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

• 批准号: 10266850
• 负责人: Franco Pestilli
• 金额: $ 22.49万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-21 至 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10266850
• 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; 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

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建议将扩大 功能以及基于云的计算平台的用户群[Brainlife.io]专门用于存储， 神经影像数据的策展，分析，共享和发布。目前，brainlife.io的用户互动和 在平台上分析基于磁共振成像[MRI]数据，该数据能够非常 大脑结构和功能的软化分析。在这里，我们的具体目标是扩大功能 该平台首次处理人类神经生理数据的平台 - 特别是磁铁 脑电图和脑电图[MEEG]数据。 MEEG数据的高临时分辨率 显着增强了基于MRI的大脑活动数据不能的方式的大脑功能研究。经过 将MEEG数据添加到Brainlife.io我们相信我们可以为神经影像社区提供独特的开放 科学和数据共享资源将加速计算，系统中的科学发现 认知和社会神经科学。为什么？我们计划在Brainlife.io上实施数据分析的“应用” 允许用户执行复杂的分析，例如结构和功能连接 - 允许大脑 通过集成MEEG和基于MRI的数据，可以更好地研究网络。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 - 现场Trip [基于MATLAB]和MNE Python [基于Python]。我们对这些软件包的开发人员的认可，重要的是 我们团队中扩展Brainlife.io功能的专业知识。我们还将利用我们的 科学专业知识 - 提案4个项目，这些项目也将在计算领域取得科学收益， 系统和认知社会神经科学。特定目标1 [项目1]提出了基本的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]提供了模拟 在进行研究之前，在MEG实验中评估所需统计能力的工具 - 使入门级和先进的用户都受益。特定目标3 [项目3]提供了来源的工具 MEEG数据的建模，并在单个受试者中提供多模式数据集[来自PI和3个co- PIS]将在美国和法国实验室进行研究。最后，特定目标4 [项目4] 将MEEG数据与人脑中的白质数据相结合[基于结构MRI和 扩散加权成像[DWI]数据]。这种综合分析是使用我们现有的 合作。具体目标3和4的目标是更多中层和经验丰富的Meeg科学家。 相关性（请参阅说明）： MEEG和基于MRI的技术的神经影像学工具的开发和集成，例如 在这个项目中，将直接帮助整合大脑功能和结构连通性的研究 多种成像方式。这是开发健康和健康的可行体内模型的下一步 患病的大脑功能 - 预防，检测和治疗中心疾病的重要步骤 神经系统。