Seizure Subtypes Research Articles

Association of SCN1A Polymorphisms rs3812718 and rs2298771 with Epilepsy.

Background/Objectives: Epilepsy is a brain disease with both environmental and genetic inputs. Ion channel dysfunction seems to be of great significance for abnormal neuronal behavior during epileptic seizures. Within neurons, the voltage-gated sodium channels are crucial proteins contributing to the initiation and propagation of action potentials. The voltage-gated sodium channel α subunit 1 (SCN1A) gene encodes for the α subunit of a voltage-gated ion channel. The aim of the study was to investigate the relation of two common SCN1A variants, i.e., rs3812718 and rs2298771, with distinct epileptic phenotypes in a South-Eastern European population. Methods: DNA was extracted from 214 unrelated participants with focal onset, focal to bilateral tonic-clonic, or generalized onset epileptic seizures and genotyped using real-time PCR (LightSNiP assays) followed by melting curve analysis. Statistical analysis of the results was performed using IBM SPSS Statistics software (version 29.0 for Windows). Results: Genotype frequency distribution analysis indicated an association for the A-allele-containing genotypes of both rs3812718 and rs2298771 polymorphisms of SCN1A with generalized onset seizures and focal to bilateral tonic-clonic seizures versus focal onset seizures. Conclusions: Consequently, the study provides evidence that supports a potential association of the investigated SCN1A polymorphisms with distinct seizure subtype susceptibility in South-Eastern Europeans.

Comparative Analysis of Lennox-Gastaut Syndrome With Different Subtypes of Tonic Seizures: A Single-Center Retrospective Cohort Study

BackgroundLennox-Gastaut syndrome (LGS) is one of the most severe childhood-onset epileptic encephalopathies, primarily characterized by tonic seizures. In clinical practice, we have identified various subtypes of tonic seizures in LGS. This study aimed to analyze the clinical characteristics, electrographic features, treatment responses, and prognosis across different subtypes of LGS. MethodsThis retrospective cohort study included 46 patients diagnosed with LGS at our center between January 2017 and January 2020. Patients were classified into four groups based on tonic seizure subtypes: Group A (tonic), Group B (spasm-tonic), Group C (myoclonic-tonic), and Group D (combination of spasm-tonic and myoclonic-tonic). Comprehensive clinical data were collected and analyzed. ResultsOf the 46 patients, 33 were male. The mean age of onset for Group B (12.38 ± 7.85 months) was significantly less than those of the other three groups (P = 0.02). No significant differences in etiology were found among the groups. Genetic analysis identified mutations in SCN8A, MCCC2, STXBP1, GABRB3, and CACNA1H. After a minimum follow-up of 24 months, the treatment outcomes were more favorable in Groups A and C, whereas psychomotor development was notably poorer in Groups B and D. ConclusionsThe findings of this study suggest that LGS may present with distinct subtypes of tonic seizures, with spasm-tonic seizures presenting at an earlier age. Patients with LGS experiencing spasm-tonic seizures, with or without myoclonic-tonic seizures, exhibited poorer treatment responses and psychomotor development than those with other subtypes.

Abnormal Cerebrospinal Fluid Cytology in Functional Movement Disorders.

The role of inflammation and neuroimmune mechanisms, which have been documented in various neuropsychiatric disorders including the seizure subtype of functional neurological disorder, remains unclear in functional movement disorders (FMD). To explore these mechanisms, we analyzed selected inflammatory markers in cerebrospinal fluid (CSF) in patients with FMD. We compared CSF markers in 26 patients with clinically established FMD (20 females; mean [SD] age = 43.3 [10.9], disease duration = 3.9 [3], range = 0.1-11 years; mean follow-up after lumbar puncture = 4.3 [2] years, range = 0.5-7 years) and 26 sex- and age-matched clinical controls with noninflammatory nonneurodegenerative neurological disorders, mostly sleep disorders. Sixty-five percent of FMD patients versus 15% of controls showed cytological abnormalities (i.e., increased white blood cells [WBC] count, signs of WBC activation, or both; odds ratio [OR] = 9.85, 95% confidence interval = 2.37-52.00, p < .01, corrected), with a significantly higher frequency of an isolated lymphocytic activation, 35% versus 0% (OR = ∞, 95% confidence interval = 2.53-∞, p < .05, corrected). There were no differences in CSF protein and albumin levels, quotient albumin, IgG index, and oligoclonal bands. CSF abnormalities were not associated with more severe motor symptoms or a higher frequency of depression in FMD. Our results suggest a possible involvement of immune mechanisms in the pathophysiology of (at least a subtype of) FMD that deserves further investigation.

Impact of variant subtype on electro-clinical phenotype of Dravet syndrome- a South Indian cohort study

ObjectiveWe aimed to compare the electroclinical correlates of truncating and missense variants of SCN1A variants in children with Dravet syndrome (DS) and to determine phenotypic features in relation to variants identified and seizure outcomes. MethodsA single center prospective study was carried out on a South Indian cohort. Patients below 18 years of age who met the clinical criteria for DS who had undergone genetic testing and completed a minimum of one year follow up were included. We compared the differences in clinical profile, seizure outcome, developmental characteristics and anti-seizure medication (ASM) responsiveness profiles between patients with missense and truncating variants. ResultsOut of a total of 3967 children with drug-resistant epilepsy during the period 2015–2021, 49 patients who fulfilled the inclusion criteria were studied. Thirty-seven had positive genetic tests, out of which 29 were SCN1A variants and 9 were other novel variants. The proportion of missense (14; 48.3%) and truncating SCN1A variants (15; 51.7%) was similar. A significant trend for developing multiple seizure types was noted among children with truncating variants (p = 0.035) and seizure freedom was more likely among children with missense variants (p = 0.042). All patients with truncating variants had ASM resistant epilepsy (p = 0.020). Developmental outcomes did not differ between the variant subtypes. ConclusionOur results show that children harbouring missense variants demonstrated a significantly lower propensity for multiple seizure subtypes and a higher proportion with seizure freedom. However developmental implications appear to be independent of variant subtype.

Multi-Branch Mutual-Distillation Transformer for EEG-Based Seizure Subtype Classification.

Cross-subject electroencephalogram (EEG) based seizure subtype classification is very important in precise epilepsy diagnostics. Deep learning is a promising solution, due to its ability to automatically extract latent patterns. However, it usually requires a large amount of training data, which may not always be available in clinical practice. This paper proposes Multi-Branch Mutual-Distillation (MBMD) Transformer for cross-subject EEG-based seizure subtype classification, which can be effectively trained from small labeled data. MBMD Transformer replaces all even-numbered encoder blocks of the vanilla Vision Transformer by our designed multi-branch encoder blocks. A mutual-distillation strategy is proposed to transfer knowledge between the raw EEG data and its wavelets of different frequency bands. Experiments on two public EEG datasets demonstrated that our proposed MBMD Transformer outperformed several traditional machine learning and state-of-the-art deep learning approaches. To our knowledge, this is the first work on knowledge distillation for EEG-based seizure subtype classification.

Deep learning distinguishes connectomes from focal epilepsy patients and controls: feasibility and clinical implications.

The application of deep learning models to evaluate connectome data is gaining interest in epilepsy research. Deep learning may be a useful initial tool to partition connectome data into network subsets for further analysis. Few prior works have used deep learning to examine structural connectomes from patients with focal epilepsy. We evaluated whether a deep learning model applied to whole-brain connectomes could classify 28 participants with focal epilepsy from 20 controls and identify nodal importance for each group. Participants with epilepsy were further grouped based on whether they had focal seizures that evolved into bilateral tonic-clonic seizures (17 with, 11 without). The trained neural network classified patients from controls with an accuracy of 72.92%, while the seizure subtype groups achieved a classification accuracy of 67.86%. In the patient subgroups, the nodes and edges deemed important for accurate classification were also clinically relevant, indicating the model's interpretability. The current work expands the evidence for the potential of deep learning to extract relevant markers from clinical datasets. Our findings offer a rationale for further research interrogating structural connectomes to obtain features that can be biomarkers and aid the diagnosis of seizure subtypes.

Functional neurological seizures and migraine: A systematic review and case series

BackgroundThe seizure subtype of functional neurological disorder (FND-seizures) is a common neuropsychiatric condition manifesting with episodic epilepsy-like events. Despite the common belief that FND-seizures are precipitated by psychological stressors, neurological disorders may also be triggers. In 1890, Babinski described four cases of FND symptoms associated with migraine attacks. Despite the passing of more than 130 years since this first clinical observation, the relationship between FND-seizures and migraine is not fully elucidated. Objectives(1) To complete a systematic review of the literature that investigated potential associations between FND-seizures and migraine and the response of FND seizures to treatment with migraine prophylactic medications (2). To undertake a retrospective study of patients with FND-seizures and migraine, including response to migraine prophylaxis. Methods(1) Using PRISMA methods, we completed a systematic review of EMBASE and Scopus databases from inception to March 31, 2021, for literature on FND-seizures and migraine. (2) Our multi-disciplinary team, including subspecialists in psychosomatic medicine, epilepsy, and headache disorders, reviewed consecutive patients diagnosed with FND-seizures and migraine to assess potential causal associations and responses to standard migraine prophylactic medications. Results(1) The search yielded seven studies from 126 screened manuscripts (N = 1,186 patients with FND-seizures; mean age 38.7 years; 72.6% female). They varied substantially in design, population, diagnostic measures, and outcomes. Nevertheless, all studies found associations between FND-seizures and migraine, which were stronger than those between epileptic seizures and migraine in comparative investigations, but provided limited information on treatment response. (2) In our case series, investigators reached unanimous consensus that migraine attacks triggered FND-seizures in 28/43 (65.1%) patients reviewed (mean age, 38.8 years; 74% female). In 19/26 (73%) patients with adequate follow-up data, treatment with migraine prophylactic medications alone (no behavioral interventions) concomitantly reduced FND-seizure and headache frequency by >50%. ConclusionOur systematic review and case series indicate that migraine attacks may trigger FND-seizures, perhaps more often that currently understood, and suggest that migraine prophylaxis may reduce FND-seizure frequency in such cases. To validate these observations, fully powered prospective investigations are required.

Novel ictal manifestations in Leucine rich Glioma Inactivated 1 Encephalitis - Transient, Alternating Hemi-body Paresthesias (P3-1.009)

<h3>Objective:</h3> To describe transient, alternating hemi-body paresthesias as an ictal manifestation of LGI1-encephalitis. <h3>Background:</h3> LGI1 encephalitis is associated with multiple seizure types, as well as cognitive decline, behavioral changes, &amp; hyponatremia. There is marked heterogeneity in seizure subtypes associated with LGI1 encephalitis. <h3>Design/Methods:</h3> Case Report <h3>Results:</h3> A 54 years old patient began experiencing paroxysmal episodes of nausea, rising epigastric sensation (NRES), and three-months later he presented with cluster of two generalized tonic clonic (GTC) seizures. GTCs and NRES resolved with levetiracetam (LEV) and lacosamide (LCS) given in the ER. At 6-months symptom onset, he reported focal-aware seizures with transient, alternating hemi-body paresthesias (TAHP), spreading from head to right, left, or bilateral hemi-body. These increased in frequency &amp; duration. Cognitive difficulties followed soon after. <h3>Neuroimaging:</h3> At presentation MRI was normal. After his 4^th GTC, MRI showed expansile T2 hyperintense mass in right hippocampus. Serial MRIs first showed evolution to right mesial temporal sclerosis. <h3>Neurophysiology:</h3> At presentation EEGs were normal. Serial ambEEGs/vEEGs captured multiple paroxysmal episodes of NRES and TAHP – neither with electrographic correlate. Additionally, patient had numerous multifocal electrographic seizures but without clinical correlate nor awareness. <h3>Clinical Diagnosis and Prognosis:</h3> At 19 months after symptom onset, CSF showed LGI1 positive Antibodies. Systemic imaging was negative for occult malignancy. Steroids followed by IVIG and Rituximab were initiated. TAHP as well as electrographic seizures gradually resolved completely at 26-months after symptom onset. Repeat CSF analysis was negative for LGI-1. <h3>Conclusions:</h3> This patient demonstrated electroclinical dissociation in his ictal presentation. There were no electrographic correlates to reported NRESs and TAHPs, and no clinical correlate to numerous, multifocal electrographic-seizures. Yet, all these phenomena gradually resolved with immunotherapy. It is common to downplay or dismiss reported symptoms of TAHP, but this diagnostic inertia may lead to delayed diagnosis and treatment of inflammatory processes, such as LGI1-associated-encephalitis. <b>Disclosure:</b> Dr. Tripathi has nothing to disclose. Dr. Khan has nothing to disclose. Dr. Aydemir has nothing to disclose. The institution of Dr. Afra has received research support from LivaNova and UCB. Dr. Afra has a non-compensated relationship as a board member (representing AAN) with CoA-NDT that is relevant to AAN interests or activities.

Source-Free Domain Adaptation (SFDA) for Privacy-Preserving Seizure Subtype Classification.

Electroencephalogram (EEG) based seizure subtype classification is very important in clinical diagnostics. Source-free domain adaptation (SFDA) uses a pre-trained source model, instead of the source data, for privacy-preserving transfer learning. SFDA is useful in seizure subtype classification, which can protect the privacy of the source patients, while reducing the amount of labeled calibration data for a new patient. This paper introduces semi-supervised transfer boosting (SS-TrBoosting), a boosting-based SFDA approach for seizure subtype classification. We further extend it to unsupervised transfer boosting (U-TrBoosting) for unsupervised SFDA, i.e., the new patient does not need any labeled EEG data. Experiments on three public seizure datasets demonstrated that SS-TrBoosting and U-TrBoosting outperformed multiple classical and state-of-the-art machine learning approaches in cross-dataset/cross-patient seizure subtype classification.

Mixture of Experts for EEG-Based Seizure Subtype Classification.

Epilepsy is a pervasive neurological disorder affecting approximately 50 million individuals worldwide. Electroencephalogram (EEG) based seizure subtype classification plays a crucial role in epilepsy diagnosis and treatment. However, automatic seizure subtype classification faces at least two challenges: 1) class imbalance, i.e., certain seizure types are considerably less common than others; and 2) no a priori knowledge integration, so that a large number of labeled EEG samples are needed to train a machine learning model, particularly, deep learning. This paper proposes two novel Mixture of Experts (MoE) models, Seizure-MoE and Mix-MoE, for EEG-based seizure subtype classification. Particularly, Mix-MoE adequately addresses the above two challenges: 1) it introduces a novel imbalanced sampler to address significant class imbalance; and 2) it incorporates a priori knowledge of manual EEG features into the deep neural network to improve the classification performance. Experiments on two public datasets demonstrated that the proposed Seizure-MoE and Mix-MoE outperformed multiple existing approaches in cross-subject EEG-based seizure subtype classification. Our proposed MoE models may also be easily extended to other EEG classification problems with severe class imbalance, e.g., sleep stage classification.

Brain network dynamics codify heterogeneity in seizure evolution.

Dynamic functional brain connectivity facilitates adaptive cognition and behaviour. Abnormal alterations within such connectivity could result in disrupted functions observed across various neurological conditions. As one of the most common neurological disorders, epilepsy is defined by the seemingly random occurrence of spontaneous seizures. A central but unresolved question concerns the mechanisms by which extraordinarily diverse propagation dynamics of seizures emerge. Here, we applied a graph-theoretical approach to assess dynamic reconfigurations in the functional brain connectivity before, during and after seizures that display heterogeneous propagation patterns despite sharing similar cortical onsets. We computed time-varying functional brain connectivity networks from human intracranial recordings of 67 seizures (across 14 patients) that had a focal origin—49 of these focal seizures remained focal and 18 underwent a bilateral spread (focal to bilateral tonic-clonic seizures). We utilized functional connectivity networks estimated from interictal periods across patients as control. Our results characterize network features that quantify the underlying functional dynamics associated with the observed heterogeneity of seizure propagation across these two types of focal seizures. Decoding these network features demonstrate that bilateral propagation of seizure activity is an outcome of the imbalance of global integration and segregation in the brain prior to seizure onset. We show that there exist intrinsic network signatures preceding seizure onset that are associated with the extent to which an impending seizure will propagate throughout the brain (i.e. staying within one hemisphere versus spreading transcallosally). Additionally, these features characterize an increase in segregation and a decrease in excitability within the brain network (i.e. high modularity and low spectral radius). Importantly, seizure-type-specific differences in these features emerge several minutes prior to seizure onset, suggesting the potential utility of such measures in intervention strategies. Finally, our results reveal network characteristics after the onset that are unique to the propagation mechanisms of two most common focal seizure subtypes, indicative of distinct reconfiguration processes that may assist termination of each seizure type. Together, our findings provide insights into the relationship between the temporal evolution of seizure activity and the underlying functional connectivity dynamics. These results offer exciting avenues where graph-theoretical measures could potentially guide personalized clinical interventions for epilepsy and other neurological disorders in which extensive heterogeneity is observed across subtypes as well as across and within individual patients.

Right Peri-insular Hemispherotomy: 2-dimensional operative video

This is a surgical technique video of a right peri-insular hemispherotomy in a child with Rasmussen’s encephalitis. The patient is a 5-year-old boy with focal onset impaired awareness seizures started 6 months before surgery, developing different subtypes of focal seizures and evolving to drug-resistant epilepsy. In neurological examination, he already had a left-sided hemiparesis with pyramidal signs. Magnetic resonance imaging showed right-sided cortical atrophy and fronto-insular subcortical hyperintensity signal areas. The preoperative electroencephalogram revealed frequent right-sided central mid temporal spikes. The history and complementary exams raised a hypothesis of Rasmussen’s encephalitis, a rare neurological condition, characterized by unilateral chronic inflammation of the cerebral cortex, causing drug-resistant epilepsy, and evolving to progressive neurological decline1. The hemispherotomy is an effective procedure aimed to isolate the entire hemisphere functionally with minimal removal of brain tissue, and remains the only cure for seizures caused by Rasmussen’s encephalitis2,3. Long-term seizure freedom may be achieved in around 80% of patients and improves quality of life4. It’s already accepted that briefer duration of epilepsy preceding the surgery is associated to better adaptive functioning postoperatively due to the high neuroplasticity of young patients4. Three years after surgery, the patient remains seizure free, walking without help.

Efficacy of cenobamate by focal seizure subtypes: Post-hoc analysis of a phase 3, multicenter, open-label study

PurposeTo report post-hoc efficacy data by focal seizure subtypes from 10 US study sites from a large, global, open-label, phase 3 study of adjunctive cenobamate. MethodsPatients 18–70 years old with uncontrolled focal seizures taking stable doses of 1–3 antiseizure medications were administered increasing daily doses of cenobamate (12.5, 25, 50, 100, 150, 200 mg/day) at 2-week intervals (target dose 200 mg/day). Further increases to 400 mg/day by 50-mg/day increments every other week were allowed. Results240 patients were evaluated; 27 (11.3%), 224 (93.3%), and 56 (23.3%) patients had focal aware motor (FAM), focal impaired awareness (FIA), and focal to bilateral tonic-clonic (FBTC) seizures, respectively (patients may have had ≥ 1 seizure subtype). Median baseline seizure frequencies/28 days were 10.5, 2.3, and 0.9 for FAM, FIA, and FBTC seizure subtypes. Reductions in median percent seizure frequency/28 days from baseline were observed during Months 1–3 (55.0%, 52.4%, and 94.1% for FAM, FIA, and FBTC). Greater reductions were observed during Months 4–5 (88.2%, 81.0%, and 100%) and during Months 25–27 (98.1%, 100%, and 100%). The percentage of patients achieving 100% seizure reduction in the FAM, FIA, and FBTC seizure subtypes was 22.2% (6/27), 21.5% (48/223), and 50% (28/56) during Months 1–3 and increased to 47.8% (11/23), 54.3% (88/162), and 90.5% (38/42) during Months 25–27, respectively. The most common treatment-emergent adverse events (≥ 20%) were fatigue, dizziness, and somnolence. No cases of DRESS were reported. ConclusionsSeizure reductions occurred in all focal seizure subtypes with cenobamate over time through Months 25–27, with the earliest onset in the FBTC group. Results from this subset analysis of the phase 3 study support the long-term efficacy of cenobamate across focal seizure types.

Efficacy of Cenobamate by Focal Seizure Subtypes: Post-hoc Analysis of a Phase 3, Multicenter, Open-Label Study (S13.006)

Efficacy of Cenobamate by Focal Seizure Subtypes: Post-hoc Analysis of a Phase 3, Multicenter, Open-Label Study (S13.006)

Efficacy and Safety of Fenfluramine for the Treatment of Seizures Associated With Lennox-Gastaut Syndrome

New treatment options are needed for patients with Lennox-Gastaut syndrome (LGS), a profoundly impairing, treatment-resistant, developmental and epileptic encephalopathy. To evaluate the efficacy and safety of fenfluramine in patients with LGS. This multicenter, double-blind, placebo-controlled, parallel-group randomized clinical trial was conducted from November 27, 2017, to October 25, 2019, and had a 20-week trial duration. Patients were enrolled at 65 study sites in North America, Europe, and Australia. Included patients were aged 2 to 35 years with confirmed diagnosis of LGS and experienced 2 or more drop seizures per week during the 4-week baseline. Using a modified intent-to-treat method, data analysis was performed from November 27, 2017, to October 25, 2019. The database lock date was January 30, 2020, and the date of final report was September 11, 2021. Patients were randomized to receive either a 0.7-mg/kg/d or 0.2-mg/kg/d (maximum 26 mg/d) dose of fenfluramine or placebo. After titration (2-week period), patients were taking their randomized dose for 12 additional weeks. Primary efficacy end point was percentage change from baseline in drop seizure frequency in patients who received 0.7 mg/kg/d of fenfluramine vs placebo. A total of 263 patients (median [range] age, 13 [2-35] years; 146 male patients [56%]) were randomized to the 0.7-mg/kg/d fenfluramine group (n = 87), 0.2-mg/kg/d fenfluramine group (n = 89), or placebo group (n = 87). The median percentage reduction in frequency of drop seizures was 26.5 percentage points in the 0.7-mg/kg/d fenfluramine group, 14.2 percentage points in the 0.2-mg/kg/d fenfluramine group, and 7.6 percentage points in the placebo group. The trial met its primary efficacy end point: patients in the 0.7-mg/kg/d fenfluramine group achieved a -19.9 percentage points (95% CI, -31.0 to -8.7 percentage points; P = .001) estimated median difference in drop seizures from baseline vs placebo. More patients in the 0.7-mg/kg/d fenfluramine group achieved a 50% or greater response (22 of 87 [25%]; P = .02) vs placebo (9 of 87 [10%]). Site investigators and caregivers gave a much improved or very much improved rating on the Clinical Global Impression of Improvement scale to more patients in the 0.7-mg/kg/d fenfluramine group than patients in the placebo group (21 [26%] vs 5 [6%]; P = .001). The seizure subtype that appeared most responsive to fenfluramine was generalized tonic-clonic seizure (120 of 263 [46%]), with a decrease in frequency of 45.7% in the 0.7-mg/kg/d fenfluramine group and 58.2% in the 0.2-mg/kg/d fenfluramine group compared with an increase of 3.7% in the placebo group. Most common treatment-emergent adverse events included decreased appetite (59 [22%]), somnolence (33 [13%]), and fatigue (33 [13%]). No cases of valvular heart disease or pulmonary arterial hypertension were observed. Results of this trial showed that, in patients with LGS, fenfluramine compared with placebo provided a significantly greater reduction in drop seizures and may be a particularly advantageous choice in patients who experience generalized tonic-clonic seizures. ClinicalTrials.gov Identifier: NCT03355209.

Improving epilepsy control among children with cerebral palsy in rural Bangladesh: a prospective cohort-based study

ObjectiveTo define the prevalence and seizure subtypes among children with cerebral palsy (CP) in rural Bangladesh and explore barriers to optimum epilepsy control.DesignProspective cohort study.SettingThe study was conducted in Shahjadpur,...

TIE-EEGNet: Temporal Information Enhanced EEGNet for Seizure Subtype Classification.

Electroencephalogram (EEG) based seizure subtype classification is very important in clinical diagnostics. However, manual seizure subtype classification is expensive and time-consuming, whereas automatic classification usually needs a large number of labeled samples for model training. This paper proposes an EEGNet-based slim deep neural network, which relieves the labeled data requirement in EEG-based seizure subtype classification. A temporal information enhancement module with sinusoidal encoding is used to augment the first convolution layer of EEGNet. A training strategy for automatic hyper-parameter selection is also proposed. Experiments on the public TUSZ dataset and our own CHSZ dataset with infants and children demonstrated that our proposed TIE-EEGNet outperformed several traditional and deep learning models in cross-subject seizure subtype classification. Additionally, it also achieved the best performance in a challenging transfer learning scenario. Both our code and the CHSZ dataset are publicized.

Evaluating vagus nerve stimulation treatment with heart rate monitoring in pediatric patients with intractable epilepsy.

Vagus nerve stimulators (VNS) have emerged as an effective treatment modality for pediatric patients suffering from intractable, drug-resistant epilepsy. Newer devices, AspireSR™ Model 106 and the SenTiva™ Model 1000 (VNS TherapyⓇ, LivaNova™), contain an "auto-stimulation" feature that detects ictal tachycardia and transmits pulsations to attenuate seizures. However, the exact benefits of auto-stimulation compared to its risks still merit further exploration. This study evaluates the utility of these specific devices in a heterogeneous population of pediatric and young adult patients with intractable epilepsy. This is a retrospective chart review of 55 patients who underwent either VNS insertion with or without an auto-stimulation-enabled VNS device at a single level four epilepsy center. Seizure frequency, seizure subtype, side effects, and change in anti-seizure medication load both before and after VNS implantations were collected from patient self-reporting at the time of VNS insertion and 12months following implantation. Information regarding output current, auto-stimulation current, duty cycling, and auto-stimulation threshold of the device was obtained from documented VNS interrogation for patients with auto-stimulation-enabled VNS devices. Patients with auto-stimulation-enabled VNS devices had a mean 56.0% (SD = 0.414) seizure frequency reduction 12months post-VNS insertion, while patients without auto-stimulation-enabled VNS devices had a mean 41.6% (SD = 0.456) seizure frequency reduction during the same interval. The mean seizure frequency reduction 12months post-VNS insertion for patients with a SenTiva™ 1000 model was 66.0% (SD = 0.426). For patients with auto-stimulation-enabled VNS devices, post-treatment seizure reduction was significantly correlated with daily auto-stimulation activation (R = 0.432, p = 0.025). This study supports the clinical safety and utility of auto-stimulation-enabled VNS models, specifically the SenTiva™ 1000, in treating pediatric patients with intractable epilepsy of various subtypes and etiologies. Further research is needed to evaluate the sustained impact of auto-stimulation on long-term outcomes (≥ 2years follow-up post-VNS).

Dementia, Subtype of Seizures, and the Risk of New Onset Seizures: A Cohort Study.

Seizure disorders have been identified in patients suffering from different types of dementia. However, the risks associated with the seizure subtypes have not been characterized. To compare the occurrence and risk of various seizure subtypes (focal and generalized) between patients with and without a dementia diagnosis. Data from 40.7 million private insured patient individual electronic health records from the U.S., were utilized. Patients 60 years of age or more from the Optum Insight Clinformatics-data Mart database were included in this study. Using ICD-9 diagnoses, the occurrence of generalized or focal seizure disorders was identified. The risk of new-onset seizures and the types of seizures associated with a dementia diagnosis were estimated in a cohort of 2,885,336 patients followed from 2005 to 2014. Group differences were analyzed using continuity-adjusted chi-square and hazard ratios with 95%confidence intervals calculated after a logistic regression analysisResults:A total of 79,561 patient records had a dementia diagnosis, and 56.38%of them were females. Patients with dementia when compared to those without dementia had higher risk for seizure disorders [Hazard ratio (HR) = 6.5 95%CI = 4.4-9.5]; grand mal status (HR = 6.5, 95%CI = 5.7-7.3); focal seizures (HR = 6.0, 95%CI = 5.5-6.6); motor simple focal status (HR = 5.6, 95%CI = 3.5-9.0); epilepsy (HR = 5.0, 95%CI = 4.8-5.2); generalized convulsive epilepsy (HR = 4.8, 95%CI = 4.5-5.0); localization-related epilepsy (HR = 4.5, 95%CI = 4.1-4.9); focal status (HR = 4.2, 95%CI = 2.9-6.1); and fits convulsions (HR = 3.5, 95%CI = 3.4-3.6). The study confirms that patients with dementia have higher risks of generalized or focal seizure than patients without dementia.

Serum Neuropeptide Y Level is Associated with Post-Ischemic Stroke Epilepsy

Background and purpose: Post-ischemic stroke epilepsy (PISE) is one of the common complications of stroke. Materials and methodsMethods To determine the risk factors of PISE, in this study, 78 patients with PISE and 86 patients without PISE were recruited. Clinical data and serum neuropeptide Y (NPY) levels were collected and the relative factors including clinical data and serum were analyzed. ResultsLogistic regression showed that low serum NPY was significantly associated with PISE. Every 5 ng/ml increment of serum NPY was associated with 62% risk decrease in patients with PISE. The area under curve of serum NPY was 0.910 with a sensitivity of 84.62% and a specificity of 86.05%. The cut-off value of serum NPY was 90 ng/ml. According to cut-off value of serum NPY, the percentage of patients with PISE decreased from 84.6% in low serum NPY group to 14.0% in high serum NPY group. Furthermore, patients were divided into different tertiles according to serum NPY. The percentage of patients with PISE reduced from 90.0% in the lowest tertile (NPY < 85 ng/ml) to 3.5% in the highest tertile (NPY ≥ 105 ng/ml). Compared with patients with normal video-electroencephalogram (VEEG), serum NPY levels significantly decreased in patients with abnormal VEEG; however, serum NPY levels were not associaated with epileptic seizure subtypes. ConclusionsSerum NPY was an independent risk factor for PISE. Targeting serum NPY may be used to the prevention and treatment of PISE.