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Abstract Although the prevalence of intraventricular hemorrhage (IVH) has remained high, no optimal strategy has been established to prevent it. This study included preterm newborns born at a gestational age of

The deadlift is a fundamental exercise in resistance training, essential for the development of overall strength and power. This review synthesizes current research on kinematics and electromyographic (EMG) activity during deadlifts, highlighting the effects of different variations and techniques on performance and muscle activation. Kinematic studies have revealed significant differences in joint angles and movement patterns between conventional and sumo deadlifts, emphasizing the importance of technique and experience in optimizing performance and reducing injury risk. EMG analysis has also revealed distinct muscle activation profiles for key muscles, such as the vastus lateralis, gluteus maximus, and hamstrings, across different deadlift variations. These findings are critical for designing effective, individualized training programs in strength and conditioning, as well as developing targeted rehabilitation and injury prevention strategies in sports medicine. By understanding the biomechanical and neuromuscular dynamics of the deadlift, practitioners can improve performance, minimize injury risk, and tailor interventions to the specific needs of athletes. Thus, this review provides a comprehensive overview of the current understanding of deadlift kinematics and EMG activity, offering valuable insights for optimizing training and rehabilitation protocols.

IntroductionLong-term electroencephalography (EEG) monitoring is advised to patients with refractory epilepsy who have a failure of anti-seizure medication and therapy. However, its real-life application is limited mainly due to the use of multiple EEG channels. We proposed a patient-specific deep learning-based single-channel seizure detection approach using the long-term scalp EEG recordings of the Children’s Hospital Boston-Massachusetts Institute of Technology (CHB-MIT) dataset, in conjunction with neurologists’ confirmation of spatial seizure characteristics of individual patients.MethodsWe constructed 18-, 4-, and single-channel seizure detectors for 13 patients. Neurologists selected a specific channel among four channels, two close to the behind-the-ear and two at the forehead for each patient, after reviewing the patient’s distinctive seizure locations with seizure re-annotation.ResultsOur multi- and single-channel detectors achieved an average sensitivity of 97.05–100%, false alarm rate of 0.22–0.40/h, and latency of 2.1–3.4 s for identification of seizures in continuous EEG recordings. The results demonstrated that seizure detection performance of our single-channel approach was comparable to that of our multi-channel ones.DiscussionWe suggest that our single-channel approach in conjunction with clinical designation of the most prominent seizure locations has a high potential for wearable seizure detection on long-term EEG recordings for patients with refractory epilepsy.

Abstract Detection and spatial distribution analyses of interictal epileptiform discharges (IEDs) are important for diagnosing, classifying, and treating focal epilepsy. This study proposes deep learning-based models to detect focal IEDs in electroencephalography (EEG) recordings of the frontal, temporal, and occipital scalp regions. This study included 38 patients with frontal (n = 15), temporal (n = 13), and occipital (n = 10) IEDs and 232 controls without IEDs from a single tertiary center. All the EEG recordings were segmented into 1.5-s epochs and fed into 1- or 2-dimensional convolutional neural networks to construct binary classification models to detect IEDs in each focal region and multiclass classification models to categorize IEDs into frontal, temporal, and occipital regions. The binary classification models exhibited accuracies of 79.3–86.4%, 93.3–94.2%, and 95.5–97.2% for frontal, temporal, and occipital IEDs, respectively. The three- and four-class models exhibited accuracies of 87.0–88.7% and 74.6–74.9%, respectively, with temporal, occipital, and non-IEDs F1-scores of 89.9–92.3%, 84.9–90.6%, and 84.3–86.0%; and 86.6–86.7%, 86.8–87.2%, and 67.8–69.2% for the three- and four-class (frontal, 50.3–58.2%) models, respectively. The deep learning-based models could help enhance EEG interpretation. Although they performed well, the resolution of region-specific focal IED misinterpretations and further model improvement are needed.

Lethal neonatal rigidity and multifocal seizure syndrome (RMFSL) is a severe autosomal recessive epileptic encephalopathy characterized by rigidity, intractable multifocal seizures, microcephaly, apnea, and bradycardia immediately after birth. RMFSL is related to a mutation in breast cancer 1-associated ataxia telangiectasia mutated activation-1 protein (BRAT1). We report a case of a female infant born to non-consanguineous Korean parents who developed hypertonia, dysmorphic features, progressive encephalopathy with refractory seizures at birth, and worsening intermittent apnea, leading to intubation and death at 137 days of age. The initial repeated electroencephalographic findings were normal; however, a pattern of focal seizures emerged at 35 days of life. Rapid trio whole-exome sequencing revealed heterozygous mutations c.1313_1314delAG p.(Gln438Argfs*51) and c.1276C>T p. (Gln426*) in BRAT1. After genetic counseling for pregnancy planning, a preimplantation genetic diagnosis for targeted BRAT1 mutations was successfully performed, and a healthy baby was born. To our knowledge, this is the first reported case of a Korean patient with compound heterozygous mutations in BRAT1. An early and accurate genetic diagnosis can help provide timely treatment to patients and indicate the need for reproductive counseling for parents for family planning.

Purpose Patients with self-limited epilepsy with centrotemporal spikes (SLECTS) rarely experience generalized tonic-clonic seizures (GTCS) after remission, and post-remission GTCS has not been thoroughly described in earlier studies. Herein, we describe the clinical and electrographic features of GTCS after a substantial period of seizure freedom in patients with SLECTS. Methods This study included six patients (three boys and three girls) diagnosed with SLECTS who later developed GTCS after or near remission. Medical records, including clinical data and serial electroencephalography (EEG) recordings, were retrospectively reviewed for all patients. Results Patients’ age at SLECTS onset ranged from 5.2 to 10.2 years (mean, 8.4 years), while seizure cessation was achieved between 8 and 12.2 years. During SLECTS, typical centrotemporal spikes were observed in all patients, and generalized spike-and-wave discharges were observed in three patients. The age at the first episode of subsequent GTCS ranged from 14.4 to 17.3 years (mean, 15.8 years), constituting an average interval of 5.6 years after the last episode of seizures (range, 4.1 to 8.1 years). EEG at subsequent episodes of GTCS revealed generalized discharges in two patients, focal discharges in two other patients, and normal discharges in the remaining two patients. Two patients had multiple episodes of GTCS. Conclusion Although rare, GTCS may occur near or after remission in patients with SLECTS, and clinicians should be aware of this. Subsequent GTCS may be a manifestation of idiopathic generalized epilepsy. However, large-scale studies are needed to determine the nature of such episodes of GTCS and their associated risk factors.

Abstract Background Phase I of the Korean Undiagnosed Diseases Program (KUDP), performed for 3 years, has been completed. The Phase I program aimed to solve the problem of undiagnosed patients throughout the country and develop infrastructure, including a data management system and functional core laboratory, for long-term translational research. Herein, we share the clinical experiences of the Phase I program and introduce the activities of the functional core laboratory and data management system. Results During the program (2018–2020), 458 patients were enrolled and classified into 3 groups according to the following criteria: (I) those with a specific clinical assessment which can be verified by direct testing (32 patients); (II) those with a disease group with genetic and phenotypic heterogeneity (353 patients); and (III) those with atypical presentations or diseases unknown to date (73 patients). All patients underwent individualized diagnostic processes based on the decision of an expert consortium. Confirmative diagnoses were obtained for 242 patients (52.8%). The diagnostic yield was different for each group: 81.3% for Group I, 53.3% for Group II, and 38.4% for Group III. Diagnoses were made by next-generation sequencing for 204 patients (84.3%) and other genetic testing for 35 patients (14.5%). Three patients (1.2%) were diagnosed with nongenetic disorders. The KUDP functional core laboratory, with a group of experts, organized a streamlined research pipeline covering various resources, including animal models, stem cells, structural modeling and metabolic and biochemical approaches. Regular data review was performed to screen for candidate genes among undiagnosed patients, and six different genes were identified for functional research. We also developed a web-based database system that supports clinical cohort management and provides a matchmaker exchange protocol based on a matchbox, likely to reinforce the nationwide clinical network and further international collaboration. Conclusions The KUDP evaluated the unmet needs of undiagnosed patients and established infrastructure for a data-sharing system and future functional research. The advancement of the KUDP may lead to sustainable bench-to-bedside research in Korea and contribute to ongoing international collaboration.

ObjectiveAlthough pediatric epilepsy is an independent disease entity, it is often observed in pediatric neurodevelopmental disorders (NDDs) as a major or minor clinical feature, which might provide diagnostic clues. This study aimed to identify the clinical and genetic characteristics of patients with epilepsy in an NDD cohort and demonstrate the importance of genetic testing.MethodsWe retrospectively analyzed the detailed clinical differences of pediatric NDD patients with epilepsy according to their genetic etiology. Among 1,213 patients with NDDs, 477 were genetically diagnosed by exome sequencing, and 168 had epilepsy and causative variants in 129 genes. Causative genes were classified into two groups: (i) the “epilepsy-genes” group resulting in epilepsy as the main phenotype listed in OMIM, Epi25, and ClinGen (67 patients) and (ii) the “NDD-genes” group not included in the “epilepsy-genes” group (101 patients).ResultsPatients in the “epilepsy-genes” group started having seizures, often characterized by epilepsy syndrome, at a younger age. However, overall clinical features, including treatment responses and all neurologic manifestations, showed no significant differences between the two groups. Gene ontology analysis revealed the close interactions of epilepsy genes associated with ion channels and neurotransmitters.ConclusionWe demonstrated a similar clinical presentation of different gene groups regarding biological/molecular processes in a large NDDs cohort with epilepsy. Phenotype-driven genetic analysis should cover a broad scope, and further studies are required to elucidate integrated pathomechanisms.

There has been significant interest in big data analysis and artificial intelligence (AI) in medicine. Ever-increasing medical data and advanced computing power have enabled the number of big data analyses and AI studies to increase rapidly. Here we briefly introduce epilepsy, big data, and AI and review big data analysis using a common data model. Studies in which AI has been actively applied, such as those of electroencephalography epileptiform discharge detection, seizure detection, and forecasting, will be reviewed. We will also provide practical suggestions for pediatricians to understand and interpret big data analysis and AI research and work together with technical expertise.

Centrotemporal spike-waves (CTSWs) are typical interictal epileptiform discharges (IEDs) observed in centrotemporal regions in self-limited epilepsy with centrotemporal spikes (SLECTS). This study aims to develop a deep learning-based approach for automated detection of CTSWs in scalp electroencephalography (EEG) recordings of patients with SLECTS. To lower the substantial burden of IED annotation on clinicians, we simplified it by limiting IEDs to CTSWs because electroencephalographic patterns of CTSWs are known to be highly consistent. Two neurologists annotated 1672 CTSWs of 20 patients with SLECTS. Thereafter, we performed a two-level CTSW detection procedure: epoch-level and EEG-level. In the epoch-level detection, we constructed convolutional neural network-based classification models for CTSW and non-CTSW binary classification using the recordings of 20 patients and 20 controls. We then set the thresholds of the classification models for 100% specificity. In the EEG-level detection, we applied the threshold-adjusted classification models to the recordings of 50 patients and 50 controls that were not used in the epoch-level detection to distinguish between CTSW-positive (with one or more CTSWs) and CTSW-negative (with no CTSW) recordings based on the detection of CTSW presence. We obtained an average sensitivity, specificity, and accuracy of 99.8%, 98.4%, and 99.1%, respectively, with an average false detection rate of 0.19/hr for the controls. Our approach showed high detectability for CTSWs despite the simplified annotation process. We expect that the proposed CTSW detectors have potential clinical usefulness for efficiently reading EEGs and diagnosing SLECTS, and can significantly reduce the burden of IED annotation on clinicians.

Purpose The purpose of this study was to expand our understanding of phenotypic and genetic variation in Allan-Herndon-Dudley syndrome (AHDS), which is a rare X-linked mental retardation syndrome characterized by hypotonia, generalized spasticity, and moderate-to-severe psychomotor retardation. AHDS is caused by a mutation of solute carrier family 16 member 2 (SLC16A2), which encodes monocarboxylate transporter 8 (MCT8), a transporter of triiodothyronine (T3) into neurons. Methods We enrolled nine patients with AHDS from unrelated families, except for two patients who were cousins, through a retrospective chart review. Clinical features, brain imaging, electroencephalograms, thyroid hormone profiles, and genetic data were reviewed retrospectively and compared with previously reported cases. Results We found three novel and five previously reported pathogenic variants in nine patients from eight families. All patients presented with hypotonia, spasticity, severe developmental delay, and elevated serum T3 levels. Cataplexy, which is a previously unreported phenotype, was found in two patients with the same mutation. In our cohort, seizures were uncommon (n=1) but intractable. Conclusion This study broadens the known phenotypic variations of AHDS, ranging from relatively mild global developmental delay to a severe form of encephalopathy with hypotonia, spasticity, and no acquisition of independent sitting. The syndromic classification or genetic etiology of global developmental delay is extremely heterogeneous; therefore, early clinical suspicion is challenging for clinicians. However, severe mental retardation with hypotonia, spasticity, and elevated serum T3 levels in boys is a highly suspicious clinical clue for the early diagnosis of AHDS.

Purpose Syntaxin-binding protein 1 (STXBP1) mutations are known to result in various phenotypes including Ohtahara syndrome, West syndrome, and autism, collectively referred as STXBP1 encephalopathy. This study aimed to expand our understanding of the genotype–phenotype spectrum of STXBP1 encephalopathy in the Korean pediatric population. Methods Ten patients with STXBP1 mutations were enrolled for a retrospective chart review. The patients were investigated for developmental delay of unknown cause and epileptic encephalopathy at a single center. Results Ten different STXBP1 mutations were identified. Three mutations had not previously been reported (c.1212A>C, c.1497C>G, c1030-2A>G). Eight patients showed early-onset epileptic encephalopathy as the main feature, while the main feature was developmental delay and non-epileptic movements in two patients. The most commonly seen electroencephalographic change was focal/multifocal epileptiform discharges, which were observed in nine patients (90%). The classical burst-suppression pattern was observed in four patients, two of which evolved to show hypsarrthymia. All patients with seizures had drug-resistant epilepsy. The patients suffered from severe developmental delay regardless of seizure frequency. Six patients showed an associated movement disorder or behavioral disorder. Conclusion This study describes the STXBP1 encephalopathy patients in Korean pediatric population, further expanding knowledge of its phenotype spectrum.

Purpose Although 1p36 deletion syndrome is the most common terminal deletion syndrome, unexplained phenotypic variability still occurs. We aimed to delineate the phenotype of this syndrome in detail and to characterize the phenotype-genotype correlation. Methods We retrospectively reviewed 15 patients diagnosed with 1p36 deletion syndrome confirmed by chromosomal microarray. Results All 15 patients revealed delayed attainment of motor milestones and speech. Seven patients (46.7%) never walked alone and only two (13.3%) could express a simple two-word sentence. They all showed subsequent intellectual disability. Two patients with large deletions of both distal and proximal critical regions of the 1p36 region shared severe intellectual disability with Rett syndrome-like behavioral features. Seizures, although frequent (73.3%), were well-controlled except in one patient with infantile spasms. Facial dysmorphism (92.9%) and ventricular mild dilatation with corpus callosum anomaly (46.7%) were common. Heart problems were identified in 14 patients, including structural abnormalities and/or functional problems associated with the gene encoding PR domain-containing protein 16. Two patients developed severe cardiac dysfunction requiring heart transplantation in their late teens. One patient with a 400 Kb deletion partly overlapping with the gene encoding calmodulin-binding transcription activator 1 did not have facial dysmorphism and presented with mild developmental delay and ataxic gait. One patient had a choledochal cyst, which was resected due to neonatal cholestasis. Conclusion Although the phenotype of 1p36 deletion syndrome is quite consistent with previous reports, additional manifestations such as certain behavioral features, ataxic gait, and severe cardiac dysfunction at an early age should be considered.

Purpose Vigabatrin has proven efficacy in the treatment of infantile spasms, but it carries the risk of irreversible visual field constriction. The incidence of vigabatrin-induced visual field constriction seems to depend on the extent of vigabatrin exposure. The aim of this study was to evaluate whether the therapeutic effect of vigabatrin is maintained in patients with infantile spasms receiving a short vigabatrin course followed by switching to another antiepileptic drug. Methods Patients with infantile spasms responsive to initial vigabatrin treatment were divided into a vigabatrin switch group (n=25) and a vigabatrin maintenance group (n=41). In the former group, vigabatrin was switched to other drugs within 6 months of spasm remission. The rate of seizure recurrence at 6 and 12 months from spasm remission was compared between the two groups. Results No statistically significant differences were found between the vigabatrin switch and maintenance groups in the age of onset, presence of concomitant seizures, time from spasm onset to vigabatrin treatment, time from vigabatrin treatment initiation to spasm remission, or vigabatrin dose at spasm remission. The number of patients with seizure recurrence at 12 months after spasm remission was 3 (3/25, 12%) in the vigabatrin switch group and 10 (10/41, 24.4%) in the vigabatrin maintenance group. The seizure recurrence rate at 12 months from spasm remission was not significantly different between groups. Conclusion A short course of vigabatrin could be considered in patients with infantile spasms who are responsive to initial vigabatrin treatment, since spasm remission was maintained after switching to other drugs.

Abstract Background The search for noninvasive biomarkers of neuroinflammation and neurodegeneration has focused on various neurological disorders, including epilepsy. We sought to determine whether α-synuclein and cytokines are correlated with the degree of neuroinflammation and/or neurodegeneration in children with epilepsy and with acquired demyelinating disorders of the central nervous system (CNS), as a prototype of autoimmune neuroinflammatory disorders. Methods We analyzed serum and exosome levels of α-synuclein and serum proinflammatory and anti-inflammatory cytokines among 115 children with epilepsy and 10 acquired demyelinating disorders of the CNS and compared to 146 controls. Patients were enrolled prospectively and blood was obtained from patients within 48 h after acute afebrile seizure attacks or relapse of neurological symptoms. Acquired demyelinating disorders of the CNS include acute disseminated encephalomyelitis, multiple sclerosis, neuromyelitis optica spectrum disorders, and transverse myelitis. The controls were healthy age-matched children. The serum exosomes were extracted with ExoQuick exosome precipitation solution. Serum α-synuclein levels and serum levels of cytokines including IFN-β, IFN-γ, IL-1β, IL-6, IL-10 and TNF-α were measured using single and multiplex ELISA kits. Data were analyzed and compared with measures of disease severity, such as age at disease onset, duration of disease, and numbers of antiepileptic drug in use. Results Serum α-synuclein levels were significantly increased in patients with epilepsy and acquired demyelinating disorders of the CNS compared to controls (both, p

Purpose Rituximab is increasingly used as a second-line treatment of neuroinflammatory disorders to improve clinical outcomes in cases refractory to conventional immunotherapy and to reduce relapses. This study aimed to demonstrate the efficacy and safety of rituximab used for pediatric autoimmune neuroinflammatory disorders. Methods We retrospectively reviewed the medical records of 32 patients (median age, 8.5 years; range, 1.1 to 17.1; 23 girls) who received rituximab treatment at Seoul National University Children’s Hospital. The disease subgroups were anti-N-methyl-D-aspartate (NMDA) receptor (anti-NMDAR) encephalitis (n=11), opsoclonus-myoclonus ataxia syndrome (OMAS) (n=10), other suspected autoimmune encephalitis (n=5), neuromyelitis optica spectrum disorder (n=4), and chronic inflammatory demyelinating polyneuropathy (n=2). Efficacy was measured by modified Rankin Scale (mRS) scores at the initiation of rituximab administration, at 2 months after initiation, and at the last follow-up. A favorable clinical outcome was defined as an improvement of ≥2 in the mRS score or achievement of an mRS score ≤2. Safety was assessed by reviewing infusion-related adverse events and infectious complications, including progressive multifocal leukoencephalopathy. Results Two months after the initiation of rituximab therapy, 21patients (65.6%) had a favorable response, while 26 (82.1%) had a favorable response at the last follow-up. Among the disease subgroups, anti-NMDAR encephalitis and OMAS showed especially good responses. Rituximab infusion-related adverse events were identified in nine patients (28.1%). All complications recovered spontaneously or with only symptomatic treatment. Conclusion Rituximab can be used safely for various pediatric autoimmune neuroinflammatory diseases. Rituximab is expected to improve clinical outcomes in pediatric patients with anti-NMDAR encephalitis and OMAS.

Purpose Cytokines demonstrate active roles in the occurrence of febrile seizures (FS). However, whether a genetic predisposition to inflammation is implicated in FS, febrile seizure plus (FS+) or genetic epilepsy with febrile seizure plus (GEFS+) are still unclear. Therefore we perform this study to find the association of promotor variants in pro-inflammatory cytokine tumor necrosis factor-α (TNF-α) genes and anti-inflammatory cytokine interleukin 10 (IL-10) genes either with FS, FS+, and GEFS+ in Korean children. Methods Fifty-seven children with FS, 32 FS+, and 12 GEFS+ patients were compared with 108 controls. The allelic and genotypic distributions were compared for TNF-α-238 (rs361525), -308 (rs1800629), -857 (rs1799724), -863 (rs1800630), and IL-10-592 (rs1800872), -819 (rs1800871), -1082 (rs1800896), and -1352 (rs1800893). Results Allelic and genotypic frequencies of TNF-α and IL-10 promotor variants showed no significant differences between FS, FS+, and GEFS+ versus controls. However, AA genotypes at TNF-α-863 were present only in controls. TNF-α-863 (rs1800630) promoter variants showed an association with FS, FS+, and GEFS+ in a recessive mode of inheritance pattern (P

Abstract Background The Korean Undiagnosed Diseases Program (KUDP) was launched in January 2017 as a one-year pilot project to address the increasing global interest in patients with undiagnosed rare diseases. The purpose of this paper is to summarize the project results and emphasize the unmet research needs among patients with undiagnosed rare diseases in Korea. Results Patient enrollment, assessment, and diagnostic processes were determined by the KUDP clinical expert consortium. Patients followed a diagnostic workflow after being categorized into one of four groups: I) insufficient clinical information or lack of standard diagnostic processes; II) undiagnosed due to low disease awareness; III) clinically diagnosed but unconfirmed genetically due to genetic heterogeneities; or IV) unknown disease due to complex, atypical clinical presentations. After excluding two patients from group I, 97 patients were enrolled, including 10 in group II, 67 in group III, and 20 in group IV. Most of them (92 of 97, 94.8%) were pediatric patients (

Sleep staging is an important part of clinical neurology. However, it is still performed manually by technical experts and is labor-intensive and time-consuming. To overcome these obstacles in the manual sleep staging process, a large number of machine learning-based classifiers with hand-engineered features have been proposed. Additionally, combinations of a deep neural network (DNN) have been recently highlighted as the state-of-the-art classifiers in view of their effectiveness for automatic sleep staging. In spite of the existence of a large number of these types of classifiers, to-this-date, no prior DNN-based approach has attempted sleep-stage classification using pediatric electroencephalographic (EEG) signals. In this paper, we propose a novel end-to-end classifier based on a multi-domain hybrid neural network (HNN-multi) approach consisting of a convolutional neural network and bidirectional long short-term memory for automatic sleep staging with pediatric scalp EEG recordings. To find effective temporal, spatial, and domain-specific conditions, we investigated noticeable changes in the classification performance corresponding to: 1) the length of input signals; 2) the number of channels; and 3) the types of input signals in the time and frequency domains. Our HNN-based classifier yielded the best performance metrics using 30-s time series in combination with an instantaneous frequency using a 19-channel, three-stage classification, with overall accuracy, F1 score, and Cohen's Kappa, equal to 92.21%, 0.90, and 0.88, respectively. We suggest that an effective combination of temporal and spatial time-domain clues with time-varying frequency domain information plays a pivotal role in pediatric, automatic sleep staging. Sufficiently reasonable performance of our HNN-based approach coping with the highly complicated pediatric EEG signatures hopefully sheds light on the clinical feasibility of the DNN-based automatic sleep staging for pediatric neurology.

We aimed to differentiate between the interictal and preictal states in epilepsy patients with focal cortical dysplasia (FCD) type-II using deep learning-based classifiers based on intracranial electroencephalography (EEG). We also investigated the practical conditions for high interictal-preictal discriminability in terms of spatiotemporal EEG characteristics and data size efficiency. Intracranial EEG recordings of nine epilepsy patients with FCD type-II (four female, five male; mean age: 10.7 years) were analyzed. Seizure onset and channel ranking were annotated by two epileptologists. We performed three consecutive interictal-preictal classification steps by varying the preictal length, number of electrodes, and sampling frequency with convolutional neural networks (CNN) using 30 s time-frequency data matrices. Classification performances were evaluated based on accuracy, F1 score, precision, and recall with respect to the above-mentioned three parameters. We found that (1) a 5 min preictal length provided the best classification performance, showing a remarkable enhancement of >13% on average compared to that with the 120 min preictal length; (2) four electrodes provided considerably high classification performance with a decrease of only approximately 1% on average compared to that with all channels; and (3) there was minimal performance change when quadrupling the sampling frequency from 128 Hz. Patient-specific performance variations were noticeable with respect to the preictal length, and three patients showed above-average performance enhancements of >28%. However, performance enhancements were low with respect to both the number of electrodes and sampling frequencies, and some patients showed at most 1–2% performance change. CNN-based classifiers from intracranial EEG recordings using a small number of electrodes and efficient sampling frequency are feasible for predicting the interictal-preictal state transition preceding seizures in epilepsy patients with FCD type-II. Preictal lengths affect the predictability in a patient-specific manner; therefore, pre-examinations for optimal preictal length will be helpful in seizure prediction.

The purpose of this pilot study was to analyze treatment pathways of pediatric epilepsy using the common data model (CDM) based on electronic health record (EHR) data. We also aimed to reveal whether CDM analysis was feasible and applicable to epilepsy research. We analyzed the treatment pathways of pediatric epilepsy patients from our institute who underwent antiseizure medication (ASM) treatment for at least 2 years, using the Observational Medical Outcomes Partnership (OMOP)-CDM. Subgroup analysis was performed for generalized or focal epilepsy, varying age of epilepsy onset, and specific epilepsy syndromes. Changes in annual prescription patterns were also analyzed to reveal the different trends. We also calculated the proportion of drug-resistant epilepsy by applying the definition of seizure persistence after application of two ASMs for a sufficient period of time (more than 6 months). We identified 1,192 patients who underwent treatment for more than 2 years (mean ± standard deviation: 6.5 ± 3.2 years). In our pediatric epilepsy cohort, we identified 313 different treatment pathways. Drug resistance, calculated as the application of more than three ASMs during the first 2 years of treatment, was 23.8%. Treatment pathways and ASM resistance differed between subgroups of generalized vs. focal epilepsy, different onset age of epilepsy, and specific epilepsy syndromes. The frequency of ASM prescription was similar between onset groups of different ages; however, phenobarbital was frequently used in children with epilepsy onset < 4 years. Ninety-one of 344 cases of generalized epilepsy and 187 of 835 cases of focal epilepsy were classified as medically intractable epilepsy. The percentage of drug resistance was markedly different depending on the specific electro-clinical epilepsy syndrome [79.0% for Lennox-Gastaut syndrome (LGS), 7.1% for childhood absence epilepsy (CAE), and 9.0% for benign epilepsy with centrotemporal spikes (BECTS)]. We could visualize the annual trend and changes of ASM prescription for pediatric epilepsy in our institute from 2004 to 2017. We revealed that CDM analysis was feasible and applicable for epilepsy research. The strengths and limitations of CDM analysis should be carefully considered when planning the analysis, result extraction, and interpretation of results.

Purpose: We aimed to evaluate the diagnostic yield of epilepsy gene panel testing in epilepsy patients whose seizures began within the first year after birth. We included 112 patients with seizure onset before 12 months and no known etiology.Methods: Deep targeted sequencing with a custom-designed capture probe was performed to ensure the detection of germline or mosaic sequence variants and copy number variations (CNVs).Results: We identified pathogenic or likely pathogenic variants in 53 patients (47.3%, 53/112), including five with pathogenic CNVs. Two putative pathogenic mosaic variants in SCN8A and KCNQ2 were also detected and validated. Those with neonatal onset (61.5%, 16/26) or early infantile onset (50.0%, 29/58) showed higher diagnostic rates than those with late infantile onset (28.5%, 8/28). The diagnostic rate was similar between patients with a specific syndrome (51.9%, 27/52) and those with no recognizable syndrome (43.3%, 26/60).Conclusion: Epilepsy gene panel testing identified a genetic cause in nearly half of the infantile onset epilepsy patients. Since the phenotypic spectrum is expanding and characterizing it at seizure onset is difficult, this group should be prioritized for epilepsy gene panel testing.

Magnetoencephalography (MEG) records the magnetic field generated by electrical activity of cortical neurons. The signal is not distorted or attenuated, and it is contactless recording that can be performed comfortably even for longer than an hour. It has excellent and decent temporal resolution, especially when it is combined with the patient's own brain magnetic resonance imaging (magnetic source imaging). Data of MEG and electroencephalography are not mutually exclusive and it is recorded simultaneously and interpreted together. MEG has been shown to be useful in detecting the irritative zone in both lesional and nonlesional epilepsy surgery. It has provided valuable and additive information regarding the lesion that should be resected in epilepsy surgery. Better outcomes in epilepsy surgery were related to the localization of the irritative zone with MEG. The value of MEG in epilepsy surgery is recruiting more patients to epilepsy surgery and providing critical information for surgical planning. MEG cortical mapping is helpful in younger pediatric patients, especially when the epileptogenic zone is close to the eloquent cortex. MEG is also used in both basic and clinical research of epilepsy other than surgery. MEG is a valuable diagnostic modality for diagnosis and treatment, as well as research in epilepsy.

Primary hypokalemic periodic paralysis (HOKPP) is an autosomal dominant disorder manifesting as recurrent periodic flaccid paralysis and concomitant hypokalemia. HOKPP is divided into type 1 and type 2 based on the causative gene. Although 2 different ion channels have been identified as the molecular genetic cause of HOKPP, the clinical manifestations between the 2 groups are similar. We report the cases of 2 patients with HOKPP who both presented with typical clinical manifestations, but with mutations in 2 different genes (CACNA1Sp.Arg528His and SCN4A p.Arg672His). Despite the similar clinical manifestations, there were differences in the response to acetazolamide treatment between certain genotypes of SCN4A mutations and CACNA1S mutations. We identified p.Arg672His in the SCN4A gene of patient 2 immediately after the first attack through a molecular genetic testing strategy. Molecular genetic diagnosis is important for genetic counseling and selecting preventive treatment.