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提议将扩大 功能性以及专用于存储的基于云的计算平台[brainlife.io]的用户群， 神经影像学数据的管理、分析、共享和发布。目前，brainlife.io的用户交互， 在平台上分析基于磁共振成像[MRI]的数据， 大脑结构和功能的复杂分析。在这里，我们的具体目标是扩大能力， 该平台首次处理人类神经生理学数据-特别是磁- 脑电图和脑电图[MEEG]数据。MEEG数据的高时间分辨率 它以基于MRI的大脑活动数据无法实现的方式显著增强了对大脑功能的研究。通过 将MEEG数据添加到brainlife.io，我们相信我们可以为神经影像学社区提供一个独特的开放式 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？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性-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 '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， 我们团队的专业知识来扩展brainlife.io的功能。我们也会利用我们的 科学专业知识-提出4个项目，也将在计算， 系统和认知社会神经科学具体目标1 [项目1]介绍了基本的MEEG预处理 和处理方法，针对brainlife.io的新用户。具体目标2 [项目2]提供模拟 在进行研究之前，用于评估MEG实验中所需统计功效的工具- 使入门级和高级用户都受益。具体目标3 [项目3]提供源代码工具 MEEG数据建模，以及提供单一受试者的多模态数据集[来自PI和3 Co- 将在美国和法国实验室进行研究。具体目标4（Project 4） 将MEEG数据与人脑中的白色物质束数据相结合[基于结构MRI， 弥散加权成像[DWI]数据]。这一综合分析是利用我们现有的 协作具体目标3和4针对更多的中级和经验丰富的MEEG科学家。 相关性（参见说明）： 开发和整合MEEG和MRI技术中的神经成像工具，例如 在这个项目中，将直接帮助对大脑功能和结构连接的综合研究， 多种成像模式。这是开发健康和健康的可行的体内模型的下一步。 患病的脑功能-预防、检测和治疗中枢神经系统疾病的重要步骤 神经系统