WooJoong Kim

The clinical and genetic characteristics of SYNGAP1 mutations in Korean pediatric patients are not well understood. We retrospectively analyzed 13 individuals with SYNGAP1 mutations from a longitudinal aspect. Clinical data, genetic profiles, and electroencephalography (EEG) patterns were examined. Genotypic analyses included gene panels and whole‐exome sequencing. All patients exhibited global developmental delay from early infancy, with motor development eventually reaching independent ambulation by 3 years of age. Language developmental delay varied significantly from nonverbal to simple sentences, which plateaued in all patients. Patients with the best language outcomes typically managed 2–3‐word sentences, corresponding to a developmental age of 2–3 years. Epilepsy developed in 77% of patients, with onset consistently following developmental delays at a median age of 31 months. Longitudinal EEG data revealed a shift from occipital to frontal epileptiform discharges with age, suggesting a correlation with synaptic maturation. These findings suggest that the critical developmental plateau occurs between the ages of 2 and 5 years and is potentially influenced by epilepsy. By analyzing longitudinal data, our study contributes to a deeper understanding of SYNGAP1 ‐related DEE, provides potential EEG biomarkers, and underlines the importance of early diagnosis and intervention to address this complex disorder.

Objective One of the clinical hallmarks of tuberous sclerosis complex (TSC) is radiologically identified cortical tubers, which are present in most patients. Intractable epilepsy may require surgery, often involving invasive diagnostic procedures such as intracranial electroencephalography (EEG). Identifying the location of the dominant tuber responsible for generating epileptic activities is a critical issue. However, the link between cortical tubers and epileptogenesis is poorly understood. Given this, we hypothesized that tuber voxel intensity may be an indicator of the dominant epileptogenic tuber. Also, via tuber segmentation based on deep learning, we explored whether an automatic quantification of the tuber burden is feasible. Methods We annotated tubers from structural magnetic resonance images across 29 TSC subjects, summarized tuber statistics in eight brain lobes, and determined suspected epileptogenic lobes from the same group using EEG monitoring data. Then, logistic regression analyses were performed to demonstrate the linkage between the statistics of cortical tuber and the epileptogenic zones. Furthermore, we tested the ability of a neural network to identify and quantify tuber burden. Results Logistic regression analyses showed that the volume and count of tubers per lobe, not the mean or variance of tuber voxel intensity, were positively correlated with electrophysiological data. In 47.6% of subjects, the lobe with the largest tuber volume concurred with the epileptic brain activity. A neural network model on the test dataset showed a sensitivity of .83 for localizing individual tubers. The predicted masks from the model correlated highly with the neurologist labels, and thus may be a useful tool for determining tuber burden and searching for the epileptogenic zone. Significance We have proven the feasibility of an automatic segmentation of tubers and a derivation of tuber burden across brain lobes. Our method may provide crucial insights regarding the treatment and outcome of TSC patients.