Device-Independent Acquisition and Real Time Spatiotemporal Analysis of Clinical Electrophysiology Data

摘要 我们建议开发一个独立于设备的实时软件平台（“MNE-CE”）， 采集、监测、分析和整合各种类型的临床电生理学的便捷性和效率， 临床数据（脑电图（EEG）、脑磁图（MEG）、皮质电图（ECoG）和脑电图（EEG））。 再定向脑电图（SEEG）。癫痫诊断的数据是今天获得和分析与各种软件 软件包，需要投入大量时间进行使用这些系统的培训。数据集成很困难， 往往是定性的。我们的统一方法不仅可以显著降低培训、收集和分析的成本， 各种类型的数据，但它通过实现所有模式的无缝集成， 和使新的方法在外科管理。MNE-CE基于我们现有的MNE和MNE-X 在过去的15年里发展起来的。MNE-X具有能够采集和分析MEG的架构 使用任何现有的MEG系统。MNE-CE将格式化来自任何记录设备的输入电图数据 对EEG、ECoG、SEEG和MEG进行数据存储，进行噪声抑制和信号调理预处理， ing，以真实的时间显示传入数据，并在源级别（活动组织）执行数据分析 与大多数现有软件不同的传感器级别。它的实时能力将提供即时反馈， 临床医生，使他们能够使用这些信息来改善手术管理，例如通过使用esti， 癫痫组织的配对位置，以引导深度或SEEG电极的插入。准确识别 传播途径可通过指定传播起始位点导致切除体积减少， 要切除的纤维。该项目将在三个机构协同开展， 三位专业人员合作多年，专业知识互补。PI将与良好的工作- 已建立癫痫科医生、放射科医生和神经外科医生协调临床评价。目标1：MGH 团队将设计和开发MNE-CE。克利夫兰的PI是另一个流行软件平台的作者之一， form（“Brainstorm”）.他们将共同致力于MNE-CE的发展。目标2：克利夫兰队将评估- 使用MNE-CE对成人癫痫患者的SEEG和SEEG/MEG数据进行分析。评估将首先在 存档的数据，重放数据并将它们视为来自虚拟EEG/ECoG仪器的输入数据。再- 结果将反馈给MGH开发团队。随着MNE-E的成熟，它将被用作现有的附加产品， 在实际临床测量过程中收集数据的硬件，而无需更换现有的FDA批准的 系统.这些结果将用于迭代改进MNE-CE。目标3：将执行相同的程序 在儿童患者的EEG、ECoG、SEEG和MEG数据上的BCH。BCH团队还将测试MNE-CE是否可以 揭示了代谢紊乱患者癫痫样活动的异常传播模式。

Abstract We propose to develop a device-independent, real-time software platform (“MNE-CE”) that will significantly increase the ease and efficiency of acquiring, monitoring, analyzing, and integrating various types of clinical electrophysiolog- ical data (electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), and ste- reotactic EEG (SEEG). Data for epilepsy diagnosis are today obtained and analyzed with a variety of software packages, requiring a significant investment of time in training to use these systems. Data integration is difficult and often qualitative. Our unified approach will not only significantly reduce the cost of training, collecting and analyzing the various types of data, but it anticipates changes in clinical practice by enabling seamless integration of all modal- ities and by enabling new approaches in surgical management. MNE-CE is based on the MNE and MNE-X we have developed during the past 15 years. MNE-X has an architecture that enables the acquisition and analysis of MEG data using any existing MEG systems. MNE-CE will format incoming electrographic data from any recording device for EEG, ECoG, SEEG and MEG, store the data, carry out preprocessing for noise rejections and signal condition- ing, display the incoming data in real time, and carry out the data analysis at the source level (active tissues) instead of the sensor level unlike most of the existing software. Its real-time capability will provide immediate feedback to clinicians, enabling them to use this information for improving surgical management, for example by using the esti- mated locations of epileptogenic tissue to guide the insertion of depth or SEEG electrodes. Accurate identification of the propagation pathway may lead to reduction in volume of resection by specifying the propagation initiation site or the fibers in the pathway to be resected. This project will be carried out synergistically at three institutions led by three PIs who have worked together for many years with complementary expertise. The PIs will work with well- established epileptologists, radiologists and neurosurgeons for coordinating the clinical evaluation. Aim 1: The MGH team will design and develop MNE-CE. The PI at Cleveland is one of the authors of another popular software plat- form (“Brainstorm”). They will work together on this MNE-CE development. Aim 2: The Cleveland team will evalu- ate MNE-CE on SEEG and SEEG/MEG data from adult epilepsy patients. The evaluation will be initially done on the archived data, replaying the data and treating them as incoming data from a virtual EEG/ECoG instrument. The re- sults will be feedback to the MGH development team. As MNE-E matures, it will be used as an add-on to the exist- ing hardware for collecting data during actual clinical measurements, without replacing the existing FDA-approved systems. These results will be used to iteratively improve MNE-CE. Aim 3: The same procedure will be carried out at BCH on EEG, ECoG, SEEG and MEG data in pediatric patients. BCH team will also test whether MNE-CE can reveal abnormal propagation patterns of epileptiform activity in patients with a metabolic disorder.