Collaborative Research: NCS-FO: Flexible Large-Scale Brain Imaging Analysis: Diversity, Individuality, and Scalability

合作研究：NCS-FO：灵活的大规模脑成像分析：多样性、个性化和可扩展性

基本信息

批准号：
1631838
负责人：
Seung-Jun Kim
金额：
$ 49.3万
依托单位：
University of Maryland Baltimore County
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2022-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1631838&HistoricalAwards=false
关键词：
Collaborative Research NCS FO Flexible

项目摘要

This project is designed to develop important analysis methods for brain imaging data, provide new educational and outreach activities to help promote the workforce, and create a software tool to foster big data analysis of the human brain. Functional magnetic resonance imaging (fMRI) enables noninvasive study of brain function, typically through the estimation of functional networks of connectivity. These networks are relatively stable, but it is also clear that there is a wide degree of differences across individuals. Given that now large-scale multi-subject data have now become available across multiple repositories, there is a pressing need for the development of a flexible analysis framework for large-scale fMRI data that can capture the global traits in brain activity, while not losing the individual aspects of a given brain. Such an accurate estimation of each subject's functional connectivity maps enables the leveraging of large and distributed fMRI repositories. It also promises effective comparisons across different conditions, groups, and time points, thus further increasing the usefulness of fMRI in human brain research. The project provides rich educational experience necessary for student training and workforce development in this fast growing field. The benefits are enhanced further via undergraduate research projects and public outreach programs such as brain awareness weeks, in addition to scholarly dissemination through publications, presentations, and workshop organization. The software toolbox developed as part of the project is freely distributed and enables wider adoption and reuse of the methods by the academia and the practitioners to move forward the brain research collectively. Ultimately, the project outcomes contribute to the NSF's mission of promoting the progress of science and advancing the national health, prosperity and welfare.Data-driven methods based on latent variable models such as independent component analysis (ICA) have been increasingly adopted in fMRI data analysis. Recently, there have been lively debates as to whether ICA leverages source independence, exploits sparsity, or both, igniting active research in sparse matrix models such as dictionary learning (DL) for fMRI analysis. Indeed, synergistically balancing multiple notions of diversity remains an important challenge. In this context, it is first recognized that jointly leveraging both independence and sparsity enables a powerful and flexible framework for analyzing large-scale fMRI data, by capturing the common traits as well as individual details in a data-driven manner. Therefore, the complementary strengths of the already widely used blind source separation approaches such as ICA, and the more recent, sparse matrix factorization models such as DL are advantageously integrated. Essential practical aspects for large-scale data integration studies, such as decentralized computation and privacy-aware sharing of the datasets across multiple repositories are also addressed by leveraging the complementary expertise of the team.

该项目旨在开发脑成像数据的重要分析方法，提供新的教育和外展活动以帮助促进劳动力发展，并创建一个软件工具来促进人脑大数据分析。功能磁共振成像（fMRI）通常通过估计功能连接网络来实现对大脑功能的无创研究。这些网络相对稳定，但也很明显，个体之间存在很大程度的差异。鉴于现在大规模的多受试者数据已经可以跨多个存储库使用，迫切需要为大规模功能磁共振成像数据开发灵活的分析框架，该框架可以捕获大脑活动的全局特征，同时不丢失特定大脑的各个方面。对每个受试者的功能连接图的如此准确的估计使得能够利用大型分布式功能磁共振成像存储库。它还有望在不同条件、群体和时间点之间进行有效比较，从而进一步提高功能磁共振成像在人脑研究中的有用性。该项目为这个快速发展的领域的学生培训和劳动力发展提供了必要的丰富的教育经验。除了通过出版物、演讲和研讨会组织进行学术传播之外，通过本科生研究项目和公共宣传计划（例如大脑意识周）进一步增强了这些好处。作为该项目的一部分开发的软件工具箱是免费分发的，使学术界和实践者能够更广泛地采用和重用这些方法，共同推进大脑研究。最终，项目成果有助于 NSF 促进科学进步、促进国民健康、繁荣和福利的使命。基于独立成分分析 (ICA) 等潜变量模型的数据驱动方法已越来越多地在 fMRI 数据中采用分析。最近，关于 ICA 是否利用源独立性、利用稀疏性，或两者兼而有之，引发了激烈的争论，引发了稀疏矩阵模型的积极研究，例如用于 fMRI 分析的字典学习 (DL)。事实上，协同平衡多种多样性概念仍然是一个重要的挑战。在这种背景下，人们首先认识到，联合利用独立性和稀疏性，可以通过以数据驱动的方式捕获共同特征和个体细节，为分析大规模功能磁共振成像数据提供强大而灵活的框架。因此，已经广泛使用的盲源分离方法（例如 ICA）和最近的稀疏矩阵分解模型（例如 DL）的互补优势被有利地集成。通过利用团队的互补专业知识，还可以解决大规模数据集成研究的基本实际问题，例如去中心化计算和跨多个存储库的数据集的隐私意识共享。

项目成果

期刊论文数量（52）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Kernel-Based Efficient Lifelong Learning Algorithm

基于内核的高效终身学习算法

DOI：
10.1109/dsw.2019.8755793
发表时间：
2019-06
期刊：
Proceedings of the 2019 IEEE Data Science Workshop
影响因子：
0
作者：
Kim, Seung;Mowakeaa, Rami
通讯作者：
Mowakeaa, Rami

Multi-Subject Analysis for Brain Developmental Patterns Discovery via Tensor Decomposition of MEG Data

通过 MEG 数据张量分解发现大脑发育模式的多主体分析

DOI：
10.1007/s12021-022-09599-y
发表时间：
2022-08-24
期刊：
Neuroinformatics
影响因子：
3
作者：
I. Belyaeva;B. Gabrielson;Yu;Tony W Wilson;Vince D Calhoun;Julia M Stephen;T. Adalı
通讯作者：
T. Adalı

Association of Neuroimaging Data with Behavioral Variables: A Class of Multivariate Methods and Their Comparison Using Multi-Task FMRI Data

神经影像数据与行为变量的关联：一类多变量方法及其使用多任务 FMRI 数据的比较