Drugs of abuse operate in part by subverting the adaptive mechanisms (algorithms) that are normally used to value actions and make decisions. It is well known that many if not most drugs of abuse affect midbrain dopamine systems, and in recent years detailed computational models of dopaminergic function (called Reinforcement Learning or RL models) have advanced to the point that they are now used profitably to interpret functional imaging experiments on reward learning in human subjects. These same models can also account for important features of the addicted state. RL models predict the existence and behavioral impact of a range of learning signals including expectation errors (ongoing differences between expectations and actual outcomes) and fictive errors (ongoing differences between `what might have happened'and actual outcomes). Consequently, the connection of RL models to dopamine systems immediately recommends their use as quantitative probes of learning and decision-making in addicted populations. Despite the intimate connection between RL models, midbrain dopamine systems, and reward-guided choice, no model-driven imaging approaches have been used to probe any addicted populations. In this proposal, we seek to fill this gap, and will pursue a rigorous, model-based approach to reward-dependent learning signals, their generation, and their mathematical character in humans undergoing functional magnetic resonance imaging while they execute sequential choice tasks. This effort will be carried out in healthy human subjects and smokers, and we have developed a substantial body of preliminary data to support the specific goals of this project. We have chosen to apply a model-based approach to smokers because they use a legal drug, there is less prevalence of poly-substance abuse, smoking is generally thought to be a gateway drug for other drugs of abuse, and smokers represent a large health burden on society especially in their later years. By using RL models to guide the design, analysis, and interpretation of a range of reward-harvesting experiments, this proposal will yield new insights into the computational underpinnings of reward- dependent choice and its pathological hijacking by a common drug of abuse.

Public Health Relevance

/ RELEVANCE Our understanding of the information distributed by midbrain dopamine systems has grown dramatically in recent years to the point that computer models of drug addiction are now usefully employed in the design and interpretation of reward-dependent learning experiments in humans. Drugs of abuse operate in part by subverting these learning mechanisms, which are normally used to value actions and make decisions. In this proposal, we plan to use computational model-based imaging studies to understand how a `gateway'drug (nicotine) perturbs experiential and `fictive'learning signals that guide human decision-making. We have chosen to use apply a model-based approach to smokers because they use a legal drug, there is less prevalence poly-substance abuse, smoking is generally thought to be a gateway drug for other drugs of abuse, and smokers represent a large health burden on society especially in their later years.

Agency
National Institute of Health (NIH)
Institute
National Institute on Drug Abuse (NIDA)
Type
Research Project (R01)
Project #
5R01DA011723-13
Application #
8437267
Study Section
Cognitive Neuroscience Study Section (COG)
Program Officer
Grant, Steven J
Project Start
1998-04-01
Project End
2014-02-28
Budget Start
2013-03-01
Budget End
2014-02-28
Support Year
13
Fiscal Year
2013
Total Cost
$328,041
Indirect Cost
$109,646
Name
Virginia Polytechnic Institute and State University
Department
Type
DUNS #
003137015
City
Blacksburg
State
VA
Country
United States
Zip Code
24061
Kirk, Ulrich; Gu, Xiaosi; Sharp, Carla et al. (2016) Mindfulness training increases cooperative decision making in economic exchanges: Evidence from fMRI. Neuroimage 138:274-83
Hétu, Sébastien; Luo, Yi; Saez, Ignacio et al. (2016) Asymmetry in functional connectivity of the human habenula revealed by high-resolution cardiac-gated resting state imaging. Hum Brain Mapp 37:2602-15
Kirk, Ulrich; Montague, P Read (2015) Mindfulness meditation modulates reward prediction errors in a passive conditioning task. Front Psychol 6:90
Hula, Andreas; Montague, P Read; Dayan, Peter (2015) Monte Carlo Planning Method Estimates Planning Horizons during Interactive Social Exchange. PLoS Comput Biol 11:e1004254
Kirk, Ulrich; Brown, Kirk Warren; Downar, Jonathan (2015) Adaptive neural reward processing during anticipation and receipt of monetary rewards in mindfulness meditators. Soc Cogn Affect Neurosci 10:752-9
McAdams, Carrie J; Lohrenz, Terry; Montague, P Read (2015) Neural responses to kindness and malevolence differ in illness and recovery in women with anorexia nervosa. Hum Brain Mapp 36:5207-19
Lu, James; Kishida, Ken; De Asis Cruz, Josepheen et al. (2015) Single stimulus fMRI produces a neural individual difference measure for Autism Spectrum Disorder. Clin Psychol Sci 3:422-432
Gu, Xiaosi; Lohrenz, Terry; Salas, Ramiro et al. (2015) Belief about nicotine selectively modulates value and reward prediction error signals in smokers. Proc Natl Acad Sci U S A 112:2539-44
Gu, Xiaosi; Kirk, Ulrich; Lohrenz, Terry M et al. (2014) Cognitive strategies regulate fictive, but not reward prediction error signals in a sequential investment task. Hum Brain Mapp 35:3738-49
Smith, Alec; Lohrenz, Terry; King, Justin et al. (2014) Irrational exuberance and neural crash warning signals during endogenous experimental market bubbles. Proc Natl Acad Sci U S A 111:10503-8

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