A study of brain functional network and alertness changes in temporal lobe epilepsy with and without focal to bilateral tonic–clonic seizures

Background Temporal lobe epilepsy (TLE) is commonly refractory. Epilepsy surgery is an effective treatment strategy for refractory epilepsy, but patients with a history of focal to bilateral tonic-clonic seizures (FBTCS) have poor outcomes. Previous network studies on epilepsy have found that TLE and idiopathic generalized epilepsy with generalized tonic-clonic seizures (IGE-GTCS) showed altered global and nodal topological properties. Alertness deficits also were found in TLE. However, FBTCS is a common type of seizure in TLE, and the implications for alertness as well as the topological rearrangements associated with this seizure type are not well understood. Methods We obtained rs-fMRI data and collected the neuropsychological assessment data from 21 TLE patients with FBTCS (TLE- FBTCS), 18 TLE patients without FBTCS (TLE-non- FBTCS) and 22 controls, and constructed their respective functional brain networks. The topological properties were analyzed using the graph theoretical approach and correlations between altered topological properties and alertness were analyzed. Results We found that TLE-FBTCS patients showed more serious impairment in alertness effect, intrinsic alertness and phasic alertness than the patients with TLE-non-FBTCS. They also showed significantly higher small-worldness, normalized clustering coefficient (γ) and a trend of higher global network efficiency (gE) compared to TLE-non-FBTCS patients. The gE showed a significant negative correlation with intrinsic alertness for TLE-non-FBTCS patients. Conclusion Our findings show different impairments in brain network information integration, segregation and alertness between the patients with TLE-FBTCS and TLE-non-FBTCS, demonstrating that impairments of the brain network may underlie the disruptions in alertness functions.

Prevalence, distribution, and clinical associations of suspected postictal changes on brain magnetic resonance imaging in epileptic dogs

OBJECTIVE To determine the prevalence of presumed postictal changes (PC) on brain MRI in epileptic dogs, describe their distribution, and recognize possible correlations with different epilepsy features. ANIMALS 540 client-owned dogs with epilepsy and a complete medical record that underwent brain MRI at 4 veterinary referral hospitals between 2016 and 2019. PROCEDURES Data were collected regarding signalment, seizure type, seizure severity, time between last seizure and MRI, and etiological classification of epilepsy. Postictal changes were considered when solitary or multiple intraparenchymal hyperintense lesions were observed on T2-weighted and fluid-attenuated inversion recovery images and were hypointense or isointense on T1-weighted sequences, which were not confined to a vascular territory and showed no to mild mass effect and no to mild contrast enhancement. RESULTS Sixty-seven dogs (12.4%) showed MRI features consistent with PC. The most common brain sites affected were the piriform lobe, hippocampus, temporal neocortex, and cingulate gyrus. Dogs having suffered cluster seizures or status epilepticus were associated with a higher probability of occurrence of PC, compared to dogs with self-limiting seizures (OR 2.39; 95% confidence interval, 1.33 to 4.30). Suspected PC were detected both in dogs with idiopathic epilepsy and in those with structural epilepsy. Dogs with unknown-origin epilepsy were more likely to have presumed PC than were dogs with structural (OR 0.15; 95% confidence interval, 0.06 to 0.33) or idiopathic epilepsy (OR 0.42; 95% confidence interval, 0.20 to 0.87). Time between last seizure and MRI was significantly shorter in dogs with PC. CLINICAL RELEVANCE MRI lesions consistent with PC were common in epileptic dogs, and the brain distribution of these lesions varied. Occurrence of cluster seizures or status epilepticus, diagnosis of unknown origin epilepsy, and lower time from last seizure to MRI are predictors of suspected PC.

Automated Deep Neural Network Approach for Detection of Epileptic Seizures

In this thesis, I focus on exploiting electroencephalography (EEG) signals for early seizure diagnosis in patients. This process is based on a powerful deep learning algorithm for times series data called Long Short-Term Memory (LSTM) network. Since manual and visual inspection (detection) of epileptic seizure through the electroencephalography (EEG) signal by expert neurologists is time-consuming, work-intensive and error-prone and it might take a couple hours for experts to analyze a single patient record and to do recognition when immediate action is needed to be taken. This thesis proposes a reliable automatic seizure/non-seizure classification method that could facilitate the identification process of characteristic epileptic patterns, such as pre-ictal spikes, seizures and determination of seizure frequency, seizure type, etc. In order to recognize epileptic seizure accurately, the proposed model exploits the temporal dependencies in the EEG data. Experiments on clinical data present that this method achieves a high seizure prediction accuracy and maintains reliable performance. This thesis also finds the most efficient lengths of EEG recording for highest accuracies of different classification in the automated seizure detection realm. It could help non-experts to predict the seizure more comprehensively and bring awareness to patients and caregivers of upcoming seizures, enhancing the daily lives of patients against unpredictable occurrence of seizures.

A channel-wise attention-based representation learning method for epileptic seizure detection and type classification

<div>Epilepsy affect almost 1% of the worldwide population. An early diagnosis of seizure types is a patient-dependent process which is crucial for the treatment selection process. The selection of the proper treatment relies on the correct identification of seizures type. As such, identifying the seizure type has the biggest immediate influence on therapy than the seizure detection, reducing the neurologist’s efforts when reading and detecting seizures in EEG recordings. Most of the existing seizure detection and classification methods are conceptualized following the patient-dependent schema thus fail to perform well with unknown cases. Our work focuses on patient-independent schema for seizure type classification and pays more attention to the explainability of the underlying attention mechanism of our method. Using a channel-wise attention mechanism, a quantification of the EEG channels contribution is enabled. Therefore, results become more interpretable and a visualization of brain lobes contribution by seizure types is allowed. We evaluate our model for seizure detection and type classification on CHB-MIT and the recently released TUH EEG Seizure, respectively. Our model is able to classify 8 seizure types with an accuracy of 98.41%, directly from raw EEG data without any preprocessing. A case study showed a high correlation between the neurological baselines and the interpretable results of our model.</div>

A channel-wise attention-based representation learning method for epileptic seizure detection and type classification

<div>Epilepsy affect almost 1% of the worldwide population. An early diagnosis of seizure types is a patient-dependent process which is crucial for the treatment selection process. The selection of the proper treatment relies on the correct identification of seizures type. As such, identifying the seizure type has the biggest immediate influence on therapy than the seizure detection, reducing the neurologist’s efforts when reading and detecting seizures in EEG recordings. Most of the existing seizure detection and classification methods are conceptualized following the patient-dependent schema thus fail to perform well with unknown cases. Our work focuses on patient-independent schema for seizure type classification and pays more attention to the explainability of the underlying attention mechanism of our method. Using a channel-wise attention mechanism, a quantification of the EEG channels contribution is enabled. Therefore, results become more interpretable and a visualization of brain lobes contribution by seizure types is allowed. We evaluate our model for seizure detection and type classification on CHB-MIT and the recently released TUH EEG Seizure, respectively. Our model is able to classify 8 seizure types with an accuracy of 98.41%, directly from raw EEG data without any preprocessing. A case study showed a high correlation between the neurological baselines and the interpretable results of our model.</div>

Clinical Characteristics and Treatment Experience of Individuals with SCN8A Developmental and Epileptic Encephalopathy (SCN8A-DEE): Findings from an Online Caregiver Survey

ABSTRACT Purpose: SCN8A developmental epileptic encephalopathy (SCN8A-DEE) is a rare and severe genetic epilepsy syndrome characterized by early-onset developmental delay, cognitive impairment, and intractable seizures. Variants in the SCN8A gene are associated with a broad phenotypic spectrum and variable disease severity. A caregiver survey, solicited by the advocacy group The Cute Syndrome Foundation (TCSF), was conducted to gather information on the demographics/disease presentation, seizure history, and treatment of patients with SCN8Arelated epilepsies. Methods: A 36-question online survey was developed to obtain de-identified data from caregivers of children with SCN8A-related epilepsy. The survey included questions on genetic diagnosis, disease manifestations/comorbidities, seizure severity/type, current/prior use of antiseizure medicines (ASMs), and best/worst treatments per caregiver perception. Results: In total, 116 survey responses (87 USA, 12 Canada, 12 UK, 5 Australia) were included in the quantitative analysis. Generalized tonic/clonic was the most common seizure type at onset and time of survey; absence and partial/focal seizures were also common. Most patients (77%) were currently taking ≥2 ASMs; 50% had previously tried and stopped ≥4 ASMs. Sodium channel blockers (oxcarbazepine, phenytoin, lamotrigine) provided the best subjective seizure control and quality of life. Conclusion: The SCN8A-DEE patient population is heterogeneous and difficult to treat, with high seizure burden and multiple comorbidities. The high proportion of patients who previously tried and stopped ASMs indicates a large unmet treatment need. Further collaboration between families, caregivers, patient advocates, clinicians, researchers, and industry can increase awareness and understanding of SCN8A-related epilepsies, improve clinical trial design, and potentially improve patient outcomes.

Evaluation of Family functioning among parents of epileptic patients

Epilepsy has aggregate of risk characteristic’s as, age of onset, triggering factors, genetics, natural history, prognosis, and it is not a condition based on single aspect or cause. Due to social problems, family functioning of epileptic patients suffers badly. The basic purpose of the study is to investigate the role of family functioning of the parents who has epileptic patients. Methods: Across-sectional study was conducted at Muzaffargarh Hospital Neurology OPD department. Total 36 parents were enrolled. All parents of children, who have 8 to18 years of age, which are diagnosis of epilepsy, were included in current study Data was collected on a predesign questionnaire and for family functioning the Family Assessment Device (FAD) was used. Statistical analysis was performed by using the Statistical Package for Social Sciences (spss) version 26. The frequencies, proportions were calculated for Qualitative variables and Mean + SD were calculated for quantitative variables. Results: The mean age of parents was 38.58+7.55 and children were 12.31+3.34. Out of 36 participants 12(33.3%) were males whereas 24(66.7%) were females. Majority of parents were holding secondary degree 13(36.1%), were unemployed 24(66.7%), 21(58.3%) were from rural area and dealing with generalized seizure type children 24(66.7%). The average seizures frequency per month was 2.64+1.15. The families of epileptic patients were more dysfunctional, especially in terms of problem solving (2.66+0.43), behavior control (2.68+0.49), affective involvement (2.62+0.64) and also family’s faces difficulties in finding their role (2.48+0.56). Conclusion: The families of epileptic patients have more dysfunctional, especially in terms of problem solving, behavior control, affective involvement and also families faces difficulties in finding their role. Therefore educational programme focusing on the importance of family functioning should be provided so that the aspect of treatments and social life of patients get improved. Keywords: Epilepsy, Family functioning, Social Support, Family support

Risk Factors for Drug Resistance in Epileptic Children with Age of Onset above Five Years: A Case-Control Study

Background. Children with epilepsy with onset above five years encompass distinct epidemiological and clinical characteristics that may have specific risk factors for resistance to antiseizure medications (ASMs). Studies on this age group are limited. Purpose. To identify risk factors for drug resistance in children with epilepsy with the age of onset above five years. Methods. A case-control study was conducted on children with epilepsy with the age of onset above five years visiting the Pediatric Neurology Clinic of Cipto Mangunkusumo and Mohammad Hoesin Hospital between September 2015 and August 2016. Cases consisted of drug-resistant children while control consisted of drug-responsive children according to 2010 ILAE classification. Risk factors studied include onset, number of seizures, illness duration before treatment, cause, seizure type, status epilepticus, initial and evolution of EEG, brain imaging, and initial treatment response. Results. Thirty-two pairs of children were included in the study. After logistic regression analysis, symptomatic etiology and failure to achieve early response to treatment were found to be associated with drug resistance with adjusted OR of 84.71 (95% CI: 5.18-1359.15) and 72.55 (95% CI: 7.08-743.85), respectively. Conclusion. Poor initial response to ASM and symptomatic etiology are independent risk factors for drug resistance in children with epilepsy with the age of onset above five years.