Neurocomputational Mechanisms for Addiction Heterogeneity, Impulsivity and Perseverance

项目总结

Project Summary Studies in substance use disorders (SUDs) have identified a profound inter-subject variability, where a wide variety of multifaceted, dissociable behavioral phenotypes are correlated with addiction development and symptomatology. Even apparently incompatible behavioral expressions, such as impulsivity and inelasticity or perseverance, have been found to co-occur in SUDs and to signal similar addiction vulnerabilities. However, few neural or computational mechanisms have been described so far to account for such seemingly contradicting findings and individual differences, thus hindering the development of individualized diagnosis and treatment. The overarching goal of this project is to validate a new model of addiction using Nicotine Use Disorder (NUD) as test case. We propose to expand on previous theories to provide a more comprehensive neuro-computational framework of addiction that includes phenotypic variability and co-occurrence of impulsivity and perseverance, characterized in terms of effective connectivity in cortico-striatal circuits. The scientific premise for this project is grounded in decades of human and non-human animal work which have demonstrated the roles played in addiction by the ventral and dorsal corticostriatal systems, respectively responsible for goal oriented and habitual behavior. The simulation of the neural dynamics in these two circuits has allowed our model to describe addiction on two independent dimensions. On a first dimension, addictive drugs such as nicotine result in increased circuit gain and state transition stability in both ventral and dorsal cortico-striatal systems, amplifying preliminary evidence (impulsivity) and making choice selections become inelastic due to a feedback effect (perseverance). On a second dimension, which is not necessarily affected by drug exposure, our models converge in suggesting that this gain-related over-stability of both cortico-striatal circuits is aggravated by the presence of a “dominance” of either of the two circuit over the other. In aim 1, we will validate the model prediction that high circuit gain predicts greater behavioral impulsivity and perseverance. In aim 2 we will validate the model prediction that the balance between the two cortico-striatal circuits predicts drug use severity. Circuit gain and circuit balance will be tested in NUD individuals (n=32) and healthy controls (n=32), tasked with decision-making tasks. Circuit gain will be measured in terms of effective connectivity between cortical and striatal areas, within each circuit, and estimated with the use of Dynamic Causal Modelling (DCM). Circuit balance will be estimated using DCM for the ventro-dorsal effective connectivity, to establish dominance on a gradient. This proof-of-concept project can provide a new computational framework for drug addiction, and a quantitative model to characterize clinical heterogeneity, eventually informing individualized treatments.