Investigation of Stereotyped High-Frequency Oscillations with Computational Intelligence for the Prediction of Seizure Onset Zone in Epilepsy

利用计算智能研究刻板高频振荡以预测癫痫发作发作区

基本信息

批准号：
10983614
负责人：
Nuri Firat Ince
金额：
$ 45.92万
依托单位：
MAYO CLINIC ROCHESTER
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-07-15 至 2025-04-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10983614
关键词：
Investigation Stereotyped Frequency Oscillations Computational

项目摘要

PROJECT SUMMARY Neurosurgical therapy of refractory epilepsy requires accurate localization of seizure onset zone (SOZ). In clinical practice, intracranial EEG (iEEG) is recorded in the epilepsy monitoring unit (EMU) over many days where multiple seizures are recorded to provide information to localize the SOZ. The prolonged monitoring in the EMU adds to the risk of complications and can include intracranial bleeding and potentially death. Recently, high frequency oscillations (HFO) of iEEG between 80 to 500 Hz are highly valued as a promising clinical biomarker for epilepsy. HFOs are believed to be clinically significant, and thus could be used for SOZ localization. However, HFOs can also be recorded from normal and non-epileptic cerebral structures. When defined only by rate or frequency, pathological HFOs are indistinguishable from physiological ones, which limit their application in epilepsy pre-surgical planning. In this proposal, to the best of our knowledge, we show of a recurrent waveform pattern that distinguishes pathological HFOs from physiological ones. In particular, we observed that the SOZ generates repeatedly a set of stereotyped HFO waveforms whereas the HFOs from nonepileptic regions were irregular in their waveform morphology. Based on these observations, using computational tools built on recent advances in sparse coding and unsupervised machine learning techniques, we propose to detect these stereotyped recurrent HFO waveform patterns directly from the continuous iEEG data of adult and pediatric patients and test their prognostic value by correlating the spatial distribution of detected events to clinical findings such as SOZ, resection zone and seizure freedom. We hypothesize that accurate detection of pathologic HFOs in brief iEEG recordings can identify the SOZ and eliminate the necessity of prolonged EMU monitoring and reduce the associated risks. With these motivations, in this project an interdisciplinary team composed of biomedical engineers, epileptologists and neurosurgeons will work together to develop and test novel computational tools to detect stereotyped HFOs and its subtypes in large iEEG datasets recorded with clinical electrodes. Developed algorithms and iEEG data will be shared with the research community to contribute to the reproducible research and help other research groups to develop novel methods. The results of this study will be essential for achieving our group's long term goal of developing an online neural signal processing system for the rapid and accurate identification of SOZ with brief invasive recording.

项目概要难治性癫痫的神经外科治疗需要准确定位癫痫发作区（SOZ）。在临床上在实践中，颅内脑电图 (iEEG) 会在癫痫监测单元 (EMU) 中记录很多天，其中记录多次癫痫发作以提供定位 SOZ 的信息。动车组内的长时间监控增加了并发症的风险，可能包括颅内出血和潜在的死亡。近期，高 80 至 500 Hz 之间的 iEEG 频率振荡 (HFO) 作为一种有前途的临床生物标志物受到高度重视用于癫痫。 HFO 被认为具有临床意义，因此可用于 SOZ 定位。然而， HFO 也可以从正常和非癫痫的大脑结构中记录。当仅由速率或由于频率高，病理性 HFO 与生理性 HFO 无法区分，这限制了它们在癫痫术前计划。在这个提案中，据我们所知，我们展示了一个循环波形区分病理性 HFO 和生理性 HFO 的模式。特别是，我们观察到 SOZ 重复生成一组定型的 HFO 波形，而来自非癫痫区域的 HFO 则为其波形形态不规则。基于这些观察，使用基于最近建立的计算工具随着稀疏编码和无监督机器学习技术的进步，我们建议检测这些直接来自成人和儿童连续 iEEG 数据的定型复发 HFO 波形模式患者并通过将检测到的事件的空间分布与临床结果相关联来测试其预后价值例如 SOZ、切除区和无癫痫发作。我们假设病理性 HFO 的准确检测简而言之，iEEG 记录可以识别 SOZ 并消除长时间 EMU 监测的必要性减少相关风险。出于这些动机，在这个项目中，一个由以下人员组成的跨学科团队生物医学工程师、癫痫学家和神经外科医生将共同开发和测试新型药物用于检测临床记录的大型 iEEG 数据集中定型 HFO 及其亚型的计算工具电极。开发的算法和 iEEG 数据将与研究界共享，为可重复的研究并帮助其他研究小组开发新方法。这项研究的结果将对于实现我们小组开发在线神经信号处理系统的长期目标至关重要通过简短的侵入性记录快速准确地识别 SOZ。