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

描述(申请人提供)：神经生理学建模是理解人脑的重要工具，然而最先进的模型受到解剖学数据的限制很差。高倍率、连续切片的显微镜图像通过提供真实的神经解剖学数据，有可能扩大神经生理学建模的领域。这个项目解决了从连续切片显微镜为神经元建立三维(3D)连接图的问题。断层数据由非常高分辨率的二维(2D)图像堆叠组成，这些图像旨在捕获细长神经元突起的横截面。这项工作的重点是两个驱动生物学应用。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。第二个是研究野生型和突变型斑马鱼视束中轴突的组织。在这两个应用中，高分辨率的连续剖面数据的复杂性和巨大的规模使得它们对于人类解释来说是不现实的。提出了两个方面的研究内容。第一个是连续显微镜数据处理的基础技术发展。这些数据表现出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，超过了2D和3D图像处理的最先进水平。第二个领域是开发实用的计算工具，科学家可以利用这些工具对与这个问题相关的非常大的数据集进行定量分析。为了最大限度地扩大其对研究界的影响，本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处理和可视化所需的算法和计算工具来系统地分析连续切片数据集。这将导致更好地理解神经电路是如何在单细胞水平上构建的，这对于推进我们对人脑基本连接的知识是重要的。显著改进的成像、分析和可视化资源将在描述与大规模神经连接异常相关的神经疾病的疾病进展、治疗性阻止和再生模式方面至关重要，如颞叶癫痫、视网膜退化、脊髓损伤和药物依赖。

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.