Childhood absence epilepsy is one of the most prevalent pediatric epilepsy syndromes, but diagnostic delay is common and consequential. Childhood absence epilepsy is diagnosed by history and physical examination including hyperventilation with electroencephalography (EEG) used to confirm the diagnosis. Hyperventilation produces generalized spike-wave discharges on EEG in >90% of patients with childhood absence epilepsy and provokes clinical absence seizures consisting of brief loss of consciousness typically within 90 seconds. Child neurologists report a high volume of referrals for children with \"staring spells\" that strain already limited health care resources. Resources are further strained by the use of EEG for monitoring antiseizure medication effectiveness with unclear benefit. In this review, we examine the safety and efficacy of hyperventilation activation as a tool for the diagnosis and management of childhood absence seizures.

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BACKGROUND: Epilepsy is a neurological disorder characterized by the predisposition for recurrent unprovoked seizures. It can broadly be classified as focal, generalized, unclassified, and unknown in its onset. Focal epilepsy originates in and involves networks localized to one region of the brain. Generalized epilepsy engages broader, more diffuse networks. The etiology of epilepsy can be structural, genetic, infectious, metabolic, immune, or unknown. Many generalized epilepsies have presumed genetic etiologies. The aim of this study is to compare the role of genetic testing to brain MRI as diagnostic tools for identifying the underlying causes of idiopathic (genetic) generalized epilepsy (IGE).
METHODS:  We evaluated the diagnostic yield of these two categories in children diagnosed with IGE. Data collection was completed using ICD10 codes filtered by TriNetX to select 982 individual electronic medical records (EMRs) of children in the Penn State Children\'s Hospital who received a diagnosis of IGE. The diagnosis was confirmed after reviewing the clinical history and electroencephalogram (EEG) data for each patient.
RESULTS: From this dataset, neuroimaging and genetic testing results were gathered. A retrospective chart review was done on 982 children with epilepsy, of which 143 (14.5%) met the criteria for IGE. Only 18 patients underwent genetic testing. Abnormalities that could be a potential cause for epilepsy were seen in 72.2% (13/18) of patients with IGE and abnormal genetic testing, compared to 30% (37/123) for patients who had a brain MRI with genetic testing.
CONCLUSIONS: This study suggests that genetic testing may be more useful than neuroimaging for identifying an etiological diagnosis of pediatric patients with IGE.

OBJECTIVE: Glucose transporter-1 (GLUT1) deficiency syndrome (GLUT1-DS) is a metabolic disorder due to reduced expression of GLUT1, a glucose transporter of the central nervous system. GLUT1-DS is caused by heterozygous SLC2A1 variants that mostly arise de novo. Here, we report a large family with heterogeneous phenotypes related to a novel SLC2A1 variant.
METHODS: We present clinical and genetic features of a five-generation family with GLUT1-DS.
RESULTS: The 14 (nine living) affected members had heterogeneous phenotypes, including seizures (11/14), behavioral disturbances (5/14), mild intellectual disability (3/14), and/or gait disabilities (2/14). Brain magnetic resonance imaging revealed hippocampal sclerosis in the 8-year-old proband, who also had drug-responsive absences associated with attention-deficit/hyperactivity disorder. His 52-year-old father, who had focal epilepsy since childhood, developed paraparesis related to a reversible myelitis associated with hypoglycorrhachia. Molecular study detected a novel heterozygous missense variant (c.446C>T) in exon 4 of SLC2A1 (NM: 006516.2) that cosegregated with the illness. This variant causes an amino acid replacement (p.Pro149Leu) at the fourth transmembrane segment of GLUT1, an important domain located at its catalytic core.
CONCLUSIONS: Our study illustrates the extremely heterogenous phenotypes in familial GLUT1-DS, ranging from milder classic phenotypes to more subtle neurological disorder including paraparesis. This novel SLC2A1 variant (c.446C>T) provides new insight into the pathophysiology of GLUT1-DS.

OBJECTIVE: This study aims to detect the seizure onset, in childhood absence epilepsy, as early as possible. Indeed, interfering with absence seizures with sensory simulation has been shown to be possible on the condition that the stimulation occurs soon enough after the seizure onset.
METHODS: We present four variations (two supervised, two unsupervised) of an algorithm designed to detect the onset of absence seizures from 4 scalp electrodes, and compare their performance with that of a state-of-the-art algorithm. We exploit the characteristic shape of spike-wave discharges to detect the seizure onset. Their performance is assessed on clinical electroencephalograms from 63 patients with confirmed childhood absence epilepsy.
RESULTS: The proposed approaches succeed in early detection of the seizure onset, contrary to the classical detection algorithm. Indeed, the results clearly show the superiority of the proposed methods for small delays of detection, under 750 ms from the onset.
CONCLUSIONS: The performance of the proposed unsupervised methods is equivalent to that of the supervised ones. The use of only four electrodes makes the pipeline suitable to be embedded in a wearable device.
CONCLUSIONS: The proposed pipelines perform early detection of absence seizures, which constitutes a prerequisite for a closed-loop system.

UNASSIGNED: Thyroid hormones (THs) play a crucial role in regulating various biological processes, particularly the normal development and functioning of the central nervous system (CNS). Epilepsy is a prevalent neurological disorder with multiple etiologies. Further in-depth research on the role of thyroid hormones in epilepsy is warranted.
UNASSIGNED: Genome-wide association study (GWAS) data for thyroid function and epilepsy were obtained from the ThyroidOmics Consortium and the International League Against Epilepsy (ILAE) Consortium cohort, respectively. A total of five indicators of thyroid function and ten types of epilepsy were included in the analysis. Two-sample Mendelian randomization (MR) analyses were conducted to investigate potential causal relations between thyroid functions and various epilepsies. Multiple testing correction was performed using Bonferroni correction. Heterogeneity was calculated with the Cochran\'s Q statistic test. Horizontal pleiotropy was evaluated by the MR-Egger regression intercept. The sensitivity was also examined by leave-one-out strategy.
UNASSIGNED: The findings indicated the absence of any causal relationship between abnormalities in thyroid hormone and various types of epilepsy. The study analyzed the odds ratio (OR) between thyroid hormones and various types of epilepsy in five scenarios, including free thyroxine (FT4) on focal epilepsy with hippocampal sclerosis (IVW, OR = 0.9838, p = 0.02223), hyperthyroidism on juvenile absence epilepsy (IVW, OR = 0.9952, p = 0.03777), hypothyroidism on focal epilepsy with hippocampal sclerosis (IVW, OR = 1.0075, p = 0.01951), autoimmune thyroid diseases (AITDs) on generalized epilepsy in all documented cases (weighted mode, OR = 1.0846, p = 0.0346) and on childhood absence epilepsy (IVW, OR = 1.0050, p = 0.04555). After Bonferroni correction, none of the above results showed statistically significant differences.
UNASSIGNED: This study indicates that there is no causal relationship between thyroid-related disorders and various types of epilepsy. Future research should aim to avoid potential confounding factors that might impact the study.

Childhood absence epilepsy (CAE) is a common type of idiopathic generalized epilepsy, manifesting as daily multiple absence seizures. Although seizures in most patients can be adequately controlled with first-line antiseizure medication (ASM), approximately 25 % of patients respond poorly to first-line ASM. In addition, an accurate method for predicting first-line medication responsiveness is lacking. We used the quantitative electroencephalogram (QEEG) features of patients with CAE along with machine learning to predict the therapeutic effects of valproic acid in this population. We enrolled 25 patients with CAE from multiple medical centers. Twelve patients who required additional medication for seizure control or who were shifted to another ASM and 13 patients who achieved seizure freedom with valproic acid within 6 months served as the nonresponder and responder groups. Using machine learning, we analyzed the interictal background EEG data without epileptiform discharge before ASM. The following features were analyzed: EEG frequency bands, Hjorth parameters, detrended fluctuation analysis, Higuchi fractal dimension, Lempel-Ziv complexity (LZC), Petrosian fractal dimension, and sample entropy (SE). We applied leave-one-out cross-validation with support vector machine, K-nearest neighbor (KNN), random forest, decision tree, Ada boost, and extreme gradient boosting, and we tested the performance of these models. The responders had significantly higher alpha band power and lower delta band power than the nonresponders. The Hjorth mobility, LZC, and SE values in the temporal, parietal, and occipital lobes were higher in the responders than in the nonresponders. Hjorth complexity was higher in the nonresponders than in the responders in almost all the brain regions, except for the leads FP1 and FP2. Using KNN classification with theta band power in the temporal lobe yielded optimal performance, with sensitivity of 92.31 %, specificity of 76.92 %, accuracy of 84.62 %, and area under the curve of 88.46 %.We used various EEG features along with machine learning to accurately predict whether patients with CAE would respond to valproic acid. Our method could provide valuable assistance for pediatric neurologists in selecting suitable ASM.

UNASSIGNED: This study aimed to evaluate the glymphatic system in childhood absence epilepsy (CAE) using diffusion tensor image analysis along the paravascular space (DTI-ALPS) index. Methods: Forty-two CAE patients and 50 age- and gender-matched healthy controls (HC) were included in this study. All participants underwent scanning using a Siemens 3.0 T magnetic resonance scanner, and the DTI-ALPS index was calculated. The study compared the differences of DTI-ALPS index between CAE patients and the healthy controls. Additionally, this study also assessed the relationship between the DTI-ALPS index and clinical characteristics such as age, seizure frequency, and duration of epilepsy.
UNASSIGNED: The DTI-ALPS index was lower in CAE patients compared to the healthy controls (1.45 ± 0.36 vs. 1.66 ± 0.30, p < 0.01). The DTI-ALPS index showed a negative correlation with the duration of epilepsy (r = -0.48, p < 0.01) and a positive correlation with age (r = 0.766, p < 0.01) in CAE patients. However, no significant correlation was observed between the DTI-ALPS index and seizure frequency.
UNASSIGNED: The results of this study indicate that children with CAE exhibit dysfunction in the glymphatic system of the brain, which might contribute to understanding the pathophysiological mechanism of CAE. The DTI-ALPS, as a non-invasive diagnostic marker, can be used to assess the function of the glymphatic system in CAE patients, providing promising applications in the diagnosis and research of CAE.

Childhood absence epilepsy (CAE) is a generalized pediatric epilepsy, which is generally considered to be a benign condition since most children become seizure-free before reaching adulthood. However, cognitive deficits and changes of brain morphological have been previously reported in CAE. These morphological changes, even if they might be very subtle, are not independent due to the underlying network structure and can be captured by the structural covariance network (SCN). In this study, SCNs were used to quantify the structural brain network for children with CAE as well as controls. Seventeen children with CAE (6-12y) and fifteen controls (6-12y) were included. To estimate the SCN, T1-weighted images were acquired and parcellated into 68 cortical regions. Graph measures characterizing the core network architecture, i.e. the assortativity and rich-club coefficient, were calculated for all individuals. Multivariable linear regression models, including age and sex as covariates, were used to assess differences between children with CAE and controls. Additionally, potential relations between the core network and cognitive performance was investigated. A lower assortativity (i.e. less efficiently organized core network organization) was found for children with CAE compared to controls. Moreover, better cognitive performance was found to relate to stronger assortative mixing pattern (i.e. more efficient core network structure). Rich-club coefficients did not differ between groups, nor relate to cognitions. The core network organization of the SCN in children with CAE tend to be less efficient organized compared to controls, and relates to cognitive performance, and therefore this study provides novel insights into the SCN organization in relation to CAE and cognition.

BACKGROUND: We aimed to find the proportion of attention-deficit/hyperactivity disorder (ADHD) among children with childhood absence epilepsy (CAE) and to describe their electroclinical features.
METHODS: Video electroencephalography (EEG) was performed on 47 children who fulfilled International League Against Epilepsy criteria for CAE. These children were also assessed for the presence of ADHD.
RESULTS: Of the 47 children, 27 (57%) met criteria for the diagnosis of ADHD. Majority (74%) of them had inattentive type of ADHD. Age at onset of absences ranged from three to 12 years (mean 7.2 ± 2.47). We analyzed 219 seizures (154 electroclinical and 65 electrographic). The average seizure duration was 7.1 seconds (range 1 to 38 [S.D. 5.81]). Of the 154 clinical absences, ictal discharges were less than or equal to two seconds in nine of 154 (5.8%); greater than two to less than or equal to four seconds in 33 of 154 (21.4%), and longer than 20 seconds in 11 of 154 (7%). The longest duration of ictal discharge recorded was 38 seconds, and the shortest duration was one second. The onset of ictal discharge had a \"lead in\" focus in 81% (177 of 219).
CONCLUSIONS: The proportion of ADHD among children with CAE is high. A \"lead in\" focus of the generalized ictal discharges was observed frequently, lending support to the theory that the origin of seizure discharges in CAE is indeed cortical. The shortest ictal discharge recorded was one second.