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

该项目旨在开发脑成像数据的重要分析方法，提供新的教育和推广活动，以帮助促进劳动力，并创建一个软件工具，以促进人类大脑的大数据分析。功能磁共振成像（fMRI）能够对大脑功能进行无创研究，通常是通过对连接功能网络的估计。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主题数据已经可以在多个存储库中获得，因此迫切需要开发一种灵活的大规模fMRI数据分析框架，以捕获大脑活动的全局特征，同时不丢失给定大脑的单个方面。这种对每个受试者的功能连接图的准确估计使得利用大型和分布式的fMRI存储库成为可能。它还承诺在不同条件、群体和时间点之间进行有效的比较，从而进一步提高功能磁共振成像在人类大脑研究中的实用性。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丰富的教育经验。除了通过出版物、演讲和研讨会组织的学术传播之外，还通过本科生研究项目和公众宣传计划（如大脑意识周）进一步增强了这些好处。作为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，项目成果有助于美国国家科学基金会促进科学进步和促进国家健康、繁荣和福利的使命。基于潜在变量模型的数据驱动方法如独立分量分析（ICA）在功能磁共振成像数据分析中得到越来越多的应用。最近，关于ICA是否利用源独立性，利用稀疏性，或两者兼有，引发了对稀疏矩阵模型（如用于fMRI分析的字典学习（DL））的积极研究。的确，协同平衡多种多样性概念仍然是一项重要挑战。在此背景下，我们首先认识到，通过以数据驱动的方式捕获共同特征和个体细节，联合利用独立性和稀疏性，可以为分析大规模fMRI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（如ICA）和最近的稀疏矩阵分解模型（如DL）的互补优势得到了有利的整合。大规模数据集成研究的基本实践方面，如分散计算和跨多个存储库的数据集的隐私意识共享，也通过利用团队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 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.