COINSTAC 2.0: decentralized, scalable analysis of loosely coupled data

COINSTAC 2.0：松散耦合数据的去中心化、可扩展分析

• 批准号: 10269008
• 负责人: VINCE D CALHOUN
• 金额: $ 61.79万
• 依托单位: GEORGIA STATE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10269008
• 关键词: Address; Adoption; Agreement; Alcohols; Algorithms; Atlases; Awareness; Brain; Brain imaging; Cannabis; Clinical Data; Cocaine; Communities; Consent; Consent Forms; Coupled; Data; Data Aggregation; Data Pooling; Data Set; Decentralization; Development; Environment; Family; Funding; Genetic; Genomics; Human; Individual; Informatics; Institution; Institutional Review Boards; International; Knowledge; Language; Learning; Legal; Link; Location; Logistics; Machine Learning; Measures; Methamphetamine; Modeling; Movement; Neurosciences; Nicotine; Opioid; Performance; Phase; Population; Positioning Attribute; Privacy; Privatization; Process; Public Health; Quality Control; Reproducibility; Research; Research Personnel; Resources; Risk; Running; Science; Security; Series; Site; Source; Source Code; Statistical Bias; Structure; Substance of Abuse; System; Testing; Time; Training; United States National Institutes of Health; Update; Visualization; Work; addiction; base; cloud based; computational platform; computerized data processing; computerized tools; data harmonization; data repository; data reuse; data sharing; data visualization; deep learning; distributed data; improved; large datasets; learning algorithm; life-long learning; negative affect; neuroimaging; novel; novel strategies; open data; open source; peer; privacy preservation; repository; scale up; structural genomics; substance use; success; supervised learning; tool; unsupervised learning; usability; virtual; Address; Adoption; Agreement; Alcohols; Algorithms; Atlases; Awareness; Brain; Brain imaging; Cannabis; Clinical Data; Cocaine; Communities; Consent; Consent Forms; Coupled; Data; Data Aggregation; Data Pooling; Data Set; Decentralization; Development; Environment; Family; Funding; Genetic; Genomics; Human; Individual; Informatics; Institution; Institutional Review Boards; International; Knowledge; Language; Learning; Legal; Link; Location; Logistics; Machine Learning; Measures; Methamphetamine; Modeling; Movement; Neurosciences; Nicotine; Opioid; Performance; Phase; Population; Positioning Attribute; Privacy; Privatization; Process; Public Health; Quality Control; Reproducibility; Research; Research Personnel; Resources; Risk; Running; Science; Security; Series; Site; Source; Source Code; Statistical Bias; Structure; Substance of Abuse; System; Testing; Time; Training; United States National Institutes of Health; Update; Visualization; Work; addiction; base; cloud based; computational platform; computerized data processing; computerized tools; data harmonization; data repository; data reuse; data sharing; data visualization; deep learning; distributed data; improved; large datasets; learning algorithm; life-long learning; negative affect; neuroimaging; novel; novel strategies; open data; open source; peer; privacy preservation; repository; scale up; structural genomics; substance use; success; supervised learning; tool; unsupervised learning; usability; virtual

Project Summary/Abstract The brain imaging community is greatly benefiting from extensive data sharing efforts currently underway. However, there is still a major gap in that much data is still not openly shareable, which we propose to address. In addition, current approaches to data sharing often include significant logistical hurdles both for the investigator sharing the data (e.g. often times multiple data sharing agreements and approvals are required from US and international institutions) as well as for the individual requesting the data (e.g. substantial computational re- sources and time is needed to pool data from large studies with local study data). This needs to change, so that the scientific community can create a venue where data can be collected, managed, widely shared and analyzed while also opening up access to the (many) data sets which are not currently available (see overview on this from our group7). The large amount of existing data requires an approach that can analyze data in a distributed way while (if required) leaving control of the source data with the individual investigator or the data host; this motivates a dynamic, decentralized way of approaching large scale analyses. During the previous funding period, we developed a peer-to-peer system called the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). Our system provides an independent, open, no-strings-attached tool that performs analysis on datasets distributed across different locations. Thus, the step of actually aggregating data is avoided, while the strength of large-scale analyses can be retained. During this new phase we respond to the need for advanced algorithms such as linear mixed effects models and deep learning, by proposing to develop decentralized models for these approaches and also implement a fully scalable cloud-based framework with enhanced security features. To achieve this, in Aim 1, we will incorporate the necessary functionality to scale up analyses via the ability to work with either local or commercial private cloud environments, together with advanced visualization, quality control, and privacy and security features. This suite of new functions will open the floodgates for the use of COINSTAC by the larger neuroscience community to enable new discovery and analysis of unprecedented amounts of brain imaging data located throughout the world. We will also improve usability, training materials, engage the community in contributing to the open source code base, and ultimately facilitate the use of COINSTAC's tools for additional science and discovery in a broad range of applications. In Aim 2 we will extend the framework to handle powerful algorithms such as linear mixed effects models and deep learning, and to perform meta-learning for leveraging and updating fit models. And finally, in Aim 3, we will test this new functionality through a partnership with the worldwide ENIGMA addiction group, which is currently not able to perform advanced machine learning analyses on data that cannot be centrally located. We will evaluate the impact of 6 main classes of substances of abuse (e.g. methamphetamines, cocaine, cannabis, nicotine, opiates, alcohol and their combinations) using the new developed functionality. 3

项目摘要/摘要 大脑成像社区从目前正在进行的广泛数据共享工作中受益匪浅。 但是，仍然存在一个很大的差距，因为我们建议解决这一数据仍然不可公开共享。 此外，当前的数据共享方法通常包括研究人员的重大后勤障碍 共享数据（例如，通常需要多个数据共享协议和批准。 国际机构）以及要求数据的个人（例如 需要来源和时间来汇总大型研究数据的数据）。这需要改变，以便 科学界可以创建一个可以收集，管理，广泛共享和分析数据的场所 同时还可以打开对当前尚不可用的（许多）数据集的访问（请参阅此处的概述 来自我们的组7）。大量现有数据需要一种可以分析分布式数据的方法 （如果需要）将源数据的控制权与单个研究者或数据主机保留；这 激励一种动态的，分散的方法来进行大规模分析。在以前的资金中 时期，我们开发了一个称为协作信息学和神经影像学套件工具包的点对点系统 用于匿名计算（CoinStac）。我们的系统提供了独立，开放，无弦的连接工具 在分布在不同位置的数据集上进行分析。因此，实际汇总的步骤 避免了数据，而大规模分析的强度可以保留。在这个新阶段，我们回应 对于需要高级算法（例如线性混合效应模型和深度学习）的需求，提议 为这些方法开发分散的模型，并实施一个完全可扩展的基于云的框架 具有增强的安全功能。为了实现这一目标，在AIM 1中，我们将结合必要的功能 通过与本地或商业私有云环境合作的能力来扩展分析 高级可视化，质量控制以及隐私和安全功能。这套新功能将打开 较大的神经科学社区使用Coinstac的闸门，以实现新发现和 分析世界各地的空前数量的大脑成像数据。我们还将改善 可用性，培训材料，让社区参与开源代码基础的贡献，并最终 促进在广泛的应用中使用Coinstac的工具进行其他科学和发现。在 目标2我们将扩展框架以处理强大的算法，例如线性混合效应模型和深层 学习，并为利用和更新拟合模型执行元学习。最后，在AIM 3中，我们将测试 通过与全球谜上瘾小组的合作伙伴关系，这种新功能 能够对无法集中位置的数据进行高级机器学习分析。我们将评估 6种主要滥用物质的影响（例如甲基苯丙胺，可卡因，大麻，尼古丁， 阿片类药物，酒精及其组合）使用新的开发功能。 3