Multimodal wearable EEG, EMG and accelerometry measurements improve the accuracy of tonic-clonic seizure detection.

Objective. This paper aims to investigate the possibility of detecting tonic-clonic seizures (TCSs) with behind-the-ear, two-channel wearable electroencephalography (EEG), and to evaluate its added value to non-EEG modalities in TCS detection.Methods. We included 27 participants with a total of 44 TCSs from the European multicenter study SeizeIT2. The wearable Sensor Dot (Byteflies) was used to measure behind-the-ear EEG, electromyography (EMG), electrocardiography, accelerometry (ACC) and gyroscope. We evaluated automatic unimodal detection of TCSs, using sensitivity, precision, false positive rate (FPR) and F1-score. Subsequently, we fused the different modalities and again assessed performance. Algorithm-labeled segments were then provided to two experts, who annotated true positive TCSs, and discarded false positives.Results. Wearable EEG outperformed the other single modalities with a sensitivity of 100% and a FPR of 10.3/24 h. The combination of wearable EEG and EMG proved most clinically useful, delivering a sensitivity of 97.7%, an FPR of 0.4/24 h, a precision of 43%, and an F1-score of 59.7%. The highest overall performance was achieved through the fusion of wearable EEG, EMG, and ACC, yielding a sensitivity of 90.9%, an FPR of 0.1/24 h, a precision of 75.5%, and an F1-score of 82.5%.Conclusions. In TCS detection with a wearable device, combining EEG with EMG, ACC or both resulted in a remarkable reduction of FPR, while retaining a high sensitivity.Significance. Adding wearable EEG could further improve TCS detection, relative to extracerebral-based systems.

EEG microstates show different features in focal epilepsy and psychogenic nonepileptic seizures.

OBJECTIVE: Electroencephalography (EEG) microstate analysis seeks to cluster the scalp's electric field into semistable topographical EEG activity maps at different time points. Our study aimed to investigate the features of EEG microstates in subjects with focal epilepsy and psychogenic nonepileptic seizures (PNES). METHODS: We included 62 adult subjects with focal epilepsy or PNES who received video-EEG monitoring at the epilepsy monitoring unit. The subjects (mean age = 42.8 ± 21.2 years) were distributed equally between epilepsy and PNES groups. We extracted microstates from a 4.4 ± 1.0-min, 21-channel resting-state EEG. We excluded subjects with interictal epileptiform discharges during resting-state EEGs. After preprocessing, we derived five main EEG microstates-MS1 to MS5-for the full frequency band (1-30 Hz) and frequency subbands (delta, 1-4 Hz; theta, 4-8 Hz; alpha, 8-12 Hz; beta, 12-30 Hz), using the MATLAB-based EEGLAB toolkit. Statistical features of microstates (duration, occurrence, contribution, global field power [GFP]) were compared between the groups, using logistic regression corrected for age and sex. RESULTS: We detected no differences in microstate parameters in the full frequency band. We found a longer duration (delta: B = -7.680, p = .046; theta: B = -16.200, p = .043) and a higher contribution (delta: B = -7.414, p = .035; theta: B = -7.509, p = .031) of MS4 in lower frequency bands in the epilepsy group. The PNES group showed a higher occurrence of MS5 in the delta subband (B = 3.283, p = .032). In the theta subband, a higher GFP of MS1 was associated with the PNES group (B = 5.674, p = .025), whereas a higher GFP of MS2 was associated with the epilepsy group (B = -6.579, p = .026). SIGNIFICANCE: Microstate features show differences between patients with focal epilepsy and PNES. EEG microstates could be a promising parameter, helping to understand changes in brain dynamics in subjects with epilepsy, and should be explored as a potential biomarker.

Genetic variation supports a causal role for valproate in prevention of ischemic stroke.

BACKGROUND: Valproate is a candidate for ischemic stroke prevention due to its anti-atherosclerotic effects in vivo. Although valproate use is associated with decreased ischemic stroke risk in observational studies, confounding by indication precludes causal conclusions. AIMS: We applied Mendelian randomization to determine whether genetic variants that influence seizure response among valproate users associate with ischemic stroke. METHODS: We derived a genetic score for valproate response using genome-wide association data of seizure response after valproate intake from the Epilepsy Pharmacogenomics Consortium. We then tested this score among valproate users of the UK Biobank for association with incident and recurrent ischemic stroke using Cox proportional hazard models. As replication, we tested found associations in an independent cohort of valproate users of the Mass General Brigham Biobank. RESULTS: Among 2150 valproate users (mean 56 years, 54% females), 82 ischemic strokes occurred over a mean 12 year follow-up. Higher valproate response genetic score was associated with higher serum valproate levels (+5.78 µg/ml per 1 standard deviation (SD), 95% confidence interval (CI) (3.45, 8.11)). After adjusting for age and sex, higher valproate response genetic score was associated with lower ischemic stroke risk (hazard ratio (HR) per 1 SD 0.73, 95% CI (0.58, 0.91)) with a halving of absolute risk in the highest compared to the lowest score tertile (4.8% vs 2.5%, p trend = 0.027). Among 194 valproate users with prevalent stroke at baseline, a higher valproate response genetic score was associated with lower recurrent ischemic stroke risk (HR per 1 SD 0.53, 95% CI (0.32, 0.86)) with reduced absolute risk in the highest compared to the lowest score tertile (3/51, 5.9% vs 13/71, 18.3%, p trend = 0.026). The valproate response genetic score was not associated with ischemic stroke among the 427,997 valproate non-users (p = 0.61), suggesting minimal pleiotropy. In 1241 valproate users of the Mass General Brigham Biobank with 99 ischemic stroke events over 6.5 years follow-up, we replicated our observed associations between the valproate response genetic score and ischemic stroke (HR per 1 SD 0.77, 95% CI (0.61, 0.97)). CONCLUSION: These results demonstrate that a genetically predicted favorable seizure response to valproate is associated with higher serum valproate levels and reduced ischemic stroke risk among valproate users, providing causal support for valproate effectiveness in ischemic stroke prevention. The strongest effect was found for recurrent ischemic stroke, suggesting potential dual-use benefits of valproate for post-stroke epilepsy. Clinical trials will be required in order to identify populations that may benefit most from valproate for stroke prevention. DATA ACCESS STATEMENT: UK Biobank participant data are available after approval of a research proposal. The weights of the used genetic scores are available in the Supplemental Tables.

Assessing 72 h vs. 24 h of long-term video-EEG monitoring to confirm the diagnosis of epilepsy: a retrospective observational study.

Introduction: Paroxysmal seizure-like events can be a diagnostic challenge. Inpatient video-electroencephalography (EEG) monitoring (VEM) can be a valuable diagnostic tool, but recommendations for the minimal duration of VEM to confirm or rule out epilepsy are inconsistent. In this study, we aim to determine whether VEM of 48 or 72 h was superior to 24 h. Methods: In this monocentric, retrospective study, we included 111 patients with paroxysmal, seizure-like events who underwent at least 72 h of VEM. Inclusion criteria were as follows: (1) Preliminary workup was inconclusive; (2) VEM admission occurred to confirm a diagnosis; (3) At discharge, the diagnosis of epilepsy was conclusively established. We analyzed the VEM recordings to determine the exact time point of the first occurrence of epileptic abnormalities (EAs; defined as interictal epileptiform discharges or electrographic seizures). Subgroup analyses were performed for epilepsy types and treatment status. Results: In our study population, 69.4% (77/111) of patients displayed EAs during VEM. In this group, the first occurrence of EAs was observed within 24 h in 92.2% (71/77) of patients and within 24-72 h in 7.8% (6/77). There was no statistically significant difference in the incidence of EA between medicated and non-medicated patients or between focal, generalized epilepsies and epilepsies of unknown type. Of the 19 recorded spontaneous electroclinical seizures, 6 (31.6%) occurred after 24 h. Discussion: A VEM of 24 h may be sufficient in the diagnostic workup of paroxysmal seizure-like events under most circumstances. Considering the few cases of first EA in the timeframe between 24 and 72 h, a prolonged VEM may be useful in cases with a high probability of epilepsy or where other strategies like sleep-EEG or ambulatory EEG show inconclusive results. Prolonged VEM increases the chance of recording spontaneous seizures. Our study also highlights a high share of subjects with epilepsy that do not exhibit EAs during 72 h of VEM.

ECG Matching: An Approach to Synchronize ECG Datasets for Data Quality Comparisons.

Clinical assessment of newly developed sensors is important for ensuring their validity. Comparing recordings of emerging electrocardiography (ECG) systems to a reference ECG system requires accurate synchronization of data from both devices. Current methods can be inefficient and prone to errors. To address this issue, three algorithms are presented to synchronize two ECG time series from different recording systems: Binned R-peak Correlation, R-R Interval Correlation, and Average R-peak Distance. These algorithms reduce ECG data to their cyclic features, mitigating inefficiencies and minimizing discrepancies between different recording systems. We evaluate the performance of these algorithms using high-quality data and then assess their robustness after manipulating the R-peaks. Our results show that R-R Interval Correlation was the most efficient, whereas the Average R-peak Distance and Binned R-peak Correlation were more robust against noisy data.

Spectral Fusion of Heartbeat and Accelerometer Data for Estimation of Breathing Rate in Wearable Patches.

Despite developments in wearable devices for detecting various bio-signals, continuous measurement of breathing rate (BR) remains a challenge. This work presents an early proof of concept that employs a wearable patch to estimate BR. We propose combining techniques for calculating BR from electrocardiogram (ECG) and accelerometer (ACC) signals, while applying decision rules based on signal-to-noise (SNR) to fuse the estimates for improved accuracy.

Modeling Clinical Guidelines for an Epilepsy-CDSS: The EDiTh Project.

The knowledge transformation process involves the guideline for the diagnosis and therapy of epilepsy to an executable and computable knowledge base that serves as the basis for a decision-support system. We present a transparent knowledge representation model which facilitates technical implementation and verification. Knowledge is represented in a plain table, used in the frontend code of the software where simple reasoning is performed. The simple structure is sufficient and comprehensible also for non-technical persons (i.e., clinicians).

Video-EEG-monitoring to guide antiseizure medication withdrawal.

BACKGROUND: Discontinuing anti-seizure medication (ASM) should be considered in persons with epilepsy with long-term seizure freedom. Clinicians should also pursue ASM withdrawal in persons with one-time seizures without increased recurrence risk and those with suspected non-epileptic events. However, ASM withdrawal is associated with the risk of recurring seizures. Monitored ASM withdrawal in an epilepsy monitoring unit (EMU) could help better evaluate the risk of seizure recurrence. Here, we investigate the practice of EMU-guided ASM withdrawal, assess its indications, and aim to determine positive and negative predictors for successful withdrawal. METHODS: We screened the medical records of all patients admitted to our EMU between November 1, 2019, and October 31, 2021, and included patients of at least 18 years admitted with the aim of permanent ASM withdrawal. We defined four groups of withdrawal indications: (1) long-term seizure freedom; (2) suspected non-epileptic events; (3) history of epileptic seizures but not fulfilling diagnostic criteria of epilepsy; and (4) seizure-freedom after epilepsy surgery. Successful withdrawal was defined according to the following criteria: no recoding of (sub)clinical seizure activity during VEM (groups 1, 2, and 3), patients did not meet the International League Against Epilepsy (ILAE) definition of epilepsy (groups 2 and 3) [14], and patients were discharged without ongoing ASM treatment (all groups). We also evaluated the prediction model by Lamberink et al. (LPM) for the risk of seizure recurrence in groups 1 and 3. RESULTS: 55/651 (8.6%) patients fulfilled the inclusion criteria. Withdrawal indications were distributed as follows; group 1: 2/55 (3.6%); group 2: 44/55 (80%); group 3: 9/55 (16,4%); group 4: 0/55. Overall, ASM withdrawal was successful in 90.9%. The sensitivity of the LPM for a 2-year 50% relapse risk threshold was 75%, the specificity 33.3%; for a 5-year relapse risk respectively 12.5% and 33.3%, suggesting that the model is not suitable for risk assessment in patients with one-time seizures or acute-symptomatic seizures, who constituted most of the evaluated patients. CONCLUSIONS: Our study suggests that EMU-guided ASM withdrawal could be a helpful tool to support clinical decision-making and improve patient safety. Prospective, randomized trials should further evaluate this method in the future.

Genetic variation supports a causal role for valproate in prevention of ischemic stroke.

Valproate is a candidate for ischemic stroke prevention due to its anti-atherosclerotic effects in vivo. Although valproate use is associated with decreased ischemic stroke risk in observational studies, confounding by indication precludes causal conclusions. To overcome this limitation, we applied Mendelian randomization to determine whether genetic variants that influence seizure response among valproate users associate with ischemic stroke. We derived a genetic score for valproate response using genome-wide association data of seizure response after valproate intake from the Epilepsy Pharmacogenomics Consortium. We then tested this score among valproate users of the UK Biobank for association with incident and recurrent ischemic stroke using Cox proportional hazard models. Among 2,150 valproate users (mean 56 years, 54% females), 82 ischemic strokes occurred over a mean 12-year follow-up. Higher valproate response genetic score was associated with higher serum valproate levels (+5.78 µg/ml per one SD, 95% CI [3.45, 8.11]). After adjusting for age and sex, higher valproate response genetic score was associated with lower ischemic stroke risk (HR per one SD 0.73, [0.58, 0.91]) with a halving of absolute risk in the highest compared to the lowest score tertile (4.8% vs 2.5%, p-trend=0.027). Among 194 valproate users with prevalent stroke at baseline, a higher valproate response genetic score was associated with lower recurrent ischemic stroke risk (HR per one SD 0.53, [0.32, 0.86]) with reduced absolute risk in the highest compared to the lowest score tertile (3/51, 5.9% vs. 13/71, 18.3%, p-trend=0.026). The valproate response genetic score was not associated with ischemic stroke among the 427,997 valproate non-users (p=0.61), suggesting minimal pleiotropy. In an independent cohort of 1,241 valproate users of the Mass General Brigham Biobank with 99 ischemic stroke events over 6.5 years follow-up, we replicated our observed associations between the valproate response genetic score and ischemic stroke (HR per one SD 0.77, 95% CI: [0.61, 0.97]). These results demonstrate that a genetically predicted favorable seizure response to valproate is associated with higher serum valproate levels and reduced ischemic stroke risk among valproate users, providing causal support for valproate effectiveness in ischemic stroke prevention. The strongest effect was found for recurrent ischemic stroke, suggesting potential dual-use benefits of valproate for post-stroke epilepsy. Clinical trials will be required in order to identify populations that may benefit most from valproate for stroke prevention.

Drosophila melanogaster as a versatile model organism to study genetic epilepsies: An overview.

Epilepsy is one of the most prevalent neurological disorders, affecting more than 45 million people worldwide. Recent advances in genetic techniques, such as next-generation sequencing, have driven genetic discovery and increased our understanding of the molecular and cellular mechanisms behind many epilepsy syndromes. These insights prompt the development of personalized therapies tailored to the genetic characteristics of an individual patient. However, the surging number of novel genetic variants renders the interpretation of pathogenetic consequences and of potential therapeutic implications ever more challenging. Model organisms can help explore these aspects in vivo. In the last decades, rodent models have significantly contributed to our understanding of genetic epilepsies but their establishment is laborious, expensive, and time-consuming. Additional model organisms to investigate disease variants on a large scale would be desirable. The fruit fly Drosophila melanogaster has been used as a model organism in epilepsy research since the discovery of "bang-sensitive" mutants more than half a century ago. These flies respond to mechanical stimulation, such as a brief vortex, with stereotypic seizures and paralysis. Furthermore, the identification of seizure-suppressor mutations allows to pinpoint novel therapeutic targets. Gene editing techniques, such as CRISPR/Cas9, are a convenient way to generate flies carrying disease-associated variants. These flies can be screened for phenotypic and behavioral abnormalities, shifting of seizure thresholds, and response to anti-seizure medications and other substances. Moreover, modification of neuronal activity and seizure induction can be achieved using optogenetic tools. In combination with calcium and fluorescent imaging, functional alterations caused by mutations in epilepsy genes can be traced. Here, we review Drosophila as a versatile model organism to study genetic epilepsies, especially as 81% of human epilepsy genes have an orthologous gene in Drosophila. Furthermore, we discuss newly established analysis techniques that might be used to further unravel the pathophysiological aspects of genetic epilepsies.

[Interactions between anti-seizure medications and recommendations for combination treatment]. / Interaktionen zwischen anfallssupprimierenden Medikamenten und Empfehlungen für Kombinationstherapien.

Pharmacotherapy is the most important pillar in the treatment of epilepsies. In approximately 50% of epilepsy patients monotherapy with anti-seizure medications (ASM) is insufficient. The knowledge of specific drug interactions in combination therapies is essential to recognize and avoid adverse side effects up to relevant therapy risks, including loss of efficiency and intoxication. Interactions can be of a pharmacokinetic or pharmacodynamic nature. Some effects of interactions in combination therapies can also be advantageous. Therapeutic drug monitoring in serum is not necessary for all ASMs and should be used rationally: however, it should be performed consistently if the indications are present. This review article provides fundamental knowledge about the most relevant interactions between ASMs and the indications for therapeutic drug monitoring.

Assessment of burden and segregation profiles of CNVs in patients with epilepsy.

OBJECTIVE: Microdeletions are associated with different forms of epilepsy but show incomplete penetrance, which is not well understood. We aimed to assess whether unmasked variants or double CNVs could explain incomplete penetrance. METHODS: We analyzed copy number variants (CNVs) in 603 patients with four different subgroups of epilepsy and 945 controls. CNVs were called from genotypes and validated on whole-genome (WGS) or whole-exome sequences (WES). CNV burden difference between patients and controls was obtained by fitting a logistic regression. CNV burden was assessed for small and large (>1 Mb) deletions and duplications and for deletions overlapping different gene sets. RESULTS: Large deletions were enriched in genetic generalized epilepsies (GGE) compared to controls. We also found enrichment of deletions in epilepsy genes and hotspots for GGE. We did not find truncating or functional variants that could have been unmasked by the deletions. We observed a double CNV hit in two patients. One patient also carried a de novo deletion in the 22q11.2 hotspot. INTERPRETATION: We could corroborate previous findings of an enrichment of large microdeletions and deletions in epilepsy genes in GGE. We could also replicate that microdeletions show incomplete penetrance. However, we could not validate the hypothesis of unmasked variants nor the hypothesis of double CNVs to explain the incomplete penetrance. We found a de novo CNV on 22q11.2 that could be of interest. We also observed GGE families carrying a deletion on 15q13.3 hotspot that could be investigated in the Quebec founder population.

The role of common genetic variation in presumed monogenic epilepsies.

BACKGROUND: The developmental and epileptic encephalopathies (DEEs) are the most severe group of epilepsies which co-present with developmental delay and intellectual disability (ID). DEEs usually occur in people without a family history of epilepsy and have emerged as primarily monogenic, with damaging rare mutations found in 50% of patients. Little is known about the genetic architecture of patients with DEEs in whom no pathogenic variant is identified. Polygenic risk scoring (PRS) is a method that measures a person's common genetic burden for a trait or condition. Here, we used PRS to test whether genetic burden for epilepsy is relevant in individuals with DEEs, and other forms of epilepsy with ID. METHODS: Genetic data on 2,759 cases with DEEs, or epilepsy with ID presumed to have a monogenic basis, and 447,760 population-matched controls were analysed. We compared PRS for 'all epilepsy', 'focal epilepsy', and 'genetic generalised epilepsy' (GGE) between cases and controls. We performed pairwise comparisons between cases stratified for identifiable rare deleterious genetic variants and controls. FINDINGS: Cases of presumed monogenic severe epilepsy had an increased PRS for 'all epilepsy' (p<0.0001), 'focal epilepsy' (p<0.0001), and 'GGE' (p=0.0002) relative to controls, which explain between 0.08% and 3.3% of phenotypic variance. PRS was increased in cases both with and without an identified deleterious variant of major effect, and there was no significant difference in PRS between the two groups. INTERPRETATION: We provide evidence that common genetic variation contributes to the aetiology of DEEs and other forms of epilepsy with ID, even when there is a known pathogenic variant of major effect. These results provide insight into the genetic underpinnings of the severe epilepsies and warrant a shift in our understanding of the aetiology of the DEEs as complex, rather than monogenic, disorders. FUNDING: Science foundation Ireland, Human Genome Research Institute; National Heart, Lung, and Blood Institute; German Research Foundation.

Reduced REM sleep: a potential biomarker for epilepsy - a retrospective case-control study.

OBJECTIVE: Establishing the diagnosis of epilepsy can be challenging if interictal epileptic discharges (IEDs) or seizures are undetectable. Many individuals with epilepsy experience sleep disturbances. A reduced percentage of REM sleep (REM%) has been observed following seizures. We aimed to assess differences of REM% in individuals with epilepsy in comparison with differential diagnoses. METHODS: We performed a retrospective, monocentric, two-armed case-control study with 128 age-matched individuals who underwent ≥72 hours of continuous video-EEG monitoring at our epilepsy monitoring unit (EMU) for diagnostic evaluation. We assessed REM% on the first and last night of EMU admission. Logistic regressions models were used to evaluate the predictive value of REM%. RESULTS: We included 64 individuals diagnosed with epilepsy and 64 with a differential diagnosis. REM% in the epilepsy group was significantly lower [12.2% (±4.7) vs. 17.2% (±5.2), p<0.001]. We found no significant influence of sex, age, anti-seizure, or other medications. A REM%-based and an IED and seizure-based regression model were not significantly different [area under the curve (AUC) 0.791 (95% confidence interval (CI): 0.713-0.870) vs. 0.853 (95% CI: 0.788-0.919), p=0.23]. A combined model, based on IEDs, seizures, and REM%, was superior to the IED model alone [0.933 (0.891-0.975), p<0.01]. INTERPRETATION: Our study shows significantly reduced REM% in individuals with epilepsy. REM%-based models show a good predictive performance. REM% assessment could improve diagnostic accuracy - especially for challenging cases, e.g., when IEDs or seizures are absent and patient history and semiology appear ambiguous. REM% as a biomarker should be evaluated in prospective, multicentric trials.

Genetic testing before epilepsy surgery - An exploratory survey and case collection from German epilepsy centers.

INTRODUCTION: Genetic testing in people with epilepsy may support presurgical decision-making. It is currently unclear to what extent epilepsy centres use genetic testing in presurgical evaluation. METHODS: We performed an exploratory survey among members of the German Society for Epileptology to study the current practice of genetic testing in presurgical evaluation at the respective sites. Survey participants contributed educational case reports. RESULTS: The majority of participants consider genetic testing to be useful in individuals with familial syndromes or phenotypic features suggesting a genetic etiology. We report 25 cases of individuals with a confirmed genetic diagnosis that have previously undergone epilepsy surgery. Our cases demonstrate that a genetic diagnosis has an impact on both the decision-making process during presurgical evaluation, as well as the postoperative outcome. CONCLUSION: Genetic testing as part of the presurgical work-up is becoming increasingly established in epilepsy centres across Germany. mTORopathies and genetic hypothalamic hamartomas seem to be associated with a generally favourable surgical outcome. Synaptopathies and channelopathies may be associated with a worse outcome and should be considered on a case-by-case level. Prospective studies are needed to examine the impact of an established genetic diagnosis on postsurgical outcome.

Role of Common Genetic Variants for Drug-Resistance to Specific Anti-Seizure Medications.

Objective: Resistance to anti-seizure medications (ASMs) presents a significant hurdle in the treatment of people with epilepsy. Genetic markers for resistance to individual ASMs could support clinicians to make better-informed choices for their patients. In this study, we aimed to elucidate whether the response to individual ASMs was associated with common genetic variation. Methods: A cohort of 3,649 individuals of European descent with epilepsy was deeply phenotyped and underwent single nucleotide polymorphism (SNP)-genotyping. We conducted genome-wide association analyses (GWASs) on responders to specific ASMs or groups of functionally related ASMs, using non-responders as controls. We performed a polygenic risk score (PRS) analyses based on risk variants for epilepsy and neuropsychiatric disorders and ASM resistance itself to delineate the polygenic burden of ASM-specific drug resistance. Results: We identified several potential regions of interest but did not detect genome-wide significant loci for ASM-specific response. We did not find polygenic risk for epilepsy, neuropsychiatric disorders, and drug-resistance associated with drug response to specific ASMs or mechanistically related groups of ASMs. Significance: This study could not ascertain the predictive value of common genetic variants for ASM responder status. The identified suggestive loci will need replication in future studies of a larger scale.

Assessing the role of rare genetic variants in drug-resistant, non-lesional focal epilepsy.

OBJECTIVE: Resistance to antiseizure medications (ASMs) is one of the major concerns in the treatment of epilepsy. Despite the increasing number of ASMs available, the proportion of individuals with drug-resistant epilepsy remains unchanged. In this study, we aimed to investigate the role of rare genetic variants in ASM resistance. METHODS: We performed exome sequencing of 1,128 individuals with non-familial non-acquired focal epilepsy (NAFE) (762 non-responders, 366 responders) and were provided with 1,734 healthy controls. We undertook replication in a cohort of 350 individuals with NAFE (165 non-responders, 185 responders). We performed gene-based and gene-set-based kernel association tests to investigate potential enrichment of rare variants in relation to drug response status and to risk for NAFE. RESULTS: We found no gene or gene set that reached genome-wide significance. Yet, we identified several prospective candidate genes - among them DEPDC5, which showed a potential association with resistance to ASMs. We found some evidence for an enrichment of truncating variants in dominant familial NAFE genes in our cohort of non-familial NAFE and in association with drug-resistant NAFE. INTERPRETATION: Our study identifies potential candidate genes for ASM resistance. Our results corroborate the role of rare variants for non-familial NAFE and imply their involvement in drug-resistant epilepsy. Future large-scale genetic research studies are needed to substantiate these findings.

[Decision support systems, assistance systems and telemedicine in epileptology]. / Decision-support-Systeme, Assistenzsysteme und Telemedizin in der Epileptologie.

BACKGROUND: Scientific knowledge about epilepsies and their clinical ramifications is rapidly expanding. This becomes an even greater challenge for non-specialists to process. Clinical decision support systems (CDSS) can play an important role as an expert-driven diagnostic and therapeutic tool which gives automated and individualized advice. In addition, medical apps and telemedical procedures for diagnostics and treatment and assistance systems for seizure detection in epilepsy patients have become available. OBJECTIVE: This article provides an overview on current tele-epileptological developments and the available telemedical applications. MATERIAL AND METHODS: Based on personal experience and a review of the literature, current epilepsy-specific CDSS, medical apps and assistance systems as well as telemedical applications are characterized and the clinical fields of application are presented. RESULTS AND CONCLUSION: Due to the chronic course and the complexity of the epilepsies and their sequelae, persons with epilepsy could profit from CDSS. Epilepsy-specific CDSS should be usable by medical professionals and patients themselves. Currently, medical apps for people with epilepsy are mostly used to document the clinical course, seizure frequency, medication compliance and side effects. Available seizure detection systems mainly detect generalized tonic-clonic seizures (GTCS). A clinical benefit of such devices is not yet sufficiently confirmed but appears to be likely, because these seizures are specifically associated with sudden unexpected death in epilepsy patients (SUDEP) and interventions are considered to be effective.

Polygenic risk scores of several subtypes of epilepsies in a founder population.

OBJECTIVE: Polygenic risk scores (PRSs) are used to quantify the cumulative effects of a number of genetic variants, which may individually have a very small effect on susceptibility to a disease; we used PRSs to better understand the genetic contribution to common epilepsy and its subtypes. METHODS: We first replicated previous single associations using 373 unrelated patients. We then calculated PRSs in the same French Canadian patients with epilepsy divided into 7 epilepsy subtypes and population-based controls. We fitted a logistic mixed model to calculate the variance explained by the PRS using pseudo-R2 statistics. RESULTS: We show that the PRS explains more of the variance in idiopathic generalized epilepsy than in patients with nonacquired focal epilepsy. We also demonstrate that the variance explained is different within each epilepsy subtype. CONCLUSIONS: Globally, we support the notion that PRSs provide a reliable measure to rightfully estimate the contribution of genetic factors to the pathophysiologic mechanism of epilepsies, but further studies are needed on PRSs before they can be used clinically.

Pharmacoresponse in genetic generalized epilepsy: a genome-wide association study.

Aim: Pharmacoresistance is a major burden in epilepsy treatment. We aimed to identify genetic biomarkers in response to specific antiepileptic drugs (AEDs) in genetic generalized epilepsies (GGE). Materials & methods: We conducted a genome-wide association study (GWAS) of 3.3 million autosomal SNPs in 893 European subjects with GGE - responsive or nonresponsive to lamotrigine, levetiracetam and valproic acid. Results: Our GWAS of AED response revealed suggestive evidence for association at 29 genomic loci (p <10-5) but no significant association reflecting its limited power. The suggestive associations highlight candidate genes that are implicated in epileptogenesis and neurodevelopment. Conclusion: This first GWAS of AED response in GGE provides a comprehensive reference of SNP associations for hypothesis-driven candidate gene analyses in upcoming pharmacogenetic studies.