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: 1734853
• 负责人: Franco Pestilli
• 金额: $ 65万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1734853&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而取得进步。通过建立软件机制来提高科学结果的可重复性，这一科学努力的成功将得到加强。该项目开发了一个软件平台，以促进公开可访问数据的发布和数据分析算法的实施。这两个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。预计开放的网上平台将促进自愿提交数据，以换取使用该系统的机会。此外，该平台将提供一个可重用的“数据衍生品”数据库，这些数据是预处理不同阶段的数据，包括皮质分割、网格、功能图、大脑连接矩阵或白质束。这个开放的衍生品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表数据和方法将促进对大脑的理解，并允许不同的科学家群体使用可重复的方法，并重复使用神经成像数据的“长尾”。该项目致力于为公众提供数据、计算和可重复算法的无缝访问，同时促进代码共享和神经科学数据长尾的升级。它有三个主要目标。首先，开发一个平台来捕获大脑数据，将算法发布为可复制的应用程序，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，开发新的算法来映射大脑连接组的个性和可变性。该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，整理大量大脑数据和数据衍生物（处理后的数据），如连接体矩阵、多参数神经束图模型、皮质分割和功能图。这些衍生品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，这是一个由计算机与信息科学与工程（CISE）、教育与人力资源（EHR）、工程（ENG）和社会、行为和经济科学（SBE）联合支持的多学科项目。它还获得了CISE高级网络基础设施办公室的资助。

项目成果

Neuroanatomical morphometric characterization of sex differences in youth using statistical learning.

• DOI: 10.1016/j.neuroimage.2018.01.065
• 发表时间: 2018-05-15
• 期刊: NeuroImage
• 影响因子: 5.7
• 作者: Sepehrband F;Lynch KM;Cabeen RP;Gonzalez-Zacarias C;Zhao L;D'Arcy M;Kesselman C;Herting MM;Dinov ID;Toga AW;Clark KA
• 通讯作者: Clark KA

Outward subcortical curvature associated with sub-clinical depression symptoms in adolescents

• DOI: 10.1016/j.nicl.2020.102187
• 发表时间: 2020-01-01
• 期刊: NEUROIMAGE-CLINICAL
• 影响因子: 4.2
• 作者: Jenkins, Lisanne M.;Chiang, Jessica J.;Wang, Lei
• 通讯作者: Wang, Lei

Age-related macular degeneration affects the optic radiation white matter projecting to locations of retinal damage

• DOI: 10.1007/s00429-018-1702-5
• 发表时间: 2018-05
• 期刊: Brain Structure and Function
• 影响因子: 3.1
• 作者: Shoyo Yoshimine;S. Ogawa;H. Horiguchi;Masahiko Terao;A. Miyazaki;Kenji Matsumoto;H. Tsuneoka;T. Nakano;Y. Masuda;F. Pestilli

Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation

• DOI: 10.1016/j.neuroimage.2020.117402
• 发表时间: 2021-01-01
• 期刊: NEUROIMAGE
• 影响因子: 5.7
• 作者: Berto, Giulia;Bullock, Daniel;Olivetti, Emanuele
• 通讯作者: Olivetti, Emanuele

Unified representation of tractography and diffusion-weighted MRI data using sparse multidimensional arrays

• 发表时间: 2017
• 作者: C. Caiafa;O. Sporns;A. Saykin;F. Pestilli