Triheptanoin Did Not Show Benefit versus Placebo for the Treatment of Paroxysmal Movement Disorders in Glut1 Deficiency Syndrome: Results of a Randomized Phase 3 Study.

BACKGROUND: Paroxysmal movement disorders are common in Glut1 deficiency syndrome (Glut1DS). Not all patients respond to or tolerate ketogenic diets. OBJECTIVES: The objective was to evaluate the effectiveness and safety of triheptanoin in reducing the frequency of disabling movement disorders in patients with Glut1DS not receiving a ketogenic diet. METHODS: UX007G-CL301 was a randomized, double-blind, placebo-controlled, phase 3 crossover study. After a 6-week run-in, eligible patients were randomized 1:1 to the first sequence (triheptanoin/placebo or placebo/triheptanoin) titration plus maintenance, followed by washout and the opposite sequence titration plus maintenance. The placebo (safflower oil) matched the appearance, taste, and smell of triheptanoin. Open-label triheptanoin was administered in the extension. The frequency of disabling paroxysmal movement disorder events per 4 weeks (recorded by diary during maintenance; primary endpoint) was assessed by Wilcoxon rank-sum test. RESULTS: Forty-three patients (children, n = 16; adults, n = 27) were randomized and treated. There was no difference between triheptanoin and placebo in the mean (interquartile range) number of disabling paroxysmal movement disorder events (14.3 [4.7-38.3] vs. 11.8; [3.2-28.7]; Hodges-Lehmann estimated median difference: 1.46; 95% confidence interval, -1.12 to 4.36; P = 0.2684). Treatment-emergent adverse events were mild/moderate in severity and included diarrhea, vomiting, upper abdominal pain, headache, and nausea. Two patients discontinued the study because of non-serious adverse events that were predominantly gastrointestinal. The study was closed early during the open-label extension because of lack of effectiveness. Seven patients continued to receive triheptanoin compassionately. CONCLUSION: There were no significant differences between the triheptanoin and placebo groups in the frequency of disabling movement disorder events during the double-blind maintenance period. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

Human organotypic brain slice cultures: a detailed and improved protocol for preparation and long-term maintenance.

The investigation of the human brain at cellular and microcircuit level remains challenging due to the fragile viability of neuronal tissue, inter- and intra-variability of the samples and limited availability of human brain material. Especially brain slices have proven to be an excellent source to investigate brain physiology and disease at cellular and small network level, overcoming the temporal limits of acute slices. Here we provide a revised, detailed protocol of the production and in-depth knowledge on long-term culturing of such human organotypic brain slice cultures for research purposes. We highlight the critical pitfalls of the culturing process of the human brain tissue and present exemplary results on viral expression, single-cell Patch-Clamp recordings, as well as multi-electrode array recordings as readouts for culture viability, enabling the use of organotypic brain slice cultures of these valuable tissue samples for basic neuroscience and disease modeling (Fig. 1).

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.

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.

Toward the Next Generation of Neural Iontronic Interfaces.

Neural interfaces are evolving at a rapid pace owing to advances in material science and fabrication, reduced cost of scalable complementary metal oxide semiconductor (CMOS) technologies, and highly interdisciplinary teams of researchers and engineers that span a large range from basic to applied and clinical sciences. This study outlines currently established technologies, defined as instruments and biological study systems that are routinely used in neuroscientific research. After identifying the shortcomings of current technologies, such as a lack of biocompatibility, topological optimization, low bandwidth, and lack of transparency, it maps out promising directions along which progress should be made to achieve the next generation of symbiotic and intelligent neural interfaces. Lastly, it proposes novel applications that can be achieved by these developments, ranging from the understanding and reproduction of synaptic learning to live-long multimodal measurements to monitor and treat various neuronal disorders.

KCNC2 variants of uncertain significance are also associated to various forms of epilepsy.

Recently, de novo variants in KCNC2, coding for the potassium channel subunit KV3.2, have been described as causative for various forms of epilepsy including genetic generalized epilepsy (GGE) and developmental and epileptic encephalopathy (DEE). Here, we report the functional characteristics of three additional KCNC2 variants of uncertain significance and one variant classified as pathogenic. Electrophysiological studies were performed in Xenopus laevis oocytes. The data presented here support that KCNC2 variants with uncertain significance may also be causative for various forms of epilepsy, as they show changes in the current amplitude and activation and deactivation kinetics of the channel, depending on the variant. In addition, we investigated the effect of valproic acid on KV3.2, as several patients carrying pathogenic variants in the KCNC2 gene achieved significant seizure reduction or seizure freedom with this drug. However, in our electrophysiological investigations, no change on the behavior of KV3.2 channels could be observed, suggesting that the therapeutic effect of VPA may be explained by other mechanisms.

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.

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).

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.

The ataxia-linked E1081Q mutation affects the sub-plasma membrane Ca2+-microdomains by tuning PMCA3 activity.

Calcium concentration must be finely tuned in all eukaryotic cells to ensure the correct performance of its signalling function. Neuronal activity is exquisitely dependent on the control of Ca2+ homeostasis: its alterations ultimately play a pivotal role in the origin and progression of many neurodegenerative processes. A complex toolkit of Ca2+ pumps and exchangers maintains the fluctuation of cytosolic Ca2+ concentration within the appropriate threshold. Two ubiquitous (isoforms 1 and 4) and two neuronally enriched (isoforms 2 and 3) of the plasma membrane Ca2+ATPase (PMCA pump) selectively regulate cytosolic Ca2+ transients by shaping the sub-plasma membrane (PM) microdomains. In humans, genetic mutations in ATP2B1, ATP2B2 and ATP2B3 gene have been linked with hearing loss, cerebellar ataxia and global neurodevelopmental delay: all of them were found to impair pump activity. Here we report three additional mutations in ATP2B3 gene corresponding to E1081Q, R1133Q and R696H amino acids substitution, respectively. Among them, the novel missense mutation (E1081Q) immediately upstream the C-terminal calmodulin-binding domain (CaM-BD) of the PMCA3 protein was present in two patients originating from two distinct families. Our biochemical and molecular studies on PMCA3 E1081Q mutant have revealed a splicing variant-dependent effect of the mutation in shaping the sub-PM [Ca2+]. The E1081Q substitution in the full-length b variant abolished the capacity of the pump to reduce [Ca2+] in the sub-PM microdomain (in line with the previously described ataxia-related PMCA mutations negatively affecting Ca2+ pumping activity), while, surprisingly, its introduction in the truncated a variant selectively increased Ca2+ extrusion activity in the sub-PM Ca2+ microdomains. These results highlight the importance to set a precise threshold of [Ca2+] by fine-tuning the sub-PM microdomains and the different contribution of the PMCA splice variants in this regulation.

Consistency of Feature Importance Algorithms for Interpretable EEG Abnormality Detection.

Recent advances in machine learning show great potential for automatic detection of abnormalities in electroencephalography (EEG). While simple and interpretable models combined with expert-comprehensible input features offer full control of the decision making process, these methods commonly lag behind complex deep learning and feature extraction methods in terms of performance. Here we study a feasibility of a bridging solution, where deep learning is combined with interpretable input and an algorithm computing the importance of particular EEG features in the decision process. We built a convolutional neural network with multi-channel EEG frequency bands as input and investigated four different methods for feature importance attribution: Layer-wise Relevance Propagation (LRP), DeepLIFT, Integrated Gradients (IG) and Guided GradCAM. Our analysis showed consistency between the first three methods, and deviating attributions of the fourth method, suggesting the importance of using a package of methods together to ensure the robustness of medical interpretation.

Loss-of-function variants in the KCNQ5 gene are implicated in genetic generalized epilepsies.

BACKGROUND: De novo missense variants in KCNQ5, encoding the voltage-gated K+ channel KV7.5, have been described to cause developmental and epileptic encephalopathy (DEE) or intellectual disability (ID). We set out to identify disease-related KCNQ5 variants in genetic generalized epilepsy (GGE) and their underlying mechanisms. METHODS: 1292 families with GGE were studied by next-generation sequencing. Whole-cell patch-clamp recordings, biotinylation and phospholipid overlay assays were performed in mammalian cells combined with homology modelling. FINDINGS: We identified three deleterious heterozygous missense variants, one truncation and one splice site alteration in five independent families with GGE with predominant absence seizures; two variants were also associated with mild to moderate ID. All missense variants displayed a strongly decreased current density indicating a loss-of-function (LOF). When mutant channels were co-expressed with wild-type (WT) KV7.5 or KV7.5 and KV7.3 channels, three variants also revealed a significant dominant-negative effect on WT channels. Other gating parameters were unchanged. Biotinylation assays indicated a normal surface expression of the variants. The R359C variant altered PI(4,5)P2-interaction. INTERPRETATION: Our study identified deleterious KCNQ5 variants in GGE, partially combined with mild to moderate ID. The disease mechanism is a LOF partially with dominant-negative effects through functional deficits. LOF of KV7.5 channels will reduce the M-current, likely resulting in increased excitability of KV7.5-expressing neurons. Further studies on network level are necessary to understand which circuits are affected and how this induces generalized seizures. FUNDING: DFG/FNR Research Unit FOR-2715 (Germany/Luxemburg), BMBF rare disease network Treat-ION (Germany), foundation 'no epilep' (Germany).

Modulating effects of FGF12 variants on NaV1.2 and NaV1.6 being associated with developmental and epileptic encephalopathy and Autism spectrum disorder: A case series.

OBJECTIVE: Fibroblast Growth Factor 12 (FGF12) may represent an important modulator of neuronal network activity and has been associated with developmental and epileptic encephalopathy (DEE). We sought to identify the underlying pathomechanism of FGF12-related disorders. METHODS: Patients with pathogenic variants in FGF12 were identified through published case reports, GeneMatcher and whole exome sequencing of own case collections. The functional consequences of two missense and two copy number variants (CNVs) were studied by co-expression of wildtype and mutant FGF12 in neuronal-like cells (ND7/23) with the sodium channels NaV1.2 or NaV1.6, including their beta-1 and beta-2 sodium channel subunits (SCN1B and SCN2B). RESULTS: Four variants in FGF12 were identified for functional analysis: one novel FGF12 variant in a patient with autism spectrum disorder and three variants from previously published patients affected by DEE. We demonstrate the differential regulating effects of wildtype and mutant FGF12 on NaV1.2 and NaV1.6 channels. Here, FGF12 variants lead to a complex kinetic influence on NaV1.2 and NaV1.6, including loss- as well as gain-of function changes in fast and slow inactivation. INTERPRETATION: We could demonstrate the detailed regulating effect of FGF12 on NaV1.2 and NaV1.6 and confirmed the complex effect of FGF12 on neuronal network activity. Our findings expand the phenotypic spectrum related to FGF12 variants and elucidate the underlying pathomechanism. Specific variants in FGF12-associated disorders may be amenable to precision treatment with sodium channel blockers. FUNDING: DFG, BMBF, Hartwell Foundation, National Institute for Neurological Disorders and Stroke, IDDRC, ENGIN, NIH, ITMAT, ILAE, RES and GRIN.

Current practice in diagnostic genetic testing of the epilepsies

Epilepsy genetics is a rapidly developing field, in which novel disease-associated genes, novel mechanisms associated with epilepsy, and precision medicine approaches are continuously being identified. In the past decade, advances in genomic knowledge and analysis platforms have begun to make clinical genetic testing accessible for, in principle, people of all ages with epilepsy. For this reason, the Genetics Commission of the International League Against Epilepsy (ILAE) presents this update on clinical genetic testing practice, including current techniques, indications, yield of genetic testing, recommendations for pre- and post-test counseling, and follow-up after genetic testing is completed. We acknowledge that the resources vary across different settings but highlight that genetic diagnostic testing for epilepsy should be prioritized when the likelihood of an informative finding is high. Results of genetic testing, in particular the identification of causative genetic variants, are likely to improve individual care. We emphasize the importance of genetic testing for individuals with epilepsy as we enter the era of precision therapy.

Natural History Study of STXBP1-Developmental and Epileptic Encephalopathy Into Adulthood.

BACKGROUND AND OBJECTIVES: Pathogenic STXBP1 variants cause a severe early-onset developmental and epileptic encephalopathy (STXBP1-DEE). We aimed to investigate the natural history of STXBP1-DEE in adults focusing on seizure evolution, the presence of movement disorders, and the level of functional (in)dependence. METHODS: In this observational study, patients with a minimum age of 18 years carrying a (likely) pathogenic STXBP1 variant were recruited through medical genetics departments and epilepsy centers. Treating clinicians completed clinical questionnaires and performed semistructured video examinations while performing tasks from the (modified) Unified Parkinson Disease Rating Scale when possible. RESULTS: Thirty adult patients were included for summary statistics, with video recordings available for 19 patients. The median age at last follow-up was 24 years (range 18-58 years). All patients had epilepsy, with a median onset age of 3.5 months. At last follow-up, 80% of adults had treatment-resistant seizures despite long periods of seizure freedom in 37%. Tonic-clonic, focal, and tonic seizures were most frequent in adults. Epileptic spasms, an unusual feature beyond infancy, were present in 3 adults. All individuals had developmental impairment. Periods of regression were present in 59% and did not always correlate with flare-ups in seizure activity. Eighty-seven percent had severe or profound intellectual disability, 42% had autistic features, and 65% had significant behavioral problems. Video examinations showed gait disorders in all 12 patients able to walk, including postural abnormalities with external rotation of the feet, broad-based gait, and asymmetric posture/dystonia. Tremor, present in 56%, was predominantly of the intention/action type. Stereotypies were seen in 63%. Functional outcome concerning mobility was variable ranging from independent walking (50%) to wheelchair dependence (39%). Seventy-one percent of adults were nonverbal, and all were dependent on caregivers for most activities of daily living. DISCUSSION: STXBP1-DEE warrants continuous monitoring for seizures in adult life. Periods of regression are more frequent than previously established and can occur into adulthood. Movement disorders are often present and involve multiple systems. Although functional mobility is variable in adulthood, STXBP1-DEE frequently leads to severe cognitive impairments and a high level of functional dependence. Understanding the natural history of STXBP1-DEE is important for prognostication and will inform future therapeutic trials.

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.

Spectrum of Phenotypic, Genetic, and Functional Characteristics in Patients With Epilepsy With KCNC2 Pathogenic Variants.

BACKGROUND AND OBJECTIVES: KCNC2 encodes Kv3.2, a member of the Shaw-related (Kv3) voltage-gated potassium channel subfamily, which is important for sustained high-frequency firing and optimized energy efficiency of action potentials in the brain. The objective of this study was to analyze the clinical phenotype, genetic background, and biophysical function of disease-associated Kv3.2 variants. METHODS: Individuals with KCNC2 variants detected by exome sequencing were selected for clinical, further genetic, and functional analysis. Cases were referred through clinical and research collaborations. Selected de novo variants were examined electrophysiologically in Xenopus laevis oocytes. RESULTS: We identified novel KCNC2 variants in 18 patients with various forms of epilepsy, including genetic generalized epilepsy (GGE), developmental and epileptic encephalopathy (DEE) including early-onset absence epilepsy, focal epilepsy, and myoclonic-atonic epilepsy. Of the 18 variants, 10 were de novo and 8 were classified as modifying variants. Eight drug-responsive patients became seizure-free using valproic acid as monotherapy or in combination, including severe DEE cases. Functional analysis of 4 variants demonstrated gain of function in 3 severely affected DEE cases and loss of function in 1 case with a milder phenotype (GGE) as the underlying pathomechanisms. DISCUSSION: These findings implicate KCNC2 as a novel causative gene for epilepsy and emphasize the critical role of KV3.2 in the regulation of brain excitability.