Una medida computacional precisa de la impulsividad que señala resultados relevantes en el tratamiento de la adicción a los opiáceos

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

Resultado de la investigación: Contribución a libro /Tipo informe o reporteContribución en conferencia

Resumen

Los modelos computacionales de toma de decisiones impulsivas, como el descuento temporal, se utilizan ampliamente para estudiar la adicción. Sin embargo, la validación clínica de un marcador supone el desarrollo de métodos que proporcionen una alta precisión y fiabilidad. En primer lugar, mostramos que un modelo modificado de descuento temporal que incorpora la sensibilidad al riesgo específica de cada individuo, proporciona una medida más precisa, imparcial y fiable de
impulsividad que el enfoque estándar. Con esta herramienta, y dada la actual epidemia de opiáceos, nos propusimos investigar longitudinalmente si el descuento indicaría resultados negativos relevantes como el consumo de drogas, la recaída y el abandono en pacientes que reciben tratamiento por adicción a los opiáceos. Se encontró que los cambios en las tasas de descuento estaban relacionados con el aumento del uso de fármacos en los pacientes, lo que indica una vulnerabilidad a la recaída total y al fracaso del tratamiento.
Título traducido de la contribuciónUna medida computacional precisa de la impulsividad que señala resultados relevantes en el tratamiento de la adicción a los opiáceos
Idioma originalEnglish (US)
Título de la publicación alojadaCognitive Computational Neuroscience
EstadoPublished - 2017

Citar esto

Lopez-Guzman, S., Konova, A., Louie, K., & Glimcher, P. (2017). A Precise Computational Measure of Impulsivity that Signals Relevant Outcomes in Opioid Addiction Treatment. En 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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title = "A Precise Computational Measure of Impulsivity that Signals Relevant Outcomes in Opioid Addiction Treatment",
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.",
author = "Silvia Lopez-Guzman and Anna Konova and Kenway Louie and Paul Glimcher",
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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. En 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.

Resultado de la investigación: Contribución a libro /Tipo informe o reporteContribución en conferencia

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 -