Morphometric Analysis of Brains and Neurons

大脑和神经元的形态测量分析

• 批准号: 6849541
• 负责人: TZUMIN LEE
• 金额: $ 13.82万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-04-01 至 2005-08-15
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6849541
• 关键词: Drosophilidae; bioimaging /biomedical imaging; brain imaging /visualization /scanning; brain mapping; brain morphology; confocal scanning microscopy; green fluorescent proteins; morphometry; neurons; statistics /biometry; technology /technique development

DESCRIPTION (provided by applicant): Modern biomedical imaging technologies allow for generation of high-resolution digital 3D images of many microscopic biological objects. However, effective strategies remain to be developed for automatic quantitative and statistical analysis of such microscopic structures' 3D morphologies. This proposal is aimed at developing algorithms for automatic morphometric analysis of neurons in intact brains. Quantitative and statistical characterization of individual neurons' spatial locations and their 3D projection patterns is not only essential for understanding brains' complexity, diversity, and plasticity with single-cell resolution, but also critical for elucidating subtle cellular pathological mechanisms underlying various neurological/mental/psychological disorders. New expertise will be explored to advance technologies in multiple areas of biomedical imaging, such as image computation and simulations of complex tissues. A GAL4-independent binary transcriptional system has been developed to label specifically the entire morphologies of the Drosophila olfactory learning and memory center, the mushroom bodies (MBs). In conjunction with MARCM (Mosaic Analysis with a Repressible Cell Marker) technologies, one can independently label various single MB neurons and the whole MBs in the same brains. Meanwhile, new algorithms have been developing to conduct automatic morphing (morphological deformation & matching) of irregular-shaped 3D objects. A virtual average MB will be constructed via statistical characterization of pair-wise morphing among multiple "standard" MBs. Morphometric analysis of distinct MBs and spatial mapping of individual MB neurons will then involve establishing point-to-point correspondence between the MBs of interest or the MBs, in which specific single MB neurons are differentially labeled, and the statistical model MB. Thus, one may be able to detect automatically and describe quantitatively any given MB's structural deviations and to identify individual MB neurons based on their 3D neuronal location/projection patterns.

描述（由申请人提供）：现代生物医学成像技术允许生成许多微观生物物体的高分辨率数字3D图像。然而，对这些微观结构的三维形态进行自动定量和统计分析的有效策略仍有待开发。本提案旨在开发完整大脑神经元的自动形态测量分析算法。单个神经元空间位置及其三维投影模式的定量和统计表征不仅对理解大脑的复杂性、多样性和单细胞分辨率的可塑性至关重要，而且对阐明各种神经/精神/心理障碍背后的微妙细胞病理机制也至关重要。将探索新的专业知识，以推进生物医学成像多个领域的技术，如图像计算和复杂组织的模拟。一个独立于gal4的双转录系统已经被开发出来，用于特异性标记果蝇嗅觉学习和记忆中心——蘑菇体（MBs）的整个形态。结合MARCM （Mosaic Analysis with a Repressible Cell Marker）技术，人们可以在同一大脑中独立标记不同的单个MB神经元和整个MB神经元。同时，对不规则形状的三维物体进行自动变形（形态变形与匹配）的新算法也在不断发展。虚拟平均MB将通过多个“标准”MB之间成对变形的统计特征来构建。不同MB的形态计量学分析和单个MB神经元的空间映射将涉及在感兴趣的MB或MB之间建立点对点对应关系，其中特定的单个MB神经元被差异标记，以及统计模型MB。因此，人们可以自动检测和定量描述任何给定MB的结构偏差，并根据其3D神经元位置/投影模式识别单个MB神经元。