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.

项目摘要 对于耐药性癫痫（DRE）的儿童，癫痫手术是阻止癫痫发作和的最佳治疗方法 防止残疾生活。对手术成功至关重要的是能够识别大脑面积的能力 负责产生癫痫发作，称为癫痫发作区（EZ）。估计EZ的最好方法是 通过颅内脑电图（ICEEG）记录大脑活动，旨在捕获 癫痫发作并找到生成它们的区域。然而，三名患者之一在 外科手术。这表明仍然有未满足的新方法超越传统冰原 解释并提供有关接受癫痫手术患者的潜在癫痫发作的新信息 评估。为了满足这一需求，我们提出了一种新颖的方法来分析冰原，以利用新的优势 “隐形”信号特征，可以告知我们癫痫发作，尽管对人类读者来说看不到。 癫痫发作是一种非常复杂的脑特性，取决于刺激和刺激变化之间的相互作用 连接性。最近的证据表明，要处理焦点，我们必须定位病理区域（描绘） 通过改变兴奋性），还欣赏它们在癫痫发电网络中的相互作用（确定变化 连接）。在此应用程序中，我们建议开发一种新型的双重方法来优化解释 量化和整合局部大脑兴奋性（通过相位振幅耦合，PAC）和 功能连接性（FC），使用“无声”的ICEEG时期（即没有坦率的癫痫样模式） 定义“相互连接 - 启用性”的新颖度量（我们称之为网络-PAC）。我们的主要目标是 开发一种新的计算机辅助方法来增强ICEEG阅读并改善患有儿童的外科手术计划 DRE，无需记录癫痫发作，甚至不需要鉴定坦率的临时癫痫病活性。 我们假设EZ的特征不仅是“当地令人兴奋的水平”（强大的PAC），还由 与其他“兴奋”组织的紧密联系，从而产生了负责的超级驱动网络 产生癫痫发作。我们将追求两个具体的目标：（1）确定高度相互连接的令人兴奋和 评估他们定义癫痫发作区（SOZ）的能力； （2）开发一个预测模型，以整合患者 - 有关本地PAC和功能网络的特定ICEEG信息（独立于存在 切除后坦率的癫痫样模式）以预测手术结果。此应用程序将结合 使用最先进的电生理和信号处理概念（跨频耦合，连通性， 和图理论）以及与儿童的广泛神经影像学和临床经验。我们的研究会 向癫痫社区呈现一种新的方法来估计癫痫手术之前的EZ，这将进行 除了视觉识别脑电图上的癫痫发作或峰值。这会极大地影响临床护理 长期患有DRE的儿童，通过增强冰上的术前解释并减少需求 对于扩展的侵入性监测 - 通常需要捕获发起癫痫发作。