A Mathematical Model of Reward-Mediated Learning in Drug Addiction

Webinar Presented By:  Professor Maria R D'Orsogna, Department of Mathematics, California State University

Speaker Bio:  Maria R D'Orsogna received her PhD in Theoretical Physics from UCLA in 2003.  She is currently the Associate Director of the Pure and Applied Math at UCLA and a professor of Mathematics at California State University at Northridge.  Her scientific interests cover a wide range of topics, primarily within biology and sociology, that she studies using the frameworks of statistical mechanics, game theory, data analysis, numerical simulations, and stochastic processes.  Prof. D'Orsogna is the author of more than sixty referred publications.  Her work is supported by the NSF, NASA, and the Army Research Office.

Abstract:  We propose a mathematical model that unifies the psychiatric concepts of drug-induced incentive salience (IST), reward prediction error (RPE) and opponent process theory (OPT) to describe the emergence of addiction within substance abuse.  The biphasic reward response (initially positive, then negative) of the OPT is activated by a drug-induced dopamine release, and evolves according to neuro-adaptative brain processes.  Successive drug intakes enhance the negative component of the reward response, which the user compensates for by increasing the drug dose.  Further neuroadaptive processes ensue, creating a positive feedback between physiological changes and user-controlled drug intake.  Our drug response model can give rise to qualitatively different pathways for an initially naive user to become fully addicted.  The path to addiction is represented by trajectories in parameter space that depend on the RPE, drug intake, and neuroadaptive changes.  We will discuss how our model can be used to guide detoxification protocols using auxiliary substances such as methadone, to mitigate withdrawal symptoms.

Co-authors:  Davide Maestrini, Tom Chou & Maria R. D'Orsogna

Date and Time:  February 26, 2021 | 4:00 p.m. Eastern Standard Time (US and Canada)

Location:  Online

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