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

利用深度学习完善癫痫的神经生理学生物标志物来指导小儿癫痫手术

• 批准号: 10664790
• 负责人: Hiroki Nariai
• 金额: $ 24.21万
• 依托单位: UNIVERSITY OF CALIFORNIA LOS ANGELES
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-04-01 至 2028-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10664790
• 关键词: Affect; Algorithms; Area; Behavioral; Biological; Biological Markers; Brain; Brain region; Characteristics; Child; Childhood; Chronic; Clinical; Clinical Research; Clinical Trials; Data; Electroencephalography; Epilepsy; Evaluation; Excision; Freedom; Frequencies; Goals; Hand; High Frequency Oscillation; Image; Joints; K-Series Research Career Programs; Knowledge; Label; Language; Link; Maps; Mentors; Mentorship; Methods; Microelectrodes; Modeling; Morbidity - disease rate; Morphology; Neocortex; Neurons; Operative Surgical Procedures; Outcome; Partial Epilepsies; Pathologic; Pharmaceutical Preparations; Physicians; Physiological; Postoperative Period; Property; Prospective cohort; Research; Research Personnel; Resistance; Retrospective cohort; Scientist; Seizures; Signal Transduction; Site; Synaptic Potentials; Testing; Time; Training; Vision; Work; biomarker identification; brain abnormalities; brain tissue; childhood epilepsy; cohort; cortex mapping; deep learning; deep learning algorithm; deep learning model; dexterity; feasibility testing; large datasets; mortality; neocortical; neurophysiology; neuroregulation; novel; postsynaptic; predictive modeling; preservation; secondary analysis; signal processing; skill acquisition; skills; statistics; theories; tool; two-dimensional

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.

项目摘要/摘要 Hroki Nariai博士是一位儿科癫痫专家/临床神经生理学家，他的长期目标是成为领先的 儿科癫痫内科科学家，使用关键生物标记物有效治疗儿童癫痫和 降低他们的死亡率和发病率。在这个项目中，Nariai博士建议研究耐药灶 通过整合计算脑电(EEG)分析、深度学习和 研究和验证高频振荡(HFO)的高级统计数据--一个很有前途的空间 癫痫脑的生物标志物。超过三分之一的癫痫儿童对药物和 因此是癫痫手术的潜在候选者。要实现术后癫痫的自由，必须 移除或干扰致痫区域(EZ)，EZ被定义为大脑产生癫痫所必需的区域 癫痫发作，在保留口才皮层(EC)的同时，被定义为控制基本功能的大脑区域。 因此，识别准确定位和区分EZ和EC的生物标志物将是开创性的。HFO 通过颅内脑电记录为高频神经元活动的短脉冲，常见于 艾兹。然而，主要的挑战是，由健康的脑组织产生的生理性HFO使 高脂血症的临床解释。因此，迫切需要区分病理性和非病理性 生理性HFO。Nariai博士假设，基于深度学习的算法可以区分病理性 以及基于与特定生物学机制相关联的微妙形态特征的生理性HFO。 通过这个K23职业发展奖，Nariai博士建议实现以下培训目标：(1) 掌握高级计算脑电分析的技能，以实现对大量HFO的定制量化 数据集，(2)了解深度学习理论及其在脑电信号处理中的应用技巧 实现对HFO的形态评估，以及(3)熟练掌握临床高级统计知识 在临床试验中验证预测模型和获取知识的研究。在Leading的共同指导下 由加州大学洛杉矶分校的小Jerome Engel博士领导的研究人员，Nariai博士将在 回顾队列定义以EZ(EHFO)表达的HFO代表病理性HFO。此外，HFO 在EC中表达的(EcHFOs)将被定义为代表生理性HFO。训练好的分类器将被分析 以获得eHFO和ecHFO的计算定义。同时展示了实时HFO 分析在前瞻性队列中是可行的，eHFO和ecHFO将被分析以证明HFO可以本地化 并将EZ与EC区分开来。Nariai博士已经展示了支持他的提议的可行性的初步结果 接近。完成拟议的目标将在将HFO用于临床方面取得重大进展 癫痫脑的有用空间生物标记物，用于指导癫痫手术，Nariai博士计划将其作为 R01项目。