Generation and Description of Dendritic Morphology

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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.