Multiscale Analysis of Neuronal Morphology

神经元形态学的多尺度分析

• 批准号: 7279961
• 负责人: SUSAN L WEARNE
• 金额: $ 32.14万
• 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/7279961
• 关键词: Multiscale; Analysis; Neuronal; Morphology

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（由申请人提供）：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的形态改变：从单个脊柱的细粒度几何形状到被占据空间的组织病理学病变扭曲的多神经元和脉管系统网络的整体拓扑。 要从机制上理解这些结构变化在产生观察到的认知缺陷中的作用，需要神经元形态的准确 3D 表示，以及可以直接将多个尺度的结构变化与改变的神经元放电模式联系起来的真实生物物理模型。 然而迄今为止，还没有能够在局部和全局尺度以及真正的 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种自动分析系统，用于对详细而准确的神经元形态进行数字化、3D 重建和几何分析，能够处理从局部脊柱几何形状到复杂的树拓扑到多神经元网络的总体空间排列的形态细节。 作为一个具体的例子，我们将分析阿尔茨海默氏病 (AD) 的 Tg2576 小鼠模型的形态变化，其中淀粉样蛋白沉积、皮质微血管系统的改变和神经异常提供了易于识别的病理病变的例子。 四个具体目标将实现这一广泛目标：(1) 开发一种半自动化系统，用于从激光扫描显微镜 (LSM) 成像数据中进行 3D 树提取和脊柱分析，具有亚体素分辨率，可在最精细的尺度上进行精确的神经元形态测量； (2) 对人类和 Tg2576 小鼠 AD 模型的 3D 单个神经元、多神经元和脉管系统网络以及老年斑进行成像和数字化； (3) 开发用于 3D 空间复杂细胞结构全局分析的工具； (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，正常衰老和神经退行性疾病中树突结构、脊柱几何形状和网络分支模式的改变决定工作记忆和认知功能的病理。 此类研究将为理解记忆诱导和维持的一般机制、正常认知功能、疾病状态下的功能障碍以及其恢复的潜在机制提供重要的见解。