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 对于患有耐药性癫痫（DRE）的儿童，癫痫手术是阻止癫痫发作的最佳治疗方法， 避免残疾的生活。手术成功的关键是能够识别出大脑的区域， 负责产生癫痫发作，称为癫痫区（EZ）。估计EZ的最佳方法是 通过颅内脑电图（icEEG）侵入性地记录大脑活动，旨在捕获 并找到产生它们的区域。然而，三分之一的患者在治疗后继续癫痫发作， 手术这表明，对于超越传统icEEG的新方法仍然存在未满足的需求 解释和提供新的信息，对潜在的癫痫致痫性的病人接受癫痫手术 评价为了满足这一需求，我们提出了一种新的方法来分析icEEG， “不可见”信号特征，其可以告知我们关于致癫痫性，尽管对人类读者不可见。 致癫痫性是一种非常复杂的大脑特性，它取决于兴奋性改变和癫痫发作之间的相互作用。 连通性。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），并了解它们如何在癫痫网络中相互作用（识别改变的 连接）。在这个应用中，我们提出了一种新的双重方法来优化解释 icEEG，它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（FC），使用“沉默”icEEG时期（即没有明显的癫痫样模式），以便 定义新的措施“相互关联的兴奋性”（我们将称为网络PAC）。我们的主要目标是 开发一种新的计算机辅助方法，以提高icEEG阅读和改善儿童的手术计划， 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。 我们假设，EZ的特点不仅是高的“局部兴奋性水平”（强PAC），而且 与其他“兴奋”组织的强连接，从而产生一个负责 导致癫痫发作我们将追求两个具体目标：（1）识别高度相互连接的兴奋性区域， 评估他们定义癫痫发作区（SOZ）的能力;（2）开发一个预测模型， 关于局部PAC和功能网络的特定icEEG信息（独立于 坦率的癫痫样模式）来预测切除术后的手术结果。此应用程序将联合收割机 使用尖端的电生理和信号处理概念（交叉频率耦合，连接， 和图论）以及广泛的神经影像学和儿童临床经验。我们的研究将 向癫痫社区提出了一种新的方法来估计癫痫手术前的EZ，这将去 除了视觉识别癫痫发作或脑电图上的尖峰之外。这可能会严重影响临床护理 通过提高术前icEEG的解释和减少对DRE的需求， 用于扩展的侵入性监测-这通常需要捕获自发性癫痫发作。