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Refining neurophysiological biomarkers of epilepsy using deep learning to guide pediatric epilepsy surgery

Award Number: K23NS128318
ORGANIZATION: NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP
PERIOD OF PERFORMANCE START DATE: 04/01/2023
PERIOD OF PERFORMANCE END DATE: 03/31/2028

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Refining neurophysiological biomarkers of epilepsy using deep learning to guide pediatric epilepsy surgery - 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 clinically useful spatial biomarker of the epileptic brain to guide epilepsy surgery, which Dr. Nariai plans to pursue as an R01 project.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $242,055 )
2026	2026	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Extramural Research Programs in the Neurosciences and Neurological Disorders	000	4	4/20/2026	NON-COMPETING CONTINUATION	$242,055
														Subtotal = $242,055

Issue Date FY: 2025 ( Subtotal = $242,055 )
2025	2025	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Extramural Research Programs in the Neurosciences and Neurological Disorders	000	3	4/18/2025	NON-COMPETING CONTINUATION	$242,055
														Subtotal = $242,055

Issue Date FY: 2024 ( Subtotal = $242,055 )
2024	2024	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Extramural Research Programs in the Neurosciences and Neurological Disorders	000	2	4/3/2024	NON-COMPETING CONTINUATION	$242,055
														Subtotal = $242,055

Issue Date FY: 2023 ( Subtotal = $242,055 )
2023	2023	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Extramural Research Programs in the Neurosciences and Neurological Disorders	001	1	3/8/2023	NEW	$37,395
2023	2023	UNIVERSITY OF CALIFORNIA, LOS ANGELES	10889 WILSHIRE BLVD STE 700	LOS ANGELES	CA	90024	LOS ANGELES	USA	Extramural Research Programs in the Neurosciences and Neurological Disorders	000	1	2/22/2023	NEW	$204,660
														Subtotal = $242,055

Grand Total All Awards = $968,220

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Refining neurophysiological biomarkers of epilepsy using deep learning to guide pediatric epilepsy surgery

Award Number: K23NS128318

ORGANIZATION: NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: TRAINING/TRAINEESHIP

PERIOD OF PERFORMANCE START DATE: 04/01/2023

PERIOD OF PERFORMANCE END DATE: 03/31/2028

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