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 我们建议开发独立于设备的实时软件平台（“ MNE-CE”），该平台将大大增加 在各种类型的临床电生理学 - ICAL数据（脑电图（EEG），磁脑摄影（MEG），电皮质学（ECOG）和Ste- 重复的脑电图（seeg）。今天获得癫痫诊断的数据并用各种软件进行分析 包装，需要大量时间用于使用这些系统的培训。数据集成很困难，并且 通常是定性的。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整合来预期临床实践的变化 通过启用手术管理中的新方法。 MNE-CE基于MNE和MNE-X，我们有 在过去的15年中开发。 MNE-X具有一个架构，可实现MEG的收购和分析 使用任何现有MEG系统的数据。 MNE-CE将从任何记录设备中格式化传入的电视数据 对于脑电图，ECOG，SEEG和MEG，存储数据，进行噪声拒绝和信号条件的预处理 - Ing，实时显示传入数据，然后在源级别（活动时间）进行数据分析 与大多数现有软件不同的传感器级别。它的实时功能将立即提供反馈 临床医生，使他们能够使用此信息来改善手术管理，例如， 癫痫组织的交配位置指导深度或SEEG电极的插入。准确的识别 传播途径可能会通过指定传播倡议地点或 要切除的路径中的纤维。该项目将在由三个机构领导的机构协同进行 三个PI与完善专业知识一起工作了很多年。 PI将与 在临床评估中进行协调的癫痫学家，放射科医生和神经外科医生。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 形式（“头脑风暴”）。他们将共同努力在这一MNE-CE开发方面。目标2：克利夫兰队将评估 - 来自成人癫痫患者的SEEG和SEEG/MEG数据的MNE-CE。评估最初将在 归档数据，重播数据并将它们视为来自虚拟EEG/ECOG仪器的传入数据。那里- Sults将成为MGH开发团队的反馈。随着MNE-E的成熟，它将用作存在的附加组件 - 在实际临床测量中收集数据的硬件，而无需更换现有的FDA批准 系统。这些结果将用于迭代改善MNE-CE。目标3：将执行相同的程序 小儿患者的脑电图，ECOG，SEEG和MEG数据的BCH。 BCH团队还将测试MNE-CE是否可以 揭示了代谢障碍患者的癫痫病活性异常的传播模式。

项目成果

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.

• DOI: 10.1016/j.pneurobio.2020.101824
• 发表时间: 2020-12
• 期刊: Progress in neurobiology
• 影响因子: 6.7
• 作者: Kozhemiako N;Nunes AS;Samal A;Rana KD;Calabro FJ;Hämäläinen MS;Khan S;Vaina LM
• 通讯作者: Vaina LM

Epileptic Activity Intrinsically Generated in the Human Cerebellum.

人类小脑内在产生的癫痫活动。

• DOI: 10.1002/ana.25779
• 发表时间: 2020
• 期刊: Annals of neurology
• 影响因子: 11.2
• 作者: Okada,Yoshio;Khan,Sheraz;Curran,Ashley;Ahtam,Banu;Hämäläinen,MattiS;Traub,RogerD;Pearl,PhillipL
• 通讯作者: Pearl,PhillipL

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

• 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

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。

• DOI: 10.1016/j.jneumeth.2019.02.002
• 发表时间: 2019
• 期刊: Journal of neuroscience methods
• 影响因子: 3
• 作者: Sun,Limin;Okada,Yoshio
• 通讯作者: Okada,Yoshio