A mathematical model of reward-mediated learning in drug addiction.

Tom Chou, Maria R D'Orsogna
Author Information
  1. Tom Chou: Department of Computational Medicine, UCLA, Los Angeles, California 90095-1766, USA. ORCID
  2. Maria R D'Orsogna: Department of Mathematics, California State University at Northridge, Los Angeles, California 91130-8313, USA. ORCID

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, drug-induced incentive salience, and opponent process theory. 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.

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Grants

  1. R01 HL146552/NHLBI NIH HHS

MeSH Term

Behavior, Addictive
Brain
Humans
Models, Theoretical
Motivation
Reward
Substance-Related Disorders

Word Cloud

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