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: 10443841
• 负责人: VINCE D CALHOUN
• 金额: $ 62.87万
• 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/10443841
• 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; 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

项目总结/摘要 大脑成像社区正在从目前正在进行的广泛数据共享工作中受益匪浅。 然而，仍然存在一个重大差距，即许多数据仍然不能公开共享，我们建议解决这个问题。 此外，目前的数据共享方法通常包括对研究者和研究者的重大后勤障碍。 共享数据（例如，通常需要美国和 国际机构）以及请求数据的个人（例如，大量计算重新计算， 将来自大型研究的数据与当地研究数据合并需要资源和时间）。这种情况需要改变， 科学界可以创建一个场所，在那里可以收集、管理、广泛共享和分析数据 同时还开放了对目前不可用的（许多）数据集的访问（见关于这一点的概述 从我们组7）。大量的现有数据需要一种可以在分布式环境中分析数据的方法。 同时（如需要）将源数据的控制权留给个体研究者或数据托管人;这 激发了一种动态的、分散的方式来进行大规模分析。在过去的融资中 在此期间，我们开发了一个点对点系统，称为协作信息学和神经成像套件工具包 匿名计算（CONOMAAC）我们的系统提供了一个独立的、开放的、没有附加条件的工具 对分布在不同位置的数据集进行分析。因此，实际聚合的步骤 避免了数据，同时可以保留大规模分析的优势。在这个新的阶段，我们回应 对线性混合效应模型和深度学习等高级算法的需求，提出 为这些方法开发分散的模型，并实施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，在目标1中，我们将纳入必要的功能， 通过使用本地或商业私有云环境的能力来扩展分析，以及 先进的可视化、质量控制以及隐私和安全功能。这套新功能将在 为更大的神经科学界使用CONOMAC打开了闸门，以实现新的发现， 分析了世界各地前所未有的大量大脑成像数据。我们还将完善 可用性，培训材料，让社区为开源代码库做出贡献，最终 促进使用CONORAC的工具，在广泛的应用中进行额外的科学和发现。在 目标2我们将扩展框架来处理强大的算法，如线性混合效应模型和深度 学习，并执行元学习以利用和更新拟合模型。最后，在目标3中，我们将测试 这项新功能是通过与全球ENIGMA成瘾组织合作实现的，该组织目前还没有 能够对无法集中定位的数据执行高级机器学习分析。我们将评估 6大类滥用物质（如甲基苯丙胺、可卡因、大麻、尼古丁、 鸦片制剂、酒精及其组合）。 3