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Online Multivariate Statistical Neural Imaging Data Analysis

在线多元统计神经成像数据分析

基本信息

批准号：
8099695
负责人：
THOMAS J ROYSTON
金额：
$ 19.14万
依托单位：
UNIVERSITY OF ILLINOIS AT CHICAGO
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-07-01 至 2014-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8099695
关键词：
Address Adopted Algorithms Animals Brain Brain imaging Cell Culture Techniques Cells Characteristics Classification Code Computer Simulation Computer software Computers Coupled Data Data Analyses Data Collection Data Set Databases Development Disease Dyes Engineering Environment Experimental Models Explosion Feedback Funding Future Generic Drugs Goals Health Image Image Analysis Imaging technology Individual Label Laboratories Meta-Analysis Methods Modeling Neurons Noise Optics Pattern Physiological Polishes Preparation Process Public Domains Public Health Reliance Reporter Research Resolution Series Signal Transduction Slice Software Tools System Systems Biology Techniques Technology Testing Time Work base computerized data processing imaging modality multicore processor neurotechnology novel programs prototype public health relevance ratiometric relating to nervous system research and development research study skills spatiotemporal technology development tool

项目摘要

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.

描述（由申请人提供）：近年来，成像技术的爆炸式发展使我们非常接近实现单神经元分辨率成像大脑功能的目标：利用标记技术，如表达报告和散装染料负载，结合成像方法，如快速帧CCD，光电二极管阵列，以及传统的多光子共聚焦成像，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，使得收集大量信息的速度远远快于我们使用标准工作流模型处理和理解信息的速度。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co-PI为该项目带来了互补的技能。我们将开发一个基于时间序列的图像数据收集、元标记和存储的通用框架。我们将开发和实现算法，允许在实验操作期间在线计算近似或精确的主成分和多元光谱特征。我们将实现典型相关分析（CCA）和其他技术来比较多个数据集。通过实施多变量方法来分析比率成像数据，我们将为将这些多变量技术和工具扩展到基于全光谱的数据收集奠定基础。