Neuronal Spines Tracking and Analysis for Time-Lapse, 3D Optical Microscopy

我们建议开发一个集成的计算系统来跟踪和分析神经元树突和 在3D延时光学显微镜中观察到的脊椎并管理所有数据。拟议的工作将 克服神经科学研究中的一个障碍，即缺乏定量分析的计算方法 大规模延时图像数据。当前的手动分析仅限于较小的数据集，并且定性地 3D图像的解释。手动分析可能会遗漏详细的神经变化。也是结果 的人工分析不能立即准备好进行数据管理和分析，因为它的工作时间长 用于将手动分析结果存储到数据库。从而寻找治疗神经退行性变的方法 条件等可能会受到阻碍。 我们计划开发集成计算系统，以提高神经元图像的吞吐量 神经科学中的分析。该系统将自动跟踪和分析神经元树突和棘突。 树突和棘突是神经细胞的两种结构，在神经退行性变中表现出变化 条件。例如，研究表明，脊柱会随着时间的推移而变化，可能会出现，也可能会消失 完全是。最新的技术是对树突和棘突进行延时成像，以获得它们的 时间行为。集成的计算系统可以在时间过程中跟踪多个脊椎，并 提取关于它们的重要特征。特征包括它们的长度、宽度、体积等。所有这些 然后，要素将以通用文件格式保存，并可方便地移植到内置数据库中 统计分析。因此，拟议的工作使神经科学家能够进行大规模的时间推移 研究和跟踪更多时间点的神经元变化。所有这些都可能是找到神经系统治疗方法的关键 条件。 今天的显微镜可以在一段时间内对神经细胞进行三维成像。但是，使用 数据有限，因为没有基于计算机的工具来分析和系统管理数据。我们 计划开发一种基于计算机的工具，以提高数据的效率和效力。 公共卫生相关性：该项目使研究人员能够快速筛选数百个神经元图像 研究疾病并确定可能的新药。总体而言，该项目具有促进治疗的影响 阿尔茨海默病等神经系统疾病的发展。

We propose to develop an integrated computational system to track and analyze neuronal dendrites and spines observed in 3D time-lapse optical microscopy and manage all the data. The proposed work will overcome one barrier in neuroscience research, i.e.,lack of computational method to quantitatively analyze large-scale time-lapse image data. Current manual analysis is confined to small datasets and qualitatively interpretation of 3D images. Detailed neurological changes may be missed by manual analysis. Also results of manual analysis are not immediately ready for data management and analysis because of long hours it takes to store manual analysis results to a database. Thus searches for treatment of neurodegenerative conditions and so on may be hindered. Our plan to develop the integrated computational system can increase the throughput of neuronal image analysis in neuroscience. The system will automatically track and analyze neuronal dendrites and spines. Dendrites and spines are two structures of neuronal cell that manifest changes in neurodegenerative conditions. For example, researches have shown that spines change over time, may appear and disappear entirely. The newest technology is to perform time-lapse imaging of the dendrites and spines to obtain their temporal behavior. The integrated computational system can track multiple spines over the time course and extract important features about them. The features include their length, width, volume, etc. All these features are then saved in common file format and can readily be ported into the built-in database for statistical analysis. Therefore, the proposed work enables neuroscientists to conduct large-scale time-lapse study and track neuronal changes at more time points. All can be critical to finding a cure to neurological conditions. Today's microscopy can image neuronal cells in three-dimensions over a period of time. However use of data is limited because there is no computer-based tool to analyze and systematically manage the data. We plan to develop a computed-based tool to increase the efficiency and effectiveness of the data. Public Health Relevance: This project enables researchers to quickly screen hundreds of neuron images to study diseases and identify possible new drugs. Overall, the project has the impact of facilitating therapy development for neurological conditions such as Alzheimer's disease.