Online Multivariate Statistical Neural Imaging Data Analysis

描述（由申请人提供）：近年来，成像技术的爆炸式增长使我们非常接近实现以单神经元分辨率对脑功能进行成像的目标：使用表达报告基因和大量染料加载等标记技术，结合快帧 CCD、光电二极管阵列、传统以及多光子共聚焦成像等成像方法，我们现在可以在细胞培养、脑切片制备甚至完整动物的水平上以单细胞分辨率可视化复杂的神经相互作用。 在我们自己的工作中，我们发现同样的技术与现代计算机相结合，能够比我们使用标准工作流程模型处理和理解信息更快地收集大量信息。 此外，迫切需要采用自动数据库存储和分析结果分类的系统生物学策略。 我们建议通过一项重点技术开发提案来解决这些问题和其他问题，该提案解决了神经技术研究、开发和增强计划所解决的基于问题/需求的标准。 具有实验和计算建模背景的 PI 和具有神经成像数据分析背景的联合 PI 为该项目带来了互补的技能。 我们将开发一个通用框架，用于基于图像的时间序列的数据收集、元标记和存储。 我们将开发和实施算法，以便在实验操作过程中在线计算近似或精确的主成分以及多元光谱特征。 我们将实施典型相关分析（CCA）和其他技术来比较多个数据集。 通过实施用于比率成像数据分析的多元方法，我们将为将这些多元技术和工具扩展到基于光谱的完整数据收集奠定基础。 。 公共健康相关性：生理成像越来越多地帮助我们了解大脑在健康和疾病中的运作方式。 这项工作与公共卫生相关，因为它将提供必要的工具，使生理成像更加高效和灵敏。 此外，它将为使用生理成像作为高通量工具铺平道路。

DESCRIPTION (provided by applicant): In recent years, an explosion of imaging technology has brought us tantalizingly close to achieving the goal of imaging brain function with single neuron resolution: Using labeling techniques such as expressed reporters and bulk dye loading combined with imaging methods such as fast frame CCD, photodiode array, and traditional as well as multiphoton confocal imaging, we can now visualize complicated neural interactions at the level of cell culture, the brain slice preparation and even the intact animal, with single cell resolution. In our own work, we have found that this same technology paired with modern computers has resulted in the ability to collect masses of information far more quickly than we can process and understand it using standard workflow models. Additionally, there is a pressing need to adopt the systems biology strategy of automated database storage and classification of analysis results. We propose to address these and other issues with a focused technology development proposal which adreses the problem/need based criteria addressed by the Neurotechnology Research, Development and Enhancement Program. The PI, with an experimental and computational modeling background, and the co-PI, with a neural imaging data analysis background, bring complementary skills to this project. We will develop a common framework for data collection, meta-tagging and storage of image based time series. We will develop and implement algorithms that will allow for the on-line calculation of approximate or exact principal components and multivariate spectral characteristics during experimental manipulations. We will implement Canonical Correlation Analysis (CCA) and other techniques for the comparison of multiple datasets. By implementing multivariate methods for the analysis of ratiometric imaging data, we will lay the groundwork for extending these multivariate techniques and tools to full optical spectrum based-data collection. . PUBLIC HEALTH RELEVANCE: Increasingly, physiological imaging is helping us to understand how the brain works in health and disease. This work is relevant to public health because it will provide necessary tools for making physiological imaging more efficient and sensitive. In addition, it wil pave the way for using physiological imaging as a high-throughput tool.