Substance use behavior (smoking/drinking) is well known to be the result of a complex interplay between genetics and environmental components. However macro- and micro-environmental effects on smoking and drinking behavior are still poorly understood and there is little work evaluating the relationship of such factors to neurobiological changes measured via brain imaging. The data are available though. There is a great amount of internationally collected imaging and genomics data, however much of this data is not being analyzed due to privacy issues or other factors which prevent sharing of the raw data. To address this barrier, we propose to leverage and extend a software platform which enables decentralized analysis of data (e.g. sharing without sharing). We will use this platform to perform an analysis which pools together data from the 10,000 participant longitudinal US-based adolescent brain cognitive development (ABCD) study with an international cohort of more than 20,000 participants from Europe, China, and India called the global imaging genetics of adolescents (GIGA). Our focus will be on assessing macro- and micro-environmental and heritability factors associated with sub- stance use behavior as well as their neuronal biomarkers in the context of factors such as cultural acceptance, urbanization, annual household income, and climate. Results will provide new insights into the factors contrib- uting to substance use behavior. We also propose to develop a persistent decentralized analysis nodes for the GIGA sites using our Collaborative Informatics and Neuroimaging Suite Toolkit for Anonymous Computation (COINSTAC) to allow for re-analyses of these large cohorts as well as pooling with one?s local data without requiring collocation of the data at the same site. 3
Data collected from large distributed cohorts provides an unprecedented opportunity to study the impact of both macro and micro genetic and environmental factors on substance use. We will leverage and extend a decentralized analysis platform which enables us to analyze data from a large multisite international consortium and combine it with the large US-based adolescent brain cognitive development (ABCD) study. Results will provide important new insights into identifying optimal intervention time windows for substance use behavior and their associated genomic and neural biomarkers. 2
