Generation and Description of Neuronal Morphology and Connectivity

神经元形态和连接性的生成和描述

• 批准号: 8066283
• 负责人: GIORGIO A ASCOLI
• 金额: $ 29.23万
• 依托单位: GEORGE MASON UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 1999
• 资助国家: 美国
• 起止时间: 1999-08-01 至 2014-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8066283
• 关键词: Address; Algorithms; Alzheimer' s Disease; Architecture; Axon; Base Sequence; Bioinformatics; Biophysics; Brain; Cells; Cellular Structures; Cognitive; Communities; Complex; Computer Simulation; Computer software; Computers; Data; Data Collection; Data Set; Databases; Dendrites; Development; Disease; Documentation; Electrophysiology (science); Epilepsy; Fiber; Funding; Generations; Goals; Grant; Growth; Health; Hippocampus (Brain); Human; Image; Individual; Informatics; Interneurons; Knowledge; Lead; Literature; Measures; Membrane; Memory; Microscopic; Mining; Modeling; Morphology; Nervous system structure; Neuroanatomy; Neurobiology; Neurons; Neurosciences; Neurosciences Research; Output; Pattern; Physiological; Play; Population Statistics; Preparation; Public Health; Quality Control; Research; Research Infrastructure; Resources; Rodent; Role; Series; Shapes; Signal Transduction; Simulate; Statistical Distributions; Structure; Structure-Activity Relationship; Synapses; Training; Trees; Validation; base; comparative; computer infrastructure; data mining; design; digital; innovation; membrane model; morphometry; nervous system disorder; neural circuit; neuroinformatics; novel; open source; programs; reconstruction; relating to nervous system; relational database; simulation; three dimensional structure; tool; usability

DESCRIPTION (provided by applicant): This continuing project is directed at describing neuroanatomical structure in a compact yet sufficiently complete fashion to allow the implementation of biologically plausible and quantitatively accurate computer simulations. Neuronal morphology plays a fundamental role in physiological and pathological brain function by integrating complex patterns of synaptic inputs, transmitting trains of spiking output, and subserving network connectivity. During the previous funding periods (under Generation and Description of Dendritic Morphology), informatics tools were successfully designed and deployed to reproduce the three-dimensional shape of dendritic trees in the same format used to represent experimentally reconstructed neurons. Digital arbors were also combined with computational models of membrane biophysics to investigate the cellular structure-activity relationship. The goal of this application is to expand these software resources and research approach from dendrites to all aspects of neuronal structure, including full axonal arborizations and synaptic connectivity. The general strategy is to resample in stochastic models the experimentally measured statistical distributions, and to compare the resulting simulations directly to the original data. Such comprehensive and parsimonious characterization constitutes an effective way to compress, store, exchange, and amplify extremely complex neuroanatomical information. The project has three logically related, but technically independent specific aims. The first aim is to enhance the power and usability of computational neuroanatomy tools for the analysis and synthesis of neuronal morphology, and to integrate them with leading bioinformatics algorithms enabling large scale knowledge mining of massive data sets. In the second aim, digital reconstruction, quantitative morphometry, and compartmental modeling of branch growth and spike propagation are applied to two distinct classes of axonal arbors, namely hippocampal CA3 interneurons and olivo-cerebellar climbing fibers. The third aim, extending to circuitry, develops a relational database of cellular-level connectivity in the rodent hippocampus. In this framework, population statistics for each neuronal class are stochastically resampled to quantify the network structure-activity relationship. The neurobiological and technological components of this project are deeply intertwined and span a variety of scientific approaches, including microscopic imaging, computational simulations, statistical analysis and data mining. The robust development and open source distribution of the underlying neuroinformatics infrastructure for data handling and integration will continue to benefit the wider neuroscience community. PUBLIC HEALTH RELEVANCE: Brain connectivity and the intricate tree-like shape of individual nerve cells underlie cognitive and physiological functions, and are dramatically altered in almost all known neurological disorders. Using state-of-the-art imaging, statistical analysis, and computational modeling, this project will quantify and synthesize a massive amount of complex neuroanatomical information to investigate the relationship between architecture and function in the nervous system. To maximize impact on the research community, powerful bioinformatics tools and databases will be developed, professionally documented, and freely distributed online for the long lasting benefit of scientific advancement and public health.

描述（由申请人提供）：该持续项目旨在以紧凑但足够完整的方式描述神经解剖结构，以允许实施生物学上合理且定量准确的计算机模拟。神经元形态通过整合突触输入的复杂模式、传递脉冲输出的序列和服务网络连接，在生理和病理脑功能中起着重要作用。在之前的资助期间（树突形态的生成和描述），信息学工具被成功地设计和部署，以相同的格式再现树突树的三维形状，用于表示实验重建的神经元。并结合膜生物物理计算模型研究了细胞的构效关系。本应用程序的目标是将这些软件资源和研究方法从树突扩展到神经元结构的各个方面，包括完整的轴突分支和突触连接。一般的策略是在随机模型中重新抽样实验测量的统计分布，并将结果模拟直接与原始数据进行比较。这种全面而简洁的表征构成了压缩、存储、交换和放大极其复杂的神经解剖信息的有效方法。该项目有三个逻辑上相关，但技术上独立的具体目标。第一个目标是增强计算神经解剖学工具的功能和可用性，用于分析和合成神经元形态，并将它们与领先的生物信息学算法相结合，从而能够对大量数据集进行大规模的知识挖掘。在第二个目标中，数字重建、定量形态测量和分支生长和穗状突起繁殖的室室建模应用于两种不同类型的轴突乔木，即海马CA3中间神经元和橄榄树-小脑攀爬纤维。第三个目标，扩展到电路，开发啮齿动物海马细胞水平连接的关系数据库。在这个框架中，每个神经元类的种群统计数据被随机重新采样，以量化网络的结构-活动关系。这个项目的神经生物学和技术组成部分是紧密交织在一起的，跨越了各种科学方法，包括微观成像、计算模拟、统计分析和数据挖掘。用于数据处理和集成的底层神经信息学基础设施的稳健发展和开源分布将继续使更广泛的神经科学界受益。公共卫生相关性：大脑连通性和个体神经细胞复杂的树状结构是认知和生理功能的基础，并且在几乎所有已知的神经系统疾病中都发生了显著改变。利用最先进的成像、统计分析和计算建模，该项目将量化和综合大量复杂的神经解剖学信息，以研究神经系统中建筑和功能之间的关系。为了最大限度地影响研究界，将开发强大的生物信息学工具和数据库，进行专业记录，并在线免费分发，以长期造福科学进步和公共卫生。