Electrophysiological Source Imaging of Partial Epilepsy

项目摘要 该研究计划的长期目标是开发和建立一种新的电生理来源 定位和成像致痫脑组织的成像技术辅助局灶性(部分)手术前计划 癫痫。癫痫是一种常见的神经系统疾病，在美国有340多万患者，其中70人患有癫痫 在全球范围内达到100万。标准的临床程序在很大程度上依赖于将颅内脑电(IEEG)植入 大脑以确定癫痫发作区域，以帮助定位致痫区域(EZ)，尽管有限 IEEG电极的覆盖范围和多天程序的侵入性。有临床上的需要 开发一种基于电生理记录的非侵入性神经成像方法，可以可靠地成像 并从相关癫痫生物标志物中圈定EZ。在这个研究项目中，我们建议建立一个小说 无监督的机器学习框架，将能够处理无标记、连续和长期的 脑电记录局灶性癫痫患者检测高频振荡(HFO)，自动识别 病理性HFO(PHFOS，即骑在尖峰上的HFO)，并定位和成像来自 已鉴定的PHFOS。建议的技术将在120多名局灶性癫痫患者中得到严格验证。 与iEEG记录和手术切除结果的临床结果相抵触。我们的具体目标是：目标1。 一种识别病理性HFO的无监督机器学习新技术的开发和评价 从连续的脑电记录中。这一目标将建立一种新的无监督机器学习方法 结合数据驱动特征，自动识别源于致痫活动的PHFO 学习方法。目的2.开发和验证一种新的脑张量分解成像方法 用于HFO源成像。我们将开发一种新的源成像框架来成像癫痫源 时间域、谱域和空间域。这一目标将建立一种新的数据驱动源成像方法，用于 从头皮记录的PHFOS准确定位和定位EZ。目标3：头皮检测的PHFOS的有效性 通过iEEG识别的PHFOS，结合局灶性癫痫患者的临床表现。我们将检验这一假设 头皮-脑电识别的PHFO事件反映了iEEG识别的PHFO的基本特征，两者都是 表明EZ由临床iEEG确定，并经手术切除结果证实。这一目标将 建立头皮识别的PHFOS与iEEG识别的PHFOS之间的关系 致痫网络。拟议研究的成功完成将建立一种新的无监督的 用机器学习技术从连续和长期头皮脑电中检测和识别病理性HFO 记录，并无创和准确地定位和成像潜在的致痫区域。这个 这种新技术的建立有望显著改善局部药物的临床管理。 难治性癫痫，目前是有限的，使许多患者和医疗保健系统受益。

Project Summary The long-term goal of the research program is to develop and establish a novel electrophysiological source imaging technology to localize and image epileptogenic brain tissues aiding pre-surgical planning in focal (partial) epilepsy. Epilepsy is a common neurological disease impacting more than 3.4 million patients in the US and 70 million globally. The standard clinical routine heavily relies on using intracranial EEG (iEEG) implanted into the brain to determine seizure onset zone, to aid in the localization of epileptogenic zone (EZ), despite the limited coverage of iEEG electrodes and invasive nature of the multiple-day procedure. There is a clinical need to develop a noninvasive neuroimaging approach based on electrophysiological recordings that can reliably image and delineate the EZ from relevant epilepsy biomarkers. In this research project, we propose to establish a novel unsupervised machine learning framework that will be able to process unmarked, continuous, and long-term EEG recordings of focal epilepsy patients to detect high frequency oscillations (HFOs), automatically identify the pathological HFOs (pHFOs, i.e., HFOs riding on spikes), and localize and image epileptogenic brain activity from the identified pHFOs. The proposed techniques will be rigorously validated in over 120 focal epilepsy patients against clinical findings from iEEG recordings and surgical resection outcomes. Our specific aims are: Aim 1. Development and evaluation of a novel unsupervised machine learning technique to identify pathological HFOs from continuous EEG recordings. This aim will establish a novel unsupervised machine learning approach for automatically identifying pHFOs originated from epileptogenic activity, by incorporating data-driven feature learning methods. Aim 2. Development and validation of a novel brain tensor decomposition imaging approach for HFO source imaging. We will develop a novel source imaging framework imaging epileptic sources in temporal, spectral, and spatial domains. This aim will establish a novel data-driven source imaging approach for accurate mapping and localization of EZ from scalp recorded pHFOs. Aim 3: Validation of scalp-detected pHFOs by iEEG-identified pHFOs, with clinical findings of EZ in focal epilepsy patients. We will test the hypothesis that the scalp-EEG identified pHFO events reflect the essential features of iEEG-identified pHFOs, and that both are indicative of the EZ determined from clinical iEEG and confirmed by surgical resection outcome. This aim will establish the relationship between scalp-identified pHFOs with iEEG-identified pHFOs and the underlying epileptogenic networks. The successful completion of the proposed research will establish a novel unsupervised machine learning technology to detect and identify pathological HFOs from continuous and long-term scalp EEG recordings, and localize and image the underlying epileptogenic zone noninvasively and accurately. The establishment of such novel technology promises to significantly improve the clinical management of focal drug- resistant epilepsy, which is currently limited, benefiting numerous patients and the healthcare system.