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

项目总结/摘要 INIAstress联盟将采用多种科学方法来了解大脑 压力和酒精相互作用的基础机制。财团承诺， 有意识地开展科学研究，通过以下方式提供跨研究组成部分的协同作用： 常见的实验设计和数据采集方法。计算和统计分析 本文提出的核心（CSAC）将实现统计和计算方法，这将有助于 整合整个联合体创建的数据，从而在 研究组成部分。这个项目作为一个核心功能，因为它并不打算测试一个特定的 总体假设，而是通过编纂各组成部分之间的数据为联合体服务。 实现这一目标将使我们更接近产生有影响力的假设的总体目标， 压力和酒精是如何影响澳元的 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”专门知识来确定和实施最佳做法，以确保数据能够被整合 跨研究组件。具体目标1概述了CSAC准备时间序列数据的活动 进行这些分析，并准备结果以供发表。这包括以下方法 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。此外，为了准备开放源代码分发的数据，所有数据都将 根据Neurodata Without Borders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 在研究部分使用的数据中，将这些数据整合到计算模型中至关重要。具体 目标2将通过清晰的数学形式主义将这些层次的分析联系起来，这将提供额外的协同作用 和僵硬此外，这提供了一种快速和严格的方法来开发新的假设，以推动未来的工作， 因为创意可以通过电脑进行探索和审查。这一目标将把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过其最初的出版物，可以成为一个持久的资源， 科学界。因此，在Specific Aim 3中，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。 已经与几个NIH资助的存储库达成了协议，这些存储库将托管这些数据。另外还有按 INIAstress网站的可搜索部分将被创建，以汇集免费的，开放获取的数据集， 与压力和酒精研究者有关。该门户网站的目标是提供一个易于访问， 研究人员可以访问的数据集的全面列表。

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.