Despite strong theoretical and clinical interest, characterization of the common and distinct neurobiological alterations across drug and behavioral addictions cannot be feasibly addressed within a single neuroimaging study. This research project will fill this knowledge gap by integratively using neuroimaging meta-analytic tools and a large amalgamated resting state fMRI (rs-fMRI) data set to rigorously characterize common (addiction- general) and distinct (drug/condition-specific) network-level brain alterations across addictive disorders. Available neuroimaging meta-analytic tools allow for synthesis of the extant literature and can be exploited to inform common and distinct neurobiological alterations across addiction. In addition, assessment of large-scale brain networks through (meta-analytic and rs-fMRI approaches) provides a more complete and coherent framework to appreciate such addiction-related alterations. As such, the innovative combination of such data streams offers the ability to inform heuristic frameworks guiding future research, fractionation of the addiction phenotype, and identification of neurobiological intervention targets. The overall objective of this project is to quantitatively synthesize the addiction-related neuroimaging literature (Aim 1), that then inform mega-analysis of a large amalgamated rs-fMRI data set (Aim 2), the behavioral interpretation of which will be facilitated by emerging meta-analytic techniques (Aim 3), thereby enabling cross-drug comparisons of network-level brain alterations. The feasibility of this overall analytic framework is evidenced by significant preliminary work in nicotine addiction. Specifically, this project will comprehensively synthesize the addiction-related neuroimaging literature to identify disrupted addiction-general and drug/condition-specific regional nodes across drug and behavioral addictions (e.g., alcohol, nicotine, marijuana, stimulants, opiates) and behavioral addictions (e.g., gambling, internet gaming) as well as obesity (Aim 1). Harnessing the accumulated volume of published neuroimaging results will allow for direct comparison of conditions that were never compared with each other in the primary studies. Meta-analytically informed hypotheses will be applied to an amalgamated rs-fMRI data set for targeted testing of altered functional connectivity across large-scale brain networks (Aim 2). To more fully contextualize the behavioral consequences of such alterations, we will employ network-level meta-analytic techniques to quantitatively delineate behavioral phenomena linked with regional and network-level alterations impacted by addiction (Aim 3). Efforts to archive, mine, and synthesize the accumulated knowledge of addictions impact on the brain are critical to inform analysis of large neuroimaging data sets generated through amalgamated sources or new data collection efforts.