Seizure Status Research Articles

Longitudinal adaptive behavioral outcomes in Ogden syndrome by seizure status and therapeutic intervention.

Ogden syndrome, also known as NAA10-related neurodevelopmental syndrome, is a rare genetic condition associated with pathogenic variants in the NAA10 N-terminal acetylation family of proteins. The condition was initially described in 2011 and is characterized by a range of neurologic symptoms, including intellectual disability and seizures, as well as developmental delays, psychiatric symptoms, congenital heart abnormalities, hypotonia, and others. Previously published articles have described the etiology and phenotype of Ogden syndrome, mostly with retrospective analyses; herein, we report prospective data concerning its progress over time. The current study involves a total of 58 distinct participants; of these, 43 caregivers were interviewed using the Vineland-3 and answered a survey regarding therapy and other questions, 10 of whom completed the Vineland-3 but did not answer the survey, and 5 participants who answered the survey but have not yet performed the Vineland-3 due to language constraints. The average age at the time of the most recent assessment was 12.4 years, with individuals ranging in age from 11 months to 40.2 years. Using Vineland-3 scores, we show decline in cognitive function over time in individuals with Ogden syndrome (n = 53). Sub-domain analysis found the decline to be present across all modalities. In addition, we describe the nature of seizures in this condition in greater detail, as well as investigate how already-available non-pharmaceutical therapies impact individuals with NAA10-related neurodevelopmental syndrome. Additional investigation between seizure and non-seizure groups showed no significant difference in adaptive behavior outcomes. A therapy investigation showed speech therapy to be the most commonly used therapy by individuals with NAA10-related neurodevelopmental syndrome, followed by occupational and physical therapy, with more severely affected individuals receiving more types of therapy than their less-severe counterparts. Early intervention analysis was only significantly effective for speech therapy, with analyses of all other therapies being non-significant. Our study portrays the decline in cognitive function over time of individuals within our cohort, independent of seizure status, and therapies being received, and highlights the urgent need for the development of effective treatments for Ogden syndrome.

Bispectral Index Monitoring in the Nursing of Patients With Paroxysmal Sympathetic Hyperactivity.

To investigate the clinical nursing effect of bispectral index (BIS) monitoring for paroxysmal sympathetic hyperactivity (PSH) patients in the neurosurgical intensive care unit (NICU). From January 2022 to June 2023, a total of 30 patients with PSH secondary to moderate to severe craniocerebral injury in the NICU were monitored for BIS. The patients' paroxysmal sympathetic hyperactivity-assessment measure (PSH-AM) scores were recorded. PSH patients generally appear in 3 states: calm state, seizure state, and postmedication state. Thirty PSH patients' BIS values were recorded during the calm period, during the seizure state, and postmedication state, and these 3 different stages' BIS values were divided into groups A, B, and C, using the Kruskal-Wallis H test to compare groups. The Kruskal-Wallis H test yielded a value of H=22.599, P <0.001. H0 was rejected against the test standard of α=0.05, and the BIS values of groups A, B, and C differed. The BIS values of group A and group B differed after a pairwise comparison, and the difference was statistically significant (adjusted P =0.001). Group B and group C had different BIS values, and the difference was statistically significant (adjusted P =0.001); group A and Group C had no difference in BIS values, and the difference was not statistically significant (adjusted P =1.00). Taking BIS value as the nursing observation index for PSH patients can make nursing work more objective, reasonable, and accurate, reduce the inducing factors of PSH attack, further reduce the attack of PSH, save nursing resources, and help guide the safety assessment of sedative use.

Steroid response encephalopathy associated with autoimmune thyroiditis

IntroductionSteroid-responsive encephalopathy associated with autoimmune thyroiditis (SREAT) is a rare autoimmune disorder affecting the central nervous system, characterized by a spectrum of neurological and psychiatric symptoms.AimThis case study aims to highlight the diagnostic challenges and the successful management of SREAT syndrome in a young woman with autoimmune thyroiditis.Case studyA 21-year-old woman with documented Hashimoto's thyroiditis and polycystic ovary syndrome was admitted to the Clinic of Neurology following a 5-minute tonic seizure and subsequent confusion state lasting several hours. Initial brain MRI showed no abnormalities, and EEG revealed generalized slowness. Comprehensive laboratory assessments, including a complete blood count, biochemical analysis, and electrolyte panels, all yielded normal results. Further investigation revealed a significantly elevated anti-thyroid peroxidase antibody (anti-TPO) titer exceeding 1000 IU/mL. The suspicion of SREAT syndrome was considered. Pulse therapy with methylprednisolone was associated with rapid recovery. The patient was discharged from the hospital with an oral corticosteroid tapering regimen.Results and discussionThe administration of pulse therapy with methylprednisolone resulted in a rapid and very good response in the patient, evidenced by the resolution of seizure activity and improvement in confusion. Laboratory investigations, particularly the markedly elevated anti-TPO titer, supported the diagnosis of SREAT syndrome. The subsequent management with an oral corticosteroid tapering regimen maintained the patient's clinical stability.ConclusionsThis case highlights the importance of considering autoimmune encephalopathy in patients with a history of autoimmune thyroiditis presenting with neurological and psychiatric symptoms. Further research is warranted to better understand the underlying pathomechanisms.

Seizure detection via deterministic learning feature extraction

Epileptic seizures have a significant impact on the well-being of a large number of individuals worldwide. Utilizing electroencephalographic (EEG) signals for automatic seizure detection proves to be a valuable solution. However, dealing with raw EEG signals is inherently complex, necessitating a preliminary step of feature extraction prior to detection. Traditional feature extraction methods often amalgamate various types of features for seizure detection, as each type typically captures specific properties. In contrast, this paper focuses on detecting seizures by analyzing the system dynamics. The proposed Deterministic Learning Feature Extraction (DLFE) method extracts a single type of nonlinear dynamical feature rooted in the EEG system dynamics. DLFE employs deterministic learning to discern the inherent system dynamics of the EEG under both seizure and normal conditions. Through the feature extraction process, the infinite-dimensional system dynamics are transformed into feature vectors, exhibiting distinct distributions in seizure and normal states. This disparity can be effectively utilized for classification using standard classifiers. The performance of the proposed seizure detection method was assessed using the CHB-MIT and Bonn datasets. The average classification accuracy was found to be 98.63% with a specificity of 99.19% and a sensitivity of 98.06% on CHB-MIT dataset. Compared with the latest similar methods, the accuracy, specificity and sensitivity are improved by 0.31%, 0.21% and 0.05% respectively. Moreover, the performance was achieved with the short-time interval EEG signals within a few channels. The average classification accuracy was found to be 99.90% with a 0.22% improvement on Bonn dataset, which indicates the good generalization performance.

Energy Dynamics in Seizure Disorder: Reexamining the Sacred Disease

In this paper we explore the dynamic basis of epilepsy, the sacred disease, and show, surprisingly, that in all cases its manifest signs and symptoms correspond to the four-tier vertical framework –material, efficient, formal and final causes – advanced by Aristotle a half-century after the Hippocratic works surfaced. Specifically, impaired energy dynamics during the seizure state transiently disrupt the couplement between the material and formal causal layers of the body precipitating the behavioral alterations that define epilepsy.

Optimization of epilepsy detection method based on dynamic EEG channel screening

To decrease the interference in the process of epileptic feature extraction caused by insufficient detection capability in partial channels of focal epilepsy, this paper proposes a novel epilepsy detection method based on dynamic electroencephalogram (EEG) channel screening. This method not only extracts more effective epilepsy features but also finds common features among different epilepsy subjects, providing an effective approach and theoretical support for across-subject epilepsy detection in clinical scenarios.Firstly, we use the Refine Composite Multiscale Dispersion Entropy (RCMDE) to measure the complexity of EEG signals between normal and seizure states and realize the dynamic EEG channel screening among different subjects, which can enhance the capability of feature extraction and the robustness of epilepsy detection. Subsequently, we discover common epilepsy features in 3–15 Hz among different subjects by the screened EEG channels. By this finding, we construct the Residual Convolutional Long Short-Term Memory (ResCon-LSTM) neural network to accomplish across-subject epilepsy detection.The experiment results on the CHB-MIT dataset indicate that the highest accuracy of epilepsy detection in the single-subject experiment is 98.523 %, improved by 5.298 % compared with non-channel screening. In the across-subject experiment, the average accuracy is 96.596 %. Therefore, this method could be effectively applied to different subjects by dynamically screening optimal channels and keep a good detection performance.

Response to photic stimulation as a measure of cortical excitability in epilepsy patients.

Studying states and state transitions in the brain is challenging due to nonlinear, complex dynamics. In this research, we analyze the brain's response to non-invasive perturbations. Perturbation techniques offer a powerful method for studying complex dynamics, though their translation to human brain data is under-explored. This method involves applying small inputs, in this case via photic stimulation, to a system and measuring its response. Sensitivity to perturbations can forewarn a state transition. Therefore, biomarkers of the brain's perturbation response or "cortical excitability" could be used to indicate seizure transitions. However, perturbing the brain often involves invasive intracranial surgeries or expensive equipment such as transcranial magnetic stimulation (TMS) which is only accessible to a minority of patient groups, or animal model studies. Photic stimulation is a widely used diagnostic technique in epilepsy that can be used as a non-invasive perturbation paradigm to probe brain dynamics during routine electroencephalography (EEG) studies in humans. This involves changing the frequency of strobing light, sometimes triggering a photo-paroxysmal response (PPR), which is an electrographic event that can be studied as a state transition to a seizure state. We investigate alterations in the response to these perturbations in patients with genetic generalized epilepsy (GGE), with (n = 10) and without (n = 10) PPR, and patients with psychogenic non-epileptic seizures (PNES; n = 10), compared to resting controls (n = 10). Metrics of EEG time-series data were evaluated as biomarkers of the perturbation response including variance, autocorrelation, and phase-based synchrony measures. We observed considerable differences in all group biomarker distributions during stimulation compared to controls. In particular, variance and autocorrelation demonstrated greater changes in epochs close to PPR transitions compared to earlier stimulation epochs. Comparison of PPR and spontaneous seizure morphology found them indistinguishable, suggesting PPR is a valid proxy for seizure dynamics. Also, as expected, posterior channels demonstrated the greatest change in synchrony measures, possibly reflecting underlying PPR pathophysiologic mechanisms. We clearly demonstrate observable changes at a group level in cortical excitability in epilepsy patients as a response to perturbation in EEG data. Our work re-frames photic stimulation as a non-invasive perturbation paradigm capable of inducing measurable changes to brain dynamics.

Management of autoimmune temporal lobe epilepsy with GAD65 antibody: four case reports.

Glutamate decarboxylase (GAD) enzyme can be a target intracellular antigen in autoimmune focal epilepsy. GAD65 antibody is in found patients diagnosed with drug-refractory temporal lobe epilepsy (TLE). We explore the clinical features of the disease and therapeutic options. We present the cases of four TLE patients, two of them with type 1 diabetes. All of them were drug-resistant and therefore underwent presurgical evaluation, which revealed GAD65 antibody positivity. We discuss the four GAD65 antibody positive temporal lobe epilepsy patients' electroclinical data, the treatments, and their effectiveness. One of them became seizure-free after right anterior temporal lobe resection, two of them did not show significant improvement with immunmodulatory agents, and the fourth patient with the shortest duration of disease had significant improvement in seizure status and normalisation of cognitive status with IVIg therapy. Our cases show that the earlier a GAD65 antibody is detected, the greater the chance of achieving seizure freedom or improvements in both seizure and cognitive status with immunomodulatory agents. However, in some cases, surgery may also bring seizure freedom, but with a risk of cognitive deterioration.

The effect of resection of gliomas of the primary motor and sensory cortex on functional recovery and seizure outcome: A 10-year retrospective study.

Gliomas, the most common primary brain tumors, pose surgical challenges in eloquent cortex regions due to potential deficits affecting patients' quality of life (QOL) and increased mortality risk. This study investigates motor and sensory recovery postresection of Rolandic cortex gliomas in 40 patients, alongside seizure outcomes and the efficacy of intraoperative techniques such as awake craniotomy. This was a 10-year monocentric retrospective study based on the experience of a neurosurgeon in the resection of Rolandic gliomas and its impact on 40 patients' QOL in a period from 2011 to 2020. The primary outcomes were tumor recurrence and the efficacy of the surgery defined as survival status, seizure status, and sensory and motor neurological deficits. Data collection included demographic, tumor, and surgical outcome variables. The extent of resection (EOR) was classified as gross total resection (GTR) (EOR ≥95%) or subtotal resection (EOR <95%). Statistical analysis involved descriptive statistics and inferential tests for outcome comparisons. Patients were aged an average of 42.3 ± 14 years and distributed between 72.5% of males and 27.5% of females. The most common presentation was seizures (65%). The tumor was located in the frontal lobe at 65%, the motor at 75%, and the top tumor pathology was oligodendroglioma (42.5%). The recurrence rate in the study was 20% (8 of 40), and the 1-year survival rate was 92.5%. After the resection, significant improvement was shown in Karnofsky's performance status (P = 0.007), in normal daily activities (P = 0.001), in fine motor skills (P = 0.020), and work hobbies (P = 0.046). No statistically significant improvement was shown in seizures and deficit rates. Recurrence was not associated with the demographic characteristics, clinical presentation, tumor-related characteristics (location, area, side, and mutation), tumor resection, and adjuvant treatment (P > 0.05). GTR of Rolandic gliomas can be achieved with the use of meticulous stimulation mapping, and complete functional recovery is attainable despite common belief.

Regulatory role of excitatory interneurons by combining electrical stimulation for absence seizures in the coupled thalamocortical model

&lt;abstract&gt;&lt;p&gt;The role of excitatory interneurons (EINs) in the cortical has received increasing attention in the discussion of absence seizures. Numerous physiological experiments have confirmed the correlation between EIN and absence seizures. However, the dynamic mechanisms underlying this relationship are not well understood, and there are some challenges in selecting appropriate stimulation strategies for pyramidal clusters. In this study, we incorporated EIN into the previous Taylor model and developed an improved thalamocortical coupled model consisting of ten neuronal populations. Initially, we investigated the excitatory induction effect of EIN to pyramidal clusters and the external input of EIN. Then, four different targeted treatment approaches (deep brain stimulation (DBS), current balanced biphasic pulse (CBBP), 1:0 coordinated resetting stimulation (1:0 CRS), and 3:2 CRS) were applied to the pyramidal clusters. Moreover, we established two quantitative indices to evaluate the stimulation effects. The results showed that modifying the external input of EIN and the coupling strength projected onto the pyramidal clusters can effectively transition the system from an absence seizure state to other normal states. Additionally, inputs from the left compartment were found to reduce the generation of abnormal discharge regions in the right compartment. Furthermore, considering the treatment effects and current consumption, the 3:2 CRS stimulation strategy appeared to be the most suitable treatment approach for the pyramidal clusters. This work introduces a novel coupled model containing EIN, which contributes new theoretical foundations and insights for the future treatment of absence seizures.&lt;/p&gt;&lt;/abstract&gt;

EEG-based epileptic seizure state detection using deep learning

EEG-based epileptic seizure state detection using deep learning

EEG-based epileptic seizure state detection using deep learning

EEG-based epileptic seizure state detection using deep learning

Prediction and Recognition of Epileptic Seizures Based on Artificial Intelligence

Repetitive seizures caused by abnormal brain activity are the hallmark of the common neurological illness epilepsy, which has a serious impact on the health of those affected. The introduction of artificial intelligence (AI) in recent years has ushered in novel ways of predicting and recognizing epileptic seizures, transforming the discipline of neurology. In particular, Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), Support Vector Machines (SVMs), and Extreme Learning Machines (ELMs) are examined in this research as potential AI approaches for the prediction and identification of epileptic episodes. The underlying working logic of these AI approaches is investigated, emphasizing their distinct strengths and capacities. RNNs and LSTMs are well-known for their ability to describe temporal dependencies in sequential data, whereas SVMs excel in robust classification and ELMs excel at rapid training and real-time prediction. Real-world case studies show how these AI models may be used to analyze electroencephalogram (EEG) data, extract relevant features, and distinguish between seizure and non-seizure states. Furthermore, the essay explores the ramifications of AI in the medical profession, emphasizing the possibility of prompt intervention, improved patient care, and resource optimization. The difficulties and ethical concerns regarding AI in healthcare are also discussed. Finally, this piece highlights AI's promising role in predicting and identifying epileptic seizures, providing a glimpse into the future of personalized, data-driven healthcare. The combination of AI approaches opens the door to more accurate and timely seizure forecasts, increasing the quality of life for those living with epilepsy and revolutionizing the landscape of neurological healthcare.

Classification of Resampled Pediatric Epilepsy EEG Data Using Artificial Neural Networks with Discrete Fourier Transforms

Epilepsy is a neurological disorder commonly observed in children. Currently, electroencephalography (EEG) is widely used as the most important diagnostic method for epilepsy in medical practice. The diagnosis of epilepsy in pediatric patients is challenging due to their high level of activity and incomplete brain development. In this study, data sampled at 256 Hz were obtained from patients between the ages of 7–12, collected by Boston Children’s Hospital. First, the image intervals that contain seizure waves were identified in the datasets, and the discrete-time Fourier transform (DFT) was applied. The amplitude-frequency features of the frequency spectrum in seizure and nonseizure states were obtained, and patients were classified for seizure detection using a multilayer perceptron (MLP) based on an artificial neural network (ANN) architecture. In the next step, the EEG signals were resampled at low frequencies, and the same analyses were repeated to minimise the disadvantages of limiting factors such as storage space and processing power, resulting in reduced storage space usage and more efficient performance.

Characteristics and Prognosis of Tumor-Related Epilepsy During Tumor Evolution in Patients With IDH Wild-Type Glioblastoma.

Tumor-related epilepsy is a well-known symptom of glioblastoma. However, the particular characteristics of epileptic seizures related to glioblastoma, isocitrate dehydrogenase (IDH)-wild-type is almost unexplored longitudinally during the whole course of the disease. We assessed tumor-related epilepsy and seizure control during tumor evolution and the prognostic significance of tumor-related epilepsy. We performed an observational, retrospective single-center study at one tertiary referral neuro-oncology surgical center (2000-2020). We included adult patients treated for a newly diagnosed supratentorial glioblastoma, IDH-wild-type with available preoperative and postoperative MRI and with available epileptic seizure status at diagnosis. To determine factors associated with tumor-related epilepsy or seizure control, univariate analyses were performed using the χ2 or Fisher exact tests for categorical variables and the unpaired t test or Mann-Whitney rank-sum test for continuous variables. Predictors associated with tumor-related epilepsy and seizure control in unadjusted analysis were entered into backward stepwise logistic regression models. One thousand six patients were enrolled. The cumulative incidence of tumor-related epilepsy increased during tumor evolution (33.1% at diagnosis, 44.7% after oncologic treatment, 52.4% at progression, and 51.8% at the end-of-life phase) and is related to tumor features (cortex involvement, no necrosis, and small volume). Uncontrolled epileptic seizures increased during tumor evolution (20.1% at diagnosis, 32.0% after oncologic treatment, 46.7% at progression, and 41.1% at the end-of-life phase). Epileptic seizure control after oncologic treatment was related to seizure features (uncontrolled before oncologic treatment and focal-to-bilateral tonic-clonic seizures) and to the extent of resection. Epileptic seizure control at tumor progression was related to seizure features (presence at diagnosis and uncontrolled after oncologic treatment) and to the time to progression. Tumor-related epilepsy at diagnosis was a predictor of a longer overall survival (adjusted hazard ratio, 0.78; 95% CI 0.67-0.90; p < 0.001) independent of age, Karnofsky Performance Status score, tumor location and volume, extent of resection, standard combined chemoradiotherapy, levetiracetam use, and MGMT promoter methylation. The progression of tumor-related epilepsy with the evolution of glioblastoma, IDH-wild-type and the effects of surgery on seizure control argue for proper antiseizure medication and maximal safe resection. Tumor-related epilepsy is an independent predictor of a longer survival.

Risk model of seizure cluster or status epilepticus and intervention in the emergency department

ObjectivesTo identify possible predictors of seizure cluster or status epilepticus (SE) and to evaluate whether these patients receive greater interventions in emergency departments. MethodologyWe conducted a secondary analysis of the ACESUR Registry, a multipurpose, observational, prospective, multicentre registry of adult patients with seizures from 18 emergency departments. Clinical and care-related variables were collected. We identified risk factors and risk models for seizure cluster or SE and assessed the effect of interventions by prehospital emergency services and the hospital emergency department. ResultsWe identified a total of 186 (28%) patients from the ACESUR registry with seizure cluster (126 [19%]) or SE (60 [9%]); the remaining 478 patients (72%) had isolated seizures. The risk model for seizure cluster or SE in the emergency department included Charlson Comorbidity Index scores ≥ 3 (OR: 1.60; 95% CI, 1.05–2.46; P=.030), ≥ 2 habitual antiepileptic drugs (OR: 2.29; 95% CI, 1.49–3.51; P<.001), and focal seizures (OR: 1.56; 95% CI, 1.05–2.32; P=.027). The area under the curve of the model was 0.735 (95% CI, 0.693–0.777; P=.021). Patients with seizure cluster and SE received more aggressive interventions both by prehospital emergency services (OR: 2.89; 95% CI, 1.91–4.36; P<.001) and at the emergency department (OR: 4.41; 95% CI, 2.69–7.22; P<.001). ConclusionsThis risk model may be of prognostic value in identifying adult patients at risk of presenting seizure cluster or SE in the emergency department. In our sample, these patients received more aggressive treatment than adult patients with isolated seizures before arriving at hospital, and even more so in the emergency department.

Role of hierarchical heterogeneity in shaping seizure onset dynamics: Insights from structurally-based whole-brain dynamical network models

The brain is a highly complex network system exhibiting nonlinear dynamics, and alterations in brain structure may give rise to pathological brain dynamics such as seizures. Recent efforts in whole-brain network modeling have provided a framework of integrating subject data with nonlinear dynamical models for reproducing empirically observed phenomenon. A question remaining largely unexplored is how the structure of brain network system support and affect seizure-like dynamics. In this paper, we simulate seizures using a structurally-based whole-brain dynamical model of coupled oscillators operating in the bistable regime, and seizure onset likelihood is quantified by the escape time for nodes to transit from background to oscillatory seizure state. Based on this model, the effect of spatial heterogeneity in brain regions on seizure onset dynamics is explored. Specifically, magnetic resonance imaging derived T1w/T2w myelin map is used to parameterize the model with biologically-relevant spatial heterogeneity that approximates structural hierarchy in the human cortex. The unique shaping effect of hierarchical heterogeneity on seizure onset dynamics is demonstrated through comparisons with surrogate models where the spatial order of heterogeneity is altered. We find that hierarchical heterogeneity could most effectively impede the propagation of excitation across brain networks. It may also improve the model’s capacity to better characterize latent seizure focus as observed clinically and to personalize for epilepsy patients. The robustness of results is demonstrated using an alternative way of representing spatial heterogeneity and a higher resolution of representing brain regions. Our findings highlight the significant role of hierarchical heterogeneity as a relevant mechanism of ictogenesis and its importance to be considered in the workflow of whole-brain modeling for epilepsy patients, which are fundamental to understanding the dynamical nature of the brain system and developing personalized treatments for epilepsy patients.

Differential patterns of very high-frequency oscillations in two seizure types of the pilocarpine-induced TLE model

AimsVery high-frequency oscillations (VHFOs, >500 Hz) are considered a highly sensitive biomarker of seizures. We hypothesized that VHFOs may exhibit specificity towards hypersynchronous (HYP) seizures and low-voltage fast (LVF) seizures in temporal lobe epilepsy (TLE). MethodsLocal field potentials were recorded from the hippocampal network in TLE mice induced by pilocarpine. Subsequently, we analyzed the VHFO features, including their temporal-frequency characteristics and VHFO/theta coupling, during three states: baseline, preictal, and postictal for both HYP- and LVF-seizure groups. ResultsSignificant changes in most of the VHFO features were observed during the preictal state in both seizure groups. In the postictal state, VHFO features in the HYP-seizure group exhibited inverse alterations and appeared to align with those observed during baseline conditions. However, such phenomena were not observed after TLE seizures in the LVF-seizure group. ConclusionOur findings highlight distinct patterns of VHFO feature changes across different states of HYP seizures and LVF seizures. These results suggest that VHFOs could serve as indicative biomarkers for seizure alterations specifically associated with HYP-seizure states

Scn2a insufficiency alters spontaneous neuronal Ca2+ activity in somatosensory cortex during wakefulness

SCN2A protein-truncating variants (PTV) can result in neurological disorders such as autism spectrum disorder and intellectual disability, but they are less likely to cause epilepsy in comparison to missense variants. While invitro studies showed PTV reduce action potential firing, consequences at invivo network level remain elusive. Here, we generated a mouse model of Scn2a insufficiency using antisense oligonucleotides (Scn2a ASO mice), which recapitulated key clinical feature of SCN2A PTV disorders. Simultaneous two-photon Ca2+ imaging and electrocorticography (ECoG) in awake mice showed that spontaneous Ca2+ transients in somatosensory cortical neurons, as well as their pairwise co-activities were generally decreased in Scn2a ASO mice during spontaneous awake state and induced seizure state. The reduction of neuronal activities and paired co-activity are mechanisms associated with motor, social and cognitive deficits observed in our mouse model of severe Scn2a insufficiency, indicating these are likely mechanisms driving SCN2A PTV pathology.

Examination of health literacy and personal impact of epilepsy in patients with epilepsy

AimWe conducted this study to examine the impact of health literacy on the personal impact of epilepsy in patients with epilepsy. MethodWe conducted this cross-sectional and correlational study with 125 individuals who were admitted to the neurology outpatient clinic of a university hospital in eastern Turkey, who met the research criteria, and who accepted to participate in the study. We collected the data by using the “Descriptive Information Form (DIF)”, “The Personal Impact of Epilepsy Scale (PIES)”, and “Health Literacy Scale (HLS)”. ResultsWe found that the mean HLS total score of the participants was 104.84 ± 22.88. We also found the mean PIES total score to be 27.08 ± 18.56. Regression results using PIES as a dependent variable reported that the model created was statistically significant (F (21,83) = 11.398, p < 0.05). Among the variables included in the model, income status and HLS were negative (β = −0.209; β = −0.194; β = −0.364, respectively) predictors of the PIES total score. However, seizure status in the last year, number of drugs used, and seizure type were positive predictors of the PIES total score. We found that the variables included in the analysis explained 74% of the total PIES score. ConclusionIn conclusion, the health literacy level (HLL) of the participants was sufficient or very good, and the effect of the disease on individuals with epilepsy was low. The effect of the disease on individuals with epilepsy decreased as the level of health literacy increased.