BIOSTATISTICAL RESEARCH CORE

生物统计研究核心(BRC)将继续通过以下应用为六个项目服务 用于实验设计和分析的经典、现代和新兴的定量方法 经验性的发现。此外，BRC将与计算和动物行为核心合作，以 为这些项目开发新的计算和生物统计技术。BRC还将发挥以下作用 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确认他们对发现 隐藏在噪声数据中的以前未检测到的信号。有了贝内斯项目和动物行为核心， BRC将应用方差模型的混合效应泊松分析来检验GABA 阻断和降低海马CA区的NMDA受体功能可导致认知方面的 通过评估治疗X环境交互作用的意义发现精神分裂症患者的功能障碍 新颖性检测研究。计算机密集的贝叶斯马尔可夫链蒙特卡罗(MCMC)方法将 也会被雇佣。在联合分析中，碟子实验将采用类似但加权的泊松 以NR2A型银粒密度为权重的GAD67阳性海马神经元计数模型 中间神经元。我们还将应用我们现有的半自动算法来检测Fos物体。与 利斯曼计划和计算核心，我们将应用MCMC技术来修正先前的连通性形态 基于现有文献的通过研究获得的新数据可能性的概率映射 在Benes项目中生成更新的后验概率映射。格林计划与动物 行为核心将受益于我们的纵向混合效应Logistic回归模型 气味熟悉和识别研究。BRC将担任科伊尔、尤格伦-托德和 戈夫根据需要进行项目设计。进一步的生物统计学应用包括测试以区分 局部空间异质性(不同区域神经元密度不同)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脑的特征 细胞在多个层次的空间分辨率上组装。

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.