Drug-Resistant Epilepsy and Surgery

Treatment strategies for encephalopathy related to status epilepticus during slow sleep, a narrative review of the literature

Encephalopathy related to Status Epilepticus during slow Sleep (ESES) is an age-dependent syndrome characterized by the appearance of neuropsychological and behavioral disorders associated with extreme activation of epileptic activity during sleep. The major goal of therapy in ESES is to prevent neuropsychological deficits. Effective therapy to reduce seizures and resolve the EEG pattern of status epilepticus during sleep (SES) may be crucial to improve long-term prognosis. However, whether to improve neurodevelopmental deficits by suppressing or eliminating SES remains unknown. The purpose of this article is to review current therapeutic options in ESES, in order to provide better alternatives. Treatment options consist of antiepileptic drugs, steroids, immunoglobulins, the ketogenic diet, and surgery. Maybe therapy targeted mechanisms can be developed with deep insight into the etiology of ESES.

Management of epileptic disorders using nanotechnology-based strategies for nose-to-brain drug delivery

INTRODUCTION

Epilepsy, a major neurological disorder affects about 1% of the Indian population. The discovery of noninvasive strategies for epilepsy presents a challenge for the scientists. Different types of nose-to-brain dosage-forms have been studied for epilepsy management. It aims to give new perspectives for developing new and existing anti-epileptic drugs. Combining nanotechnology with nose-to-brain approach can help in promoting the treatment efficacy by site-specific delivery. Also, it will minimize the side-effects and patient noncompliance observed in conventional administration routes. Peptide delivery can be an interesting approach for the management of epilepsy. Drug-loaded intranasal nanoformulations exhibit diverse prospective potentials in the management of epilepsy. Considering that, nanotherapy using nose-to-brain delivery as a prospective technique for the efficient management of epilepsy is reviewed.

AREAS COVERED

The authors have compiled all recently available data pertaining to the nose-to-brain delivery of therapeutics using nanotechnological strategies. The fundamental mechanism of nose-to-brain delivery, claims for intranasal delivery and medical devices for epilepsy are discussed.

EXPERT OPINION

Drug-loaded intranasal nanoformulations exhibit different prospective potentials in the management of epilepsy. Considering the foregoing research done in the field of nanotechnology, globally, authors propose nose-to-brain delivery of nanoformulations as a potential technique for the efficient management of epilepsy.

Levetiracetam Reduced the Basal Excitability of the Dentate Gyrus without Restoring Impaired Synaptic Plasticity in Rats with Temporal Lobe Epilepsy

Temporal lobe epilepsy (TLE), the most common type of focal epilepsy, affects learning and memory; these effects are thought to emerge from changes in synaptic plasticity. Levetiracetam (LEV) is a widely used antiepileptic drug that is also associated with the reversal of cognitive dysfunction. The long-lasting effect of LEV treatment and its participation in synaptic plasticity have not been explored in early chronic epilepsy. Therefore, through the measurement of evoked field potentials, this study aimed to comprehensively identify the alterations in the excitability and the short-term (depression/facilitation) and long-term synaptic plasticity (long-term potentiation, LTP) of the dentate gyrus of the hippocampus in a lithium–pilocarpine rat model of TLE, as well as their possible restoration by LEV (1 week; 300 mg/kg/day). TLE increased the population spike (PS) amplitude (input/output curve); interestingly, LEV treatment partially reduced this hyperexcitability. Furthermore, TLE augmented synaptic depression, suppressed paired-pulse facilitation, and reduced PS-LTP; however, LEV did not alleviate such alterations. Conversely, the excitatory postsynaptic potential (EPSP)-LTP of TLE rats was comparable to that of control rats and was decreased by LEV. LEV caused a long-lasting attenuation of basal hyperexcitability but did not restore impaired synaptic plasticity in the early chronic phase of TLE.

A New Method Based on CEEMD Combined With Iterative Feature Reduction for Aided Diagnosis of Epileptic EEG

In the clinical diagnosis of epileptic diseases, the intelligent diagnosis of epileptic electroencephalogram (EEG) signals has become a research focus in the field of brain diseases. In order to solve the problem of time-consuming and easily influenced by human subjective factors, artificial intelligence pattern recognition algorithm has been applied to EEG signals recognition. However, at present, the common empirical mode decomposition (EMD) signal decomposition algorithm does not consider the problem of mode aliasing. The EEG features obtained by feature extraction may be mixed with some unimportant features that affect the classification accuracy. In this paper, we proposed a new method based on complementary ensemble empirical mode decomposition (CEEMD) combined with iterative feature reduction for aided diagnosis of epileptic EEG. First of all, the evaluation indexes of decomposing and reconstructing signals by several methods were compared. The CEEMD was selected as the decomposition method of the signals. Then, the support vector machine recursive elimination (SVM-RFE) was used to reduce 9 features extracted from EEG data. The support vector classification of the gray wolf optimizer (GWO-SVC) recognition model was established for different feature subsets. By comparing the classification accuracy of training set and test set of different feature subsets, and considering the complexity of the model reflected by the number of features selected by SVM-RFE, the analysis showed that the 6 feature subsets with fewer features and higher classification accuracy could reflect the key information of epileptic EEG. The accuracy of the training set classification was 99.38% and the test set was as high as 100%. The recognition time was only 1.6551 s. Finally, in order to verify the reliability of the algorithm proposed in this paper, the proposed algorithm compared with the classification model established by the raw EEG signals and the optimization model established by other intelligent optimization algorithms. It is found that the algorithm used in this paper has higher classification accuracy and faster recognition time than other processing methods. The experimental results show that CEEMD combined with SVM-RFE is feasible for rapid and accurate recognition of EEG signals, which provides a theoretical basis for the aided diagnosis of epilepsy.

Evaluation of Wearable Electronics for Epilepsy: A Systematic Review

Epilepsy is a neurological disorder that affects 50 million people worldwide. It is characterised by seizures that can vary in presentation, from short absences to protracted convulsions. Wearable electronic devices that detect seizures have the potential to hail timely assistance for individuals, inform their treatment, and assist care and self-management. This systematic review encompasses the literature relevant to the evaluation of wearable electronics for epilepsy. Devices and performance metrics are identified, and the evaluations, both quantitative and qualitative, are presented. Twelve primary studies comprising quantitative evaluations from 510 patients and participants were collated according to preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines. Two studies (with 104 patients/participants) comprised both qualitative and quantitative evaluation components. Despite many works in the literature proposing and evaluating novel and incremental approaches to seizure detection, there is a lack of studies evaluating the devices available to consumers and researchers, and there is much scope for more complete evaluation data in quantitative studies. There is also scope for further qualitative evaluations amongst individuals, carers, and healthcare professionals regarding their use, experiences, and opinions of these devices.

Static magnetic fields reduce epileptiform activity in anesthetized rat and monkey

Increasing evidence indicates that static magnetic fields (SMF) reduce cortical activity in both human and animal models. The aim of this work was to investigate the effect of SMF on epileptiform cortical activity, a condition related to an abnormal increase in neuronal excitability. The first experimental block included a Pilocarpine rat model of epilepsy, in which a magnetic neodymium nickel-plated cylinder, a magnetic field of 0.5 T, or “sham” were placed over the skull. In the second experimental block, we recorded epileptic-like activity in the visual cortex of a monkey (Macaca mulatta) under control conditions and in the presence of the magnet. Between 15 and 30 minutes after the second dose of Pilocarpine, EEG changes compatible with seizure like events induced by Pilocarpine were clearly observed in the control animals (sham stimulation). Similar effects were visible in the animals exposed to the real magnet after 1–2 hours. In the monkey, SMF over the cortical focus clearly reduced abnormal activity: the intensity threshold for visual induction increased and the severity and duration decreased. These results reinforce the view that static magnets modulate cortical activity and open the door to the future therapeutic use of SMF in epilepsy as a complement to current pharmacological treatments.