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CRCNS: US-France Data Sharing Proposal: Open science & cloud computing of MEEG

CRCNS：美法数据共享提案：开放科学

基本信息

批准号：
10428625
负责人：
Franco Pestilli
金额：
$ 20.52万
依托单位：
TRUSTEES OF INDIANA UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-09-21 至 2024-05-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10428625
关键词：
Address Base of the Brain Beds Brain Brain Diseases Central Nervous System Diseases Cloud Computing Cognition Cognitive Collaborations Communities Computer software Data Data Analyses Data Set Development Diffusion Diffusion Magnetic Resonance Imaging Documentation Educational workshop Electroencephalography Event Excision Flowers France Functional Magnetic Resonance Imaging General Population Goals Grant Head Home Human Image Individual Instruction Laboratories Magnetic Resonance Imaging Magnetoencephalography Methods Modeling Morphologic artifacts Neurosciences Pathway interactions Perception Periodicity Positioning Attribute Power Sources Procedures Publications Pythons Resolution Resources Rest Running Sampling Science Scientist Source Standardization Structure Study models System Techniques Technology Testing Time Training Weight base cloud based cognitive neuroscience computational neuroscience computational platform computerized data processing data format data sharing data sharing networks data standards density design digital experience experimental study imaging modality in vivo Model indexing information processing multimodal data multimodal neuroimaging multimodality neuroimaging neurophysiology open data open source prevent relating to nervous system sensor simulation social neuroscience software development symposium teacher temporal measurement tool white matter

项目摘要

This data sharing proposal between existing collaborators in the USA and France will expand the functionality and also user base of a cloud-based computing platform [brainlife.io] devoted to the storage, curation, analysis, sharing and publication of neuroimaging data. Currently, users of brainlife.io interact and analyze magnetic resonance imaging [MRI] based data on the platform, which is capable of very sophisticated analyses of brain structure and function. Here, our specific goal is to expand the capabilities of this platform to handle human neurophysiological data for the first time – specifically magneto- encephalographic and electroencephalographic [MEEG] data. The high temporal resolution of MEEG data significantly enhances studies of brain function in ways that MRI-based brain activity data cannot. By adding MEEG data to brainlife.io we believe that we can offer the neuroimaging community a unique open science and data sharing resource that will accelerate scientific discovery in computational, systems, cognitive and social neuroscience. Why? We plan to implement data analysis ‘Apps’ on brainlife.io that will allow users to perform sophisticated analyses, e.g. structural and functional connectivity – allowing brain networks to be better studied by integrating MEEG and MRI-based data. We will implement MEEG ‘Apps’ using 2 widely-used open source MEEG software suites – FieldTrip [MATLAB-based] and MNE Python [Python-based]. We have the endorsement of the developers of these software packages and, importantly, the expertise within our team to expand the functionality of brainlife.io. We will also make use of our scientific expertise – proposing 4 projects that will also make scientific gains in the fields of computational, systems and cognitive-social neuroscience. Specific Aim 1 [Project 1] presents basic MEEG preprocessing and processing methods, targeting new users of brainlife.io. Specific Aim 2 [Project 2] provides simulation tools for evaluating the required statistical power in a MEG experiment prior to running the study – benefitting both entry-level and sophisticated users. Specific Aim 3 [Project 3] provides tools for source modelling of MEEG data, as well as providing multimodal datasets in single subjects [from the PI and 3 Co- PIs] who will be studied in both the USA and French laboratories. Finally, Specific Aim 4 [Project 4] integrates MEEG data with white matter tracts data in the human brain [based on structural MRI and diffusion weighting imaging [DWI] data]. This integrative analysis has been generated using our existing collaboration. Specific Aims 3 and 4 target more mid-level and experienced MEEG scientists. RELEVANCE (See instructions): The development and integration of neuroimaging tools across MEEG and MRI-based techniques such as in this project will directly aid the integrated study of brain functional and structural connectivity across multiple imaging modalities. This is the next step to developing viable in vivo models of both healthy and diseased brain function – an essential step for preventing, detecting and treating diseases of the central nervous system.

这项在美国和法国的现有合作者之间的数据共享提议将扩大致力于存储的基于云的计算平台[Brainlife.io]的功能和用户基础，管理、分析、共享和发布神经成像数据。目前，Brainlife.io的用户交互和在该平台上分析基于磁共振成像[MRI]的数据，该平台能够非常对大脑结构和功能的复杂分析。在这里，我们的具体目标是扩展功能首次使用这个平台处理人类神经生理数据--特别是磁电机-- 脑电和脑电[MEEG]数据。脑电数据的高时间分辨率极大地增强了对大脑功能的研究，这是基于MRI的大脑活动数据无法做到的。通过将脑电数据添加到脑生活中。我们相信，我们可以为神经成像社区提供一个独特的开放科学和数据共享资源，将加速计算、系统、认知和社会神经科学。为什么？我们计划在Brainlife上实现数据分析‘Apps’。允许用户执行复杂的分析，例如结构和功能连接-允许大脑通过整合MEEG和基于MRI的数据，可以更好地研究网络。我们将实施MEEG‘Apps’ 使用两个广泛使用的开源MEEG软件套件-FieldTrip[基于MatLab]和MNE Python [基于Python的]。我们得到了这些软件包开发商的认可，而且，重要的是，我们团队中的专业知识来扩展Brainlife的功能。我们还将利用我们的科学专长-提出4个项目，这些项目也将在计算领域取得科学成果，系统和认知-社会神经科学。特定目标1[项目1]介绍了基本的脑电预处理和处理方法，目标是Brainlife.io的新用户。特定目标2[项目2]提供模拟在进行研究之前，用于评估MEG实验所需统计能力的工具- 对入门级和高级用户都有好处。特定目标3[项目3]为源代码提供了工具脑电数据的建模，以及提供单一受试者的多模式数据集[来自PI和3Co. 他将在美国和法国的实验室进行研究。最后，具体目标4[项目4] 将脑电数据与人脑中的白质束数据相结合[基于结构磁共振和扩散加权成像[DWI]数据]。这一综合分析是使用我们现有的协作。具体目标3和4的目标是更多的中层和有经验的脑电科学家。相关性(请参阅说明)：跨MEEG和基于MRI技术的神经成像工具的开发和集成，例如该项目将直接帮助对大脑功能和结构连接的综合研究多种成像方式。这是开发健康和健康的人体模型的下一步脑功能异常--预防、发现和治疗中枢神经系统疾病的重要环节神经系统。