COINSTAC: decentralized, scalable analysis of loosely coupled data

COINSTAC：松散耦合数据的去中心化、可扩展分析

• 批准号: 9268713
• 负责人: VINCE D CALHOUN
• 金额: $ 65.51万
• 依托单位: THE MIND RESEARCH NETWORK
• 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 2020-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9268713
• 关键词: AODD relapse; Accounting; Address; Agreement; Alcohol or Other Drugs use; Algorithmic Analysis; Algorithms; Attention; Brain imaging; Classification; Clinical Data; Clinical Research; Collaborations; Communities; Consent Forms; Coupled; Data; Data Aggregation; Data Set; Data Sources; Decentralization; Development; Ensure; Family; Functional Magnetic Resonance Imaging; Funding; Genetic; Genetic Markers; Health; Hippocampus (Brain); Human; Individual; Informatics; Institution; International; Knowledge; Language; Letters; Linear Models; Location; Logistics; Machine Learning; Manuals; Measures; Methods; Movement; Paper; Plant Roots; Poaceae; Population; Privacy; Privatization; Procedures; Process; Reproducibility; Research; Research Personnel; Resources; Risk; Running; Science; Site; Stream; Substance abuse problem; System; Testing; Time; United States National Institutes of Health; base; commune; computer framework; computing resources; connectome; cost; data sharing; distributed data; flexibility; imaging genetics; imaging modality; independent component analysis; neuroimaging; novel; open data; peer; public health relevance; quality assurance; repository; statistics; tool; virtual

 DESCRIPTION (provided by applicant): The brain imaging community is greatly benefiting from extensive data sharing efforts currently underway5,10. However, there is a significant gap in existing strategies which focus on anonymized, post-hoc sharing of either 1) full raw or preprocessed data [in the case of open studies] or 2) manually computed summary measures [such as hippocampal volume11, in the case of closed (or not yet shared) studies] which we propose to address. Current approaches to data sharing often include significant logistical hurdles both for the investigator sharing the dat as well as for the individual requesting the data (e.g. often times multiple data sharing agreements and approvals are required from US and international institutions). This needs to change, so that the scientific community becomes 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 recent overview on this from our group2). The large amount of existing data requires an approach that can analyze data in a distributed way while also leaving control of the source data with the individual investigator; this motivates dynamic, decentralized way of approaching large scale analyses. We are proposing a peer-to-peer system called the Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC). The system will provide an independent, open, no-strings-attached tool that performs analysis on datasets distributed across different locations. Thus, the step of actually aggregating data can be avoided, while the strength of large-scale analyses can be retained. To achieve this, in Aim 1, the uniform data interfaces that we propose will make it easy to share and cooperate. Robust and novel quality assurance and replicability tools will also be incorporated. Collaboration and data sharing will be done through forming temporary (need and project-based) virtual clusters of studies performing automatically generated local computation on their respective data and aggregating statistics in global inference procedures. The communal organization will provide a continuous stream of large scale projects that can be formed and completed without the need of creating new rigid organizations or project-oriented storage vaults. In Aim 2, we develop, evaluate, and incorporate privacy-preserving algorithms to ensure that the data used are not re-identifiable even with multiple re-uses. We also will develop advanced distributed and privacy preserving approaches for several key multivariate families of algorithms (general linear model, matrix factorization [e.g. independent component analysis], classification) to estimate intrinsic networks and perform data fusion. Finally, in Aim 3, we will demonstrate the utility of this approach in a proof of concept study through distributed analyses of substance abuse datasets across national and international venues with multiple imaging modalities.

 描述（由申请人提供）： 脑成像界从目前正在进行的广泛数据共享工作中受益匪浅5,10，但现有策略存在重大差距，这些策略侧重于 1) 完整原始数据或预处理数据的匿名、事后共享。开放研究]或2）手动计算的汇总测量[例如海马体积11，在封闭（或尚未共享）研究的情况下]，我们建议解决当前的数据共享方法通常包括对研究者而言的重大后勤障碍。共享数据以及请求数据的个人（例如，通常需要美国和国际机构的多个数据共享协议和批准），这种情况需要改变，以便科学界成为可以收集数据的场所，管理、广泛共享和分析，同时还开放对当前不可用的（许多）数据集的访问（请参阅我们组2最近对此的概述）。 大量的现有数据需要一种能够以分布式方式分析数据的方法，同时将源数据的控制权留给个体研究者；这激发了动态、分散的方法来进行大规模分析。名为“匿名计算协作信息学和神经影像套件工具包”（COINSTAC）的对等系统将提供一个独立、开放、无附加条件的工具，对分布在不同地点的数据集进行分析。可以避免实际聚合数据的步骤，同时可以保留大规模分析的优势，为了实现这一目标，我们提出的统一数据接口将使共享和协作变得容易。还将纳入保证和可复制性工具，通过形成临时的（基于需求和项目的）虚拟研究集群，对各自的数据执行自动生成的本地计算，并在全球推理程序中汇总统计数据。将提供连续的大型项目流，这些项目可以形成和完成，而无需创建新的僵化组织或面向项目的存储库。在目标 2 中，我们开发、评估和合并隐私保护算法以确保数据。即使多次重复使用，也无法重新识别所使用的数据，我们还将为几个关键的多元算法系列（一般线性模型、矩阵分解[例如独立成分分析]、分类）开发先进的分布式和隐私保护方法来估计内在网络。并进行数据融合。最后，在目标 3 中，我们将通过使用多种成像模式对国内和国际场所的药物滥用数据集进行分布式分析，展示这种方法在概念验证研究中的实用性。