Study of fiber anatomy in mouse development via MRI/DTI

通过 MRI/DTI 研究小鼠发育中的纤维解剖学

• 批准号: 7099469
• 负责人: Christos Davatzikos
• 金额: $ 45.25万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-07-15 至 2008-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7099469
• 关键词: axon; bioimaging /biomedical imaging; brain imaging /visualization /scanning; brain morphology; computational neuroscience; corpus callosums; developmental disease /disorder; developmental neurobiology; embryo /fetus; genetically modified animals; image enhancement; laboratory mouse; magnetic resonance imaging; morphometry; mutant; neuroanatomy; neuronal guidance; technology /technique development; three dimensional imaging /topography; white matter

DESCRIPTION (provided by applicant): The main goal of this project is to characterize the development of the murine brain, with emphasis on white matter anatomy, using magnetic resonance micro-imaging in conjunction with mathematical methodologies for quantitative image analysis. The traditionally used histological methods for examination of murine brain sections are limited by tissue distortion or loss, by difficulties in constructing a spatially consistent volumetric image from sections, by extensive effort in preparation, and by lack of capability for in vivo examination of the mouse brain. Magnetic resonance imaging (MRI) is emerging as a technology with strengths complementary to histology, with respect to these limitations. In this project, we will develop methods for imaging and analysis of the murine brain, and we will use them to generate normative data for brain development of the C57BL/6J mouse strain. Our emphasis will be on using diffusion tensor imaging (DTI) to characterize the white matter architecture. Building upon current work by several groups in the Human Brain Project, we propose to develop mathematical methodologies for computational anatomy, which complement traditional analysis methods in mainly two ways. First, they can identify very subtle and localized shape characteristics, without the need to know the location of an affected brain region a priori. Second, they are highly automated and quantitative, thus enabling the examination of a large number of animals with minimal effort, using statistical image analysis techniques. Our image analysis methodology will involve shape analysis methods for the reconstruction and spatial normalization of murine brain structures, and it will utilize the well-established framework of stereotaxic space analysis. After mass-preserving spatial normalization of MRI images to a stereotaxic space of the respective developmental stage, the normal anatomic variation of grey and white matter structures will be measured at a number of different developmental stages. This normative data will be useful in subsequent DTI-based studies aiming to identify regions of abnormal development in neurogenetic mice, by finding regions that fall outside this normal range. We will test this methodology on a pilot study of the Emx-1 knockout mouse, a well-characterized strain with abnormal cortical lamination and defasciculated white matter fiber tracts, including the corpus callosum, and we will validate our MR-based measurements using histological sections.

描述（由申请人提供）：该项目的主要目标是描述小鼠大脑的发育，重点是白质解剖，使用磁共振微成像结合数学方法进行定量图像分析。传统的用于小鼠脑切片检查的组织学方法受到组织畸变或丢失、难以从切片中构建空间一致的体积图像、大量准备工作以及缺乏对小鼠脑进行体内检查的能力的限制。鉴于这些局限性，磁共振成像（MRI）正在成为一种与组织学互补的技术。在本项目中，我们将开发小鼠脑成像和分析方法，并将利用这些方法为C57BL/6J小鼠品系的脑发育提供规范性数据。我们的重点将是使用扩散张量成像（DTI）来表征白质结构。在人脑计划中几个小组目前工作的基础上，我们建议发展计算解剖学的数学方法，主要在两方面补充传统的分析方法。首先，他们可以识别非常微妙和局部的形状特征，而不需要先验地知道受影响的大脑区域的位置。其次，它们是高度自动化和定量的，因此可以使用统计图像分析技术以最小的努力检查大量动物。我们的图像分析方法将包括用于重建和空间归一化小鼠大脑结构的形状分析方法，并将利用立体定向空间分析的成熟框架。在将MRI图像的质量保持空间归一化到各自发育阶段的立体定向空间后，将在多个不同发育阶段测量灰质和白质结构的正常解剖变化。这一规范性数据将有助于后续基于dti的研究，旨在通过发现超出正常范围的区域来识别神经遗传小鼠的异常发育区域。我们将在Emx-1基因敲除小鼠的初步研究中测试该方法，Emx-1基因敲除小鼠是一种具有异常皮质层压和白质纤维束（包括胼胝体）失血的典型品系，我们将使用组织学切片验证我们基于mr的测量结果。