Multiscale Analysis of Neuronal Morphology

神经元形态学的多尺度分析

• 批准号: 7113127
• 负责人: SUSAN L WEARNE
• 金额: $ 33.1万
• 依托单位: ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
• 依托单位国家: 美国
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7113127
• 关键词: Alzheimer' s disease; bioimaging /biomedical imaging; biophysics; cell morphology; cognition disorders; computational neuroscience; computer program /software; computer simulation; confocal scanning microscopy; digital imaging; genetically modified animals; histology; histopathology; image processing; laboratory mouse; mathematical model; memory disorders; neural degeneration; neuroanatomy; neurons; neuropathology; psychological aspect of aging; three dimensional imaging /topography

DESCRIPTION (provided by applicant): Cognitive impairment in normal aging and neurodegenerative disease is accompanied by altered morphologies on multiple scales: from the fine-grained geometry of individual spines to the global topologies of multi-neuron and vasculature networks that are distorted by space-occupying histopathologic lesions. A mechanistic understanding of the role of these structural changes in producing the observed cognitive deficits requires accurate 3D representations of neuronal morphology, and realistic biophysical modeling that can directly relate structural changes on multiple scales to altered neuronal firing patterns. To date however, no tools capable of resolving, digitizing and analyzing neuronal morphology on both local and global scales, and in true 3D, have been available. The central goal of this project is development of an automated analysis system for digitization, 3D reconstruction and geometric analysis of detailed and accurate neuronal morphology, capable of handling morphologic details on scales spanning local spine geometry through complex tree topology to the gross spatial arrangement of multi-neuron networks. As a specific example we will analyze morphologic changes in a Tg2576 mouse model of Alzheimer's disease (AD), in which amyloid deposition, altered cortical microvasculature and neural abnormalities provide easily identifiable examples of pathologic lesions. Four Specific Aims will address this broad objective: (1) To develop a semi-automated system for 3D tree extraction and spine analysis from laser scanning microscopy (LSM) imaged data, with sub-voxel resolution for accurate neuronal morphometry at the finest scales; (2) to image and digitize in 3D individual neurons, multineuron and vasculature networks, and senile plaques from human and Tg2576 mouse models of AD; (3) to develop tools for global analysis of spatially complex cellular structures in 3D; (4) to distribute and maintain all software, and develop a database-driven web repository for distribution of digitized neurons and networks. By providing true 3D morphometry of complex neural structures on multiple scales, the tools developed in this study will enable future multiscale biophysical modeling studies capable of testing hypothesized mechanisms by which altered dendritic structure, spine geometry and network branching patterns in normal aging and neurodegenerative disease determine pathologies of working memory and cognitive function. Such studies will provide crucial insight into general mechanisms of memory induction and maintenance that underlie normal cognitive function, its dysfunction in diseased states, and potential mechanisms for its restoration.

描述（由申请方提供）：正常衰老和神经退行性疾病中的认知障碍伴随着多个尺度上的形态学改变：从个体脊柱的细粒度几何结构到多神经元和脉管系统网络的全局拓扑结构，其被占位性组织病理学病变扭曲。 对这些结构变化在产生所观察到的认知缺陷中的作用的机械理解需要神经元形态的准确3D表示，以及可以将多个尺度上的结构变化与改变的神经元放电模式直接相关的现实生物物理建模。 然而，迄今为止，还没有能够在局部和全局尺度上以及在真正的3D中解析、数字化和分析神经元形态的工具。 该项目的中心目标是开发一个自动分析系统，用于详细和准确的神经元形态的数字化，3D重建和几何分析，能够通过复杂的树形拓扑结构处理跨越局部脊柱几何结构的尺度上的形态细节，以多神经元网络的总体空间排列。 作为一个具体的例子，我们将分析Tg 2576小鼠模型的阿尔茨海默病（AD），其中淀粉样蛋白沉积，改变皮质微血管和神经异常提供了容易识别的病理病变的例子形态学变化。 四个具体目标将解决这一广泛目标：（1）开发一种半自动系统，用于从激光扫描显微镜（LSM）成像数据中提取3D树木和脊柱分析，具有亚体素分辨率，可在最细尺度上进行准确的神经元形态测量;（2）对来自AD的人和Tg 2576小鼠模型的3D个体神经元、多神经元和脉管系统网络以及老年斑进行成像和再现;（3）开发用于三维空间复杂细胞结构的全球分析工具;（4）分发和维护所有软件，并开发数据库驱动的网络存储库，用于分发数字化神经元和网络。 通过在多个尺度上提供复杂神经结构的真实3D形态测量，本研究中开发的工具将使未来的多尺度生物物理建模研究能够测试假设的机制，通过这些机制，在正常衰老和神经退行性疾病中改变的树突结构，脊柱几何形状和网络分支模式确定工作记忆和认知功能的病理。 这些研究将提供重要的洞察记忆诱导和维持的一般机制，这些机制是正常认知功能的基础，疾病状态下的功能障碍及其恢复的潜在机制。