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: 10646209
• 负责人: VINCE D CALHOUN
• 金额: $ 61.84万
• 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/10646209
• 关键词: Address; Adoption; Agreement; Alcohols; Algorithms; Atlases; Awareness; Brain; Brain imaging; Cannabis; Classification; Clinical Data; Clinical Research; 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; Machine Learning; Maps; 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; community engagement; computational platform; computerized data processing; computerized tools; computing resources; data harmonization; data repository; data reuse; data sharing; data visualization; deep learning; distributed data; forgetting; improved; large datasets; learning algorithm; life-long learning; negative affect; neuroimaging; novel; novel strategies; open data; open source; peer; privacy preservation; repository; research study; 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

项目摘要/摘要 脑成像社区正在从目前正在进行的广泛的数据共享努力中受益匪浅。 然而，在许多数据仍然不能公开共享方面仍然存在一个重大差距，我们建议解决这一问题。 此外，目前的数据共享方法通常包括调查员面临的重大后勤障碍 共享数据(例如，通常需要从美国和 国际机构)以及请求数据的个人(例如，大量的计算重新 需要资源和时间来汇集来自大型研究的数据和本地研究数据)。这种情况需要改变，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 同时还开放了对当前不可用的(许多)数据集的访问(参见关于此的概述 来自我们这一组。大量的现有数据需要一种方法来分析分布式环境中的数据 在将源数据的控制权留给个人调查员或数据主机的同时(如果需要)；这 激发了一种动态的、分散的方式来进行大规模分析。在之前的资助期间 期间，我们开发了一个称为协作信息学和神经成像套件工具包的点对点系统 用于匿名计算(COINSTAC)。我们的系统提供了一个独立的、开放的、无附加条件的工具 它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。在这个新阶段，我们对此作出回应 针对线性混合效应模型和深度学习等高级算法的需求，提出了 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。为了实现这一点，在目标1中，我们将纳入必要的功能，以 通过能够与本地或商业私有云环境配合使用，以及 高级可视化、质量控制以及隐私和安全功能。这套新功能将开放 更大的神经科学界使用COINSTAC的闸门使新的发现和 分析世界各地史无前例的大量脑成像数据。我们也会改进 可用性、培训材料、让社区参与到开源代码库中来，并最终 促进在广泛的应用中使用COINSTAC的工具进行更多的科学和发现。在……里面 目标2我们将扩展框架以处理强大的算法，如线性混合效果模型和深度 学习，并执行元学习以利用和更新FIT模型。最后，在目标3中，我们将测试 这一新功能是通过与世界范围内的谜成瘾组织合作实现的，目前该组织还没有 能够对无法集中放置的数据执行高级机器学习分析。我们将评估 6类主要滥用物质(如甲基苯丙胺、可卡因、大麻、尼古丁、 鸦片、酒精及其组合)使用新开发的功能。 3.

项目成果

A Correlated Noise-assisted Decentralized Differentially Private Estimation Protocol, and its application to fMRI Source Separation.

• DOI: 10.1109/tsp.2021.3126546
• 发表时间: 2021
• 期刊: IEEE TRANSACTIONS ON SIGNAL PROCESSING
• 影响因子: 5.4
• 作者: Imtiaz, Hafiz;Mohammadi, Jafar;Silva, Rogers;Baker, Bradley;Plis, Sergey M.;Sarwate, Anand D.;Calhoun, Vince D.
• 通讯作者: Calhoun, Vince D.

Enhancing collaborative neuroimaging research: introducing COINSTAC Vaults for federated analysis and reproducibility.

• DOI: 10.3389/fninf.2023.1207721
• 发表时间: 2023
• 期刊: FRONTIERS IN NEUROINFORMATICS
• 影响因子: 3.5
• 作者: Martin, Dylan;Basodi, Sunitha;Panta, Sandeep;Rootes-Murdy, Kelly;Prae, Paul;Sarwate, Anand D.;Kelly, Ross;Romero, Javier;Baker, Bradley T.;Gazula, Harshvardhan;Bockholt, Jeremy;Turner, Jessica A.;Esper, Nathalia B.;Franco, Alexandre R.;Plis, Sergey;Calhoun, Vince D.
• 通讯作者: Calhoun, Vince D.

Multi-Site Mild Traumatic Brain Injury Classification with Machine Learning and Harmonization.

• DOI: 10.1109/embc48229.2022.9871869
• 发表时间: 2022-07-01
• 期刊: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
• 作者: Bostami, Biozid;Espinoza, Flor A;Vergara, Victor M
• 通讯作者: Vergara, Victor M

Abnormal dynamic functional connectivity is linked to recovery after acute ischemic stroke.

• DOI: 10.1002/hbm.25366
• 发表时间: 2021-05
• 期刊: Human brain mapping
• 影响因子: 4.8
• 作者: Bonkhoff AK;Schirmer MD;Bretzner M;Etherton M;Donahue K;Tuozzo C;Nardin M;Giese AK;Wu O;D Calhoun V;Grefkes C;Rost NS
• 通讯作者: Rost NS

Federated Analysis of Neuroimaging Data: A Review of the Field.

• DOI: 10.1007/s12021-021-09550-7
• 发表时间: 2022-04
• 期刊: Neuroinformatics
• 影响因子: 3