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

该项目旨在开发重要的脑成像数据分析方法，提供新的教育和推广活动以帮助提升劳动力，并创建一个软件工具来促进对人脑的大数据分析。功能磁共振成像(FMRI)能够对大脑功能进行非侵入性研究，通常是通过估计连接的功能网络。这些网络是相对稳定的，但很明显，不同的人之间存在很大程度的差异。鉴于现在大规模多主题数据现在已经可以在多个储存库中使用，迫切需要为大规模fMRI数据开发一种灵活的分析框架，能够捕捉大脑活动的全局特征，同时不会丢失给定大脑的个别方面。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和分布式的fMRI存储库。它还承诺对不同条件、组和时间点进行有效的比较，从而进一步增加功能磁共振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教育经验。除了通过出版物、演讲和研讨会组织的学术传播外，这些好处还通过本科生研究项目和公共外展计划(如大脑意识周)进一步渗透。作为该项目的一部分开发的软件工具箱是免费分发的，并使学术界和实践者能够更广泛地采用和重用这些方法，以共同推动脑研究。最终，项目成果有助于NSF促进科学进步和促进国民健康、繁荣和福祉的使命。基于潜在变量模型的数据驱动方法，如独立成分分析(ICA)，已越来越多地被用于功能磁共振数据分析。最近，关于ICA是利用源独立性，还是利用稀疏性，或者两者兼而有之，引发了对稀疏矩阵模型的活跃研究，例如用于功能磁共振分析的字典学习(DL)。事实上，协同平衡多样性的多种概念仍然是一个重要的挑战。在此背景下，首先认识到，通过以数据驱动的方式捕获共同特征以及个体细节，联合利用独立性和稀疏性能够实现用于分析大规模fMRI数据的强大而灵活的框架。因此，已经广泛使用的盲源分离方法(例如ICA)和较新的稀疏矩阵分解模型(例如DL)的互补优势被有利地集成在一起。大型数据集成研究的基本实践方面，如分散计算和在多个储存库之间共享具有隐私意识的数据集，也通过利用该小组的互补专业知识来解决。

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 permeated even 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.