BIOSTATISTICAL RESEARCH CORE

生物统计研究核心

• 批准号: 8074012
• 负责人: JOSEPH T. COYLE
• 金额: $ 10.6万
• 依托单位: MCLEAN HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-06-01 至 2011-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8074012
• 关键词: Algorithms; Analysis of Variance; Animals; Behavioral; Cereals; Computers; Data; Dependence; Detection; Environment; Experimental Designs; Familiarity; Heterogeneity; Hippocampus (Brain); Impaired cognition; Interneurons; Literature; Location; Logistic Regressions; Maps; Markov Chains; Methods; Modeling; Monte Carlo Method; N-Methyl-D-Aspartate Receptors; Neurons; Odors; Probability; Research; Resolution; Schizophrenia; Signal Transduction; Silver; Technology; Testing; Update; Weight; Work; base; brain cell; cell assembly; density; expectation; gamma-Aminobutyric Acid; receptor function; research study

The Biostatistical Research Core (BRC) will continue to serve the six projects through applications of classical, modern, and newly-emerging quantitative methods for experimental design and analysis of empirical findings. In addition, the BRC will work with the Computational and Animal Behavioral Cores to develop new computational and biostatistical technologies for the projects. The BRC will also serve to integrate the efforts of all three cores. The major continuing challenge of the BRC is to push biostatistical interactions with the projects beyond mere confirmation of their expectations towards the discovery of previously undetected signals hidden in noisy data. With the Benes project and the Animal Behavioral Core, the BRC will apply mixed-effects Poisson analyses of variance models to test the hypotheses that GABA blockade and reduced NMDA receptor function in hippocampal CA sectors can result in aspects of cognitive dysfunction found in schizophrenia by assessing the significance of Treatment X Environment interactions in the novelty detection study. Computer-intense Bayesian Markov Chain Monte Carlo (MCMC) methods will also be employed. In a combined analysis, the DISH experiments will employ similar yet weighted Poisson models with silver grain NR2A densities as weights for the counts of GAD67-positive hippocampal interneurons. We will also apply our existing semi-automatic algorithms for detection of Fos bodies. With the Lisman project and the Computational Core, we will apply MCMC technology to revise prior connectivitymorphology probability mappings based on existing literature by new data likelihoods obtained from studies in the Benes project to yield updated posterior probability mappings. The Greene project and the Animal Behavioral Core will benefit from our longitudinal mixed-effects logistic regression models applied to their odor familiarity and recognition study. The BRC will serve as consultant to the Coyle, Yurgelun-Todd and Goff projects on an as needed basis. Further biostatistical applications include tests to distinguish between local spatial heterogeneity (different neuronal densities in different regions) and spatial dependence (correlations between neuronal locations) and Poisson random field methods to detect features of 3D brain cell assemblies at multiple levels of spatial resolution.

生物统计研究核心（BRC）将继续通过申请为六个项目服务 经典，现代和新出现的定量方法，用于实验设计和分析 经验发现。此外，BRC将与计算和动物行为核心一起工作 为项目开发新的计算和生物统计技术。 BRC也将用于 整合所有三个核心的努力。 BRC的主要持续挑战是推动生物统计学 与项目的互动不仅仅是他们对发现的期望 以前未发现的信号隐藏在嘈杂的数据中。伴随着贝恩斯项目和动物行为核心， BRC将应用差异模型的混合效应泊松分析来测试GABA的假设 海马CA部门的封锁和降低的NMDA受体功能可能导致认知方面 通过评估治疗x环境相互作用在精神分裂症中发现的功能障碍 新颖的检测研究。计算机强贝叶斯马尔可夫链蒙特卡洛（MCMC）方法将 也被雇用。在组合分析中，Dish实验将采用类似但加权的泊松 带有银谷物NR2A密度的模型作为GAD67阳性海马计数的权重 中间神经元。我们还将应用现有的半自动算法来检测FOS体。与 利斯曼项目和计算核心，我们将应用MCMC技术修改先前的连接态度 从研究中获得的新数据可能性基于现有文献的概率映射 在BENES项目中，以产生更新的后验概率映射。绿色项目和动物 行为核心将受益于我们应用于其的纵向混合效应的逻辑回归模型 气味熟悉和识别研究。 BRC将担任Coyle，Yurgelun-Todd和 Goff根据需要进行项目。进一步的生物统计应用包括测试以区分 局部空间异质性（不同区域的不同神经元密度）和空间依赖性 （神经元位置之间的相关性）和泊松随机场方法检测3D脑特征 在多个空间分辨率的多个级别的细胞组件。