM hidden neurons capture biological significance, while reservoir SNN activities and NeuCube spiking dynamics identify EEG channels as diagnostic biomarkers. BiLSTM and reservoir SNN achieve 90 and 85% classification accuracy, while NeuCube achieves 97%, all methods pinpointing potential biomarkers like T6, F7, C4, and F8. The research bears implications for refining online EEG classification, analysis, and early brain state diagnosis, enhancing AI models with interpretability and discovery. The proposed techniques hold promise for streamlined brain-computer interfaces and clinical applications, representing a significant advancement in pattern discovery across the three most popular neural network methods for addressing a crucial problem. Further research is planned to study how early can these diagnostic biomarkers predict an onset of brain states.

[Risk of epilepsy after a first epileptic seizure with unknown etiology in elderly patients]. / Riesgo de epilepsia tras una primera crisis epiléptica de etiología desconocida en pacientes de edad avanzada.

AIM: Patients whose epilepsy begins with seizures with unknown etiology in old age have been studied to a limited extent. The aim is to clinically characterise these patients, and predict their risk of developing epilepsy in the long term. MATERIALS AND METHODS: This is a retrospective observational study of patients over 55 years old experiencing a first epileptic seizure with unknown etiology. The data were collected from their clinical history, including electroencephalogram (EEG) and brain magnetic resonance imaging (MRI) results. RESULTS: Eighty-seven patients (58.6% male; 71.5 ± 8.1 years) were included. The mean follow-up was 7.3 ± 4.9 years. The most common vascular risk factor was arterial hypertension (77%; n = 67). Focal seizures with altered consciousness were the most frequent type of seizure (44.8%; n = 39), followed by focal seizures evolving to bilateral tonic-clonic seizures (39.1%; n = 34). Brain MRI showed cortical atrophy (50%; n = 42) and signs of small-vessel vascular disease (SVVD) (67.8%; n = 57). Interictal epileptiform EEG abnormalities were observed in 43.7% (n = 38) of the patients, mostly with temporal localisations (94.7%; n = 36). 44.8% (n = 39) had mild cognitive impairment at baseline. Recurrence of seizures, which was observed in 49 patients (56.1%), occurred after a median of 12 months (interquartile range: 4.4-25.9). Finally, 71 patients (81.6%) developed epilepsy. CONCLUSION: The risk of epilepsy in the long term following a single seizure of unknown etiology in elderly patients is greater than 80%. Arterial hypertension and mild cognitive impairment at baseline are the most common clinical features. Cortical atrophy and the presence of SVVD are frequent in MRI, and routine EEGs do not usually show epileptiform alterations.

TITLE: Riesgo de epilepsia tras una primera crisis epiléptica de etiología desconocida en pacientes de edad avanzada.Objetivo. Los pacientes que comienzan con crisis de origen desconocido en la edad avanzada no están bien estudiados. El objetivo es caracterizar clínicamente a estos pacientes y predecir el riesgo de desarrollar epilepsia a largo plazo. Materiales y métodos. Es un estudio observacional retrospectivo en pacientes mayores de 55 años con una primera crisis epiléptica de causa desconocida. Se recogieron los datos desde la historia clínica, incluyendo electroencefalograma (EEG) y resonancia magnética (RM) cerebral. Resultados. Se incluyó a 87 pacientes (58,6% varones; 71,5 ± 8,1 años). El seguimiento medio fue de 7,3 ± 4,9 años. El factor de riesgo vascular más frecuente fue la hipertensión arterial (77%; n = 67). Las crisis focales con alteración de la conciencia fueron el tipo de crisis más frecuente (44,8%; n = 39), seguidas de las crisis focales con evolución a bilaterales tonicoclónicas (39,1%; n = 34). La RM cerebral mostró atrofia cortical (50%; n = 42) y signos de enfermedad vascular de pequeño vaso (EVPV) (67,8%; n = 57). Se observaron anomalías epileptiformes intercríticas en el EEG en un 43,7% (n = 38) de los pacientes, mayoritariamente con localización temporal (94,7%; n = 36). Hasta un 44,8% (n = 39) presentaba deterioro cognitivo leve basalmente. La recurrencia de crisis, observada en 49 pacientes (56,1%), sucedió con una mediana de 12 meses (rango intercuartílico: 4,4-25,9). Finalmente, 71 pacientes (81,6%) desarrollaron epilepsia. Conclusión. El riesgo de epilepsia a largo plazo tras una crisis única de etiología desconocida en pacientes de edad avanzada es superior al 80%. La hipertensión arterial y el deterioro cognitivo leve en el inicio son las características clínicas más frecuentes. En la RM, la atrofia cortical y la presencia de EVPV son frecuentes, y los EEG de rutina no suelen mostrar alteraciones epileptiformes.

Lead-free dual-frequency ultrasound implants for wireless, biphasic deep brain stimulation.

Ultrasound-driven bioelectronics could offer a wireless scheme with sustainable power supply; however, current ultrasound implantable systems present critical challenges in biocompatibility and harvesting performance related to lead/lead-free piezoelectric materials and devices. Here, we report a lead-free dual-frequency ultrasound implants for wireless, biphasic deep brain stimulation, which integrates two developed lead-free sandwich porous 1-3-type piezoelectric composite elements with enhanced harvesting performance in a flexible printed circuit board. The implant is ultrasonically powered through a portable external dual-frequency transducer and generates programmable biphasic stimulus pulses in clinically relevant frequencies. Furthermore, we demonstrate ultrasound-driven implants for long-term biosafety therapy in deep brain stimulation through an epileptic rodent model. With biocompatibility and improved electrical performance, the lead-free materials and devices presented here could provide a promising platform for developing implantable ultrasonic electronics in the future.

The value of linear and non-linear quantitative EEG analysis in paediatric epilepsy surgery: a machine learning approach.

Epilepsy surgery is effective for patients with medication-resistant seizures, however 20-40% of them are not seizure free after surgery. Aim of this study is to evaluate the role of linear and non-linear EEG features to predict post-surgical outcome. We included 123 paediatric patients who underwent epilepsy surgery at Bambino Gesù Children Hospital (January 2009-April 2020). All patients had long term video-EEG monitoring. We analysed 1-min scalp interictal EEG (wakefulness and sleep) and extracted 13 linear and non-linear EEG features (power spectral density (PSD), Hjorth, approximate entropy, permutation entropy, Lyapunov and Hurst value). We used a logistic regression (LR) as feature selection process. To quantify the correlation between EEG features and surgical outcome we used an artificial neural network (ANN) model with 18 architectures. LR revealed a significant correlation between PSD of alpha band (sleep), Mobility index (sleep) and the Hurst value (sleep and awake) with outcome. The fifty-four ANN models gave a range of accuracy (46-65%) in predicting outcome. Within the fifty-four ANN models, we found a higher accuracy (64.8% ± 7.6%) in seizure outcome prediction, using features selected by LR. The combination of PSD of alpha band, mobility and the Hurst value positively correlate with good surgical outcome.

Spectrum of NMDA Receptor Variants in Neurodevelopmental Disorders and Epilepsy.

N-methyl-D-aspartate receptors (NMDAR) are ligand-gated ion channels mediating excitatory neurotransmission and are important for normal brain development, cognitive abilities, and motor functions. Pathogenic variants in the Glutamate receptor Ionotropic N-methyl-D-aspartate (GRIN) genes (GRIN1, GRIN2A-D) encoding NMDAR subunits have been associated with a wide spectrum of neurodevelopmental disorders and epilepsies ranging from treatable focal epilepsies to devastating early-onset developmental and epileptic encephalopathies. Genetic variants in NMDA receptor genes can cause a range of complex alterations to receptor properties resulting in various degrees of loss-of-function, gain-of-function, or mixtures thereof. Understanding how genetic variants affect the function of the receptors, therefore, represents an important first step in the ongoing development towards targeted therapies. Currently, targeted treatment options for GRIN-related diseases are limited. However, treatment with memantine has been reported to significantly reduce seizure frequency in a few individuals with developmental and epileptic encephalopathies harboring de novo gain-of-function GRIN2A missense variants, and supplementary treatment with L-serine has been associated with improved motor and cognitive performance as well as reduced seizure frequency in patients with GRIN2B loss-of-function missense variants as well as GRIN2A and GRIN2B null variants.

A multiscale symbolic approach to decoding delta and ripple oscillation bands as biomarkers for epileptiform discharges.

We use a multiscale symbolic approach to study the complex dynamics of temporal lobe refractory epilepsy employing high-resolution intracranial electroencephalogram (iEEG). We consider the basal and preictal phases and meticulously analyze the dynamics across frequency bands, focusing on high-frequency oscillations up to 240 Hz. Our results reveal significant periodicities and critical time scales within neural dynamics across frequency bands. By bandpass filtering neural signals into delta, theta, alpha, beta, gamma, and ripple high-frequency bands (HFO), each associated with specific neural processes, we examine the distinct nonlinear dynamics. Our method introduces a reliable approach to pinpoint intrinsic time lag scales τ within frequency bands of the basal and preictal signals, which are crucial for the study of refractory epilepsy. Using metrics such as permutation entropy (H), Fisher information (F), and complexity (C), we explore nonlinear patterns within iEEG signals. We reveal the intrinsic τmax that maximize complexity within each frequency band, unveiling the nonlinear subtle patterns of the temporal structures within the basal and preictal signal. Examining the H×F and C×F values allows us to identify differences in the delta band and a band between 200 and 220 Hz (HFO 6) when comparing basal and preictal signals. Differences in Fisher information in the delta and HFO 6 bands before seizures highlight their role in capturing important system dynamics. This offers new perspectives on the intricate relationship between delta oscillations and HFO waves in patients with focal epilepsy, highlighting the importance of these patterns and their potential as biomarkers.

Therapeutic Drug Monitoring of Levetiracetam in Different Trimesters of Pregnant Women with Epilepsy in a Tertiary Care Center: A Prospective Study.

BACKGROUND: Levetiracetam is the most commonly used antiepileptic drug in pregnant women due to its low teratogenic risk profile, favorable pharmacokinetic characteristics, and safety profile. Serum levels of levetiracetam vary in epilepsy during pregnancy. Therefore, the aim of the study was to evaluate the serum levels of levetiracetam during different trimesters of pregnancy by using therapeutic drug monitoring (TDM). MATERIALS AND METHODS: This was a single-center, prospective study. Pregnant women with epilepsy on levetiracetam were enrolled after getting written informed consent from them. Serum trough levels of levetiracetam were estimated at all trimesters by high-performance liquid chromatography (HPLC). RESULTS: The study included 16 participants with mean ± standard deviation (SD) age of 27.75 ± 4 years. There were nine (56.2%) participants with generalized seizure disorder and seven (43.8%) participants of focal seizure disorder. Among 16 patients, 10 (62.5%) participants were on levetiracetam alone and six (37.5%) participants were on levetiracetam combined with other antiepileptic drugs. In a total of 48 trough samples, 45 sample concentrations were below the therapeutic range of 12-46 mg/l and three sample concentrations were within the therapeutic range. There was a statistically significant difference in the concentration-dose ratio (CDR) of levetiracetam between the third and first trimesters (P-value 0.018). CONCLUSION: There was a statistically significant difference in serum levetiracetam concentration between the third and first trimesters. A well-conducted, intensive pharmacokinetic sampling study in PWWE with a control group is needed in future to evaluate the whole pharmacokinetic profile of levetiracetam and to correlate the clinical outcome.

Approach to Patients with Seizures and Epilepsy: A Guide for Primary Care Physicians.

Seizures and epilepsy are common neurologic conditions that are frequently encountered in the outpatient primary care setting. An accurate diagnosis relies on a thorough clinical history and evaluation. Understanding seizure semiology and classification is crucial in conducting the initial assessment. Knowledge of common seizure triggers and provoking factors can further guide diagnostic testing and initial management. The pharmacodynamic characteristics and side effect profiles of anti-seizure medications are important considerations when deciding treatment and counseling patients, particularly those with comorbidities and in special populations such as patient of childbearing potential.

Role of Functional neurosurgery in Improving Patient Outcomes in Epilepsy, Movement Disorders, and Chronic Pain.

Functional neurosurgery encompasses surgical procedures geared towards treating movement disorders (such as Parkinson's disease and essential tremor), drug-resistant epilepsy, and various types of pain disorders. It is one of the most rapidly expanding fields within neurosurgery and utilizes both traditional open surgical methods such as open temporal lobectomy for epilepsy as well as neuromodulation-based treatments such as implanting brain or nerve stimulation devices. This review outlines the role functional neurosurgery plays in treatment of epilepsy, movement disorders, and pain, and how it is being implemented at the University of Missouri by the Department of Neurosurgery.

Novel Surgical Approaches in Childhood Epilepsy: Laser, Brain Stimulation, and Focused Ultrasound.

Pediatric epilepsy has a worldwide prevalence of approximately 1% (Berg et al., Handb Clin Neurol 111:391-398, 2013) and is associated with not only lower quality of life but also long-term deficits in executive function, significant psychosocial stressors, poor cognitive outcomes, and developmental delays (Schraegle and Titus, Epilepsy Behav 62:20-26, 2016; Puka and Smith, Epilepsia 56:873-881, 2015). With approximately one-third of patients resistant to medical control, surgical intervention can offer a cure or palliation to decrease the disease burden and improve neurological development. Despite its potential, epilepsy surgery is drastically underutilized. Even today only 1% of the millions of epilepsy patients are referred annually for neurosurgical evaluation, and the average delay between diagnosis of Drug Resistant Epilepsy (DRE) and surgical intervention is approximately 20 years in adults and 5 years in children (Solli et al., Epilepsia 61:1352-1364, 2020). It is still estimated that only one-third of surgical candidates undergo operative intervention (Pestana Knight et al., Epilepsia 56:375, 2015). In contrast to the stable to declining rates of adult epilepsy surgery (Englot et al., Neurology 78:1200-1206, 2012; Neligan et al., Epilepsia 54:e62-e65, 2013), rates of pediatric surgery are rising (Pestana Knight et al., Epilepsia 56:375, 2015). Innovations in surgical approaches to epilepsy not only minimize potential complications but also expand the definition of a surgical candidate. In this chapter, three alternatives to classical resection are presented. First, laser ablation provides a minimally invasive approach to focal lesions. Next, both central and peripheral nervous system stimulation can interrupt seizure networks without creating permanent lesions. Lastly, focused ultrasound is discussed as a potential new avenue not only for ablation but also modulation of small, deep foci within seizure networks. A better understanding of the potential surgical options can guide patients and providers to explore all treatment avenues.

Performance investigation of epilepsy detection from noisy EEG signals using base-2-meta stacking classifier.

Epilepsy is a chronic neurological disease, characterized by spontaneous, unprovoked, recurrent seizures that may lead to long-term disability and premature death. Despite significant efforts made to improve epilepsy detection clinically and pre-clinically, the pervasive presence of noise in EEG signals continues to pose substantial challenges to their effective application. In addition, discriminant features for epilepsy detection have not been investigated yet. The objective of this study is to develop a hybrid model for epilepsy detection from noisy and fragmented EEG signals. We hypothesized that a hybrid model could surpass existing single models in epilepsy detection. Our approach involves manual noise rejection and a novel statistical channel selection technique to detect epilepsy even from noisy EEG signals. Our proposed Base-2-Meta stacking classifier achieved notable accuracy (0.98 ± 0.05), precision (0.98 ± 0.07), recall (0.98 ± 0.05), and F1 score (0.98 ± 0.04) even with noisy 5-s segmented EEG signals. Application of our approach to the specific problem like detection of epilepsy from noisy and fragmented EEG data reveals a performance that is not only superior to others, but also is translationally relevant, highlighting its potential application in a clinic setting, where EEG signals are often noisy or scanty. Our proposed metric DF-A (Discriminant feature-accuracy), for the first time, identified the most discriminant feature with models that give A accuracy or above (A = 95 used in this study). This groundbreaking approach allows for detecting discriminant features and can be used as potential electrographic biomarkers in epilepsy detection research. Moreover, our study introduces innovative insights into the understanding of these features, epilepsy detection, and cross-validation, markedly improving epilepsy detection in ways previously unavailable.

Review of the spectrum of tuberous sclerosis complex: The Saudi Arabian Experience.

OBJECTIVES: To determine the prevalence of tuberous sclerosis complex (TSC) in the paediatric Saudi population and to characterise the range of clinical symptoms, neurocutaneous findings, neuroimaging results, and complications of the disease. METHODS: A total of 61 genetically confirmed TSC patients from the National Guard Health Affairs (NGHA) in Saudi Arabia were the subject of this retrospective descriptive analysis. The data were presented using descriptive measures. RESULTS: The mean age at diagnosis was found to be 4.9 years. Subependymal nodules (86.9%), numerous cortical tubers and/or radial migration lines (63.9%), and hypomelanotic macules (63.9%) were the 3 most common significant criteria. The vast majority (86.9%) of those diagnosed had epilepsy, of which 50% were considered medically intractable. Nearly half of our subjects underwent genetic testing, which revealed that TSC2 predominated over TSC1. Symptoms of Tuberous Sclerosis Complex-Associated Neuropsychiatric Disorders (TAND) were present in 66.7% of TSC1 patients and 73.9% of TSC2 patients. CONCLUSION: The findings of this study demonstrate that the clinical spectrum of TSC among Saudi children is consistent with the body of existing literature. The TSC2 was more prevalent than TSC1. The most frequent signs were cutaneous and neurological. Monitoring TSC patients regularly is crucial to identify any issues as soon as possible.

Ketogenic Diet in the Treatment of Epilepsy.

Epilepsy is one of the most disabling neurological diseases. Despite proper pharmacotherapy and the availability of 2nd and 3rd generation antiepileptic drugs, deep brain stimulation, and surgery, up to 30-40% of epilepsy patients remain drug-resistant. Consequences of this phenomenon include not only decreased a quality of life, and cognitive, behavioral, and personal disorders, but also an increased risk of death, i.e., in the mechanism of sudden unexpected death in epilepsy patients (SUDEP). The main goals of epilepsy treatment include three basic issues: achieving the best possible seizure control, avoiding the undesired effects of treatment, and maintaining/improving the quality of patients' lives. Therefore, numerous attempts are made to offer alternative treatments for drug-resistant seizures, an example of which is the ketogenic diet. It is a long-known but rarely used dietary therapy for intractable seizures. One of the reasons for this is the unpalatability of the classic ketogenic diet, which reduces patient compliance and adherence rates. However, its antiseizure effects are often considered to be worth the effort. Until recently, the diet was considered the last-resort treatment. Currently, it is believed that a ketogenic diet should be used much earlier in patients with well-defined indications. In correctly qualified patients, seizure activity may be reduced by over 90% or even abolished for long periods after the diet is stopped. A ketogenic diet can be used in all age groups, although most of the available literature addresses pediatric epilepsy. In this article, we focus on the mechanisms of action, effectiveness, and adverse effects of different variants of the ketogenic diet, including its classic version, a medium-chain triglyceride diet, a modified Atkins diet, and a low glycemic index treatment.

Evaluation of the Effect of Parenting Style and Parental Mealtime Actions on the Eating Behavior of Children with Epilepsy.

BACKGROUND: Research on the interaction of parenting style, parents' mealtime behaviors, and children's eating behavior in the presence of chronic disease is limited. This study aimed to investigate the impact of parenting style and parental mealtime actions on the eating behavior of children with epilepsy. METHODS: Thirty-one children with epilepsy, thirty-one healthy children (aged 4-9 years), and their parents were included. The Multidimensional Assessment of Parenting Scale (MAPS), Parent Mealtime Action Scale, Children's Eating Behavior Questionnaire, and Healthy Eating Index (HEI)-2015 were applied. The MAPS, HEI-2015 scores, and body mass index for age Z scores were similar in both groups (p > 0.05). In the epilepsy group, the food approach behavior score was higher, and positive correlations were noted between broadband negative parenting and food approach behavior, and the HEI-2015 score and broadband positive parenting (p < 0.05). Regression analysis showed that broadband negative parenting and snack modeling increased the food approach behavior in the epilepsy group. Owing to the chronic disease, the effects of parent-child interaction on the child's eating behavior in the epilepsy group differed from those of healthy children reported in the literature.

Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot Images.

The treatment of epilepsy, the second most common chronic neurological disorder, is often complicated by the failure of patients to respond to medication. Treatment failure with anti-seizure medications is often due to the presence of non-epileptic seizures. Distinguishing non-epileptic from epileptic seizures requires an expensive and time-consuming analysis of electroencephalograms (EEGs) recorded in an epilepsy monitoring unit. Machine learning algorithms have been used to detect seizures from EEG, typically using EEG waveform analysis. We employed an alternative approach, using a convolutional neural network (CNN) with transfer learning using MobileNetV2 to emulate the real-world visual analysis of EEG images by epileptologists. A total of 5359 EEG waveform plot images from 107 adult subjects across two epilepsy monitoring units in separate medical facilities were divided into epileptic and non-epileptic groups for training and cross-validation of the CNN. The model achieved an accuracy of 86.9% (Area Under the Curve, AUC 0.92) at the site where training data were extracted and an accuracy of 87.3% (AUC 0.94) at the other site whose data were only used for validation. This investigation demonstrates the high accuracy achievable with CNN analysis of EEG plot images and the robustness of this approach across EEG visualization software, laying the groundwork for further subclassification of seizures using similar approaches in a clinical setting.