Interictal high frequency oscillations as non-invasive biomarkers of epileptogenicity in pediatric patients

发作间期高频振荡作为儿科患者致癫痫性的非侵入性生物标志物

• 批准号: 9816907
• 负责人: Christos Papadelis
• 金额: $ 0.34万
• 依托单位: BOSTON CHILDREN'S HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-01 至 2019-11-12
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9816907
• 关键词: 18 year old; 3-Dimensional; 3D Print; Address; Adult; Affect; Area; Biological Markers; Brain; Child; Child Development; Childhood; Clinical; Data; Development; Electrodes; Electroencephalography; Epilepsy; Evaluation; Excision; Freedom; Gold; Head; High Frequency Oscillation; Lead; Life; Link; Literature; Location; Magnetoencephalography; Measures; Medical; Methods; Mission; Monitor; Morbidity - disease rate; National Institute of Neurological Disorders and Stroke; Operative Surgical Procedures; Outcome; Pathologic; Patient-Focused Outcomes; Patients; Pharmaceutical Preparations; Physiological; Predictive Value; Printing; Procedures; Public Health; Publishing; Quality of life; Refractory; Research; Resected; Sampling; Seizures; Sensitivity and Specificity; Spatial Distribution; Specificity; Techniques; Technology; Test Result; Testing; Time; Tissues; base; blind; density; experience; experimental study; improved; improved outcome; innovation; instrument; nervous system disorder; neuroimaging; neurosurgery; novel; pediatric patients; signal processing; success; surgery outcome; tool; unsupervised learning; visual motor

Project Summary Crucial to the success of epilepsy surgery is the availability of a robust presurgical biomarker to identify the epileptogenic zone (EZ). Complete resection of the EZ may lead to medication and seizure freedom. Since the EZ cannot be measured directly, its location is estimated indirectly based on concordant data from a multitude of noninvasive tests. Yet, the results of these tests are often insufficiently concordant or inconclusive. Intracranial electroencephalography (iEEG) serves as the gold standard for the delineation of the seizure onset zone (SOZ). However, the SOZ does not always predict the surgical outcome, and its delineation requires many days of recordings to capture clinical seizures. High-frequency oscillations (HFOs), recorded with iEEG are promising interictal biomarkers of the EZ. Yet, their clinical value for epilepsy surgery is still debated since the HFO-generating area is often relatively large and its complete resection may overlap with eloquent areas. This is often attributed to the presence of physiological HFOs in non-epileptogenic areas. In our recent iEEG study, we showed that interictal HFOs are initiated by an onset generator and spread to other brain areas over time. This generator constitutes a promising interictal biomarker of the EZ since its resection is associated with good surgical outcome. Despite the copious literature on HFOs, the clinical value of HFOs for surgery has been only investigated using iEEG, which presents serious limitations due to its invasiveness and its limited spatial sampling. This application aims to noninvasively localize interictal HFOs with high-density electroencephalography (HD-EEG) and magnetoencephalography (MEG) in children with medically refractory epilepsy (MRE), distinguish pathological from physiological HFOs, and assess the noninvasive localization of the HFO-onset generator with respect to the surgical resection and patients’ outcome. Our hypothesis is that HD-EEG and MEG can distinguish pathological from physiological HFOs non-invasively and can localize the HFO-onset generator whose removal leads to better surgical outcome than the removal of the area of secondary spread. To test our hypothesis, we specifically aim to: (i) assess the ability of HD-EEG and MEG to localize HFOs; (ii) differentiate physiological (nHFOs) from pathological (pHFOs) HFOs using unsupervised machine learning; (iii) localize noninvasively the HFO-onset generator and compare it with the clinical gold standard for resection tailoring, i.e. the iEEG-defined SOZ; and (iv) assess the predictive value of the HFO- onset generator in terms of surgical outcome. To pursue these aims, we will record HD-EEG and MEG data from 50 children (0-18 years old) with MRE and 50 typically developing (TD) children. This application combines the use of cutting-edge pediatric neuroimaging instruments, 3D printing technology, and innovative signal processing tools together with extensive neuroimaging experience with children. Our research will have a direct impact on the life of children with MRE since it will provide a new noninvasive biomarker of the EZ that would limit invasive long-term monitoring, augment presurgical planning, and improve the surgical outcome.

项目摘要 癫痫手术成功的关键是一个强大的术前生物标志物的可用性，以确定 致痫区（EZ）。完全切除EZ可能导致药物和癫痫发作自由。以来 EZ无法直接测量，其位置是根据来自多个 非侵入性测试。然而，这些测试的结果往往不够一致或不确定。 颅内脑电图（iEEG）是描述癫痫发作的金标准 区域（SOZ）。然而，SOZ并不总是预测手术结果，其划定需要 许多天的记录来捕捉临床癫痫发作。高频振荡（HFO），用iEEG记录 是有希望的发作间期生物标志物的EZ。然而，它们对癫痫手术的临床价值仍然存在争议， HFO产生区域通常相对较大，其完全切除可能与功能区重叠。 这通常归因于非致癫痫区域中存在生理HFO。在我们最近的iEEG中 研究表明，发作间期HFO由发作发生器启动，并扩散到其他大脑区域， 时间该发生器构成了EZ的有希望的发作间期生物标志物，因为其切除与 手术效果良好。尽管有大量关于HFO的文献，但HFO在外科手术中的临床价值仍有待进一步研究。 仅使用iEEG进行了研究，由于其侵入性和有限的 空间抽样该应用旨在非侵入性地定位发作间期高密度HFO， 脑电图（HD-EEG）和脑磁图（MEG）在儿童医学难治性 癫痫（MRE），区分病理性和生理性HFO，并评估 HFO发作发生器与手术切除和患者结局的关系。我们的假设是 HD-EEG和MEG可以无创地区分病理性和生理性HFO，并可以定位HFO的位置。 HFO发作发生器，其移除导致的手术结局优于移除 二次扩散。为了检验我们的假设，我们的具体目标是：（i）评估HD-EEG和MEG的能力， 定位HFO;（ii）使用无监督HFO将生理（nHFO）与病理（pHFO）HFO区分开来 机器学习;（iii）非侵入性定位HFO发作发生器并将其与临床黄金进行比较 切除量身定制的标准，即iEEG定义的SOZ;以及（iv）评估HFO的预测价值- 在手术结果方面的发作发生器。为了实现这些目标，我们将记录HD-EEG和MEG数据 来自50名患有MRE的儿童（0-18岁）和50名典型发育（TD）儿童。本申请 结合使用尖端的儿科神经成像仪器，3D打印技术， 信号处理工具以及丰富的儿童神经成像经验。我们的研究将有 对MRE儿童的生活产生直接影响，因为它将提供一种新的非侵入性EZ生物标志物， 将限制侵入性长期监测，增强术前计划，并改善手术结果。