Treatment and management of epilepsy in the elderly demented patient

Levetiracetam, from broad-spectrum use to precision prescription: A narrative review and expert opinion

Levetiracetam (LEV) is an antiseizure medication (ASM) whose mechanism of action involves the modulation of neurotransmitters release through binding to the synaptic vesicle glycoprotein 2A. It is a broad-spectrum ASM displaying favorable pharmacokinetic and tolerability profiles. Since its introduction in 1999, it has been widely prescribed, becoming the first-line treatment for numerous epilepsy syndromes and clinical scenarios. However, this might have resulted in overuse. Increasing evidence, including the recently published SANAD II trials, suggests that other ASMs are reasonable therapeutic options for generalized and focal epilepsies. Not infrequently, these ASMs show better safety and effectiveness profiles compared to LEV (partially due to the latter's well-known cognitive and behavioral adverse effects, present in up to 20% of patients). Moreover, it has been shown that the underlying etiology of epilepsy is significantly linked to ASMs response in particular scenarios, highlighting the importance of an etiology-based ASM choice. In the case of LEV, it has demonstrated an optimal effectiveness in Alzheimer's disease, Down syndrome, and PCDH19-related epilepsies whereas, in other etiologies such as malformations of cortical development, it may show negligible effects. This narrative review analyzes the current evidence related to the use of LEV for the treatment of seizures. Illustrative clinical scenarios and practical decision-making approaches are also addressed, therefore aiming to define a rational use of this ASM.

Combination of Ocimum sanctum extract and Levetiracetam ameliorates cognitive dysfunction and hippocampal architecture in rat model of Alzheimer's disease

Alzheimer's disease (AD) is the most common neurodegenerative disease which affects more than 40 million people worldwide with progressive loss of memory and cognitive functions. It is reported, persistent AD is also one of the main causes of epilepsy in elders and comorbidity of both these will contribute to worsening the health status of AD patients. Recently, herbal plants with potent neuroprotective and antioxidant properties were used for increasing the quality of life in neurodegenerative disease patients. The present study was conceptualized to access the protective effect of Ocimum sanctum extract (OSE) and Levetiracetam (LEV) and their combination (OSE+LEV) against AD and epilepsy associated with AD in the rat AD model. AD was induced in aged male Wistar albino rats with Amyloid-β (Aβ) by intracerebroventricular administration. The results reveal, treatment with OSE, LEV and OSE+LEV significantly reversed the memory impairment, increases the BDNF expressions and decreases AChE activity in Aβ induced AD animals. Expression of A-β and p-tau in the hippocampus was significantly reduced in treatment group when compared to the control animals. Treatment with OSE and OSE+LEV also restored the hippocampal architecture by ameliorating the neuronal count in CA1, CA3 and DG regions. It also observed that treatment has decreased the excitoneurotoxicity evidenced by decreased glutamate and increased GABA levels and thus provided protection against epilepsy. Treatment groups also exhibited a potent antioxidant activity when tested endogenous antioxidant enzymes SOD, GSH and LPO in the brain hippocampus. Our findings provide evidence for use of OSE, LEV and OSE+LEV against AD and epilepsy associated with AD in Aβ induced AD animal model. However, further clinical studies are required to prove the use of OSE, LEV and OSE+LEV in the management of AD and AD-associated epilepsy in human volunteers.

Analysis of Shared Genetic Regulatory Networks for Alzheimer's Disease and Epilepsy

Alzheimer's disease (AD) and epilepsy are neurological disorders that affect a large cohort of people worldwide. Although both of the two diseases could be influenced by genetic factors, the shared genetic mechanism underlying the pathogenesis of them is still unclear. In this study, we aimed to identify the shared genetic networks and corresponding hub genes for AD and epilepsy. Firstly, the gene coexpression modules (GCMs) were constructed by weighted gene coexpression network analysis (WGCNA), and 16 GCMs were identified. Through further integration of GCMs, genome-wide association studies (GWASs), and expression quantitative trait loci (eQTLs), 4 shared GCMs of AD and epilepsy were identified. Functional enrichment analysis was performed to analyze the shared biological processes of these GCMs and explore the functional overlaps between these two diseases. The results showed that the genes in shared GCMs were significantly enriched in nervous system-related pathways, such as Alzheimer's disease and neuroactive ligand-receptor interaction pathways. Furthermore, the hub genes of AD- and epilepsy-associated GCMs were captured by weighted key driver analysis (wKDA), including TRPC1, C2ORF40, NR3C1, KIAA0368, MMT00043109, STEAP1, MSX1, KL, and CLIC6. The shared GCMs and hub genes might provide novel therapeutic targets for AD and epilepsy.

Frailty for neurologists: a practical guide

Neurologists increasingly care for people with significant frailty in both clinic and ward settings. Such care demands a balanced approach to investigation, diagnosis and treatment, as well-intentioned actions can produce adverse effects. This article presents a practical approach to the identification and management of patients with frailty and neurological conditions. We address medicines optimisation, common causes of deterioration in those with frailty, communication, decisions about intensity of treatment, and shared decision-making including ethical aspects of withholding or withdrawing life-prolonging treatment, with a view to improving the experience both of people living with frailty and of the teams who care for them.

Symbolic time series analysis of electroencephalographic (EEG) epileptic seizure and brain dynamics with eye-open and eye-closed subjects during resting states

Objective

Epilepsy is a neuronal disorder for which the electrical discharge in the brain is synchronized, abnormal and excessive. To detect the epileptic seizures and to analyse brain activities during different mental states, various methods in non-linear dynamics have been proposed. This study is an attempt to quantify the complexity of control and epileptic subject with and without seizure as well as to distinguish eye-open (EO) and eye-closed (EC) conditions using threshold-based symbolic entropy.

Methods

The threshold-dependent symbolic entropy was applied to distinguish the healthy and epileptic subjects with seizure and seizure-free intervals (i.e. interictal and ictal) as well as to distinguish EO and EC conditions. The original time series data was converted into symbol sequences using quantization level, and word series of symbol sequences was generated using a word length of three or more. Then, normalized corrected Shannon entropy (NCSE) was computed to quantify the complexity. The NCSE values were not following the normal distribution, and the non-parametric Mann–Whitney–Wilcoxon (MWW) test was used to find significant differences among various groups at 0.05 significance level. The values of NCSE were presented in a form of topographic maps to show significant brain regions during EC and EO conditions. The results of the study were compared to those of the multiscale entropy (MSE).

Results

The results indicated that the dynamics of healthy subjects are more complex compared to epileptic subjects (during seizure and seizure-free intervals) in both EO and EC conditions. The comparison of the dynamics of epileptic subjects revealed that seizure-free intervals are more complex than seizure intervals. The dynamics of healthy subjects during EO conditions are more complex compared to those during EC conditions. Further, the results clearly demonstrated that threshold-dependent symbolic entropy outperform MSE in distinguishing different physiological and pathological conditions.

Conclusion

The threshold symbolic entropy has provided improved accuracy in quantifying the dynamics of healthy and epileptic subjects during EC an EO conditions for each electrode compared to the MSE.

A high-performance seizure detection algorithm based on Discrete Wavelet Transform (DWT) and EEG

In the past decade, Discrete Wavelet Transform (DWT), a powerful time-frequency tool, has been widely used in computer-aided signal analysis of epileptic electroencephalography (EEG), such as the detection of seizures. One of the important hurdles in the applications of DWT is the settings of DWT, which are chosen empirically or arbitrarily in previous works. The objective of this study aimed to develop a framework for automatically searching the optimal DWT settings to improve accuracy and to reduce computational cost of seizure detection. To address this, we developed a method to decompose EEG data into 7 commonly used wavelet families, to the maximum theoretical level of each mother wavelet. Wavelets and decomposition levels providing the highest accuracy in each wavelet family were then searched in an exhaustive selection of frequency bands, which showed optimal accuracy and low computational cost. The selection of frequency bands and features removed approximately 40% of redundancies. The developed algorithm achieved promising performance on two well-tested EEG datasets (accuracy >90% for both datasets). The experimental results of the developed method have demonstrated that the settings of DWT affect its performance on seizure detection substantially. Compared with existing seizure detection methods based on wavelet, the new approach is more accurate and transferable among datasets.

Association Between Benzodiazepine Use and Epilepsy Occurrence: A Nationwide Population-Based Case-Control Study

We conducted a retrospective case-control study to evaluate the association between the risk of benzodiazepine (BZD) use and epilepsy occurrence by using data from the Taiwan National Health Insurance Research Database. We recruited 1065 participants who ages 20 years or older and newly diagnosed with epilepsy (International Classification of Diseases, Ninth Revision, Clinical Modification 345) between 2004 and 2011 and assigned them to the epilepsy group. We subsequently frequency-matched them with participants in a control group (n = 4260) according to sex, age, and index year at a 1:4 ratio. A logistic regression model was employed to calculate the odds ratio (OR) for association of epilepsy with BZD exposure. Multivariate logistic regression was conducted to estimate the dose-response relationship between BZD levels and epilepsy risk. The adjusted OR (aOR) for the association of epilepsy with BZD exposure was 2.02 (95% confidence interval [CI] = 1.68-2.42). The aOR for an average BZD dose increased to 1.26 for the participants on <0.01 defined daily dose (DDD), and increased to 4.32 for those on ≥1.50 DDD. On average, when the DDD of BZD exposure increased by 100 units, the epilepsy risk increase by 1.03-fold (95% CI = 1.01-1.04, P = 0.003). The annual BZD exposure day ranges were significantly associated with epilepsy (2-7 days: aOR = 1.67; 8-35 days: aOR = 3.16; and ≥35 days: aOR = 5.60). Whenever the annual BZD exposure increased by 30 days, the risk of epilepsy notably increased by 1.03-fold (95% CI = 1.01-1.04, P < 0.001). In addition, users who quit BZD for more than 6 months still exhibited a higher risk of epilepsy than did the non-BZD users. A considerable increase in epilepsy occurrence was observed in ones with BZD use, particularly in those with prolonged use, multiple exposure, and high-dose consumption.

Epilepsy in patients with Alzheimer's disease: A systematic review

Alzheimer's disease (AD) and epilepsy are common disorders in the elderly. Evidence demonstrates that patients with AD have an increased risk of developing epilepsy and seizures.

Objective

To review epidemiological, clinical and treatment aspects of epilepsy and AD.

Methods

We reviewed databases (PubMED, LiLACS, Scielo) conducting a search for manuscripts using the terms Alzheimer's disease and epilepsy.

Results

Manuscripts related to the areas of interest were reviewed. Studies revealed that epilepsy is more frequent among AD patients. The combined presence of the two disorders may be related to mechanisms of neuronal hyperexcitability as a consequence of amyloid-beta protein (Aβ) or phosphorylated tau accumulation, as well as to structural changes in cortical and hippocampal regions. Available data suggest that the new generation of antiepileptic drugs (AEDs) are better tolerated in the elderly population, and may also be the best option in patients with AD and epilepsy.

Conclusion

Further prospective studies involving evaluation of concomitant dementia and epilepsy, neurophysiological findings and biomarkers need to be performed.