CAREER: Foundational statistical theory and methods for analyzing populations of attributed connectomes

职业：用于分析归因连接体群体的基础统计理论和方法

• 批准号: 1942963
• 负责人: Joshua Vogelstein
• 金额: $ 63.02万
• 依托单位: Johns Hopkins University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1942963&HistoricalAwards=false
• 关键词: CAREER; Foundational; statistical; theory; methods

Understanding the brain has been called science’s final frontier due to its astonishing complexity. It provides people with their unmatched cognitive capabilities and is the source of some of our most debilitating disabilities. A widespread view among brain scientists is that brains are essentially large complex networks, with each node and edge of the network itself being a complex object. Thus, to understand ourselves and to fight disorders including depression (the world’s leading burden of disease) and suicide (a current epidemic), the scientist on this project will develop mathematical and statistical tools to study networks and apply them to brain networks. This will provide a deeper understanding of human brains and contribute to preventing dysfunction and restoring function. All the tools they develop will also be integrated into a course, and all of the science, including papers, code, and educational materials, will be generated in the open source, so that everyone in society will have access to this content. The investigator will establish foundational theory and methods for analyzing populations of attributed connectomes. Their approach, “connectal coding,” will enable brain scientists to (1) infer latent structure from individual connectomes, (2) identify meaningful clusters among populations of connectomes, and (3) detect relationships between connectomes and multivariate phenotypes. The methods they develop will naturally overcome the challenges inherent in connectomics: high-dimensional non-Euclidean data with multi-level nonlinear interactions. Their procedures will extend the current state-of-the-art in terms of theoretical guarantees, computational scalability, empirical performance, and interpretability of results. The efficacy of the methods will be demonstrated on datasets spanning experimental modalities, scales, and taxa, in collaboration with domain experts who acquired the data. They will also generate educational resources to complement their methods, data, and code to democratize connectomics. All project results will be available at https://neurodata.io/graspyThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

由于大脑惊人的复杂性，了解大脑被称为科学的最后前沿。它为人们提供了无与伦比的认知能力，也是我们一些最令人衰弱的残疾的根源。脑科学家普遍认为，大脑本质上是一个庞大的复杂网络，网络的每个节点和边缘本身就是一个复杂的对象。因此，为了了解我们自己并对抗包括抑郁症（世界上最大的疾病负担）和自杀（当前流行病）在内的疾病，该项目的科学家将开发数学和统计工具来研究网络并将其应用于大脑网络。这将提供对人类大脑的更深入了解，并有助于预防功能障碍和恢复功能。他们开发的所有工具也将被整合到一门课程中，所有的科学，包括论文、代码和教育材料，都将以开源的方式生成，这样社会上的每个人都可以访问这些内容。研究者将建立基础理论和方法来分析归属连接组的群体。他们的方法，“连接编码”，将使脑科学家能够（1）从单个连接体中推断潜在结构，（2）在连接体群体中识别有意义的簇，（3）检测连接体和多变量表型之间的关系。他们开发的方法将自然克服连接组学固有的挑战：具有多级非线性相互作用的高维非欧几里得数据。他们的程序将在理论保证、计算可扩展性、经验性能和结果的可解释性方面扩展当前最先进的技术。这些方法的有效性将在跨越实验模式，规模和分类的数据集上与获得数据的领域专家合作进行证明。他们还将产生教育资源，以补充他们的方法，数据和代码，使连接组学民主化。所有项目结果将在www.example.com上公布https://neurodata.io/graspyThis奖项反映了NSF的法定使命，并被认为值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估来支持。

项目成果

ReX: an integrative tool for quantifying and optimizing measurement reliability for the study of individual differences

• DOI: 10.1038/s41592-023-01901-3
• 发表时间: 2023-06
• 期刊: Nature Methods
• 影响因子: 48
• 作者: Ting Xu;Gregory Kiar;J. Cho;Eric W. Bridgeford;A. Nikolaidis;J. Vogelstein;M. Milham