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

癫痫是一种毁灭性的疾病，影响着全世界5000多万人（世卫组织）。大约30%的患者 对药物没有积极反应，被诊断为耐药性癫痫（DRE）。焚毁去除率原因 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），即触发缉获活动的区域。SOZ的本地化至关重要， 手术成功不幸的是，手术成功率从30%-70%不等，因为没有可靠的 SOZ的生物标志物。我们建议开发一种联合的SOZ颅内EEG-fMRI生物标志物， 患者没有抽搐或处于“休息”状态。有人可能会问：“如何确定癫痫发作在大脑中的起始位置 而从来没有观察到癫痫发作，如果这是可能的，为什么以前的方法失败了？“根本 静息状态fMRI（rs-fMRI）和颅内EEG（rsiEEG）当前计算方法的局限性 SOZ局部化在于这样一个事实，即他们计算静态措施，从观测产生的一个 动态癫痫网络我们相信，一种计算方法，可以提供一个表征如何 首先，观察结果是动态生成的，内部网络属性如何触发 癫痫发作或预防癫痫发作将成功SOZ定位。因此，我们将构建动态 网络模型（DNMs）。DNM是生成模型，它捕捉每个网络节点如何 （集中式网络信号处理和传输的位置）与每个其它节点动态地交互。 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 连接性。我们认为，当一个病人没有癫痫发作，这是因为SOZ 被邻近的节点（大脑区域）抑制。因此，我们将以一种新颖的方式应用DNM算法， 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”）和被禁止节点本身（表示为“宿”）。因此，在本发明中， 根据精准医疗的最新进展，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，优化定位主要的SOZ成因 癫痫网络中的节点及其连通性。我们将利用功能成像数据 在接受癫痫手术的DRE儿童研究人群中， 评价具体来说，我们将从rs-fMRI和rs-iEEG数据构建DNM，并测试我们新的“源-汇”。 这一假设可能指向SOZ时，病人没有抽搐。如果成功，拟议的DNM可以 通过增加手术的产量，显著增加手术候选人并改善手术结果 可操作的结果和SOZ定位的精度。此外，通过消除捕获缉获量的需要， 这种新的基于动态网络模型的SOZ定位生物标志物可以大大减少侵入性 监测时间，避免对患者的进一步风险，并降低医院的成本。

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.