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A mathematical model of reward-mediated learning in drug addiction
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.
If this is useful here are my co-authors:
Davide Maestrini, Tom Chou, Maria R. D’Orsogna
Date:
5 March 2021, 14:00
Venue:
Mathematical Institute, Woodstock Road OX2 6GG
Speaker:
Professor Maria D'Orsogna (Dept of Mathematics California State University Northridge)
Organising department:
Mathematical Institute
Organiser:
Sara Jolliffe (University of Oxford)
Organiser contact email address:
sara.jolliffe@maths.ox.ac.uk
Host:
Dr Ruth Baker (University of Oxford)
Part of:
Mathematical Biology and Ecology
Booking required?:
Not required
Audience:
Members of the University only
Editor:
Sara Jolliffe
