Generation and Description of Dendritic Morphology

描述（由申请人提供）：该持续项目旨在以紧凑但足够完整和详细的方式描述树突结构，以允许计算机生成形态学上准确的神经元模型。树突形态在生理和病理脑功能中起着重要作用，它通过维护和塑造网络连接性以及整合神经元接收的突触输入的复杂模式。树突形状的简洁和算法描述是神经系统结构-活性关系定量表征的关键一步，它构成了表示、压缩、存储、交换和放大极其复杂的神经解剖数据的有效方法。神经解剖学的算法和模型已经被开发来模拟和定量分析树突树的三维结构，其格式与用于表示实验重建的神经元的格式相同。 本项目的具体目标是：（1）通过包括三维分支方向的决定因素和生长对局部和全局影响的依赖性，扩展和改进神经解剖学上合理的树突结构和发育算法（例如，分别为直径和神经元大小）;（2）增强和分发分析、建模和数据库软件，以便为实验和计算神经科学家提供基于网络的工具来查询、检索，测量、分类和综合树突形态学数据;（3）继续用不同的实验方案和在出生后早期对海马锥体细胞和脊髓运动神经元进行实验重建和分析;并将这些数据与神经元电生理学的详细生物物理模型相结合。这项研究的信息学和神经科学组成部分深深交织在一起，并跨越各种科学方法，包括“湿”实验，计算模拟，统计分析和数据挖掘。这将需要设计和实施新的神经信息学工具，用于数据处理和集成，并将其分发给更广泛的神经科学界。

DESCRIPTION (provided by applicant): This continuing project is directed at describing dendrite structure in a compact yet sufficiently complete and detailed fashion to allow the computer generation of morphologically accurate neuronal models. Dendrite morphology plays a fundamental role in physiological and pathological brain function by subserving and shaping network connectivity and by integrating the complex pattern of synaptic inputs received by the neuron. A parsimonious and algorithmic description of dendritic shape is a crucial step towards the quantitative characterization of the structure-activity relationship in the nervous system and it constitutes an effective way to represent, compress, store, exchange, and amplify extremely complex neuroanatomical data. Neuroanatomical algorithms and models have been developed to simulate and quantitatively analyze the three-dimensional structure of dendritic trees in the same format used to represent experimentally reconstructed neurons. The specific aims of this project are: (1) to expand and improve neuroanatomically plausible algorithms of dendritic structure and development by including determinants of three-dimensional branch orientation and dependence of growth upon local and global influences (e.g. diameter and neuronal size, respectively); (2) to enhance and distribute the analysis, modeling, and data basing software in order to provide experimental and computational neuroscientists with web-based tools to query, retrieve, measure, classify, and synthesize dendritic morphology data; (3) to continue the experimental reconstruction and analysis of hippocampal pyramidal cells and spinal motoneurons with different experimental protocols and in early postnatal periods; and to integrate these data with detailed biophysical models of neuronal electrophysiology. The informatics and neuroscience components of this research are deeply intertwined and span a variety of scientific approaches, including "wet" experiments, computational simulations, statistical analysis and data mining. This will require the design and implementation of novel neuroinformatics tools for data handling and integration, and their distribution to the wider neuroscience community.