Literature DB >> 19822738

Genetic variation in dopaminergic neuromodulation influences the ability to rapidly and flexibly adapt decisions.

Lea K Krugel1, Guido Biele, Peter N C Mohr, Shu-Chen Li, Hauke R Heekeren.   

Abstract

The ability to rapidly and flexibly adapt decisions to available rewards is crucial for survival in dynamic environments. Reward-based decisions are guided by reward expectations that are updated based on prediction errors, and processing of these errors involves dopaminergic neuromodulation in the striatum. To test the hypothesis that the COMT gene Val(158)Met polymorphism leads to interindividual differences in reward-based learning, we used the neuromodulatory role of dopamine in signaling prediction errors. We show a behavioral advantage for the phylogenetically ancestral Val/Val genotype in an instrumental reversal learning task that requires rapid and flexible adaptation of decisions to changing reward contingencies in a dynamic environment. Implementing a reinforcement learning model with a dynamic learning rate to estimate prediction error and learning rate for each trial, we discovered that a higher and more flexible learning rate underlies the advantage of the Val/Val genotype. Model-based fMRI analysis revealed that greater and more differentiated striatal fMRI responses to prediction errors reflect this advantage on the neurobiological level. Learning rate-dependent changes in effective connectivity between the striatum and prefrontal cortex were greater in the Val/Val than Met/Met genotype, suggesting that the advantage results from a downstream effect of the prefrontal cortex that is presumably mediated by differences in dopamine metabolism. These results show a critical role of dopamine in processing the weight a particular prediction error has on the expectation updating for the next decision, thereby providing important insights into neurobiological mechanisms underlying the ability to rapidly and flexibly adapt decisions to changing reward contingencies.

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Year:  2009        PMID: 19822738      PMCID: PMC2760487          DOI: 10.1073/pnas.0905191106

Source DB:  PubMed          Journal:  Proc Natl Acad Sci U S A        ISSN: 0027-8424            Impact factor:   11.205


  44 in total

Review 1.  Phasic versus tonic dopamine release and the modulation of dopamine system responsivity: a hypothesis for the etiology of schizophrenia.

Authors:  A A Grace
Journal:  Neuroscience       Date:  1991       Impact factor: 3.590

Review 2.  A neural substrate of prediction and reward.

Authors:  W Schultz; P Dayan; P R Montague
Journal:  Science       Date:  1997-03-14       Impact factor: 47.728

3.  Modulation of memory fields by dopamine D1 receptors in prefrontal cortex.

Authors:  G V Williams; P S Goldman-Rakic
Journal:  Nature       Date:  1995-08-17       Impact factor: 49.962

4.  Effect of COMT Val108/158 Met genotype on frontal lobe function and risk for schizophrenia.

Authors:  M F Egan; T E Goldberg; B S Kolachana; J H Callicott; C M Mazzanti; R E Straub; D Goldman; D R Weinberger
Journal:  Proc Natl Acad Sci U S A       Date:  2001-05-29       Impact factor: 11.205

5.  Dissociable roles of ventral and dorsal striatum in instrumental conditioning.

Authors:  John O'Doherty; Peter Dayan; Johannes Schultz; Ralf Deichmann; Karl Friston; Raymond J Dolan
Journal:  Science       Date:  2004-04-16       Impact factor: 47.728

6.  6-Hydroxydopamine lesions of the prefrontal cortex in monkeys enhance performance on an analog of the Wisconsin Card Sort Test: possible interactions with subcortical dopamine.

Authors:  A C Roberts; M A De Salvia; L S Wilkinson; P Collins; J L Muir; B J Everitt; T W Robbins
Journal:  J Neurosci       Date:  1994-05       Impact factor: 6.167

7.  Gene-gene interaction associated with neural reward sensitivity.

Authors:  Juliana Yacubian; Tobias Sommer; Katrin Schroeder; Jan Gläscher; Raffael Kalisch; Boris Leuenberger; Dieter F Braus; Christian Büchel
Journal:  Proc Natl Acad Sci U S A       Date:  2007-05-02       Impact factor: 11.205

8.  Meta-analysis of the cognitive effects of the catechol-O-methyltransferase gene Val158/108Met polymorphism.

Authors:  Jennifer H Barnett; Linda Scoriels; Marcus R Munafò
Journal:  Biol Psychiatry       Date:  2008-03-14       Impact factor: 13.382

9.  Variation in dopamine genes influences responsivity of the human reward system.

Authors:  Jean-Claude Dreher; Philip Kohn; Bhaskar Kolachana; Daniel R Weinberger; Karen Faith Berman
Journal:  Proc Natl Acad Sci U S A       Date:  2008-12-22       Impact factor: 11.205

Review 10.  The catechol-O-methyltransferase polymorphism: relations to the tonic-phasic dopamine hypothesis and neuropsychiatric phenotypes.

Authors:  Robert M Bilder; Jan Volavka; Herbert M Lachman; Anthony A Grace
Journal:  Neuropsychopharmacology       Date:  2004-11       Impact factor: 7.853

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Authors:  Tim Klucken; Onno Kruse; Sina Wehrum-Osinsky; Juergen Hennig; Jan Schweckendiek; Rudolf Stark
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2.  Social stress reactivity alters reward and punishment learning.

Authors:  James F Cavanagh; Michael J Frank; John J B Allen
Journal:  Soc Cogn Affect Neurosci       Date:  2010-05-07       Impact factor: 3.436

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5.  The effects of methylphenidate on cerebral responses to conflict anticipation and unsigned prediction error in a stop-signal task.

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Journal:  J Psychopharmacol       Date:  2016-01-11       Impact factor: 4.153

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Journal:  Neuropsychologia       Date:  2013-11-05       Impact factor: 3.139

8.  Changes in Endogenous Dopamine Induced by Methylphenidate Predict Functional Connectivity in Nonhuman Primates.

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Journal:  J Neurosci       Date:  2018-12-10       Impact factor: 6.167

9.  COMT influences on prefrontal and striatal blood oxygenation level-dependent responses during working memory among individuals with schizophrenia, their siblings, and healthy controls.

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Journal:  Cogn Neuropsychiatry       Date:  2012-10-03       Impact factor: 1.871

10.  The drift diffusion model as the choice rule in reinforcement learning.

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