Scalable and Sensor-Agnostic Software for Distributed Processing and Visualization of Multi-Site MEG/EEG Datasets

可扩展且与传感器无关的软件，用于多站点 MEG/EEG 数据集的分布式处理和可视化

• 批准号: 10442915
• 负责人: MATTI HAMALAINEN
• 金额: $ 67.13万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-08-01 至 2023-02-28
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10442915
• 关键词: Algorithms; Alzheimer' s Disease; Aptitude; Basic Science; Brain; Brain imaging; Brain region; Clinical; Clinical Research; Code; Cognitive deficits; Communities; Computer software; Data; Data Analyses; Data Set; Databases; Development; Diagnosis; Disease; Documentation; Educational workshop; Electrocorticogram; Electrodes; Electroencephalography; Electromagnetics; Electrophysiology (science); Ensure; Epilepsy; Frequencies; Functional Magnetic Resonance Imaging; Grant; Human; Individual; Institution; Knowledge; Laboratories; Language Development; Machine Learning; Magnetoencephalography; Manufacturer Name; Maps; Measurement; Measures; Mental disorders; Methodology; Methods; Modeling; Modernization; Neurologic; Neurosciences Research; Noise; Obsessive-Compulsive Disorder; Online Systems; Optics; Population; Positioning Attribute; Process; Pump; Pythons; Reading; Reproducibility; Research; Research Personnel; Resolution; Sampling; Scalp structure; Schizophrenia; Science; Signal Transduction; Site; Source; Statistical Data Interpretation; Stream; Structure; Surface; System; Techniques; Technology; Temperature; Testing; Training; United States National Institutes of Health; Visualization; Work; Writing; autism spectrum disorder; base; computerized data processing; data analysis pipeline; data format; data standards; design; flexibility; hemodynamics; human data; improved; innovation; light weight; magnetic field; millisecond; novel; pedagogy; reconstruction; sensor; software development; source localization; tool

Project Summary During the past three decades non-invasive functional brain imaging has developed immensely in terms of measurement technologies, analysis methods, and innovative paradigms to capture information about brain function both in healthy and diseased individuals. While functional MRI (fMRI) provides a wealth of information by measuring the indirect slow hemodynamic signals. Magnetoencephalography (MEG) and electroencephalography (EEG) remain the only noninvasive techniques capable of directly measuring the electrophysiological activity directly with a millisecond resolution. During the past twelve years we have developed, with NIH support, the MNE-Python software, which covers multiple methods of data preprocessing, source localization, statistical analysis, and estimation of functional connectivity between distributed brain regions. All algorithms and utility functions are implemented in a consistent manner with well-documented interfaces, enabling users to create M/EEG data analysis pipelines by writing Python scripts. To further extend our software to meet the needs of a growing user base and reflect recent developments in MEG/EEG as well as in invasive electrophysiological recordings. Optically Pumped Magnetometers (OPMs) are sensitive room- temperature magnetic field sensors that have begun to provide movable, flexible, lightweight, on-scalp MEG systems, and may soon provide higher signal-to-noise ratio and more complete spatial frequency sampling than SQUID-based systems. However, analysis tools optimal processing of OPM-MEG data are largely missing. Therefore, in Aim 1, we will introduce tools for High-Resolution On-Scalp OPM-MEG Data Analysis. Electrocorticography (ECoG) and subcortical EEG (sEEG) provide focal spatial measurements of the electrophysiological activity. In Aim 2, we will develop sEEG and ECoG workflows, which includes electrode localization and intracranial inverse and forward modeling. Recent methodological advances by our group and the availability of on-scalp OPM-MEG systems (Aim 1) and ECoG/sEEG (Aim 2) have expanded the possibilities for improved localization of deep (cortical and subcortical) sources in basic and clinical research applications. In Aim 3, we will introduce these methods to the repertoire of MNE-Python and will use phantom recordings, human data with known ground truth, and existing MEG databases to validate the new methods. Finally, in Aim 4, we will continue to develop MNE-Python using best programming practices ensuring multiplatform compatibility, extensive web-based documentation, training and forums, and hands-on training workshops.

项目总结