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

项目摘要 对于患有抗药性癫痫(DRE)的儿童，癫痫手术是阻止癫痫发作和 防止伤残的生活。手术成功的关键是识别大脑中 负责产生癫痫发作，称为致痫带(EZ)。估计EZ的最好方法是 通过颅内脑电(IcEEG)有创地记录大脑活动，旨在捕获 并定位产生癫痫的区域。然而，三分之一的患者在治疗后仍有癫痫发作。 做手术。这表明，对超越传统脑电的新方法的需求仍未得到满足。 解释并提供癫痫手术患者潜在致痫因素的新信息 评估。为了解决这一需求，我们提出了一种新的方法来分析ICEEG，该方法利用了新的 “看不见”的信号特征，它可以告诉我们癫痫的致病性，尽管人类读者看不到。 癫痫是一种非常复杂的大脑属性，依赖于兴奋性改变和 连通性。最近的证据表明，为了治疗局灶性DRE，我们必须定位病理性区域(如图所示 通过改变兴奋性)，并且还理解它们如何在致痫网络内相互作用(识别改变 连接)。在这一应用中，我们建议开发一种新的双重方法来优化口译 它量化和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“无声的”icEEG时期(即没有明显的癫痫样模式)，以便 定义“相互关联的兴奋性”的新度量(我们将其称为Network-PAC)。我们的主要目标是 开发一种新的计算机辅助方法来促进ICEEG阅读并改进儿童的手术计划 DRE，不需要记录癫痫发作，甚至不需要识别坦率的发作间期癫痫样活动。 我们假设EZ不仅具有较高的“局部兴奋性水平”(强PAC)，而且还具有 与其他“可兴奋”组织的紧密联系，从而产生一个高度兴奋的网络，负责 导致癫痫发作。我们将追求两个具体目标：(1)确定相互关联的高兴奋性区域和 评估他们定义癫痫发作起始区(SOZ)的能力；(2)开发一个预测模型，将患者- 有关本地PAC和功能网络的特定ICEEG信息(与 Frank癫痫样改变)来预测切除后的手术结果。此应用程序将结合 使用尖端电生理和信号处理概念(交叉频率耦合、连接、 和图论)，以及丰富的神经成像和儿童临床经验。我们的研究将 在癫痫手术前向癫痫社区提出一种新的方法来估计EZ，该方法将 超出了脑电波上癫痫发作或棘波的视觉识别。这可能会显著影响临床护理 通过加强术前ICEEG的解释并减少对DRE儿童的需求，从长远来看， 用于延长侵入性监测--这通常是捕捉自发性癫痫发作所必需的。

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.