A Precise Computational Measure of Impulsivity that Signals Relevant Outcomes in Opioid Addiction Treatment

Silvia Lopez-Guzman, Anna Konova, Kenway Louie, Paul Glimcher

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Computational models of impulsive decision-making, like temporal discounting, are widely used to study addiction. However, clinically validating a marker supposes developing methods that provide high accuracy and reliability. We first show that a modified model of temporal discounting incorporating individual-specific risk sensitivity - provides a more precise, unbiased, and reliable measure of
impulsivity than the standard approach. Using this tool, and given the current opioid epidemic, we set out to investigate longitudinally whether discounting would signal relevant negative outcomes like drug use, relapse and dropout in patients undergoing treatment for opioid addiction. We found that changes in discount rates were related to increased drug use in patients, indicating a vulnerability to full relapse and treatment failure.
Original languageEnglish (US)
Title of host publicationCognitive Computational Neuroscience
StatePublished - 2017

Cite this

Lopez-Guzman, S., Konova, A., Louie, K., & Glimcher, P. (2017). A Precise Computational Measure of Impulsivity that Signals Relevant Outcomes in Opioid Addiction Treatment. In Cognitive Computational Neuroscience
Lopez-Guzman, Silvia ; Konova, Anna ; Louie, Kenway ; Glimcher, Paul. / A Precise Computational Measure of Impulsivity that Signals Relevant Outcomes in Opioid Addiction Treatment. Cognitive Computational Neuroscience. 2017.
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abstract = "Computational models of impulsive decision-making, like temporal discounting, are widely used to study addiction. However, clinically validating a marker supposes developing methods that provide high accuracy and reliability. We first show that a modified model of temporal discounting incorporating individual-specific risk sensitivity - provides a more precise, unbiased, and reliable measure ofimpulsivity than the standard approach. Using this tool, and given the current opioid epidemic, we set out to investigate longitudinally whether discounting would signal relevant negative outcomes like drug use, relapse and dropout in patients undergoing treatment for opioid addiction. We found that changes in discount rates were related to increased drug use in patients, indicating a vulnerability to full relapse and treatment failure.",
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Lopez-Guzman, S, Konova, A, Louie, K & Glimcher, P 2017, A Precise Computational Measure of Impulsivity that Signals Relevant Outcomes in Opioid Addiction Treatment. in Cognitive Computational Neuroscience.

A Precise Computational Measure of Impulsivity that Signals Relevant Outcomes in Opioid Addiction Treatment. / Lopez-Guzman, Silvia; Konova, Anna; Louie, Kenway; Glimcher, Paul.

Cognitive Computational Neuroscience. 2017.

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - A Precise Computational Measure of Impulsivity that Signals Relevant Outcomes in Opioid Addiction Treatment

AU - Lopez-Guzman, Silvia

AU - Konova, Anna

AU - Louie, Kenway

AU - Glimcher, Paul

PY - 2017

Y1 - 2017

N2 - Computational models of impulsive decision-making, like temporal discounting, are widely used to study addiction. However, clinically validating a marker supposes developing methods that provide high accuracy and reliability. We first show that a modified model of temporal discounting incorporating individual-specific risk sensitivity - provides a more precise, unbiased, and reliable measure ofimpulsivity than the standard approach. Using this tool, and given the current opioid epidemic, we set out to investigate longitudinally whether discounting would signal relevant negative outcomes like drug use, relapse and dropout in patients undergoing treatment for opioid addiction. We found that changes in discount rates were related to increased drug use in patients, indicating a vulnerability to full relapse and treatment failure.

AB - Computational models of impulsive decision-making, like temporal discounting, are widely used to study addiction. However, clinically validating a marker supposes developing methods that provide high accuracy and reliability. We first show that a modified model of temporal discounting incorporating individual-specific risk sensitivity - provides a more precise, unbiased, and reliable measure ofimpulsivity than the standard approach. Using this tool, and given the current opioid epidemic, we set out to investigate longitudinally whether discounting would signal relevant negative outcomes like drug use, relapse and dropout in patients undergoing treatment for opioid addiction. We found that changes in discount rates were related to increased drug use in patients, indicating a vulnerability to full relapse and treatment failure.

M3 - Conference contribution

BT - Cognitive Computational Neuroscience

ER -