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.

美国和法国现有合作者之间的数据共享提案将扩大致力于存储的基于云的计算平台 [brainlife.io] 的功能和用户群，目前，brainlife.io 的用户可以进行交互和神经影像数据的管理、分析、共享和发布。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能力。该平台首次处理人类神经生理学数据——特别是磁力数据脑电图和脑电图 [MEEG] 数据 MEEG 数据的高时间分辨率。显着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。将 MEEG 数据添加到 Brainlife.io，我们相信我们可以为神经影像学界提供独特的开放科学和数据共享资源将加速计算、系统、为什么我们计划在 Brainlife.io 上实施数据分析“应用程序”？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 的数据来更好地研究网络我们将实施 MEEG“应用程序”。使用 2 个广泛使用的开源 MEEG 软件套件 – FieldTrip [基于 MATLAB] 和 MNE Python [基于Python]。我们得到了这些软件包开发人员的认可，重要的是，我们还将利用我们团队的专业知识来扩展 Brainlife.io 的功能。科学专业知识——提出 4 个项目，这些项目也将在计算、系统和认知社会神经科学具体目标 1 [项目 1] 介绍基本的 MEEG 预处理。和处理方法，针对 Brainlife.io 的新用户特定目标 2 [项目 2] 提供模拟。在进行研究之前评估 MEG 实验所需统计功效的工具 – 具体目标 3 [项目 3] 提供了源代码工具，使入门级和高级用户受益。 MEEG 数据建模，以及提供单个受试者的多模态数据集 [来自 PI 和 3 Co- PIs] 将在美国和法国实验室进行研究最后，具体目标 4 [项目 4]。将 MEEG 数据与人脑白质束数据相结合 [基于结构 MRI 和该综合分析是使用我们现有的数据生成的。具体目标 3 和 4 针对更多中级和经验丰富的 MEEG 科学家。相关性（参见说明）：跨 MEEG 和 MRI 技术的神经影像工具的开发和集成，例如在这个项目中将直接帮助大脑功能和结构连接的综合研究这是开发健康和健康体内可行模型的下一步。患病的大脑功能——预防、检测和治疗中枢疾病的重要步骤神经系统。