# Refining neurophysiological biomarkers of epilepsy using deep learning to guide pediatric epilepsy surgery > **NIH NIH K23** · UNIVERSITY OF CALIFORNIA LOS ANGELES · 2024 · $242,055 ## Abstract PROJECT SUMMARY/ABSTRACT Dr. Hiroki Nariai is a pediatric epileptologist/clinical neurophysiologist whose long-term goal is to be a leading physician-scientist in pediatric epilepsy, using key biomarkers to effectively treat children with epilepsy and reduce their mortality and morbidity. In this project, Dr. Nariai proposes to study medication-resistant focal epilepsy in children by integrating computational electroencephalogram (EEG) analysis, deep learning, and advanced statistics to investigate and validate high-frequency oscillations (HFOs)—a promising spatial biomarker of the epileptic brain. More than one-third of children with epilepsy are resistant to medications and are therefore potential candidates for epilepsy surgery. To achieve postoperative seizure freedom, one must remove or disrupt the epileptogenic zone (EZ), defined as the brain area that is indispensable for generating seizures, while preserving the eloquent cortex (EC), defined as the brain area that controls essential functions. Thus, identifying biomarkers that accurately localize and discriminate EZ from EC will be groundbreaking. HFOs are recorded via intracranial EEG as short bursts of high-frequency neuronal activity and are often observed in EZ. However, the major challenge is that physiological HFOs generated by healthy brain tissue complicate the clinical interpretation of HFOs. Therefore, there is a critical need to distinguish between pathological and physiological HFOs. Dr. Nariai hypothesizes that deep learning-based algorithms can distinguish pathological and physiological HFOs based on subtle morphological features linked to specific biological mechanisms. Through this K23 career development award, Dr. Nariai proposes to accomplish the following training goals: (1) acquire skills in an advanced computational EEG analysis to enable customized quantification of HFOs in a large dataset, (2) gain knowledge of the theory of deep learning and skills in its application in EEG signal processing to enable morphological assessment of HFOs, and (3) develop proficiency in advanced statistics in clinical research to validate prediction models and gain knowledge in clinical trials. Under the joint mentorship of leading researchers led by Dr. Jerome Engel, Jr., at UCLA, Dr. Nariai will build deep learning-based models in a large retrospective cohort to define HFOs expressed in EZ (eHFOs) to represent pathological HFOs. In addition, HFOs expressed in EC (ecHFOs) will be defined to represent physiological HFOs. The trained classifier will be analyzed to obtain the computational definition of eHFOs and ecHFOs. Along with demonstrating that real-time HFO analysis is feasible in a prospective cohort, eHFOs and ecHFOs will be analyzed to prove that HFOs can localize and discriminate EZ from EC. Dr. Nariai has shown preliminary results supporting the feasibility of his proposed approach. Completing the proposed goals will provide significant progress toward utilizing HFOs as a clin... ## Key facts - **NIH application ID:** 10793591 - **Project number:** 5K23NS128318-02 - **Recipient organization:** UNIVERSITY OF CALIFORNIA LOS ANGELES - **Principal Investigator:** Hiroki Nariai - **Activity code:** K23 (R01, R21, SBIR, etc.) - **Funding institute:** NIH - **Fiscal year:** 2024 - **Award amount:** $242,055 - **Award type:** 5 - **Project period:** 2023-04-01 → 2028-03-31 ## Primary source NIH RePORTER: https://reporter.nih.gov/project-details/10793591 ## Citation > US National Institutes of Health, RePORTER application 10793591, Refining neurophysiological biomarkers of epilepsy using deep learning to guide pediatric epilepsy surgery (5K23NS128318-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10793591. Licensed CC0. --- *[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*