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Neurology
Network-based biomarkers in pediatric drug-resistant epilepsy
Joseph Lim, Jaeso Cho, Hunmin Kim
Clin Exp Pediatr. 2026;69(8):636-638.   Published online July 24, 2026
Conventional preoperative evaluation in pediatric drug-resistant epilepsy relies on clinical surrogates that quantify seizure exposure but not its impact on brain networks. A newly developed diffusion-weighted imaging connectome biomarker, anchored to intracranial electroencephalography-confirmed seizure onset zones and resolved across 6 neurocognitive domains, addresses this gap. This individualized approach classifies cognitive impairment with high accuracy and may offer a more precise, clinically interpretable tool to inform surgical timing.
Original Article
Neurology
Patient-independent automated pediatric seizure monitoring based on expert-labeled electrographic ictal data as core reference knowledge
Yoon Gi Chung, Jaeso Cho, Anna Cho, Hunmin Kim, Byung Chan Lim
Clin Exp Pediatr. 2026;69(8):665-675.   Published online July 14, 2026
Question: Can deep learning models trained on expert-verified electroencephalography segments reliably detect seizures and be applied to single-channel wearable monitoring?
Finding: This technique demonstrated 80.1%–100% sensitivity and low false-alarm rates. On 1,604 expert-labeled segments, the model outperformed previous studies in terms of latency and accuracy across unseen datasets.
Meaning: Expert-labeled data are essential for achieving universal seizure detection. This channel-specific approach enabled efficient and continuous monitoring using wearable devices.
Clinical Note
Neurology
Expanding association between BICD2 variants and brain malformations and associated lissencephaly
Jaeso Cho, Haeryung Kim, Seoungbok Lee, Jihoon G Yoon, HyeJin Kim, Minhye Kim, Seoyun Jang, Woojoong Kim, Soo Yeon Kim, Jong Hee Chae
Clin Exp Pediatr. 2024;67(1):54-56.   Published online December 21, 2023


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