GENERATION AND DESCRIPTION OF DENDRITIC MORPHOLOGY

树枝状形态的产生和描述

• 批准号: 2884854
• 负责人: GIORGIO A ASCOLI
• 金额: $ 10万
• 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 2003-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/2884854
• 关键词: computational neuroscience; computer simulation; dendrites; electrophysiology; informatics; motor neurons; single cell analysis

DESCRIPTION: (Applicant's Abstract) Despite a general agreement among neuroscientists that dendritic morphology plays an important role in shaping cellular physiology and network connectivity, computational tools for detailed neuromorphological modeling are so far lacking. Such a gap is particularly surprising considering the vast amount of experimental data on the three-dimensional shape of many neuronal classes available in the literature, and the increasingly powerful sophistication of computer graphics and virtual reality. This research project aims at filling this gap. Cajal envisioned neuronal shape as determined by a finite number of intrinsic phenomena, modulated by the extrinsic effect of the environment. Based on this notion, several local rules correlating morphological parameters (e.g. branch diameter and length) have proved to be powerful and parsimonious descriptors of specific aspects of dendritic topology. We are using these successful correlations, together with global geometrical constraints, to implement descriptive algorithms for dendritic morphology. These algorithms will be assembled into a software package, named L-Neuron, for the generation and study of anatomically plausible neuronal analogs. Our implementation is based on L-system, a well-known mathematical formalism particularly suitable to describe branching and recursive structures, and extensively developed in computer graphics. L-Neuron will use experimental distributions of parameters from real-cell anatomical data to generate virtual neurons of various morphological classes. Within each class, the statistically constrained stochastic implementation of the algorithm will produce multiple, non-identical neurons. The generation of sets of virtual neurons is biologically relevant because it discriminates between important morphological parameters and emergent byproducts, which represent redundancies. If the algorithm actually produces accurate and realistic structures, it must contain all the required information and thus completely describes the original morphological family. If there are residual discrepancies between virtual and real neurons, their analysis may lead to the discovery of new geometric constraints and quantitative correlations between dendritic parameters. Generating complete models of dendritic geometry in virtual reality thus stimulates the development of analytical strategies to test whether the virtual neurons are morphologically equivalent to the real ones. L-Neuron will output neuronal structures into various formats, including virtual reality, standard graphic, and anatomical files, also used by compartmental modeling programs such as GENESIS. This variety of options will allow the display, dynamical rendering and quantitative analysis of data as well as their efficient exchange among research groups. The limitation of L-Neuron consists in being oriented toward single-cell analysis, thus making it less suitable for studying the effect of neuronal morphology on network connectivity. However, the simplicity of this system also represents an important advantage because it allows the analysis of the influence of specific intrinsic and extrinsic determinants on neuronal shape, and consequently on neuronal electrophysiology. We believe that this package, portable to all major platforms and freely distributed, will further neuroanatomy, computational modeling, and scientific education.

描述：（申请人摘要） 尽管神经科学家普遍认为树突形态 在塑造细胞生理学和网络方面发挥着重要作用 连接性，用于详细神经形态学建模的计算工具是 至今还缺乏。考虑到巨大的差距，这种差距尤其令人惊讶。 许多神经元三维形状的实验数据量 文献中提供的类，以及日益强大的 计算机图形学和虚拟现实的复杂性。本研究项目 旨在填补这一空白。卡哈尔设想神经元的形状是由 有限数量的内在现象，由外部效应调制 环境。基于这一理念，一些地方相关法规 形态参数（例如分枝直径和长度）已被证明是 树突特定方面的强大且简洁的描述符 拓扑。我们正在利用这些成功的相关性以及全球 几何约束，实现树突的描述算法 形态学。这些算法将被组装成一个软件包，命名为 L-Neuron，用于生成和研究解剖学上合理的神经元 类似物。我们的实现基于 L 系统，这是一种著名的数学 形式主义特别适合描述分支和递归结构， 并在计算机图形学领域得到广泛发展。 L-Neuron 将使用实验性 来自真实细胞解剖数据的参数分布以生成虚拟 各种形态类别的神经元。在每个班级中，统计 该算法的约束随机实现将产生多个， 不相同的神经元。虚拟神经元组的生成为 具有生物学相关性，因为它区分了重要的形态学 参数和紧急副产品，代表冗余。如果 算法实际上会产生准确且真实的结构，它必须包含 所有必需的信息，从而完整地描述了原始内容 形态家族。如果虚拟和实际之间存在残余差异 真实的神经元，它们的分析可能会导致新几何的发现 树突参数之间的约束和定量相关性。 在虚拟现实中生成完整的树突几何模型 刺激分析策略的发展，以测试虚拟 神经元在形态上与真实神经元相同。 L-神经元将输出 神经元结构转化为各种格式，包括虚拟现实、标准 图形和解剖文件，也由房室建模程序使用 例如创世记。这种多种选项将允许显示、动态 数据的呈现、定量分析及其高效交换 研究小组之间。 L-神经元的局限性在于定向 偏向于单细胞分析，因此不太适合研究 神经元形态对网络连接的影响。然而，简单性 该系统的另一个重要优点是，它允许 分析特定内在和外在决定因素对 神经元形状，从而影响神经元电生理学。我们相信 该软件包可移植到所有主要平台并免费分发，将 进一步的神经解剖学、计算建模和科学教育。