From Terabytes of Pixels to Intuitive Brain Networks (Administrative Supplement)

从 TB 级像素到直观的大脑网络（行政补充）

• 批准号: 9243849
• 负责人: Hong-Wei Dong
• 金额: $ 21.45万
• 依托单位: UNIVERSITY OF SOUTHERN CALIFORNIA
• 依托单位国家: 美国
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-06-01 至 2018-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9243849
• 关键词: Administrative Supplement; Affect; Algorithms; Archives; Atlases; BRAIN initiative; Behavior; Big Data; Biological Neural Networks; Brain; Code; Cognition; Communities; Computer software; Data; Data Set; Disease; Electronic Mail; Functional disorder; Goals; Grant; Graph; Health; Image; Imagery; Informatics; Information Networks; Investigation; Laboratories; Lens development; Lesion; Licensing; Maps; Methods; Microscopy; Mining; Mus; Neurons; Online Systems; Outcome; Output; Pathway Analysis; Pathway interactions; Phase; Printing; Process; Production; Publishing; Reporting; Research; Research Personnel; Route; Scanning; Science; Scientist; Semantics; Specific qualifier value; Structure; System; Time; Visual; Weight; base; big biomedical data; connectome; data visualization; design; empowered; image archival system; improved; indexing; innovation; interest; lens; light microscopy; microscopic imaging; neural circuit; open source; programs; reconstruction; response; software development; terabyte; web app; web based interface

 DESCRIPTION (provided by applicant): In the midst of a connectomics zeitgeist, microscopy images are collected at an unprecedented rate. The amount of data collected is so overwhelming that it is a challenge for researchers to extract the organizational information of the neural networks embedded within. Developing software that provides researchers intuitive visual representations of their connections of interest would greatly aid them to generate testable hypotheses regarding the functional significance of neural networks -- the impetus for creating the mammalian connectome in the first place. The primary and initial challenge toward developing such a visualization system is the daunting task of thoroughly and reliably quantifying the enormous amount of image data. Achieving this without significant computational aid is intractable. Although algorithms for registering images and automatically reconstructing neuronal processes and axonal pathways for analysis of data exist, they are not efficient for connectivity Big Data given their protracted processing times. Utilizing the data fro the Mouse Connectome Project (MCP) at USC, we developed a beta version of Connection Lens, an innovative informatics pipeline for efficiently and expediently warping, segmenting, and quantifying connectivity data. We have successfully applied Connection Lens toward a limited set of our microscopy image data and we propose to extend its functionality to process our entire archive. Furthermore, leveraging our Connection Lens quantified data we propose to develop a complementary visualization web application. Called Projection Lens, it will render publishable visualizations of user specified connections of interest as connectivity maps, adjacency matrices, network graphs, and flatmaps. Similar to a roadmap, the program will be equipped to show all possible routes between two regions of interest and illustrate how a dysfunctioning node will affect overall information flow within the network. These features will empower researchers to quickly browse, comprehend, and publish fundamental findings regarding functionally distinct neural networks, thereby maximizing the utility of connectomics data nested in terabytes of microscopy scans. The web-based interface of Projection Lens will grant easy access to scientists world-wide. In addition, the code developed for the Connection/Projection Lens (C/PL) framework will be published freely online, and released via an open source license enabling other laboratories to quantify and visualize their data.

 描述（由申请人提供）：在连接组学时代精神中，显微镜图像以前所未有的速度收集。收集的数据量是如此巨大，以至于研究人员难以提取嵌入其中的神经网络的组织信息。开发软件，为研究人员提供他们感兴趣的连接的直观视觉表示将极大地帮助他们产生关于神经网络功能意义的可测试假设-首先创造哺乳动物连接体的动力。开发这种可视化系统的主要和最初的挑战是彻底和可靠地量化大量图像数据的艰巨任务。在没有大量计算辅助的情况下实现这一点是棘手的。尽管存在用于配准图像和自动重建神经元过程和轴突通路以分析数据的算法，但由于其漫长的处理时间，它们对于连接大数据并不有效。利用南加州大学小鼠连接组项目（MCP）的数据，我们开发了一个测试版的连接透镜，一个创新的信息学管道，用于有效和方便地扭曲，分割和量化连接数据。我们已经成功地应用连接透镜对我们的显微镜图像数据的一个有限的集合，我们建议扩展其功能来处理我们的整个档案。此外，利用我们的连接透镜量化数据，我们建议开发一个互补的可视化Web应用程序。称为投影透镜，它将呈现用户指定的连接感兴趣的可扩展的可视化连接图，邻接矩阵，网络图，和平面图。与路线图类似，该程序将被配置为显示两个感兴趣区域之间的所有可能路线，并说明功能失调的节点将如何影响网络内的整体信息流。这些功能将使研究人员能够快速浏览，理解和发布关于功能不同的神经网络的基本发现，从而最大限度地利用嵌套在TB显微镜扫描中的连接组学数据。投影透镜基于网络的界面将使世界各地的科学家能够轻松访问。此外，为连接/投影透镜（C/PL）框架开发的代码将在网上免费发布，并通过开源许可证发布，使其他实验室能够量化和可视化他们的数据。