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

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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.