A Multilevel Computational Characterization of Endophenotypes in Addiction

Abstract

Substance use disorders are 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 literature. To investigate 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 drug consumption. 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.

Modeling subjective belief states in computational psychiatry: interoceptive inference as a candidate framework

“…Computational psychiatry is a new and interdisciplinary field that seeks to understand the mechanisms underlying mental function and dysfunction using computational approaches (Friston et al 2014;Maia and Frank 2011;Montague et al 2012). This nascent field has enjoyed much success since its inception, providing powerful explanations and predictions for a wide range of disorders; including schizophrenia and psychosis (Adams et al 2013b;Braver et al 1999;Powers et al 2017), depression (Rutledge et al 2017), bipolar disorder (Mason et al 2017), anxiety (Browning et al 2015), autism (Lawson et al 2017;Lawson et al 2014), ADHD (Hauser et al 2016;Hauser et al 2014;Hauser et al 2017b), OCD (Gillan et al 2016;Hauser et al 2017a), and addiction (Fiore et al 2018;Gu 2018;Gu and Filbey 2017;Redish and Johnson 2007). The majority of these studies and theories have focused on overt behaviors, and in particular choice behaviors, using computational models of learning and decision-making.…”