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

基本信息

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

项目摘要

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.

神经科学正在通过溶解学科界限并促进心理学家，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进步。通过建立软件机制来提高科学结果的可重复性，可以增强这项科学努力的成功。该项目开发了一个软件平台，可促进公开访问数据的发布和数据分析算法的实现。在高性能计算环境中，这两个功能都可以实现。该平台将启用可再现的代码，并访问国家超级计算机。它还将提供参考数据集，以验证结果和数据质量。预计开放的在线平台将促进自愿数据提交，以换取访问系统。此外，该平台将提供可重复使用的“数据衍生物”数据库，该数据库是在预处理的不同阶段的数据，包括皮质分割，网格，功能图，脑连接性矩阵或白色 - 材料。该开放式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善其域中的方法。 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.它有三个主要目标。首先，要开发一个捕获大脑数据的平台，将算法作为可重复的应用程序发布，并对高性能计算簇和公共云进行数据密集型计算。其次，开发新的算法来绘制脑连通的个性和可变性。该算法将通过利用在线平台进行大型数据集的数据密集处理来增强发现。第三，为了整理大量的大脑数据和数据衍生物（已处理数据）的数据集，例如Connectome矩阵，多参数拖拉机模型，皮质分割和功能图。这些导数将使科学家受益，以开发用于功能映射，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（NSF-NCS）的综合策略提供资金，这是一项多学科计划，由计算机和信息科学与工程局（CISE），教育与人力资源（EHR），工程（ENG）以及社交，行为，行为和经济科学（SBE）共同支持。它还从Cise Cyberinfrastructure办公室获得了资金。