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

临床电生理学数据的独立于设备的采集和实时时空分析

• 批准号: 10225499
• 负责人: John Compton Mosher
• 金额: $ 113.57万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-15 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10225499
• 关键词: Adult; Affect; Anatomy; Architecture; Archives; Boston; Brain; Clinic; Clinical; Collaborations; Communities; Computer software; Data; Data Analyses; Data Set; Data Store; Development; Devices; Diagnosis; Electrocorticogram; Electrodes; Electroencephalography; Electrophysiology (science); Environment; Epilepsy; Evaluation; Excision; FDA approved; Feedback; Fiber; Foundations; General Hospitals; Image; Institution; Investigation; Investments; Lead; Location; Magnetic Resonance Imaging; Magnetoencephalography; Massachusetts; Measurement; Metabolic Diseases; Methods; Modality; Monitor; Neurologist; Neurosurgeon; Noise; Operative Surgical Procedures; Partner in relationship; Pathway interactions; Patients; Pattern; Pediatric Hospitals; Plug-in; Procedures; Property; Public Domains; Public Health; Resected; Scalp structure; Signal Transduction; Site; Software Design; Source; Specific qualifier value; Surgical Management; System; Techniques; Testing; Time; Tissues; Training; Visualization; Work; archive data; archived data; base; clinical examination; clinical practice; conditioning; cost; data integration; design; experience; improved; instrument; novel strategies; pediatric patients; radiologist; research clinical testing; sensor; simulation environment; spatiotemporal; virtual

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.

抽象的 我们建议开发一个独立于设备的实时软件平台（“MNE-CE”），该平台将显着提高 获取、监测、分析和整合各种类型的临床电生理学数据的简便性和效率 数据（脑电图（EEG）、脑磁图（MEG）、皮层电图（ECoG）和 如今，通过多种软件获取并分析癫痫诊断数据。 包，需要投入大量时间进行培训才能使用这些系统。数据集成是困难且困难的。 我们的统一方法不仅会显着降低培训、收集和分析的成本。 各种类型的数据，但它通过实现所有模态的无缝集成来预测临床实践的变化 MNE-CE是基于我们拥有的MNE和MNE-X MNE-X 是在过去 15 年中开发的，具有能够采集和分析 MEG 的架构。 使用任何现有 MEG 系统的数据将格式化来自任何记录设备的传入电图数据。 对于 EEG、ECoG、SEEG 和 MEG，存储数据，执行噪声抑制和信号条件的预处理 - ing，实时显示传入的数据，并在源级别（活性组织）进行数据分析 与大多数现有软件不同，它的实时功能将提供即时反馈。 支持者，使他们能够利用这些信息来改善手术管理，例如通过使用估计 癫痫组织的配合位置，以指导深度或 SEEG 电极的插入。 通过指定传播起始位点，传播路径可能会导致切除体积减少或 该项目将由以下三个机构协同开展。 三位 PI 合作多年，专业知识互补。 设立癫痫科医生、放射科医生和神经外科医生来协调临床评估 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。 他们将共同致力于这一 MNE-CE 开发目标 2：克利夫兰团队将评估- MNE-CE 对成人癫痫患者的 SEEG 和 SEEG/MEG 数据进行评估。 存档数据、重播数据并将其视为来自虚拟 EEG/ECoG 仪器的传入数据。 随着 MNE-E 的成熟，结果将反馈给 MGH 开发团队，它将用作现有的附加组件。 用于在实际临床测量期间收集数据的硬件，无需更换现有的 FDA 批准的硬件 这些结果将用于迭代改进 MNE-CE 目标 3：将执行相同的程序。 BCH 儿科患者的 EEG、ECoG、SEEG 和 MEG 数据也将测试 MNE-CE 是否可以。 揭示代谢紊乱患者癫痫样活动的异常传播模式。

项目成果

Epileptic Activity Intrinsically Generated in the Human Cerebellum.

人类小脑内在产生的癫痫活动。

• 发表时间: 2020
• 影响因子: 11.2
• 作者: Okada, Yoshio;Khan, Sheraz;Curran, Ashley;Ahtam, Banu;Hämäläinen, Matti S;Traub, Roger D;Pearl, Phillip L
• 通讯作者: Pearl, Phillip L

Neural activity underlying the detection of an object movement by an observer during forward self-motion: Dynamic decoding and temporal evolution of directional cortical connectivity.

观察者在向前自我运动过程中检测物体运动的神经活动：方向皮层连接的动态解码和时间演化。

• 发表时间: 2020
• 影响因子: 6.7
• 作者: Kozhemiako, N;Nunes, A S;Samal, A;Rana, K D;Calabro, F J;Hämäläinen, M S;Khan, S;Vaina, L M
• 通讯作者: Vaina, L M

Vibrotactile piezoelectric stimulation system with precise and versatile timing control for somatosensory research.

振动触觉压电刺激系统具有精确且多功能的时序控制，适用于体感研究。

• 发表时间: 2019
• 影响因子: 3
• 作者: Sun, Limin;Okada, Yoshio
• 通讯作者: Okada, Yoshio

High-Density EEG in Current Clinical Practice and Opportunities for the Future.

当前临床实践中的高密度脑电图和未来的机会。

• DOI: 10.1097/wnp.0000000000000807
• 发表时间: 2021-03-01
• 期刊: Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society
• 作者: Stoyell SM;Wilmskoetter J;Dobrota MA;Chinappen DM;Bonilha L;Mintz M;Brinkmann BH;Herman ST;Peters JM;Vulliemoz S;Seeck M;Hämäläinen MS;Chu CJ
• 通讯作者: Chu CJ