Establishment and characterization of ZJUCHi003: an induced pluripotent stem cell line from a patient with Temple-Baraitser/Zimmermann-Laband syndrome carrying KCNH1 c.1070G > A (p.R357Q) variant.

Pathogenic variants of the KCNH1 gene can cause dominant-inherited Temple-Baraitser/Zimmermann-Laband syndrome with severe mental retardation, seizure, gingival hyperplasia and nail hypoplasia. This study established an induced pluripotent stem cell (iPSC) line using urinary cells from a girl with KCNH1 recurrent/hotspot pathogenic variant c.1070G > A (p.R357Q). The cell identity, pluripotency, karyotypic integrity, absence of reprogramming virus and mycoplasma contamination, and differential potential to three germ layers of the iPSC line, named as ZJUCHi003, were characterized and confirmed. Furthermore, ZJUCHi003-derived neurons manifested slower action potential repolarization process and wider action potential half-width than the normal neurons. This cell line will be useful for investigating the pathogenic mechanisms of KCNH1 variants-associated symptoms, as well as for evaluating novel therapeutic approaches.

An improved BECT spike detection method with functional brain network features based on PLV.

Background: Children with benign childhood epilepsy with centro-temporal spikes (BECT) have spikes, sharps, and composite waves on their electroencephalogram (EEG). It is necessary to detect spikes to diagnose BECT clinically. The template matching method can identify spikes effectively. However, due to the individual specificity, finding representative templates to detect spikes in actual applications is often challenging. Purpose: This paper proposes a spike detection method using functional brain networks based on phase locking value (FBN-PLV) and deep learning. Methods: To obtain high detection effect, this method uses a specific template matching method and the 'peak-to-peak' phenomenon of montages to obtain a set of candidate spikes. With the set of candidate spikes, functional brain networks (FBN) are constructed based on phase locking value (PLV) to extract the features of the network structure during spike discharge with phase synchronization. Finally, the time domain features of the candidate spikes and the structural features of the FBN-PLV are input into the artificial neural network (ANN) to identify the spikes. Results: Based on FBN-PLV and ANN, the EEG data sets of four BECT cases from the Children's Hospital, Zhejiang University School of Medicine are tested with the AC of 97.6%, SE of 98.3%, and SP 96.8%.

Seizure Prediction Analysis of Infantile Spasms.

Infantile spasms (IS) is a typical childhood epileptic disorder with generalized seizures. The sudden, frequent and complex characteristics of infantile spasms are the main causes of sudden death, severe comorbidities and other adverse consequences. Effective prediction is highly critical to infantile spasms subjects, but few related studies have been done in the past. To address this, this study proposes a seizure prediction framework for infantile spasms by combining the statistical analysis and deep learning model. The analysis is conducted on dividing the continuous scalp electroencephalograms (sEEG) into 5 phases: Interictal, Preictal, Seizure Prediction Horizon (SPH), Seizure, and Postictal. The brain network of Phase-Locking Value (PLV) of 5 typical brain rhythms is constructed, and the mechanism of epileptic changes is analyzed by statistical methods. It is found that 1) the connections between the prefrontal, occipital, and central regions show a large variability at each stage of seizure transition, and 2) 4 sub-bands of brain rhythms ( θ , α , ß , γ ) are predominant. Group and individual variabilities are validated by using the Resnet18 deep model on data from 25 patients with infantile spasms, where the consistent results to statistical analyses can be observed. The optimized model achieves an average of 79.78 % , 94.46% , 75.46% accuracy, specificity, and recall rate, respectively. The method accomplishes the analysis of the synergy between infantile spasms mechanism, model, data and algorithm, providing a guideline to build an intelligent and systematic model for comprehensive IS seizure prediction.

Efficacy, Retention Rate, and Influencing Factors of Ketogenic Diet Therapy in Children with Refractory Epilepsy: A Retrospective Study.

BACKGROUND: This study aimed to evaluate the efficacy and retention rate of a ketogenic diet (KD) and assess factors that influence the efficacy of KD therapy in children with refractory epilepsy (RE). METHODS: We retrospectively studied the efficacy and retention rate of 56 RE children who accepted KD therapy from January 2013 to December 2019. Patients who had a ≥50% reduction in seizure frequency were defined as responders. The retention rate was calculated as the proportion of children who continued KD/the total number of children who were followed up at the time of enrollment. We also analyzed the effects of different factors (such as gender, KD initial age, KD duration, the type of epilepsy syndrome, and others) on the efficacy of the KD. RESULTS: (1) The efficacy rates for the KD at 3, 6, 12, and 18 months were 51.8, 53.6, 39.2, and 23.2%, respectively. (2) The retention rates for the KD at 3, 6, 12 and 18 months were 100, 69.6, 41.1, and 23.2%, respectively. (3) There was no correlation between efficacy and gender, epilepsy onset age, the type of epilepsy syndrome, electroencephalogram improvement, or the number of antiseizure medications, while cranial magnetic resonance imaging (MRI) abnormalities, KD duration, and KD initial age affected its efficacy at 3 months. CONCLUSION: (1) KD therapy for refractory childhood epilepsy was effective and produced a high retention rate. (2) MRI abnormalities and the initial age and duration of KD influenced its short-term efficacy in RE children.

Clinical characteristics and associated factors of pediatric acute disseminated encephalomyelitis patients with MOG antibodies: a retrospective study in Hangzhou, China.

BACKGROUND: To explore the clinical characteristics and related factors of children with acute disseminated encephalomyelitis (ADEM) with positive anti-myelin oligodendrocyte glycoprotein (MOG) antibody. METHODS: A retrospective study was conducted and enrolled pediatric ADEM patients who underwent serum MOG antibody detection from May 2017 to August 2020. The patients were divided into two groups: MOG- immunoglobulin G (IgG) positive (n = 35) and MOG-IgG negative (n = 50). We analyzed the clinical characteristics of MOG-IgG-positive ADEM pediatric patients and conducted a comparative analysis between the two groups. RESULTS: Thirty-five patients (21 males and 14 females) in the MOG-IgG-positive group with encephalopathy, multifocal neurological symptoms, and typical magnetic resonance imaging (MRI) abnormalities were enrolled. They usually had a favorable outcome, while some suffered from relapse. Compared to the MOG-IgG-negative group, MOG-IgG-positive ADEM patients had a longer disease duration (median: 10 vs. 6 days), more meningeal involvement (31.4% vs. 8%) and frontal lobe involvement (82.8% vs. 68%), higher relapse rates (14.3% vs. 2%), lower serum tumor necrosis factor (1-12.4 pg/ml, median 1.7 vs. 1-34 pg/ml, median 2.2) and interferon-gamma (1-9.4 pg/ml, median 1.3 vs. 1-64 pg/ml, median 3) (P < 0.05, respectively). Multivariate logistic regression analysis showed that the longer disease duration, meningeal involvement and frontal lobe involvement were the correlated factors of patients with ADEM with MOG antibody (P < 0.05). CONCLUSIONS: Our findings provide clinical evidence that MOG-IgG positivity is associated with longer disease duration, meningeal involvement, and frontal lobe involvement.

Scalp EEG functional connection and brain network in infants with West syndrome.

The common age-dependent West syndrome can be diagnosed accurately by electroencephalogram (EEG), but its pathogenesis and evolution remain unclear. Existing research mainly aims at the study of West seizure markers in time/frequency domain, while less literature uses a graph-theoretic approach to analyze changes among different brain regions. In this paper, the scalp EEG based functional connectivity (including Correlation, Coherence, Time Frequency Cross Mutual Information, Phase-Locking Value, Phase Lag Index, Weighted Phase Lag Index) and network topology parameters (including Clustering coefficient, Feature path length, Global efficiency, and Local efficiency) are comprehensively studied for the prognostic analysis of the West episode cycle. The scalp EEGs of 15 children with clinically diagnosed string spasticity seizures are used for prospective study, where the signal is divided into pre-seizure, seizure, and post-seizure states in 5 typical brain wave rhythm frequency bands (δ (1-4 Hz), θ (4-8 Hz), α (8-13 Hz), ß (13-30 Hz), and γ (30-80 Hz)) for functional connectivity analysis. The study shows that recurrent West seizures weaken connections between brain regions responsible for cognition and intelligence, while brain regions responsible for information synergy and visual reception have greater variability in connectivity during seizures. It is observed that the changes inßandγfrequency bands of the multiband brain network connectivity patterns calculated by Corr and WPLI can be preliminarily used as judgment of seizure cycle changes in West syndrome.

Multidimensional Feature Optimization Based Eye Blink Detection Under Epileptiform Discharges.

OBJECTIVES: Eye blink artifact detection in scalp electroencephalogram (EEG) of epilepsy patients is challenging due to its similar waveforms to epileptiform discharges. Developing an accurate detection method is urgent and critical. METHODS: In this paper, we proposed a novel multi-dimensional feature optimization based eye blink artifact detection algorithm for EEGs containing rich epileptiform discharges. An unsupervised clustering algorithm based on smoothed nonlinear energy operator (SNEO) and variational mode extraction (VME) is proposed to detect epileptiform discharges in the frontal leads. Then, multi-dimensional time/frequency EEG features extracted from forehead electrodes (FP1 and FP2 channels) combining with the improved VME (IVME) threshold are derived for EEG representation. A variance filtering method is further applied for discriminative feature selection and a machine learning model is finally learned to perform detection. RESULTS: Experiments on EEGs of 16 subjects from the Children's Hospital of Zhejiang University School of Medicine (CHZU) show that our method achieves the highest average sensitivity, specificity and accuracy of 95.04, 89.52, and 93.01, respectively. That outperforms 5 recent and state-of-the-art (SOTA) eye blink detection algorithms. SIGNIFICANCE: The proposed method is robust in eye blink artifact detection for EEGs containing high-frequency epileptiform discharges. It is also effective in dealing with individual differences in EEGs, which is usually ignored in conventional methods.

Deep feature fusion based childhood epilepsy syndrome classification from electroencephalogram.

Accurate classification of the children's epilepsy syndrome is vital to the diagnosis and treatment of epilepsy. But existing literature mainly focuses on seizure detection and few attention has been paid to the children's epilepsy syndrome classification. In this paper, we present a study on the classification of two most common epilepsy syndromes: the benign childhood epilepsy with centro-temporal spikes (BECT) and the infantile spasms (also known as the WEST syndrome), recorded from the Children's Hospital, Zhejiang University School of Medicine (CHZU). A novel feature fusion model based on the deep transfer learning and the conventional time-frequency representation of the scalp electroencephalogram (EEG) is developed for the epilepsy syndrome characterization. A fully connected network is constructed for the feature learning and syndrome classification. Experiments on the CHZU database show that the proposed algorithm can offer an average of 92.35% classification accuracy on the BECT and WEST syndromes and their corresponding normal cases.

Longitudinally tracking personal physiomes for precision management of childhood epilepsy.

Our current understanding of human physiology and activities is largely derived from sparse and discrete individual clinical measurements. To achieve precise, proactive, and effective health management of an individual, longitudinal, and dense tracking of personal physiomes and activities is required, which is only feasible by utilizing wearable biosensors. As a pilot study, we implemented a cloud computing infrastructure to integrate wearable sensors, mobile computing, digital signal processing, and machine learning to improve early detection of seizure onsets in children. We recruited 99 children diagnosed with epilepsy and longitudinally tracked them at single-second resolution using a wearable wristband, and prospectively acquired more than one billion data points. This unique dataset offered us an opportunity to quantify physiological dynamics (e.g., heart rate, stress response) across age groups and to identify physiological irregularities upon epilepsy onset. The high-dimensional personal physiome and activity profiles displayed a clustering pattern anchored by patient age groups. These signatory patterns included strong age and sex-specific effects on varying circadian rhythms and stress responses across major childhood developmental stages. For each patient, we further compared the physiological and activity profiles associated with seizure onsets with the personal baseline and developed a machine learning framework to accurately capture these onset moments. The performance of this framework was further replicated in another independent patient cohort. We next referenced our predictions with the electroencephalogram (EEG) signals on selected patients and demonstrated that our approach could detect subtle seizures not recognized by humans and could detect seizures prior to clinical onset. Our work demonstrated the feasibility of a real-time mobile infrastructure in a clinical setting, which has the potential to be valuable in caring for epileptic patients. Extension of such a system has the potential to be leveraged as a health management device or longitudinal phenotyping tool in clinical cohort studies.

Cluster Embedding Joint-Probability-Discrepancy Transfer For Cross-subject Seizure Detection.

Transfer learning (TL) has been applied in seizure detection to deal with differences between different subjects or tasks. In this paper, we consider cross-subject seizure detection that does not rely on patient history records, that is, acquiring knowledge from other subjects through TL to improve seizure detection performance. We propose a novel domain adaptation method, named the Cluster Embedding Joint-Probability-Discrepancy Transfer (CEJT), for data distribution structure learning. Specifically, 1) The joint probability distribution discrepancy is minimized to reduce the distribution shift in the source and target domains, and strengthen the discriminative knowledge of classes. 2) A clustering is performed on the target domain, and the class centroids of sources is used as the clustering prototype of the target domain to enhance data structure. It is worth noting that the manifold regularization is used to improve the quality of clustering prototypes. In addition, a correlation-alignment-based source selection metric (SSC) is designed for most favorable subject selection, reducing the computational cost as well as avoiding some negative transfer. Experiments on 15 patients with focal epilepsy from the Children's Hospital, Zhejiang University School of Medicine (CHZU) database shown that CEJT outperforms several state-of-the-art approaches, and can promote the application of seizure detection.

Application of Trio-Whole Exome Sequencing in Genetic Diagnosis and Therapy in Chinese Children With Epilepsy.

Epilepsy is one of the most common neurological disorders in pediatric patients with other underlying neurological defects. Identifying the underlying etiology is crucial for better management of the disorder. We performed trio-whole exome sequencing in 221 pediatric patients with epilepsy. Probands were divided into seizures with developmental delay/intellectual disability (DD/ID) and seizures without DD/ID groups. Pathogenic (P) or likely pathogenic (LP) variants were identified in 71/110 (64.5%) patients in the seizures with DD/ID group and 21/111 (18.9%) patients in the seizures without DD/ID group (P < 0.001). Eighty-seven distinct P/LP single nucleotide variants (SNVs)/insertion deletions (Indels) were detected, with 55.2% (48/87) of them being novel. All aneuploidy and P/LP copy number variants (CNVs) larger than 100 Kb were identifiable by both whole-exome sequencing and copy number variation sequencing (CNVseq) in 123 of individuals (41 pedigrees). Ten of P/LP CNVs in nine patients and one aneuploidy variant in one patient (Patient #56, #47, XXY) were identified by CNVseq. Herein, we identified seven genes (NCL, SEPHS2, PA2G4, SLC35G2, MYO1C, GPR158, and POU3F1) with de novo variants but unknown pathogenicity that were not previously associated with epilepsy. Potential effective treatment options were available for 32 patients with a P/LP variant, based on the molecular diagnosis. Genetic testing may help identify the molecular etiology of early onset epilepsy and DD/ID and further aid to choose the appropriate treatment strategy for patients.

BECT Spike Detection Based on Novel EEG Sequence Features and LSTM Algorithms.

The benign epilepsy with spinous waves in the central temporal region (BECT) is the one of the most common epileptic syndromes in children, that seriously threaten the nervous system development of children. The most obvious feature of BECT is the existence of a large number of electroencephalogram (EEG) spikes in the Rolandic area during the interictal period, that is an important basis to assist neurologists in BECT diagnosis. With this regard, the paper proposes a novel BECT spike detection algorithm based on time domain EEG sequence features and the long short-term memory (LSTM) neural network. Three time domain sequence features, that can obviously characterize the spikes of BECT, are extracted for EEG representation. The synthetic minority oversampling technique (SMOTE) is applied to address the spike imbalance issue in EEGs, and the bi-directional LSTM (BiLSTM) is trained for spike detection. The algorithm is evaluated using the EEG data of 15 BECT patients recorded from the Children's Hospital, Zhejiang University School of Medicine (CHZU). The experiment shows that the proposed algorithm can obtained an average of 88.54% F1 score, 92.04% sensitivity, and 85.75% precision, that generally outperforms several state-of-the-art spike detection methods.

Eye Blink Artifact Detection With Novel Optimized Multi-Dimensional Electroencephalogram Features.

Accurate eye blink artifact detection is essential for electroencephalogram (EEG) analysis and auxiliary analysis of nervous system diseases, especially in the presence of the frontal epileptiform discharges. In this paper, we develop a novel eye blink artifact detection algorithm based on optimally selected multi-dimensional EEG features. Specific efforts have been paid to filtering the frontal epileptiform discharges, where an unsupervised learning exploiting the EEG signal physiological characteristics and smooth nonlinear energy operator (SNEO) based on the K-means clustering has been firstly proposed. Multiple statistical EEG features derived from the frontal electrodes and other electrodes are then extracted to characterize eye blink artifacts. Discriminative feature selection scheme based on the variance filtering and Relief algorithms has been respectively studied, and the average correlation coefficient (ACC) is applied for feature optimization evaluation. The eye blink artifact detection is finally achieved based on the support vector machine (SVM) trained on the optimized EEG features. The effectiveness of the proposed algorithm is demonstrated by experiments carried out on the EEG database of 11 subjects recorded from the Children's Hospital, Zhejiang University School of Medicine (CHZU). Comparisons to several state-of-the-art (SOTA) eye blink artifact detection methods are also presented.

Unsupervised Eye Blink Artifact Detection From EEG With Gaussian Mixture Model.

Eye blink is one of the most common artifacts in electroencephalogram (EEG) and significantly affects the performance of the EEG related applications, such as epilepsy recognition, spike detection, encephalitis diagnosis, etc. To achieve an accurate and efficient eye blink detection, a novel unsupervised learning algorithm based on a hybrid thresholding followed with a Gaussian mixture model (GMM) is presented in this paper. The EEG signal is priliminarily screened by a cascaded thresholding method built on the distributions of signal amplitude, amplitude displacement, as well as the cross channel correlation. Then, the channel correlation of the two frontal electrodes (FP1, FP2), the fractal dimension, and the mean of amplitude difference between FP1 and FP2, are extracted to characterize the filtered EEGs. The GMM trained on these features is applied for the eye blink detection. The performance of the proposed algorithm is studied on two EEG datasets collected by the Temple University Hospital (TUH) and the Children's Hospital, Zhejiang University School of Medicine (CHZU), where the datasets are recorded from epilepsy and encephalitis patients, and contain a lot of eye blink artifacts. Experimental results show that the proposed algorithm can achieve the highest detection precision and F1 score over the state-of-the-art methods.

Clinical and molecular analysis of epilepsy-related genes in patients with Dravet syndrome.

Dravet syndrome is considered to be one of the most severe types of genetic epilepsy. Mutations in SCN1A gene have been found to be responsible for at least 80% of patients with Dravet syndrome, and 90% of these mutations arise de novo. The variable clinical phenotype is commonly observed among these patients with SCN1A mutations, suggesting that genetic modifiers may influence the phenotypic expression of Dravet syndrome. In the present study, we described the clinical, pathological, and molecular characteristics of 13 Han Chinese pedigrees clinically diagnosed with Dravet syndrome. By targeted-exome sequencing, bioinformatics analysis and Sanger sequencing verification, 11 variants were identified in SCN1A gene among 11 pedigrees including 7 missense mutations, 2 splice site mutations, and 2 frameshift mutations (9 novel variants and 2 reported mutations). Particularly, 2 of these Dravet syndrome patients with SCN1A variants also harbored SCN9A, KCNQ2, or SLC6A8 variants. In addition, 2 subjects were failed to detect any pathogenic mutations in SCN1A and other epilepsy-related genes. These data suggested that SCN1A variants account for about 84.6% of Dravet syndrome in our cohort. This study expanded the mutational spectrum for the SCN1A gene, and also provided clinical and genetic evidence for the hypothesis that genetic modifiers may contribute to the variable manifestation of Dravet syndrome patients with SCN1A mutations. Thus, targeted-exome sequencing will make it possible to detect the interactions of epilepsy-related genes and reveal their modification on the severity of SCN1A mutation-related Dravet syndrome.

Novel Mutation of the Dystrophin Gene in a Child with Duchenne Muscular Dystrophy.

INTRODUCTION: Duchenne muscular dystrophy (DMD) is an X-linked autosomal recessive genetic disorder caused by mutations in DMD gene. Approximately 70% of the mutations are caused by deletions or duplications of DMD exons, while the remaining were minor mutations. CASE REPORT: We present a 5-year-old boy with typical clinical features of DMD. A novel mutation was identified as a c.9358_9359insA of DMD gene by next-generation sequencing. This mutation which was origined from mother, generated a frameshift mutation and resulted in abnormal synthesis of protein polypeptide chains. CONCLUSION: We demonstrated a novel mutation of DMD gene and expanded the spectrum of mutations causing DMD.

[Clinical and genetic analyses of a family with atypical nonketotic hyperglycinemia caused by compound heterozygous mutations in the GLDC gene].

Nonketotic hyperglycinemia (NKH) is an autosomal recessive hereditary disease caused by a defect in the glycine cleavage system and is classified into typical and atypical NKH. Atypical NKH has complex manifestations and is difficult to diagnose in clinical practice. This article reports a family of NKH. The parents had normal phenotypes, and the older brother and the younger sister developed this disease in the neonatal period. The older brother manifested as intractable epilepsy, severe spastic diplegia, intellectual disability, an increased level of glycine in blood and cerebrospinal fluid, an increased glycine/creatinine ratio in urine, and an increased ratio of glycine concentration in cerebrospinal fluid and blood. The younger sister manifested as delayed language development, ataxia, chorea, mental and behavior disorders induced by pyrexia, hypotonia, an increased level of glycine in cerebrospinal fluid, and an increased ratio of glycine concentration in cerebrospinal fluid and blood. High-throughput sequencing found a maternal missense mutation, c.3006C>G (p.C1002W), and a paternal nonsense mutation, c.1256C>G (p.S419X), in the GLDC gene in both patients. These two mutations were thought to be pathogenic mutations by a biological software. H293T cells transfected with these two mutants of the GLDC gene had a down-regulated activity of glycine decarboxylase. NKH has various phenotypes, and high-throughput sequencing helps to make a confirmed diagnosis. Atypical NKH is associated with the downregulated activity of glycine decarboxylase caused by gene mutations.