Abstract Substance use disorders (SUD) and obesity are both major public health concerns in the United States, with an estimated 20.8 million Americans struggling with at least one SUD in 2015 and 78.6 million adults and 12.7 million children who are obese. Cue-elicited craving is a central symptom of both drug addiction and binge eating and a strong predictor of relapse. Compared to other SUD symptoms, craving is also much more resistant to treatment. Unfortunately, our understanding of the neurobiological basis of cue-induced craving is still limited, especially compared to the wealth of existing human neuroimaging data. This is partially due to the lack of big data collectives (i.e. fMRI studies have mostly been conducted in isolation from each other) as well as the scarcity of model-based computational analysis in neuroimaging studies on addiction and obesity. The overarching goal of this project is to use multi-level, model-based computational methods to re-analyze six existing fMRI datasets that examine cue reactivity and craving across a total of 954 individuals with substance use or binge eating (59 tobacco smokers, 254 cannabis users, 598 binge drinkers, and 43 binge eating adults). We will address three timely aims using novel computational modeling methods: 1) conduct Bayesian model- based analyses to examine the common and distinct computational mechanisms of drug and food craving across different groups; 2) use dynamic causal modeling to quantify directed coupling between neural regions involved in cue reactivity shared by or unique to different substance using and binge eating groups; 3) explore how models of cue-elicited craving are modulated by the severity of substance use and binge eating. Findings from this project will greatly enhance our understanding of the neural and computational mechanisms underlying craving and cue reactivity in drug addiction and binge eating. The implication of these results could be far-reaching, because 1) craving is a common and core phenotype across different substance use and binge eating groups; 2) these advanced modeling methods could be applied to many other pathologies related to dysfunctional craving and reward processing; and 3) how these mechanisms differ between more severe (e.g. SUD) and less severe (e.g. non-SUD) individuals could provide mechanisms that might protect an individual from developing SUD.