Scalable Software for Reverse Engineering Neural Circuits from Histology

用于组织学逆向工程神经电路的可扩展软件

• 批准号: 8314294
• 负责人: CHRISTOPHER CHARLES LAW
• 金额: $ 49.57万
• 依托单位: KITWARE, INC.
• 依托单位国家: 美国
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-12-07 至 2014-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8314294
• 关键词: Address; Algorithms; Automation; Axon; Behavior; Boston; Brain; Brain Stem; Bread; Cells; Cereals; Cities; Client; Cognition; Cognitive; Collaborations; Color; Complex; Computer software; Confocal Microscopy; Conscious; Custom; Data; Data Set; Databases; Development; Distant; Documentation; Electron Microscope; Electron Microscopy; Electrons; Engineering; Face; Future; Histology; Human; Image; Image Analysis; Imaging Techniques; Individual; Intelligence; Journals; Label; Lateral Geniculate Body; Learning; Link; Manuals; Maps; Memory; Methods; Microscope; Modeling; Motor Cortex; Mus; Neurons; Neurosciences; Phase; Preparation; Process; Research; Research Personnel; Resolution; Scanning; Scanning Electron Microscopy; Science; Scientist; Silicon Dioxide; Staging; Support System; Synapses; System; Techniques; Testing; Thick; Three-Dimensional Image; Time; Tissues; Transgenic Mice; Visual Cortex; Work; brain tissue; clay; computer infrastructure; data sharing; detector; nanometer; neural circuit; open source; relating to nervous system; software systems

DESCRIPTION (provided by applicant): A human brain is estimated to have roughly 100 billion neurons connected through more than 100 thousand miles of axons and a quadrillion of synaptic connections (~10^15 or 2^50 connections). As a comparison, there are more synaptic connections in human brains in the city of Boston alone than grains of sand in all the desserts and beaches in the world (~10^20). The neural circuit within each brain is called its connectome, and understanding how it works and enables cognition, consciousness, or intelligence is one of the most fundamental questions in science. Given this complexity it is not surprising that the neural circuits underlying even the simplest of behaviors are not understood. Until recently, attempts to fully describe such circuits were never even seriously entertained, as it was considered too numerically complex. However, modern advances in the preparation, sectioning, and imaging of brain tissue in the last five years have enabled biologists to image neural connectivity at scales of only a few nanometers in a highly automated manner. Neuroscience researchers are using confocal and electron microscopy techniques to image serial sections at high resolution. Current three-dimensional image datasets are up to several terabytes in size. With automation and faster imaging, we expect dataset sizes to increase by orders of magnitude. Unfortunately, processing and analyzing these images in order to identify the connectome of any mammalian brain is still an incredibly difficult task, and only a few groups across the world have started to address this problem. We propose to develop the computational infrastructure necessary for mapping the wiring of neurons in a large volume of neural tissue that has been cut into ultrathin serial sections. We will develop an open- source system that supports analysis of arbitrarily large image volumes. By being able to trace every neural process in a volume within a reasonable amount of time (days or weeks instead of years), our system will enable a collaborative effort to develop efficient automatic methods for segmentation and tracing. The proposed system will support remote data access so that the enormous datasets can be accessed simultaneously by geographically diverse research groups. Custom clients will be developed to implement various segmentation algorithms, with results uploaded to a central database. In this way the segmentation results obtained with one algorithm can be compared against those obtained with another algorithm on the same datasets. We will also implement fusion methods that will take as input the segmentation results from different algorithms and that will generate the tracings of neural processes by linking segmentation results from one section to the next. PUBLIC HEALTH RELEVANCE: Connectome scientists are using ultra-thin serial sections and nanometer resolution electron microscopes to reverse engineer neural circuits of the brain. In the not too distant future we will be able to compare neural activity with their circuit diagrams o understand how higher level cognitive tasks such as learning, memory, association and inductive reasoning are implemented in the mammalian brain. We propose to create a scalable open-source software system that will help biologists trace neurons through massive 50-terabyte connectome datasets.

描述（由申请人提供）：据估计，人脑的神经元大约有1000亿个神经元，通过超过1万英里的轴突和四十四张突触连接（〜10^15或2^50连接）。相比之下，仅波士顿市的人类大脑中的突触连接比世界上所有甜点和海滩的沙子（〜10^20）中更多。每个大脑内的神经回路称为其连接组，了解其工作原理并使认知，意识或智力是科学中最基本的问题之一。 鉴于这种复杂性，即使是最简单的行为也不理解的神经回路也就不足为奇了。直到最近，尝试完全描述此类电路的尝试从未得到认真娱乐，因为 被认为太复杂了。 然而，过去五年来，脑组织的制备，切片和成像的现代进步使生物学家能够以高度自动化的方式仅在少数纳米的尺度上对神经连通性进行图像。 神经科学研究人员正在使用共聚焦和电子显微镜技术以高分辨率对串行切片进行成像。当前的三维图像数据集的大小高达几个。 随着自动化和更快的成像，我们期望 数据集大小以数量级增加。 不幸的是，处理和分析这些图像以识别任何哺乳动物大脑的连接仍然是一项非常艰巨的任务，全世界只有少数几组开始解决 这个问题。 我们建议开发在已切成超薄串行切片的大量神经组织中映射神经元接线所需的计算基础设施。 我们将开发一个开放源系统，该系统支持任意大型图像量的分析。通过能够在合理的时间内（几天或几年而不是几年）中的一个卷中的每个神经过程，我们的系统将有助于开发有效的自动方法进行分割和跟踪。 提出的系统将支持远程数据访问，以便可以通过地理上多样化的研究小组同时访问庞大的数据集。 将开发自定义客户端以实现各种细分算法，并将结果上传到中央数据库。 这样，可以将用一种算法获得的分割结果与同一数据集上另一种算法获得的算法进行比较。我们还将实施融合方法，将其作为输入分割结果的输入结果，并通过将分割结果从一个部分链接到另一个部分来生成神经过程的跟踪。 公共卫生相关性：Connectome科学家正在使用超薄的串行部分和纳米分辨率电子显微镜来反向大脑的神经回路。 在不太遥远的未来，我们将能够将神经活动与它们的电路图进行比较。 我们建议创建一个可扩展的开源软件系统，该系统将帮助生物学家通过大量的50型连接组数据集跟踪神经元。