CRCNS: Following the BOLD lightening at rest strikes the seizure onset zone!

癫痫是一种破坏性疾病，全世界有5000多万人受到影响(世卫组织)。约30%的患者这样做 对药物没有积极反应，并被诊断为耐药癫痫(DRE)。DRE原因 巨大的成本、发病率和死亡率。最有效的治疗方法是手术切除癫痫发作。 区域(SOZ)，触发癫痫活动的区域。SOZ的本地化对于 手术成功了。不幸的是，手术成功率从30%到70%不等，因为没有可靠的 SOZ的生物标志物。我们建议开发一种SOZ的联合颅内EEG-fMRI生物标志物，同时 病人没有抽搐或处于“休息”状态。有人可能会问，如何识别癫痫发作在大脑中的起始位置？ 在没有观察到癫痫发作的情况下，如果这是可能的，为什么以前的方法失败了？最基本的 目前静息功能磁共振成像(rs-fMRI)和颅内脑电(RsiEEG)计算方法的局限性 SOZ本地化在于他们根据由 动态癫痫网络。我们相信，一种计算方法可以提供如何描述 观测首先是动态生成的，以及内部网络属性如何触发 癫痫发作或防止癫痫发作将在SOZ定位中成功。因此，我们将构建动力学 本研究中的网络模型(DNM)。DNM是生成性模型，可捕获每个网络节点 (集中网络信号处理和传输的位置)与每一个其他节点动态交互。 DNM揭示了内部属性，包括带宽、稳定性、可控性、系统增益以及最重要的 连接到此应用程序。我们认为，当患者没有癫痫发作时，这是因为SOZ 正受到邻近节点(大脑区域)的抑制。因此，我们将以一种新颖的方式将DNM算法应用于 从rS-fMRI和rS-iEEG中识别两组网络节点：那些持续抑制一组 它们的相邻节点(表示为“源”)和被禁止的节点本身(表示为“汇点”)。因此， 根据精准医学的最新进展，我们将为每位患者定制DNM 通过评分确定和量化关键源和汇，优化以定位主要致病SOZ 致痫网络中的节点及其连通性。我们将利用功能成像数据 而在接受癫痫手术的DRE儿童的研究人群中，患者们处于“休息”状态 评估。具体地说，我们将从rs-fmri和rs-iEEG数据中构建dnms，并测试我们的新的“源-接收器”。 当患者没有癫痫发作时，可能指向SOZ的假说。如果成功，拟议的DNMS可以 通过增加外科手术的产量，显著增加手术的可能性并改善手术结果 SOZ本地化的可操作结果和精度。此外，通过消除捕获癫痫发作的需要， 这种新的基于动态网络模型的SOZ定位生物标记物可以显著降低侵袭性 监测时间，避免给患者带来进一步的风险，降低医院的成本。

Epilepsy is a devastating disease affecting over 50 million people worldwide (WHO). About 30% of patients do not respond positively to medication and are diagnosed as having drug resistant epilepsy (DRE). DRE causes significant costs, morbidity, and mortality. The most effective treatment is to surgically remove the seizure onset zone (SOZ), the region from which seizure activity is triggered. The localization of the SOZ is essential for surgical success. Unfortunately, surgical success rates range from 30%-70% because there is no reliable biomarker of the SOZ. We propose to develop a combined intracranial EEG-fMRI biomarker of the SOZ while the patient is not seizing or at “rest”. One may ask, “how does one identify where seizures start in the brain without ever observing a seizure, and if this is possible why have previous methods failed?” The fundamental limitation of current computational approaches for both resting state fMRI (rs-fMRI) and intracranial EEG (rsiEEG) SOZ localization lies in the fact that they compute static measures from observations produced by a dynamic epileptic network. We believe that a computational method that can provide a characterization of how the observations are dynamically generated in the first place, and how internal network properties can trigger seizures or prevent seizures will be successful in SOZ localization. Therefore, we will construct dynamical network models (DNMs) in this study. DNMs are generative models that capture how every network node (location of centralized network signal processing and transfer) interacts with every other node dynamically. DNMs uncover internal properties including bandwidth, stability, controllability, system gain, and most important to this application - connectivity. We propose that when a patient is not having a seizure, it is because the SOZ is being inhibited by neighboring nodes (brain regions). We thus will apply DNM algorithms in a novel manner to identify two groups of network nodes from rs-fMRI and rs-iEEG: those that are continuously inhibiting a set of their neighboring nodes (denoted as “sources”) and the inhibited nodes themselves (denoted as “sinks”). Thus, in line with the most recent advancement in precision medicine, for each patient, we will build DNMs customized to identify and quantify, via a score, key sources and sinks, optimized to localize the primary causative SOZ nodes in the epileptogenic network and their connectivity properties. We will leverage functional imaging data while patients are “at rest” in a study population of children with DRE who are undergoing epilepsy surgery evaluation. Specifically, we will construct DNMs from rs-fMRI and rs-iEEG data and test our novel “source-sink” hypothesis that may point to the SOZ when patients are not seizing. If successful, the proposed DNMs could significantly increase surgical candidacy and improve surgical outcomes by increasing the yield of surgically actionable results and precision of SOZ localization. Furthermore, by removing the need to capture seizures, this novel dynamic network model-based SOZ localization biomarker may substantially reduce invasive monitoring times, avoiding further risks to patients and reducing costs to hospitals.