Epilepsy Research最新文献

Background

The rate of spontaneous Intracerebral Hemorrhage (sICH) is rising among young Americans. Trends in acute seizure (AS) incidence in this age group is largely unknown. Further, the association of AS with mortality has not been reported in this age group. The aim of this study is to determine trends in AS among young individuals with sICH.

Methods

The Merative MarketScan® Commercial Claims and Encounters database, for the years 2005 through 2015, served as the data source for this retrospective in-hospital population study. This period was chosen as spontaneous ICH incidence increased among young individuals between 2005 and 2015. Our study population included patients aged 18–64 years with ICH identified using the International Classification of Diseases, Ninth and Tenth Revision (ICD-9/10) codes 430, 431, 432.0, 432.1, 432.9, I61, I61.0, I61.1, I61.2, I61.3, I61.4, I61.5, I61.6, I61.8, and I61.9, excluding those with a prior diagnosis of seizures (ICD-9/10 codes 345.x,780.3x, G40, G41, and R56.8). We computed yearly AS incidence, mortality (in patients with and without seizures), and analyzed trends. We applied a logistic regression model to determine the independent association of AS with mortality accounting for demographic and clinical variables.

Results

AS incidence increased linearly between 2005 (incidence rate: 8.1 %) and 2015 (incidence rate: 11.0 %), which represents a 26 % relative increase (P for trends <0.0001). In-hospital mortality rate was 14.3 % among those who developed AS and 11.5 % among those who did not have AS. Overall, between 2005 and 2015, in-hospital mortality decreased from 13.0 % to 9.7 % among patients without AS but remained unchanged among those with AS. Patients who developed AS were 10 % more likely to die than those who did not (OR: 1.10, 95 % confidence interval: 1.02–1.18).

Conclusions

Between 2005 and 2015, the incidence of AS increased by nearly 26 % among young Americans with sICH. In-patient mortality remained unchanged among those who developed seizures but declined among those who did not. The occurrence of AS was independently associated with a 10 % higher risk of in-hospital death.

Objectives

This study aimed to evaluate seizure semiology, electroencephalogram (EEG), magnetic resonance imaging (MRI), and genetic findings, as well as treatment choices in Rett syndrome (RTT).

Methods

A retrospective analysis was conducted on one hundred and twenty cases diagnosed with RTT with a genetic mutation. Data were obtained from nine participating centers.

Results

In this study, 93.3 % of patients were female, with typical RTT found in 70 % of cases. Genetic etiology revealed MECP2, FoxG1, and CDKL5 in 93.8 %, 2.7 %, and 1.8 % of cases, respectively. Atypical RTT clinics were observed in 50 % of male cases, with the first EEG being normal in atypical RTT cases (p = 0.01). Generalized tonic-clonic and myoclonic epilepsy were the most common seizure semiologies, while absence and focal epilepsy were less prevalent. Valproate, levetiracetam, lamotrigine, and clobazam were the most commonly used antiepileptic drugs, affecting the severity and frequency of seizures (p = 0.015, p=<0.001, p = 0.022, and p=<0.001, respectively). No significant differences were observed in EEG findings. The initiation of anti-seizure medications significantly altered seizure characteristics (Table 4). A ketogenic diet and vagal nerve stimulation (VNS) correlated with a 50 % improvement in cognitive function, while steroid treatment showed a 60 % improvement. Remarkably, seizures were substantially reduced after VNS application.

Conclusion

This study underscores the importance of genetic diagnosis in RTT cases with a clinical diagnosis. These preliminary results will be further validated with the inclusion of clinically diagnosed RTT cases in our ongoing study.

Thalamic neuromodulation has emerged as a treatment option for drug-resistant epilepsy (DRE) with widespread and/or undefined epileptogenic networks. While deep brain stimulation (DBS) and responsive neurostimulation (RNS) depth electrodes offer means for electrical stimulation of the thalamus in adult patients with DRE, the application of thalamic neuromodulation in pediatric epilepsy remains limited. To address this gap, the Neuromodulation Expert Collaborative was established within the Pediatric Epilepsy Research Consortium (PERC) Epilepsy Surgery Special Interest Group. In this expert review, existing evidence and recommendations for thalamic neuromodulation modalities using DBS and RNS are summarized, with a focus on the anterior (ANT), centromedian(CMN), and pulvinar nuclei of the thalamus. To-date, only DBS of the ANT is FDA approved for treatment of DRE in adult patients based on the results of the pivotal SANTE (Stimulation of the Anterior Nucleus of Thalamus for Epilepsy) study. Evidence for other thalamic neurmodulation indications and targets is less abundant. Despite the lack of evidence, positive responses to thalamic stimulation in adults with DRE have led to its off-label use in pediatric patients. Although caution is warranted due to differences between pediatric and adult epilepsy, the efficacy and safety of pediatric neuromodulation appear comparable to that in adults. Indeed, CMN stimulation is increasingly accepted for generalized and diffuse onset epilepsies, with recent completion of one randomized trial. There is also growing interest in using pulvinar stimulation for temporal plus and posterior quadrant epilepsies with one ongoing clinical trial in Europe. The future of thalamic neuromodulation holds promise for revolutionizing the treatment landscape of childhood epilepsy. Ongoing research, technological advancements, and collaborative efforts are poised to refine and improve thalamic neuromodulation strategies, ultimately enhancing the quality of life for children with DRE.

Objective

This study aims to assess the clinical, inflammatory, and genetic profiles of traumatic brain injury (TBI) patients over a 2-year follow-up period, focusing on the development of posttraumatic epilepsy (PTE).

Methods

Fifty-nine patients with acute TBI were recruited in the emergency unit of a hospital in Brazil. Clinical data and blood samples were collected after 10 days of hospitalization for posterior genetic profile (Apolipoprotein E- ApoE and Glutamic Acid Descarboxylase-GAD sequencing) analyses. A subset of 19 patients were assessed for cytokine markers (mRNA expression). The development of PTE was investigated for two years following TBI. Statistical analyses including univariate analysis, multiple correspondence analysis, and Mann-Whitney test were performed.

Results

Analysis revealed an association between severe TBI and requirement for neurosurgery and polytrauma (p<0.05), as well as the development of PTE over a two-year follow-up period (p<0.05). Multiple correspondence analysis identified two distinct profiles associated with PTE and Non-PTE outcomes. The PTE profile showed a higher prevalence of the ApoE genotype E3/E3 and GAD1 SNP (rs769391) genotype AA in our study, while the Non-PTE profile showed a higher presence of E3/E4. mRNA expression analysis demonstrated acute elevated levels of TNF-α in the PTE group as compared to Non-PTE patients (6.70±1.53 vs 5.31 ±0.33, p<0.01).

Significance

Our findings underscore the multifactorial nature of aspects potentially contributing to PTE. It is unlikely that any single factor might in isolation have a strong causative influence over the development of epilepsy after TBI. Our results provide a suggestion of potential clustering that might be relevant as prognostic factors for PTE.

In medial temporal lobe epilepsy (MTLE), the benefits of surgery must be balanced against the risk of post-operative memory decline. Prediction of postoperative outcomes based on functional magnetic resonance imaging (fMRI) tasks is increasingly common but remains uncertain. The aim of this retrospective study was to determine whether hippocampal activations elicited by fMRI language tasks could enhance or refine memory fMRI in MTLE patients candidates to surgery. Forty-six patients were included: 30 right and 16 left MTLE, mostly with hippocampal sclerosis. Preoperative assessment included neuropsychological tests and fMRI with language (syntactic verbal fluency) and memory tasks (encoding, delayed, and immediate recognition of images of objects). Thirty patients underwent surgery and had neuropsychological evaluations one year after surgery. Worsening was defined as a degradation of more than 10% in postoperative forgetting scores compared to preoperative scores in verbal, non-verbal and global memory. Memory fMRI had the best sensitivity with hippocampal activations obtained in 95% of patients, versus 65% with language fMRI. Considering the patients who elicited an hippocampal activation, language fMRI led to 80%, 65% and 85% of correct predictions for respectively global, verbal and non verbal memory (versus 71%, 64% and 68% with memory fMRI). Memory and language fMRI predictions outperformed those made by neuropsychological tests. In summary, language fMRI was less sensitive than memory fMRI to elicit hippocampal activations but when it did, the proportion of correct memory predictions was better. Moreover, it proved to be an independent predictive factor regardless of the side of the epileptic focus. Given the ease of setting up a language task in fMRI, we recommend the systematic combination of memory and language tasks to predict the post-operative memory outcome of MTLE patients undergoing epilepsy surgery.

Lennox–Gastaut syndrome (LGS) is a severe form of childhood onset epileptic encephalopathy characterized by multiple drug-resistant seizures, cognitive impairment, and diffuse slow spike and wave (SSW), and generalized paroxysmal fast activity (GPFA) on electroencephalogram (EEG). Systematic review following the Preferred Reporting Items for Systematic Reviews and Meta Analysis (PRISMA) guidelines was done to investigate EEG findings in LGS. PubMed and MEDLINE were systematically searched for English-language studies published until15th may 2023. Original articles and research with patients between age group 1–30 years, and studies with description of EEG findings were included. Search identified 20 studies with 1167 patients. In this analysis 62.6 % of patients were male. The median age was 9.6 years. Etiology was structural abnormality in 42.6 %, genetic in 8.7 % but was unknown in 48.7%. Tonic seizures (74.5 %) were most frequent followed by atypical absences (44.3 %), myoclonic (39.2 %), generalized (38.5 %), atonic (34.8 %), epileptic spasm (15.9 %), focal (11.4 %) and non-convulsive status epilepticus (7.0 %). Out of 20 studies, only 15 studies mentioned GPFA in 46.6 % patients and SSW in 91.7 % patients. Unilateral and focal discharges were more common in patients with unilateral structural abnormalities. Seizure discharges on EEG longer than 10 second duration correlated with seizure diary counts. Combination of atonic, tonic, and atypical absence seizures correlated with SSW, and myoclonic seizures correlated with GPFA. EEG helps in diagnosis and prognosis of LGS. SSW is present in almost all EEG, and GPFA in 46.6 % patients. Longer duration of SSW discharges and disorganized background are associated with poor outcome.

Surgical resection of the epileptogenic zone (EZ) is an effective method for treating drug-resistant epilepsy. At present, the accuracy of EZ localization needs to be further improved. The characteristics of graph theory based on partial directed coherence networks have been applied to the localization of EZ, but the application of network control theory to effective networks to locate EZ is rarely reported. In this study, the method of partial directed coherence analysis was utilized to construct the time-varying effective brain networks of stereo-electroencephalography (SEEG) signals from 20 seizures in 12 patients. Combined with graph theory and network control theory, the differences in network characteristics between epileptogenic and non-epileptogenic zones during seizures were analyzed. We also used dung beetle optimized support vector machine classification model to evaluate the localization effect of EZ based on brain network characteristics of graph theory and controllability. The results showed that the classification of the average controllability feature was the best, and the area under the receiver operating characteristic (ROC) curve (AUC) was 0.9505, which is 1.32 % and 1.97 % higher than the traditional methods. The AUC value increased to 0.9607 after integrating the average controllability with other features. This study proved the effectiveness of controllability characteristic in identifying the EZ and provided a theoretical basis for the clinical application of network controllability in the EZ.

Detailed descriptions of violent postictal episodes are rare. We provide evidence from an index case and from a systematic review of violent postictal episodes that demonstrates the encephalopathic features of some violent postictal behaviors. We discuss how these cases may fit in the legal framework of culpability. The data support the view that some episodes of violent postictal behavior are more accurately classified as a neurological delirium or encephalopathy rather than as a postictal psychosis. Current medical terminology may present unwarranted (and presumably unintended) barriers to exculpation for patients who exhibit post-ictal violence during an episode of delirium during which the patient was unaware of his or her violent conduct.

Purpose

This study aimed to develop a classifier using supervised machine learning to effectively assess the impact of clinical, demographical, and biochemical factors in accurately predicting the antiseizure medications (ASMs) treatment response in people with epilepsy (PWE).

Methods

Data was collected from 786 PWE at the Outpatient Department of Neurology, Institute of Human Behavior and Allied Sciences (IHBAS), New Delhi, India from 2005 to 2015. Patients were followed up at the 2nd, 4th, 8th, and 12th month over the span of 1 year for the drugs being administered and their dosage, the serum drug levels, the frequency of seizure control, drug efficacy, the adverse drug reactions (ADRs), and their compliance to ASMs. Several features, including demographic details, medical history, and auxiliary examinations electroencephalogram (EEG) or Computed Tomography (CT) were chosen to discern between patients with distinct remission outcomes. Remission outcomes were categorized into ‘good responder (GR)’ and ‘poor responder (PR)’ based on the number of seizures experienced by the patients over the study duration. Our dataset was utilized to train seven classical machine learning algorithms i.e Extreme Gradient Boost (XGB), K-Nearest Neighbor (KNN), Support Vector Classifier (SVC), Decision Tree (DT), Random Forest (RF), Naïve Bayes (NB) and Logistic Regression (LR) to construct classification models.

Results

Our research findings indicate that 1) among the seven algorithms examined, XGB and SVC demonstrated superior predictive performances of ASM treatment outcomes with an accuracy of 0.66 each and ROC-AUC scores of 0.67 (XGB) and 0.66 (SVC) in distinguishing between PR and GR patients. 2) The most influential factor in discerning PR to GR patients is a family history of seizures (no), education (literate) and multitherapy with Chi-square (χ2) values of 12.1539, 8.7232 and 13.620 respectively and odds ratio (OR) of 2.2671, 0.4467, and 1.9453 each. 3). Furthermore, our surrogate analysis revealed that the null hypothesis for both XGB and SVC was rejected at a 100 % confidence level, underscoring the significance of their predictive performance. These findings underscore the robustness and reliability of XGB and SVC in our predictive modelling framework.

Significance

Utilizing XG Boost and SVC-based machine learning classifier, we successfully forecasted the likelihood of a patient's response to ASM treatment, categorizing them as either PR or GR, post-completion of standard epilepsy examinations. The classifier’s predictions were found to be statistically significant, suggesting their potential utility in improving treatment strategies, particularly in the personalized selection of ASM regimens for individual epilepsy patients.

Objective

Approximately 20–30 % of epilepsy patients exhibit negative findings on routine magnetic resonance imaging, and this condition is known as nonlesional epilepsy. Absence epilepsy (AE) is a prevalent form of nonlesional epilepsy. This study aimed to investigate the clinical diagnostic utility of regional homogeneity (ReHo) assessed through the support vector machine (SVM) approach for identifying AE.

Methods

This research involved 102 healthy individuals and 93 AE patients. Resting-state functional magnetic resonance imaging was employed for data acquisition in all participants. ReHo analysis, coupled with SVM methodology, was utilized for data processing.

Results

Compared to healthy control individuals, AE patients demonstrated significantly elevated ReHo values in the bilateral putamen, accompanied by decreased ReHo in the bilateral thalamus. SVM was used to differentiate patients with AE from healthy control individuals based on rs-fMRI data. A composite assessment of altered ReHo in the left putamen and left thalamus yielded the highest accuracy at 81.64 %, with a sensitivity of 95.41 % and a specificity of 69.23 %.

Significance

According to the results, altered ReHo values in the bilateral putamen and thalamus could serve as neuroimaging markers for AE, offering objective guidance for its diagnosis.