CRCNS: Large-scale computational reconstruction of three-dimensional neural

CRCNS：三维神经网络的大规模计算重建

• 批准号: 7432501
• 负责人: Tolga Tasdizen
• 金额: $ 29.14万
• 依托单位: UNIVERSITY OF UTAH
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2010-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7432501
• 关键词: Address; Algorithms; Area; Arts; Automobile Driving; Axon; Biological; Brain; Cells; Communities; Computer software; Data; Data Set; Development; Disease Progression; Drug Addiction; Exhibits; Exposure to; Human; Image; Image Analysis; Imagery; Knowledge; Maps; Microscopy; Modeling; Natural regeneration; Neurons; Optic tract structure; Pattern; Process; Property; Research; Resolution; Resources; Retina; Retinal Degeneration; Scientist; Spinal Injuries; Temporal Lobe Epilepsy; Texture; Therapeutic; Three-Dimensional Image; Three-Dimensional Imaging; Work; Zebrafish; computerized data processing; computerized tools; ganglion cell; image processing; improved; mutant; nervous system disorder; neural circuit; neurophysiology; open source; reconstruction; relating to nervous system; size; technology development; tool; two-dimensional

DESCRIPTION (provided by applicant): Neurophysiological modeling is an important tool in understanding the human brain, and yet state-of-the-art models are poorly constrained by anatomical data. High-magnification, serial-section microscopy images have the potential to expand the field of neurophysiological modeling by providing ground-truth neuroanatomical data. This project addresses the problem of building three-dimensional (3D) connectivity maps for neurons from serial-section microscopy. Sectional data consists of stacks of very high-resolution, two-dimensional (2D) images that are oriented to capture cross sections of elongated neuronal processes. The work focuses on two driving biological applications. The first application is the development of complete connectivity maps for ganglion cells in the mammalian retina. The second is the study of the organization of axons in the optic tract of wildtype and mutant zebrafish. In both applications, the complexity and vast size of the high-resolution, serial-section data make them impractical for human interpretation. Two areas of research are proposed. The first is basic technology development for serial microscopy data processing. Such data exhibits unique structural and statistical properties (e.g. textures), which present a distinct set of challenges that surpass the state-of-the-art in 2D and 3D image processing. The second area is the development of practical, computational tools with which scientists can quantitatively analyze the very large data sets associated with this problem. In order to maximize its exposure to the research community, the software produced in this project will be made publicly available as part of the open source toolkit ITK (www.itk.org). This research will allow scientists to systematically analyze serial section data sets by developing the necessary algorithms and computational tools for processing and visualization. This will result in an improved understanding how neural circuits are constructed at the level of single cells, which is important in advancing our knowledge of the basic wiring of the human brain. Significantly improved imaging, analysis and visualization resources will be critical in characterizing disease progressions, therapeutic arrests, and regeneration patterns in neurological diseases, such as temporal lobe epilepsy, retinal degenerations, spinal injury, and drug dependence, which are associated with anomalies in large-scale neural connectivity.

描述（由申请人提供）：神经生理学建模是理解人类大脑的重要工具，但最先进的模型受解剖数据的约束很差。高放大倍数，连续切片显微镜图像有可能扩大神经生理学建模的领域，通过提供真实的神经解剖数据。这个项目解决了从连续切片显微镜建立神经元三维（3D）连接图的问题。截面数据由一堆非常高分辨率的二维（2D）图像组成，这些图像被定向为捕获细长神经元过程的横截面。这项工作集中在两个驱动生物应用。第一个应用是开发哺乳动物视网膜神经节细胞的完整连接图。第二个是野生型和突变型斑马鱼视束轴突组织的研究。在这两种应用中，高分辨率连续切片数据的复杂性和巨大尺寸使得它们对于人类解释是不切实际的。提出了两个研究领域。首先是串行显微数据处理的基础技术开发。这些数据具有独特的结构和统计特性（例如纹理），这带来了一系列独特的挑战，超越了2D和3D图像处理的最新技术水平。第二个领域是开发实用的计算工具，使科学家能够定量分析与此问题相关的非常大的数据集。为了最大限度地使研究界了解该项目，该项目制作的软件将作为开放源码工具包ITK（www.itk.org）的一部分公开提供。这项研究将使科学家能够通过开发必要的算法和计算工具来系统地分析连续切片数据集，以进行处理和可视化。这将有助于我们更好地理解神经回路是如何在单细胞水平上构建的，这对于推进我们对人类大脑基本布线的认识非常重要。显著改善的成像，分析和可视化资源将在表征疾病进展，治疗停滞和神经系统疾病的再生模式方面至关重要，例如颞叶癫痫，视网膜变性，脊髓损伤和药物依赖，这些都与大规模神经连接异常有关。