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.

项目总结/摘要 博士Hiroki Nariai是一名儿科癫痫学家/临床神经生理学家，其长期目标是成为一名领先的 儿科癫痫的医生-科学家，使用关键生物标志物有效治疗癫痫儿童， 降低其死亡率和发病率。在这个项目中，成合博士建议研究耐药病灶 通过整合计算脑电图（EEG）分析、深度学习和 先进的统计调查和验证高频振荡（HFO）-一个有前途的空间 癫痫大脑的生物标志物。超过三分之一的癫痫儿童对药物有抗药性， 因此是癫痫手术的潜在候选者。为了实现术后无癫痫发作，必须 消除或破坏癫痫区（EZ），定义为大脑区域，是必不可少的产生 癫痫发作，同时保留功能皮层（EC），定义为控制基本功能的大脑区域。 因此，确定生物标志物，准确地定位和区分EZ从EC将是开创性的。HFOs 通过颅内EEG记录为高频神经元活动的短脉冲，并且经常在 EZ.然而，主要的挑战是由健康脑组织产生的生理HFO使脑损伤复杂化。 HFO的临床解释。因此，迫切需要区分病理性和 生理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机制相关的细微形态特征的生理HFO。 通过K23职业发展奖，成合博士提出了以下培训目标：（1） 获得先进的计算EEG分析技能，以实现大型HFO的定制量化 数据集，（2）获得深度学习理论的知识及其在脑电信号处理中的应用技能 使HFO的形态评估，（3）发展在临床先进的统计学熟练 研究以验证预测模型并在临床试验中获得知识。在领导的共同指导下， 由小杰罗姆恩格尔博士领导的研究人员，在加州大学洛杉矶分校，Nariai博士将在一个大型的 回顾性队列定义EZ中表达的HFO（eHFO），以代表病理性HFO。此外，氢氟烯烃 以EC（ecHFOs）表示的术语将被定义为代表生理HFOs。将对训练好的分类器进行分析 以获得eHFO和ecHFO的计算定义。沿着的是， 分析在前瞻性队列中是可行的，将对eHFO和ecHFO进行分析，以证明HFO可以定位 并将EZ与EC区分开来。Nariai博士已经展示了初步结果，支持他提出的可行性。 approach.完成拟议的目标将为利用HFO作为临床治疗提供重大进展。 癫痫大脑的有用的空间生物标志物，以指导癫痫手术，Nariai博士计划将其作为 R 01项目。