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

CORE 1/2：INIA 压力和慢性酒精相互作用：计算和统计分析核心 (CSAC)

• 批准号: 10574618
• 负责人: CHRISTOPHER COURT LAPISH
• 金额: $ 49.39万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-02-16 至 2027-01-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10574618
• 关键词: Adoption; Agreement; Alcohols; Animal Model; Animals; Archives; Artificial Intelligence; Behavior; Big Data; Brain; Chronic; Communities; Complex; Computer Analysis; Computer Models; Consult; Data; Data Aggregation; Data Analyses; Data Set; Dimensions; Electrophysiology (science); Ensure; Experimental Designs; Fiber; Fracture; Funding; Future; Goals; Human; Knowledge; Lead; Link; Mathematics; Methods; Mission; Modeling; Neurons; Outcome; Photometry; Play; Positioning Attribute; Publications; Research; Research Personnel; Resources; Risk; Risk Factors; Risk Marker; Role; Science; Secure; Series; Signal Transduction; Site; Sorting; Source; Standardization; Statistical Data Interpretation; Statistical Models; Stress; System; Techniques; Testing; Time; Time Series Analysis; Translations; United States National Institutes of Health; Work; alcohol use disorder; biophysical properties; complex data; data access; data acquisition; data integration; data repository; denoising; density; experimental study; human data; improved; in silico; innovation; large datasets; neural; novel; open data; patch clamp; pre-clinical; repository; synergism; translational impact; web portal; web site

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.

项目概要/摘要 INIAstress 联盟将采用多种科学方法来了解大脑 酒精压力和相互作用的机制 该联盟承诺： 有意识地以一种在研究组成部分之间提供协同作用的方式开展科学研究 常见的实验设计和数据采集方法。 本文提出的核心（CSAC）将实施统计和计算方法，以促进 整合整个联盟创建的数据，从而在各联盟之间提供协同互动 该项目作为核心发挥作用，因为它并不打算测试特定的内容。 总体假设，相反，它通过在组件之间编码数据来为联盟服务。 实现这一点将使我们更接近产生有影响力的假设的总体目标，这些假设描述了 压力和酒精如何成为澳元的前因。 每个参与的研究组件都会生成大型、复杂的数据集，因此“大”。 需要“数据”专业知识来识别和实施最佳实践，以确保数据可以集成 具体目标 1 概述了 CSAC 准备时间序列数据的活动。 进行分析，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 此外，为了准备用于开源分发的数据，所有数据都将被公开。 根据 Neurodata Without Borders 中描述的标准进行格式化。 增强各种实验方法和动物模型之间的协同作用 在研究组件中使用时，将这些数据集成到特定的计算模型中至关重要。 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，从而提供额外的协同作用 此外，这提供了一种快速而严格的方法来发展新的假设来推动未来的工作， 因为可以在计算机中探索和审查想法，这一目标将把组件中收集的数据集成到其中。 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，并且可以成为持久的资源 因此，在具体目标 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。 已与 NIH 资助的多个存储库达成了协议，这些存储库将托管这些数据。 将创建 INIAstress 网站的可搜索部分，以汇总免费、开放获取的数据集，这些数据集 该门户网站的目标是为压力和酒精研究人员提供一个易于访问、 研究人员可以访问的数据集的完整列表。