Reconstruction & Imaging of Living Nerve Cells

描述（由申请人提供）：脑功能是基于同时发生在神经细胞内部和之间的计算。在单个神经细胞的水平上，来自数千个突触输入的信息被树突棘接收，在那里，这些信息被树突的特定形态和电压门控离子通道的分布处理。我们的两个参与实验室一直在研究这种神经元计算的各个方面，包括突触电位的非线性求和和树突动作电位的信号功能。为了理解这些树突的功能，分析结构和功能之间复杂的相互作用是绝对必要的。这种分析需要神经元的计算模型，同时包含神经元结构和离子通道分布的信息。建立这样的模型是困难的，因为技术上的考虑决定了结构和功能是分开获得的。光学成像技术的最新进展，现在使我们能够获得活神经细胞的结构，并在一次实验中进行多位点的神经元功能记录。然而，仍然必须选择功能成像的部位。我们建议根据所研究的神经细胞的在线模拟来选择这些最佳的记录位置。这样的模拟需要在急性实验的短时间框架内获得结构信息，进行形态重建，并构建神经元的室室模型。模拟的输出将通过确定将产生关于所研究过程的最多信息的位置来指导功能成像。最后，将采集到的功能成像数据纳入计算模型，进行进一步实验。

DESCRIPTION (provided by applicant): Brain function is based on the computation that occurs concurrently within and among nerve cells. At the level of the single nerve cell, information from thousands of synaptic inputs is received at the dendritic spines, where it is processed by the dendrites' specific morphology and distribution of voltage-gated ion channels. Two of our participating labs have been investigating various aspects of this Neuronal Computation, including nonlinear summation of synaptic potentials and signaling functions of dendritic action potentials. To understand these dendritic functions, it is absolutely necessary to analyze the complex interplay of structure and function. Such analysis requires computational models of the neuron incorporating both information about the neuron's structure and its distribution of ion channels. Building such models has been difficult because technical considerations have dictated that structure and function be acquired separately. Recent advances in optical imaging techniques, now allow us to acquire the structure of living nerve cells and perform multi-site recording of neuronal function during a single experiment. However, choices as of the sites for functional imaging must still be made. We propose to choose these optimal recording sites based on an on-line simulation of the nerve cell under study. Such a simulation requires that structural information be acquired, a morphological reconstruction be performed, and a compartmental model of the neuron be constructed, during the short time frame of an acute experiment. The output of the simulation will guide the functional imaging by identifying sites that will yield the most information about the process under study. Finally, the acquired functional imaging data would be incorporated into the computational model for further experiments. The goal of this project is the development of a computational and experimental framework to allow real-time mapping or functional imaging data (e.g., spatio-temporal patterns of dendritic voltages or intracellular ion) to neuronal structure, during the very limited duration of an acute experiment. In order to accomplish this goal, the research objectives of this proposal are the following: . To develop the theoretical framework and computational techniques for on-line, robust, and accurate morphological reconstruction of a fluorescently labeled live nerve cell from stack of optical sections obtained using non-invasive structural imaging. . To predict on-line a nerve cell's behavior using the reconstructed morphology and a priori knowledge regarding the distribution of ion channels embodied in a compartmental model. . To guide functional imaging based on predictions of the model and the reconstructed morphology. . To optimize the computational model of the neuron by minimizing error between the predictions and the data acquired during functional imaging. The impact of the proposed project is in its enhancement of the data acquisition process, particularly in optimizing the value of multi-site optical recordings, and in the focused and directed incorporation of data into quantitative computational models of nerve cells. Our computational and experimental framework will guide the efficient design of experiments and the generation of new hypotheses that can help reveal functional mechanisms underlying both normal and diseased states of the nervous system, both for us and for other researchers. The successful completion of the proposed research requires input from neuroscience, bio-imaging, biophysics, and computer science. Thus, our project demands collaboration and complementary expertise for its success - our team is uniquely suited to accomplish this challenge and also to attract and train excellent students.