Epileptic biomarkers and big data: identifying brain regions to resect in patients with refractory epilepsy

 描述（由申请人提供） 在高分辨率颅内脑电图（iEEG）记录中发现了一种新的电生物标志物，称为高频振荡（HFO）。研究表明，这种生物标志物在识别癫痫发作网络和改善难治性癫痫患者的手术结果方面具有很大的前景。然而，HFO向临床实践的转化受到许多因素的阻碍，例如HFO检测率的空间、时间和患者间变化、假阳性和假阴性检测以及显著的背景噪声。需要使用从许多患者获得的大量HFO的大数据方法来量化这些影响并允许HFO的临床使用。该项目详细介绍了一个计划，其中候选人在大规模高能核物理数据集中量化测量和检测偏差的经验将与多学科导师团队相结合，以解决这个问题。计算神经科学，大数据网络分析和转化神经工程研究的培训相结合将是解决这个问题的关键，并为候选人提供职业轨迹。该提案的具体目的是解决三个特定的混杂因素：1）假阴性HFO检测率，2）非癫痫引起的HFO特征变化，3）警戒状态对HFO的影响。这些目标中的每一个都涉及新颖的大数据方法和/或可推广到其他情况的应用：1）使用组合的实验/模拟数据方法来估计假阳性检测率，2）对数据点的分布进行聚类和分类，而不是对数据点的分布进行聚类和分类。 数据点直接，以及3）用于评估分布之间的有意义（而非统计）差异的一般消歧统计。 申请人的职业目标是成为一名颅内EEG数据分析和建模的学术研究员，重点是转化癫痫和睡眠生理学研究。随着临床EEG分辨率的快速发展，对此类研究专业知识的需求已经很大。该基金将提供教学课程，正式的研究和方法培训，以及来自专家导师团队的职业指导。这三位导师的任命涵盖神经学、麻醉学、数学、统计学、生物医学工程、电气工程和计算机科学。候选人还将建立和指导一个研究团队，并建立外部合作。 密歇根大学是一所首屈一指的研究型大学，在生物医学和物理科学、工程、转化和学术研究以及高级研究计算方面拥有强大的课程和培训机会。这项建议广泛利用了大学的大型计算机集群。导师团队和外部合作者将为候选人提供来自150多名患者的预先记录的去识别数据，估计有超过4000万HFO。环境和导师团队将为候选人提供培训，设施和数据，以成功完成拟议的目标。

 DESCRIPTION (provided by applicant) A new electrical biomarker has been identified in high resolution, intracranial electroencephalogram (iEEG) recordings, called a high frequency oscillation (HFO). Studies have suggested this biomarker has great promise to identify seizure networks and improve surgical outcomes for patients with refractory epilepsy. However, translation of HFOs to clinical practice is hampered by many factors such as spatial, temporal and inter-patient variation in HFO detection rates, false positive and false negative detections, and significant background noise. Big data approaches using large numbers of HFOs acquired from many patients are needed to quantify these effects and allow clinical usage of HFOs. This project details a plan in which the candidate's experience quantifying measurement and detection bias in massive high energy nuclear physics datasets will be combined with a multidisciplinary mentor team to address this problem. The combination of training in computational neuroscience, big data network analysis, and translational neural engineering research will be critical to approach this problem and provide a career trajectory for the candidate. The specific aims of this proposal address three specific confounding factors: 1) the false negative HFO detection rate, 2) variations in HFO features not due to epilepsy, and 3) effects of the state of vigilance on HFOs. Each of these aims involve novel big data methods and/or applications generalizable to other situations: 1) estimating false positive detection rates using a combined experimental/simulated data approach, 2) clustering and classification of distributions of data points, rather than of the data points directly, and 3) a general disambiguation statistic to assess meaningful (rather than statistical) difference between distributions. The applicant's career goal is to become an academic researcher in the analysis and modeling of intracranial EEG data with a focus on translational epilepsy and sleep physiology research. With the rapid advancement in the resolution of clinical EEG, there is already a strong need for this type of research expertise. Thi grant will provide didactic coursework, formal research and methods training, and career guidance from an expert mentor team. The three mentors have appointments spanning Neurology, Anesthesiology, Mathematics, Statistics, Biomedical Engineering, and Electrical Engineering and Computer Science. The candidate will also build and mentor a research team and establish external collaborations. The University of Michigan is a premier research university with strong programs and training opportunities in biomedical and physical sciences, engineering, translational and academic research, and advanced research computing. This proposal makes extensive use of the University's large computer cluster. The mentor team and an external collaborator will provide candidate access to prerecorded, deidentified data from over 150 patients, estimated to have over 40 million HFOs. The environment and mentor team will provide the training, facilities, and data for the candidate to successfully complete the proposed goals.