NCS-FO: Connectome mapping algorithms with application to community services for big data neuroscience

NCS-FO：连接组映射算法及其应用于大数据神经科学社区服务

• 批准号: 2203524
• 负责人: Franco Pestilli
• 金额: $ 65万
• 依托单位: University of Texas at Austin
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2203524&HistoricalAwards=false
• 关键词: NCS; FO; Connectome; mapping; algorithms

Neuroscience is advancing by dissolving disciplinary boundaries and promoting transdisciplinary research between psychologists, cognitive neuroscientists, computer scientists, and engineers, to name a few. The success of this scientific endeavor would be enhanced by establishing software mechanisms to improve reproducibility of scientific results. This project develops a software platform that facilitates publication of publicly-accessible data and implementation of data-analysis algorithms. Both functions will be achievable within high-performance computing environments. The platform will enable publication of reproducible code, and access to national supercomputers. It will also make available reference datasets for validating results and data quality. It is expected that the open online platform will promote voluntary data submissions in exchange for access to the system. In addition, this platform will provide a reusable database of "data derivatives," which are data at different stages of preprocessing, including cortical segmentations, meshes, functional maps, brain connectivity matrices, or white-matter tracts. This open-derivatives database will allow computer scientists, mathematical scientists and engineers to use these data to develop and improve methods in their domains. Most generally, providing easy-to-use published data and methods will promote understanding the brain and allow diverse communities of scientists to use reproducible methods, and reuse the "long tail" of neuroimaging data.The project focuses on providing seamless public access to data, computing, and reproducible algorithms, while promoting code sharing and upcycling the long tail of neuroscience data. It has three main objectives. First, to develop a platform to capture brain data, publish algorithms as reproducible applications, and perform data-intensive computing on high-performance compute clusters, as well as public clouds. Second, to develop novel algorithms for mapping brain-connectome individuality and variability. The algorithms will enhance discovery by leveraging the online platform for data intensive processing of large datasets. Third, to collate a large data set of brain data and data derivatives (processed data), such as connectome matrices, multi-parameters tractography models, cortical segmentation and functional maps. These derivatives will benefit scientists to develop algorithms for functional mapping, anatomical computing, and model optimization. This project is funded by Integrative Strategies for Understanding Neural and Cognitive Systems (NSF-NCS), a multidisciplinary program jointly supported by the Directorates for Computer and Information Science and Engineering (CISE), Education and Human Resources (EHR), Engineering (ENG), and Social, Behavioral, and Economic Sciences (SBE). It has also received funding from the CISE Office of Advanced Cyberinfrastructure.

神经科学正在通过消除学科界限和促进心理学家，认知神经科学家，计算机科学家和工程师之间的跨学科研究来发展。通过建立软件机制来提高科学结果的可重复性，将促进这一科学奋进的成功。该项目开发了一个软件平台，便于公布可公开访问的数据和实施数据分析算法。这两种功能都可以在高性能计算环境中实现。该平台将允许发布可复制的代码，并访问国家超级计算机。它还将提供用于验证结果和数据质量的参考数据集。预计开放的在线平台将促进自愿提交数据，以换取进入该系统的机会。此外，该平台将提供一个可重复使用的“数据衍生物”数据库，这些数据是预处理不同阶段的数据，包括皮层分割、网格、功能图、大脑连接矩阵或白质束。这个开放衍生数据库将允许计算机科学家，数学科学家和工程师使用这些数据来开发和改进他们领域的方法。最普遍的是，提供易于使用的已发布数据和方法将促进对大脑的理解，并允许不同的科学家社区使用可重复的方法，并重用神经成像数据的“长尾”。该项目专注于提供无缝的公共访问数据，计算和可重复的算法，同时促进代码共享和升级神经科学数据的长尾。它有三个主要目标。首先，开发一个平台来捕获大脑数据，将算法作为可复制的应用程序发布，并在高性能计算集群和公共云上执行数据密集型计算。其次，开发用于绘制大脑连接体个体性和变异性的新算法。这些算法将通过利用在线平台对大型数据集进行数据密集型处理来增强发现。第三，整理大脑数据和数据衍生物（处理后的数据）的大数据集，如连接体矩阵，多参数纤维束成像模型，皮层分割和功能图。这些衍生物将有利于科学家开发功能映射，解剖计算和模型优化的算法。该项目由理解神经和认知系统的综合策略（NSF-NCS）资助，这是一个由计算机和信息科学与工程（CISE），教育和人力资源（EHR），工程（ENG）以及社会，行为和经济科学（SBE）董事会共同支持的多学科计划。它还获得了CISE高级网络基础设施办公室的资助。

项目成果

Tractography dissection variability: what happens when 42 groups dissect 14 white matter bundles on the same dataset?

纤维束成像解剖变异性：当 42 个组在同一数据集上解剖 14 个白质束时会发生什么？

• DOI: 10.1101/2020.10.07.321083
• 发表时间: 2020
• 作者: Schilling K

Development of white matter tracts between and within the dorsal and ventral streams

• DOI: 10.1007/s00429-021-02414-5
• 发表时间: 2021-01
• 期刊: Brain Structure and Function
• 影响因子: 3.1
• 作者: S. Vinci-Booher;B. Caron;D. Bullock;K. James;F. Pestilli