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eNeuro. 2018 Jul 17;5(4). pii: ENEURO.0151-18.2018. doi: 10.1523/ENEURO.0151-18.2018. eCollection 2018 Jul-Aug.

A Multilevel Computational Characterization of Endophenotypes in Addiction.

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School of Behavioral and Brain Sciences, University of Texas at Dallas, Richardson, TX 75080.
Department of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, United Kingdom.
Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona 08018, Spain.
University of Texas Southwestern Medical Center, Dallas, TX 75390.
VA North Texas Health Care System, Dallas, TX 75216.
Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY 10029.


Addiction is characterized by a profound intersubject (phenotypic) variability in the expression of addictive symptomatology and propensity to relapse following treatment. However, laboratory investigations have primarily focused on common neural substrates in addiction and have not yet been able to identify mechanisms that can account for the multifaceted phenotypic behaviors reported in the literature. To fill this knowledge gap theoretically, here we simulated phenotypic variations in addiction symptomology and responses to putative treatments, using both a neural model, based on cortico-striatal circuit dynamics, and an algorithmic model of reinforcement learning (RL). These simulations rely on the widely accepted assumption that both the ventral, model-based, goal-directed system and the dorsal, model-free, habitual system are vulnerable to extra-physiologic dopamine reinforcements triggered by addictive rewards. We found that endophenotypic differences in the balance between the two circuit or control systems resulted in an inverted-U shape in optimal choice behavior. Specifically, greater unbalance led to a higher likelihood of developing addiction and more severe drug-taking behaviors. Furthermore, endophenotypes with opposite asymmetrical biases among cortico-striatal circuits expressed similar addiction behaviors, but responded differently to simulated treatments, suggesting personalized treatment development could rely on endophenotypic rather than phenotypic differentiations. We propose our simulated results, confirmed across neural and algorithmic levels of analysis, inform on a fundamental and, to date, neglected quantitative method to characterize clinical heterogeneity in addiction.


addiction; neural model; phenotyping; reinforcement learning

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