CORE 1/2: INIA Stress and Chronic Alcohol Interactions: Computational and Statistical Analysis Core (CSAC)

项目摘要/摘要 INIAStress联盟将使用一套不同的科学方法来了解大脑 压力和酒精相互作用的机制。财团之间有一项承诺： 有意识地开展科学，通过以下方式提供跨研究组成部分的协同效应 常见的实验设计和数据获取方法。计算和统计分析 本文建议的核心(CSAC)将实施统计和计算方法，以促进 整合整个联合体创建的数据，从而在 研究组成部分。此项目作为核心运行，因为它并不是要测试特定的 最重要的假设，但更确切地说，它通过编撰各组成部分之间的数据来为财团服务。 实现这一点将使我们更接近生成有效的假设来描述 压力和酒精如何作为AUD的前驱因素。 每个参与研究的组成部分都将产生庞大而复杂的数据集。因此，“大” 将需要“数据”专业知识来确定和实施最佳做法，以确保数据可以集成 研究的各个组成部分。具体目标1概述国资委编制时间序列数据的活动 为了进行分析，请执行这些分析，并准备结果以供发布。这包括方法，如 创新的数据预处理方法、降维方法和人工智能 方法以及其他方法。此外，为了准备开放源码分发的数据，所有数据都将 按照《神经数据无国界》中描述的标准进行格式化。 增加广泛的实验方法和动物模型之间的协同作用 在研究的组成部分中，将这些数据整合到计算模型中是至关重要的。特定的 目标2将通过清晰的数学形式将这些层次的分析联系起来，从而提供更多的协同效应 和严谨。此外，这提供了一种快速而严格的方式来开发新的假设以推动未来的工作， 因为可以在Silico中探索和审查想法。这一目标将把在组件中收集的数据整合到 酒精和压力如何改变大脑功能，并最终改变行为的计算模型。 大型数据集的影响远远超出其最初发布的范围，并可能成为 科学界。因此，在具体目标3中，在组件中创建的数据将在 根据科学界接受的标准，公开存档，并免费提供。 已经与NIH资助的几个储存库达成了协议，这些储存库将托管这些数据。此外，a INIAStress网站的可搜索部分将被创建，以聚合以下免费、开放访问的数据集 与压力和酒精研究人员有关。该门户网站的目标将是提供一个易于访问的、 研究人员可以访问的数据集的完整列表。

Project Summary/Abstract The INIAstress consortium will employ a diverse set of scientific approaches to understand the brain mechanisms that underlie stress and alcohol interactions. There is a commitment among the consortium to deliberately carry out the science in a way that provides synergy across the research components through common experimental designs and data acquisition approaches. The Computational and Statistical Analyses Core (CSAC) proposed herein will implement statistical and computational approaches that will facilitate the integration of the data created throughout the consortium, thus providing synergistic interactions amongst the research components. This project functions as a core because it does not set out to test a specific overarching hypothesis, but rather, it serves the consortium by codifying data among the components. Accomplishing this will bring us closer to the overarching goal of generating impactful hypotheses that describe how stress and alcohol act as an antecedent for an AUD. Each of the participating research components will generate large, complex data sets. Therefore, “Big Data” expertise will be required to identify and implement best practices to ensure that data can be integrated across the research components. Specific Aim 1 outlines the activities of the CSAC to prepare time series data for analysis, perform these analyses, and prepare the results for publication. This includes methods such as innovative data preprocessing methods, dimensionality reduction approaches, and artificial intelligence approaches as well as others. In addition, to prepare data for open source distribution, all data will be formatted in accordance with the standards described in Neurodata Without Borders. To increase synergy amongst the wide-range of experimental approaches and animal models employed in the research components, it is critical to integrate these data into computational models. Specific Aim 2 will link these levels of analysis through clear, mathematical formalisms that will provide added synergy and rigor. Furthermore, this provides a rapid and rigorous way to develop novel hypothesis to drive future work, as ideas can be explored and vetted in silico. This aim will integrate the data gathered in the components into computational models of how alcohol and stress alters brain function and, ultimately, behavior. Large data sets have an impact well beyond their initial publication and can be an enduring resource for the scientific community. Therefore, in Specific Aim 3, the data created in the components will be curated in accordance with standards accepted by the scientific community, publicly archived, and made freely available. Agreements have been reached with several NIH-funded repositories that will host these data. In addition, a searchable section of the INIAstress website will be created to aggregate free, open access data sets that are relevant to stress and alcohol researchers. The goal of this web portal will be to provide an easy to access, comprehensive list of data sets that researchers can access.