Computational Infrastructure for Brain Research: EAGER: A Scalable Solution for Processing High Resolution Brain Connectomics Data

脑研究的计算基础设施：EAGER：处理高分辨率脑连接组数据的可扩展解决方案

• 批准号: 1649923
• 负责人: Valerio Pascucci
• 金额: $ 30万
• 依托单位: University of Utah
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1649923&HistoricalAwards=false
• 关键词: Computational; Infrastructure; Brain; Research; EAGER

Obtaining a "connectome" or map of the wiring of the brain is crucial to understanding brain structure and function, and has been set as long-term goals of several international government-funded initiatives due to the potential benefits for improving health, treating brain diseases, and understanding development. As technologies for sample preparation and microscopy advance, it is becoming feasible to image large sections of brain tissue. However, the vast quantities of data produced with these techniques is far outpacing the ability of neuroscientists to analyze the data. This project will address the data analysis challenge by developing new computational software tools that facilitate use of advanced computing for connectomics studies, in alignment with NSF's mission to promote the progress of science and advance national health, prosperity and welfare.Understanding the microarchitecture and neuronal morphologies that comprise neural circuitry in the brain is crucial to understanding brain function. This EAGER project aims to build the computational and data infrastructure that is necessary to manage and process large microscopy imaging data sets for connectomics studies, bringing High Performance Computing (HPC) resources into the neuroscience workflow. The project will employ a data model that enables scientists to visualize, interact with, and process data of any size that is stored in any remote location, from USB drives to high-performance parallel file systems. The software infrastructure will furthermore enable automatic mapping of analysis procedures designed by neuroscientists to remote HPC systems. The system will leverage state-of-the-art tools and practices developed in the HPC community, and aims to result in greatly accelerated studies of connectivity in the brain at scale.This Early-concept Grants for Exploratory Research (EAGER) award by the CISE Division of Advanced Cyberinfrastructure is jointly supported by the CISE Division of Information and Intelligent Systems, with funds associated with the NSF Understanding the Brain, BRAIN Initiative activities, and for developing national research infrastructure for neuroscience. This project also aligns with NSF objectives under the National Strategic Computing Initiative.

获得“连接体”或大脑布线图对于了解大脑结构和功能至关重要，并且由于其对改善健康，治疗脑部疾病和了解发育的潜在益处，已被设定为几个国际政府资助计划的长期目标。随着样品制备和显微镜技术的进步，对脑组织的大切片进行成像变得可行。然而，这些技术产生的大量数据远远超出了神经科学家分析数据的能力。该项目将通过开发新的计算软件工具来解决数据分析的挑战，这些工具有助于将先进的计算用于连接组学研究，与NSF的使命保持一致，以促进科学进步和促进国家健康，繁荣和福利。了解大脑中神经回路的微结构和神经元形态对于了解大脑功能至关重要。EAGER项目旨在建立必要的计算和数据基础设施，以管理和处理用于连接组学研究的大型显微镜成像数据集，将高性能计算（HPC）资源引入神经科学工作流程。该项目将采用一种数据模型，使科学家能够可视化，交互和处理存储在任何远程位置的任何大小的数据，从USB驱动器到高性能并行文件系统。此外，软件基础设施将使神经科学家设计的分析程序自动映射到远程HPC系统。该系统将利用HPC社区开发的最先进的工具和实践，旨在大大加快大脑连接的大规模研究。CISE高级网络基础设施部门的探索性研究（EAGER）早期概念赠款奖由CISE信息和智能系统部门共同支持，与NSF理解大脑，BRAIN Initiative活动，以及发展神经科学的国家研究基础设施。该项目也符合国家战略计算计划下的NSF目标。

项目成果

Shared-Memory Parallel Computation of Morse-Smale Complexes with Improved Accuracy

• DOI: 10.1109/tvcg.2018.2864848
• 发表时间: 2019-01
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: A. Gyulassy;P. Bremer;Valerio Pascucci

Ray Tracing Generalized Tube Primitives: Method and Applications

光线追踪广义管基元：方法和应用

• DOI: 10.1111/cgf.13703
• 发表时间: 2019
• 期刊: Computer Graphics Forum
• 影响因子: 2.5
• 作者: Han, Mengjiao;Wald, Ingo;Usher, Will;Wu, Qi;Wang, Feng;Pascucci, Valerio;Hansen, Charles D.;Johnson, Chris R.
• 通讯作者: Johnson, Chris R.

Direct Multifield Volume Ray Casting of Fiber Surfaces

• DOI: 10.1109/tvcg.2016.2599040
• 发表时间: 2017
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Kui Wu;A. Knoll;Benjamin J. Isaac;Hamish A. Carr;Valerio Pascucci

Toward Localized Topological Data Structures: Querying the Forest for the Tree

走向局部拓扑数据结构：从森林中查询树

• DOI: 10.1109/tvcg.2019.2934257
• 发表时间: 2020
• 期刊: IEEE Transactions on Visualization and Computer Graphics
• 影响因子: 5.2
• 作者: Klacansky, Pavol;Gyulassy, Attila;Bremer, Peer-Timo;Pascucci, Valerio
• 通讯作者: Pascucci, Valerio

Flexible Live-Wire: Image Segmentation with Floating Anchors

灵活的火线：使用浮动锚点进行图像分割

• DOI: 10.1111/cgf.13364
• 发表时间: 2018
• 期刊: Computer Graphics Forum
• 影响因子: 2.5
• 作者: Summa, B.;Faraj, N.;Licorish, C.;Pascucci, V.
• 通讯作者: Pascucci, V.