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.

[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.

[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.

Testing association of rare genetic variants with resistance to three common antiseizure medications.

OBJECTIVE: Drug resistance is a major concern in the treatment of individuals with epilepsy. No genetic markers for resistance to individual antiseizure medication (ASM) have yet been identified. We aimed to identify the role of rare genetic variants in drug resistance for three common ASMs: levetiracetam (LEV), lamotrigine (LTG), and valproic acid (VPA). METHODS: A cohort of 1622 individuals of European descent with epilepsy was deeply phenotyped and underwent whole exome sequencing (WES), comprising 575 taking LEV, 826 LTG, and 782 VPA. We performed gene- and gene set-based collapsing analyses comparing responders and nonresponders to the three drugs to determine the burden of different categories of rare genetic variants. RESULTS: We observed a marginally significant enrichment of rare missense, truncating, and splice region variants in individuals who were resistant to VPA compared to VPA responders for genes involved in VPA pharmacokinetics. We also found a borderline significant enrichment of truncating and splice region variants in the synaptic vesicle glycoprotein (SV2) gene family in nonresponders compared to responders to LEV. We did not see any significant enrichment using a gene-based approach. SIGNIFICANCE: In our pharmacogenetic study, we identified a slightly increased burden of damaging variants in gene groups related to drug kinetics or targeting in individuals presenting with drug resistance to VPA or LEV. Such variants could thus determine a genetic contribution to drug resistance.

Frequent genes in rare diseases: panel-based next generation sequencing to disclose causal mutations in hereditary neuropathies.

Hereditary neuropathies comprise a wide variety of chronic diseases associated to more than 80 genes identified to date. We herein examined 612 index patients with either a Charcot-Marie-Tooth phenotype, hereditary sensory neuropathy, familial amyloid neuropathy, or small fiber neuropathy using a customized multigene panel based on the next generation sequencing technique. In 121 cases (19.8%), we identified at least one putative pathogenic mutation. Of these, 54.4% showed an autosomal dominant, 33.9% an autosomal recessive, and 11.6% an X-linked inheritance. The most frequently affected genes were PMP22 (16.4%), GJB1 (10.7%), MPZ, and SH3TC2 (both 9.9%), and MFN2 (8.3%). We further detected likely or known pathogenic variants in HINT1, HSPB1, NEFL, PRX, IGHMBP2, NDRG1, TTR, EGR2, FIG4, GDAP1, LMNA, LRSAM1, POLG, TRPV4, AARS, BIC2, DHTKD1, FGD4, HK1, INF2, KIF5A, PDK3, REEP1, SBF1, SBF2, SCN9A, and SPTLC2 with a declining frequency. Thirty-four novel variants were considered likely pathogenic not having previously been described in association with any disorder in the literature. In one patient, two homozygous mutations in HK1 were detected in the multigene panel, but not by whole exome sequencing. A novel missense mutation in KIF5A was considered pathogenic because of the highly compatible phenotype. In one patient, the plasma sphingolipid profile could functionally prove the pathogenicity of a mutation in SPTLC2. One pathogenic mutation in MPZ was identified after being previously missed by Sanger sequencing. We conclude that panel based next generation sequencing is a useful, time- and cost-effective approach to assist clinicians in identifying the correct diagnosis and enable causative treatment considerations.

Comparative effectiveness of antiepileptic drugs in patients with mesial temporal lobe epilepsy with hippocampal sclerosis.

OBJECTIVE: Mesial temporal lobe epilepsy with hippocampal sclerosis (MTLE-HS) is a common epilepsy syndrome that is often poorly controlled by antiepileptic drug (AED) treatment. Comparative AED effectiveness studies in this condition are lacking. We report retention, efficacy, and tolerability in a cohort of patients with MTLE-HS. METHODS: Clinical data were collected from a European database of patients with epilepsy. We estimated retention, 12-month seizure freedom, and adverse drug reaction (ADR) rates for the 10 most commonly used AEDs in patients with MTLE-HS. RESULTS: Seven hundred sixty-seven patients with a total of 3,249 AED trials were included. The highest 12-month retention rates were observed with carbamazepine (85.9%), valproate (85%), and clobazam (79%). Twelve-month seizure freedom rates varied from 1.2% for gabapentin and vigabatrin to 11% for carbamazepine. Response rates were highest for AEDs that were prescribed as initial treatment and lowest for AEDs that were used in a third or higher instance. ADRs were reported in 47.6% of patients, with the highest rates observed with oxcarbazepine (35.7%), topiramate (30.9%), and pregabalin (27.4%), and the lowest rates with clobazam (6.5%), gabapentin (8.9%), and lamotrigine (16.6%). The most commonly reported ADRs were lethargy and drowsiness, dizziness, vertigo and ataxia, and blurred vision and diplopia. SIGNIFICANCE: Our results did not demonstrate any clear advantage of newer versus older AEDs. Our results provide useful insights into AED retention, efficacy, and ADR rates in patients with MTLE-HS.

Early-onset familial hemiplegic migraine due to a novel SCN1A mutation.

Introduction Familial hemiplegic migraine (FHM) is a rare autosomal dominant subtype of migraine with aura. The FHM3 subtype is caused by mutations in SCN1A, which is also the most frequent epilepsy gene encoding the voltage-gated Na+ channel NaV1.1. The aim of this study was to explore the clinical, genetic and pathogenetic features of a pure FHM3 family. Methods A three-generation family was enrolled in this study for genetic testing and assessment of clinical features. Whole cell patch-clamp was performed to determine the functions of identified mutant NaV1.1 channels, which were transiently expressed in human tsA201 cells together with ß1 and ß2 subunits. Results and conclusions We identified a novel SCN1A (p.Leu1624Pro) mutation in a pure FHM family with notably early-onset attacks at mean age of 7. L1624P locates in S3 of domain IV, the same domain as two of four known pure FHM3 mutations. Compared to WT channels, L1624P displayed an increased threshold-near persistent current in addition to other gain-of-function features such as: a slowing of fast inactivation, a positive shift in steady-state inactivation, a faster recovery and higher channel availability during repetitive stimulation. Similar to the known FHM3 mutations, this novel mutation predicts hyperexcitability of GABAergic inhibitory neurons.

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.

Cenobamate: real-world data from a retrospective multicenter study.

OBJECTIVE: To report the effects of adjunctive cenobamate and concomitant antiseizure medications (ASMs) on weight from two double-blind, placebo-controlled, phase 2 studies (YKP3089C013 [C013] and YKP3089C017 [C017]) and their open-label extensions (OLEs) and from a long-term, open-label phase 3 safety study, YKP3089C021 (C021). BACKGROUND: Cenobamate is an ASM approved in the US and EU for treatment of focal seizures in adults. Some ASMs are associated with weight gain (e.g., valproate, gabapentin, pregabalin), which can negatively affect patient health. DESIGN/METHODS: Patients with uncontrolled focal seizures taking stable doses of 1-3 ASMs were enrolled in each study. In C013, cenobamate was titrated to a target dose of 200 mg/day (max OLE dose 400 mg/day). In C017, patients were randomized to cenobamate 100, 200, or 400 mg/day (max OLE dose 400 mg/day). In C021, cenobamate was titrated to a target dose of 200 mg/day (max dose 400 mg/day). Median weight changes at 1 and 2 years from baseline were analyzed post hoc. RESULTS: Analyses included 39, 206, and 1054 patients from C013, C017 (dose groups combined), and C021, respectively. Median weight changes from baseline ranged from -0.2 to -0.9 kg at 1 year and from -1.0 to +1.0 kg at 2 years. Some numerical reductions in weight were noted in patients who discontinued valproate by 1 (-13.0 kg, C013, n=1) or 2 years (-24.5 kg, C017, n=2) and in patients who discontinued gabapentin by 1 (-7.1 kg, C017, n=2) or 2 years (-7.0 kg, C017, n=2). Otherwise, median weight changes from baseline for patients receiving concomitant valproate, gabapentin, or pregabalin ranged from -3.1 to +2.6 kg at 1 year and from -1.6 to +2.7 kg at 2 years. CONCLUSIONS: Adjunctive cenobamate was not associated with clinically significant changes in weight from baseline in patients treated for 1 and 2 years, including those receiving concomitant valproate, gabapentin, or pregabalin.

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.

Episodic itch in a case of spinal glioma.

BACKGROUND: Itch is a frequent complaint reported by patients and is usually ascribed to dermatological or metabolic causes. In neurological disorders, however, it is a very unusual symptom and thus its neurological aetiology is likely to be overlooked. There are only very few reports about permanent itch related to lesions of the central nervous system. To our knowledge we report the first case of episodic itch associated with a central nervous lesion. CASE PRESENTATION: A 74-year-old female suffered from long-standing episodes of itch of the dermatomes C2 to C6 on the right side that was refractory to any treatment. On occurrence it propagated in a proximal to distal fashion. Between the episodes the patient was asymptomatic. MRI of the cervical spine uncovered a spinal glioma that matched the location of the symptoms. Treatment with gabapentin led to a prompt reduction of the symptoms. CONCLUSION: Patients with intractable pruritus and dermatomal presentation ought to undergo neurological examination and spinal cord imaging. Thus, ongoing frustrating and sometimes even harmful treatment trials could be avoided.

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.

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.

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.

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.

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).

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.

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.