Study of fiber anatomy in mouse brain development via MRI/DTI

通过 MRI/DTI 研究小鼠大脑发育中的纤维解剖结构

• 批准号: 7791108
• 负责人: Christos Davatzikos
• 金额: $ 53.99万
• 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 2013-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7791108
• 关键词: Address; Adopted; Affect; Anatomy; Animal Model; Animals; Appearance; Architecture; Atlases; Autistic Disorder; Binding; Biological; Brain; Brain imaging; Brain region; Characteristics; Complement; Complex; Data; Development; Diffusion Magnetic Resonance Imaging; Disease; Fiber; Gene Mutation; Genes; Genetically Engineered Mouse; Genotype; Goals; Gray unit of radiation dose; Growth; Histology; Human; Image; Image Analysis; Imaging Techniques; Induced Mutation; Location; Magnetic Resonance; Magnetic Resonance Imaging; Measures; Methodology; Methods; Microscopic; Morphologic artifacts; Morphology; Mouse Strains; Mus; Mutant Strains Mice; Nature; Neuroanatomy; Pattern; Phenotype; Play; Preparation; Research; Residual state; Resolution; Rett Syndrome; Role; Scanning; Schizophrenia; Screening procedure; Staining method; Stains; Structure; Techniques; Technology; Testing; Tissues; Transgenic Animals; Transgenic Mice; Transgenic Model; Transgenic Organisms; Variant; Weight; Work; axonal degeneration; base; brain morphology; brain tissue; computational anatomy; computer framework; fetal; image registration; image warping; in vivo; interest; method development; mouse development; mouse model; myelination; neuroimaging; neuroinformatics; novel strategies; postnatal; programs; public health relevance; rapid growth; shape analysis; tool; water diffusion; white matter

DESCRIPTION (provided by applicant): The main goal of this research program is to characterize the development of the mouse brain, with emphasis on white matter anatomy, using magnetic resonance micro-imaging in conjunction with mathematical methodologies for quantitative image and shape analysis following the field of computational neuroanatomy. The development of methods for altering the genotype of model animals, including mice, has opened enormous possibilities for better understanding the role of different genes in brain development and diseases, by studying differences in the course of brain development between normal and transgenic animals. Imaging, and in particular MRI, is bound to play an important role in structural phenotyping of mouse models, since it can serve as an effective screening tool pointing to more detailed yet laborious histological analyses, and does not suffer from sectioning and staining distortion artifacts, but provides spatially consistent 3D volumes. However, conventional imaging techniques cannot reveal the internal white matter architecture, which consists of various fiber tracts and is of great interest in understanding brain development. This is especially the case for fetal and young mouse brains, in which myelination is not well developed and the T1- or T2-weighted images cannot even distinguish the gray and white matter with adequate contrast. Diffusion tensor imaging (DTI) provides excellent contrast, and is able to distinguish various structures in the white matter of even young brains, by imaging microscopic water diffusion which is known to be relatively higher along axonal fibers. Therefore, DTI is bound to be critical in studying brain development and brain connectivity, as well in understanding how genetic mutations affect the formation, myelination, or degeneration of axonal fibers. Concurrent with advances in micro-MR imaging have been advances in mathematical methodologies for computational anatomy, a rapidly maturing field of quantitative techniques for analysis of brain morphology from volumetric images. These new approaches are now adopted by a number of neuroimaging and neuroinformatics groups, and they constitute powerful tools for structural phenotyping in highly automated and detailed ways; they are particularly sensitive to subtle and spatially complex patterns of morphological change. In this project, we will continue to develop methods for quantitative analysis of the mouse brain, and use them to generate normative data for brain development of the C57BL/6J mouse strain, and to investigate phenotypic differences between wildtype and transgenic mouse models. Emphasis will continue to be the mathematical and computational challenges posed by the complexity of tensor images, which are obtained in DTI (Aims 1 and 2), especially challenges in image registration and morphological analysis that are posed by the rapid changes observed during early postnatal development as well as by morphological differences between transgenic and wildtype mice. Moreover, we will test the utility of our methodologies in studies of 3 specific projects involving mouse models of schizophrenia, autism, and Rett's syndrome (Aim 3), in collaborative efforts. PUBLIC HEALTH RELEVANCE: This project seeks to investigate normal mouse brain development via high-resolution diffusion tensor imaging, an MRI contrast that provides good definition of brain tissues and of fiber architecture. Advanced computational image analysis tools for diffusion tensor images will be further developed and validated, emphasizing two of the challenges faced by this project: 1) rapid anatomical changes during early postnatal development; 2) morphological differences between wildtype and transgenic mice. These neuroinformatics tools will be applied to 3 collaborative projects, seeking to identify brain differences between wildtype mice and mouse models of schizophrenia, autism, and Rett's syndrome.

描述（由申请人提供）：本研究计划的主要目标是描述小鼠大脑发育的特征，重点是白色物质解剖，使用磁共振显微成像结合计算神经解剖学领域的定量图像和形状分析的数学方法。 改变包括小鼠在内的模型动物基因型的方法的发展，通过研究正常动物和转基因动物之间大脑发育过程的差异，为更好地理解不同基因在大脑发育和疾病中的作用开辟了巨大的可能性。 成像，特别是MRI，必然在小鼠模型的结构表型中发挥重要作用，因为它可以作为一种有效的筛选工具，指向更详细但费力的组织学分析，并且不会遭受切片和染色失真伪影，但提供空间一致的3D体积。 然而，传统的成像技术不能揭示内部的白色物质结构，它由各种纤维束组成，对理解大脑发育具有极大的兴趣。 对于胎儿和幼年小鼠的大脑尤其如此，在这些大脑中，髓鞘形成并不发育良好，T1或T2加权图像甚至不能以足够的对比度区分灰色和白色物质。 扩散张量成像（DTI）提供了极好的对比度，并且能够通过对已知沿沿着轴突纤维相对较高的微观水扩散成像来区分甚至年轻脑的白色物质中的各种结构。 因此，DTI在研究大脑发育和大脑连接以及了解基因突变如何影响轴突纤维的形成、髓鞘形成或变性方面至关重要。 与微磁共振成像的进展同时，计算解剖学的数学方法也取得了进展，这是一个快速成熟的定量技术领域，用于从体积图像分析大脑形态。 这些新方法现在被许多神经影像学和神经信息学小组采用，它们以高度自动化和详细的方式构成了结构表型分析的强大工具;它们对形态变化的微妙和空间复杂模式特别敏感。 在本项目中，我们将继续开发小鼠脑的定量分析方法，并使用它们来生成C57 BL/6 J小鼠品系脑发育的标准数据，并研究野生型和转基因小鼠模型之间的表型差异。 重点将继续是张量图像的复杂性所带来的数学和计算挑战，这些张量图像是在DTI中获得的（目标1和2），特别是在出生后早期发育期间观察到的快速变化以及转基因小鼠和野生型小鼠之间的形态差异所带来的图像配准和形态分析方面的挑战。 此外，我们将测试我们的方法在3个具体项目的研究中的效用，涉及精神分裂症，自闭症和Rett综合征（Aim 3）的小鼠模型，在合作的努力。 公共卫生相关性：该项目旨在通过高分辨率扩散张量成像研究正常小鼠大脑发育，这是一种MRI对比，可提供脑组织和纤维结构的良好定义。 将进一步开发和验证用于扩散张量图像的高级计算图像分析工具，强调该项目面临的两个挑战：1）出生后早期发育期间的快速解剖变化; 2）野生型和转基因小鼠之间的形态差异。 这些神经信息学工具将应用于3个合作项目，旨在确定野生型小鼠与精神分裂症，自闭症和Rett综合征小鼠模型之间的大脑差异。