Combining Brain Connectivity and Excitability to Plan Epilepsy Surgery in Children: A New Approach to Augment Presurgical Intracranial Electroencephalography

结合大脑连接性和兴奋性来规划儿童癫痫手术：增强术前颅内脑电图的新方法

• 批准号: 10592653
• 负责人: Eleonora Tamilia
• 金额: $ 8.85万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10592653
• 关键词: Address; Area; Biological Markers; Boston; Brain; Brain region; Characteristics; Child; Child Care; Clinical; Cognition; Communities; Complement; Complex; Computer Assisted; Coupling; Data; Disease; Electroencephalography; Electrophysiology (science); Epilepsy; Evaluation; Excision; FarGo; Freedom; Frequencies; Goals; Human; Information Networks; Intractable Epilepsy; Life; Link; Logistic Regressions; Measures; Methodology; Methods; Mission; Monitor; National Institute of Neurological Disorders and Stroke; Operative Surgical Procedures; Outcome; Pathologic; Patients; Pattern; Pediatric Hospitals; Performance; Phase; Property; Public Health; Quality of life; ROC Curve; Reader; Reading; Research; Retrospective Studies; Risk; Seizures; Signal Transduction; Techniques; Testing; Tissues; Visual; analytical method; clinical care; disability; epileptiform; experience; graph theory; improved; neuroimaging; neurosurgery; novel; novel strategies; predictive modeling; prevent; signal processing; success; surgery outcome

Project Summary For children with drug-resistant epilepsy (DRE), epilepsy surgery is the best treatment to stop seizures and prevent a life of disability. Crucial to the success of surgery is the ability to identify the area of the brain that is responsible for generating seizures, called epileptogenic zone (EZ). The best way to estimate the EZ is by recording the brain activity invasively via intracranial electroencephalography (icEEG), aiming to capture seizures and locate the area that generates them. Yet, one of three patients continue to have seizures after surgery. This suggests that there is still an unmet need for new methods that go beyond traditional icEEG interpretation and offer novel information on underlying epileptogenicity in patients undergoing epilepsy surgery evaluation. To address this need, we propose a novel approach to analyze icEEG that takes advantage of new “invisible” signal characteristics, which can inform us on epileptogenicity, albeit not visible to the human reader. Epileptogenicity is a very complex brain property that depends on the interplay between altered excitability and connectivity. Recent evidence suggests that, to treat focal DRE, we must localize pathological regions (depicted by altered excitability) and also appreciate how they interact within the epileptogenic network (identifying altered connections). In this application, we propose to develop a novel twofold approach to optimize the interpretation of icEEG, which quantifies and integrates both local brain excitability (via phase-amplitude coupling, PAC) and functional connectivity (FC), using “silent” icEEG epochs (i.e. without frank epileptiform patterns), in order to define novel measures of “interconnected-excitability” (which we will call Network-PAC). Our main goal is to develop a new computer-aided approach to boost icEEG reading and improve surgical planning in children with DRE, without requiring the recording of seizures or even the identification of frank interictal epileptiform activity. We hypothesize that the EZ is characterized not only by a high ‘local excitability level’ (strong PAC) but also by strong connections with other ‘excitable’ tissue, thus generating a hyper-excitable network that is responsible for generating seizures. We will pursue two specific aims: (1) Identify regions of high inter-connected excitability and assess their ability to define the seizure onset zone (SOZ); (2) Develop a predictive model that integrates patient- specific icEEG information about both local PAC and functional networks (independently from the presence of frank epileptiform patterns) to predict surgical outcome following a resection. This application will combine the use of cutting-edge electrophysiological and signal processing concepts (cross-frequency coupling, connectivity, and graph theory) together with extensive neuroimaging and clinical experience with children. Our research will present to the epilepsy community a new approach to estimate the EZ before epilepsy surgery, which will go beyond the visual identification of seizures or spikes on the EEG. This can significantly impact the clinical care of children with DRE in the long-term, by boosting the pre-surgical interpretation of icEEG and reducing the need for extended invasive monitoring - which is often needed to capture spontaneous seizures.

项目概要 对于患有耐药性癫痫（DRE）的儿童，癫痫手术是阻止癫痫发作和治疗的最佳治疗方法。 手术成功的关键是能够识别大脑的哪个区域。 负责产生癫痫发作的区域，称为致癫痫区 (EZ)。估计 EZ 的最佳方法是 通过颅内脑电图（icEEG）侵入性记录大脑活动，旨在捕获 然而，三名患者中的一名在之后仍然出现癫痫发作。 这表明对传统 icEEG 之外的新方法的需求仍然未得到满足。 解释并提供有关接受癫痫手术的患者潜在致癫痫性的新信息 为了满足这一需求，我们提出了一种利用新方法来分析 icEEG 的新方法。 “看不见的”信号特征，可以告诉我们致癫痫性，但人类读者看不到。 致癫痫性是一种非常复杂的大脑特性，取决于兴奋性改变和癫痫发作之间的相互作用。 最近的证据表明，为了治疗局灶性 DRE，我们必须定位病理区域（如图所示） 通过改变兴奋性）并了解它们如何在致癫痫网络中相互作用（识别 在此应用中，我们建议开发一种新颖的双重方法来优化解释。 icEEG，它量化并整合局部大脑兴奋性（通过相位幅度耦合，PAC）和 功能连接（FC），使用“沉默”的 icEEG 纪元（即没有明显的癫痫样模式），以便 定义“互联兴奋性”的新颖衡量标准（我们将其称为 Network-PAC）。 开发一种新的计算机辅助方法来提高 icEEG 读数并改进患有以下疾病的儿童的手术计划 DRE，不需要记录癫痫发作，甚至不需要识别明显的发作间期癫痫样活动。 我们追求 EZ 的特点不仅在于高“局部兴奋性水平”（强 PAC），还在于 与其他“兴奋”组织的紧密联系，从而产生一个超兴奋网络，负责 我们将追求两个具体目标：（1）识别高相互关联的兴奋性和癫痫发作的区域。 评估他们定义癫痫发作区（SOZ）的能力；（2）开发一个整合患者的预测模型； 有关本地 PAC 和功能网络的特定 icEEG 信息（独立于是否存在 坦率的癫痫样模式）来预测切除后的手术结果。 使用尖端的电生理学和信号处理概念（跨频耦合、连接性、 和图论）以及广泛的神经影像学和儿童临床经验。 向癫痫界提出一种在癫痫手术前估计 EZ 的新方法，该方法将 超出了脑电图上癫痫发作或尖峰的视觉识别范围，这可能会显着影响临床护理。 通过加强 icEEG 的术前解释并减少需要，从长远来看，可以减少患有 DRE 的儿童 用于扩展侵入性监测 - 通常需要捕获自发性癫痫发作。