A mathematical model of reward-mediated learning in drug addiction

TL;DR

This paper presents a dynamical systems model of drug addiction that integrates reward prediction error, incentive salience, and opponent process theory to explain neuroadaptive processes leading to addiction.

Contribution

It introduces a simple, interpretable model capturing diverse pathways to addiction and the impact of neuroadaptive responses on drug intake behavior.

Findings

Users with strong negative b-process responses are at higher risk of addiction.

The model explains how positive feedback leads to habituation, tolerance, and addiction.

Mechanisms like methadone can mitigate withdrawal symptoms in the model.

Abstract

Substances of abuse are known to activate and disrupt neuronal circuits in the brain reward system. We propose a simple and easily interpretable dynamical systems model to describe the neurobiology of drug addiction that incorporates the psychiatric concepts of reward prediction error (RPE), drug-induced incentive salience (IST), and opponent process theory (OPT). Drug-induced dopamine releases activate a biphasic reward response with pleasurable, positive "a-processes" (euphoria, rush) followed by unpleasant, negative "b-processes" (cravings, withdrawal). Neuroadaptive processes triggered by successive intakes enhance the negative component of the reward response, which the user compensates for by increasing drug dose and/or intake frequency. This positive feedback between physiological changes and drug self-administration leads to habituation, tolerance and eventually to full addiction. Our model gives rise to qualitatively different pathways to addiction that can represent a diverse set of user profiles (genetics, age) and drug potencies. We find that users who have, or neuroadaptively develop, a strong b-process response to drug consumption are most at risk for addiction. Finally, we include possible mechanisms to mitigate withdrawal symptoms, such as through the use of methadone or other auxiliary drugs used in detoxification.