Abstract
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. 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.
Significance Statement Addiction is known to encompass heterogeneity in its development, maintenance, and treatment response. While previous work has mostly focused on the common mechanisms underlying vulnerabilities in addiction at a group level, the neurocomputational causes for such intersubject variability in addition are not well-understood. To fill this knowledge gap, we combine a neural and a reinforcement learning model to reveal that the balance between neural circuits or computational control modalities characterizes the presence of behavioral phenotypes in addiction. The presence of converging effects, validated across neural and algorithmic levels of analysis, informs on a quantitative method to characterize clinical heterogeneity, and potentially helps future development of precision treatments.
Footnotes
Authors report no conflict of interest.
This work is supported by the Dallas Foundation and a startup grant from UT Dallas.
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
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