Online Multivariate Statistical Neural Imaging Data Analysis

在线多元统计神经成像数据分析

• 批准号: 7989731
• 负责人: THOMAS J ROYSTON
• 金额: $ 24.2万
• 依托单位: UNIVERSITY OF ILLINOIS AT CHICAGO
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-01 至 2012-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7989731
• 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; 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; Route; 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

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(申请人提供)：近年来，成像技术的爆炸性发展使我们非常接近使用单神经元分辨率成像脑功能的目标：使用表达报告和批量染料加载等标记技术，结合快速帧CCD、光电二极管阵列等成像方法，以及传统和多光子共聚焦成像，我们现在可以以单细胞分辨率在细胞培养、脑切片制备甚至完整动物的水平上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，导致收集大量信息的能力比我们使用标准工作流模型处理和理解信息的速度快得多。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。我们建议通过有重点的技术开发提案来解决这些和其他问题，该提案符合神经技术研究、开发和增强计划解决的基于问题/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。我们将为基于图像的时间序列的数据收集、元标记和存储开发一个通用框架。我们将开发和实施算法，允许在实验操作期间在线计算近似或精确的主成分和多变量光谱特性。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。通过实施比率成像数据的多变量分析方法，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。。 与公共健康相关：越来越多的生理成像正在帮助我们了解大脑在健康和疾病中是如何工作的。这项工作与公共健康相关，因为它将为提高生理成像的效率和灵敏度提供必要的工具。此外，它还将为生理成像作为高通量工具的使用铺平道路。