Management and Analysis of Mouse Behavioral Datasets

小鼠行为数据集的管理和分析

• 批准号: 7540463
• 负责人: Laurence H. Tecott
• 金额: $ 47.64万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2007
• 资助国家: 美国
• 起止时间: 2007-01-24 至 2009-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/7540463
• 关键词: Algorithms; Appearance; Archives; Attention; Behavior; Behavioral; Biological; Biomedical Research; Brain; Cluster Analysis; Collection; Data; Data Collection; Data Quality; Data Reporting; Data Set; Detection; Development; Devices; Elements; Environmental Risk Factor; Evaluation; Event; Exhibits; Failure; Feeding behaviors; Generations; Genes; Genetic; Genomics; Head; Home environment; Imagery; Inbred Strains Mice; Individual; Information Systems; Intake; Laboratories; Light; Measurement; Mechanics; Methods; Modification; Monitor; Mus; Neural Pathways; Pattern; Procedures; Process; Production; Proteomics; Quality Control; Research; Research Personnel; Resolution; Retrieval; Structure; System; Techniques; Technology; Time; Update; Work; analytical tool; comparison group; data management; design; drinking; experience; feeding; graphical user interface; insight; neurobehavioral disorder; neuropsychological; novel; relating to nervous system; research study; tool; tool development

DESCRIPTION (provided by applicant): A high-resolution quantitative approach to the monitoring of spontaneous home cage behavior in the mouse has a potential to reveal novel insights into the organization of mammalian behaviors relevant to common neurobehavioral disorders. In addition, it can provide a sensitive tool for detecting the impact of genetic factors on brain function. We have developed monitoring systems for the collection of high- resolution home cage behavioral data, and now plan to develop tools for managing the large volumes of information contained within these novel datasets. The proposal consists of three Specific Aims. In Aim 1, we will develop methods for the efficient evaluation of data quality. In particular, tools will be developed to detect errors and variability resulting from device malfunction, idiosyncratic interactions between mice and devices, environmental factors and experimenter error. In Aim 2, we will develop systems for behavioral data management with the aid of Dr. Victor Markowitz, head of the Biological Data Management and Technology Center at Lawrence Berkeley National Laboratory to. These systems will be designed for the efficient organization, storage, retrieval and dissemination of both raw and analyzed home cage behavioral data. In Aim 3, we will establish methods for the identification of basic behavioral elements. Data reduction methods will facilitate the development of analytical approaches to uncover mouse behavioral patterns and their sensitivity to genetic background. Procedures will also be developed for cluster analysis of behavioral patterns and for group comparisons. To assess the sensitivity of these analytical tools, they will be applied to datasets reflecting the behavioral patterns of genetically diverse strains of inbred mice. We anticipate that the tools and datasets to be developed in this proposal will be broadly applicable to the use of mice for biomedical research.

描述（由申请人提供）：一种高分辨率的定量方法来监测小鼠自发的家庭笼子行为，有可能揭示与常见神经行为障碍相关的哺乳动物行为组织的新见解。此外，它可以为检测遗传因素对脑功能的影响提供一个敏感的工具。我们已经开发了用于收集高分辨率家庭笼子行为数据的监测系统，现在计划开发用于管理这些新数据集中包含的大量信息的工具。该建议包括三个具体目标。在目标1中，我们将开发有效评估数据质量的方法。特别是，将开发工具来检测由设备故障、鼠标和设备之间的特殊相互作用、环境因素和实验者错误引起的错误和可变性。在目标2中，我们将在劳伦斯伯克利国家实验室生物数据管理和技术中心主任Victor Markowitz博士的帮助下开发行为数据管理系统。这些系统将被设计用于有效地组织、存储、检索和传播原始和分析的家庭笼子行为数据。在Aim 3中，我们将建立识别基本行为元素的方法。数据简化方法将促进分析方法的发展，以揭示小鼠的行为模式及其对遗传背景的敏感性。还将制定行为模式聚类分析和群体比较的程序。为了评估这些分析工具的敏感性，它们将被应用于反映遗传多样性的近交系小鼠行为模式的数据集。我们预计，本提案中开发的工具和数据集将广泛适用于使用小鼠进行生物医学研究。