0
Get Alert
Please Wait... Processing your request... Please Wait.
You must sign in to sign-up for alerts.

Please confirm that your email address is correct, so you can successfully receive this alert.

Articles   |    
Reduced Neural Tracking of Prediction Error in Substance-Dependent Individuals
Jody Tanabe, M.D.; Jeremy Reynolds, Ph.D.; Theodore Krmpotich, B.S.; Eric Claus, Ph.D.; Laetitia L. Thompson, Ph.D.; Yiping P. Du, Ph.D.; Marie T. Banich, Ph.D.
Am J Psychiatry 2013;170:1356-1363. doi:10.1176/appi.ajp.2013.12091257
View Author and Article Information

The authors report no financial relationships with commercial interests.

Supported by grants DA024104 and DA02774 from the National Institute on Drug Abuse and the National Key Basic Research Program of China (2013CB329501).

From the Departments of Radiology and Psychiatry, University of Colorado School of Medicine, Denver; the Department of Psychology, Denver University, Denver; the Mind Research Network, Albuquerque; the Department of Biomedical Engineering, Zhejiang University, Hangzhou, China; Key Laboratory for Biomedical Engineering of Education Ministry of China, Hangzhou; and the Institute of Cognitive Science, University of Colorado Boulder.

Address correspondence to Dr. Tanabe (jody.tanabe@ucdenver.edu).

Copyright © 2013 by the American Psychiatric Association

Received September 28, 2012; Revised January 30, 2013; Revised April 01, 2013; Accepted April 25, 2013.

Abstract

Objective  Substance-dependent individuals make poor decisions on the Iowa Gambling Task, a reward-related decision-making task that involves risk and uncertainty. Task performance depends on several factors, including how sensitive individuals are to feedback and how well they learn based on such feedback. A physiological signal that guides decision making based on feedback is prediction error. The authors investigated whether disruptions in the neural systems underlying prediction error processing in substance-dependent individuals could account for decision-making performance on a modified Iowa Gambling Task.

Methods  Thirty-two substance-dependent individuals and 30 healthy comparison subjects played a modified version of the Iowa Gambling Task during MR scanning. Trial-to-trial behavior and functional MRI (fMRI) blood-oxygen-level-dependent (BOLD) signal were analyzed using a computational model of prediction error based on internal expectancies. The authors investigated how well BOLD signal tracked prediction error in the striatum and the orbitofrontal cortex as well as over the whole brain in patients relative to comparison subjects.

Results  Compared with healthy subjects, substance-dependent patients were less sensitive to loss compared with gain, made less consistent choices, and performed worse on the modified Iowa Gambling Task. The ventral striatum and medial orbitofrontal cortex did not track prediction error as strongly in patients as in healthy subjects.

Conclusions  Weaker tracking of prediction error in substance-dependent relative to healthy individuals suggests that altered frontal-striatal error learning signals may underlie decision-making impairments in drug abusers. Computational fMRI may help bridge the knowledge gap between physiology and behavior to inform research aimed at substance abuse treatment.

Abstract Teaser
Figures in this Article

Your Session has timed out. Please sign back in to continue.
Sign In Your Session has timed out. Please sign back in to continue.
Sign In to Access Full Content
 
Username
Password
Sign in via Athens (What is this?)
Athens is a service for single sign-on which enables access to all of an institution's subscriptions on- or off-site.
Not a subscriber?

Subscribe Now/Learn More

PsychiatryOnline subscription options offer access to the DSM-5 library, books, journals, CME, and patient resources. This all-in-one virtual library provides psychiatrists and mental health professionals with key resources for diagnosis, treatment, research, and professional development.

Need more help? PsychiatryOnline Customer Service may be reached by emailing PsychiatryOnline@psych.org or by calling 800-368-5777 (in the U.S.) or 703-907-7322 (outside the U.S.).

FIGURE 1. Decision Making on a Modified Iowa Gambling Taska

a Net score is the number of plays on good decks minus the number of plays on bad decks; it was calculated for the first half and for the second half of the task, consisting of 25 trials each. The figure shows improvement over time in net score in the comparison group but not in the substance-dependent group.

FIGURE 2. Neural Tracking of Trial-to-Trial Prediction Error in Healthy Comparison Subjects and Substance-Dependent Individualsa

a Difference maps show higher activity in the medial orbitofrontal cortex in the comparison group relative to the substance-dependent group. All maps are thresholded at p<0.01, corrected for multiple comparisons using cluster correction, voxel-level p<0.005.

Anchor for Jump
TABLE 1.Drug Parameters in Substance-Dependent Patients and Healthy Comparison Subjects
Table Footer Note

a “Other” includes only hallucinogens and phencyclidine.

Anchor for Jump
TABLE 2.Significant Clusters of Group Differences in Prediction Error Processing Using Whole-Brain Analyses
Table Footer Note

a MNI=Montreal Neurological Institute.

Anchor for Jump
TABLE 3.Prediction Error Tracking in Anatomically Defined Regions of Interesta
Table Footer Note

a The table lists p values for one-sample t tests across all subjects and two-sample t tests.

+

References

Schultz  W;  Dickinson  A:  Neuronal coding of prediction errors.  Annu Rev Neurosci 2000; 23:473–500
[CrossRef] | [PubMed]
 
Redish  AD:  Addiction as a computational process gone awry.  Science 2004; 306:1944–1947
[CrossRef] | [PubMed]
 
Takahashi  Y;  Roesch  MR;  Stalnaker  TA;  Schoenbaum  G:  Cocaine exposure shifts the balance of associative encoding from ventral to dorsolateral striatum.  Front Integr Neurosci 2007; 1:11
[CrossRef] | [PubMed]
 
Schultz  W;  Dayan  P;  Montague  PR:  A neural substrate of prediction and reward.  Science 1997; 275:1593–1599
[CrossRef] | [PubMed]
 
Hyman  SE:  Addiction: a disease of learning and memory.  Am J Psychiatry 2005; 162:1414–1422
[CrossRef] | [PubMed]
 
Volkow  ND;  Wang  GJ;  Fowler  JS;  Tomasi  D;  Telang  F:  Addiction: beyond dopamine reward circuitry.  Proc Natl Acad Sci USA 2011; 108:15037–15042
[CrossRef] | [PubMed]
 
Montague  PR;  Dolan  RJ;  Friston  KJ;  Dayan  P:  Computational psychiatry.  Trends Cogn Sci 2012; 16:72–80
[CrossRef] | [PubMed]
 
Chiu  PH;  Lohrenz  TM;  Montague  PR:  Smokers’ brains compute, but ignore, a fictive error signal in a sequential investment task.  Nat Neurosci 2008; 11:514–520
[CrossRef] | [PubMed]
 
Park  SQ;  Kahnt  T;  Beck  A;  Cohen  MX;  Dolan  RJ;  Wrase  J;  Heinz  A:  Prefrontal cortex fails to learn from reward prediction errors in alcohol dependence.  J Neurosci 2010; 30:7749–7753
[CrossRef] | [PubMed]
 
Mazas  CA;  Finn  PR;  Steinmetz  JE:  Decision-making biases, antisocial personality, and early-onset alcoholism.  Alcohol Clin Exp Res 2000; 24:1036–1040
[CrossRef] | [PubMed]
 
Grant  S;  Contoreggi  C;  London  ED:  Drug abusers show impaired performance in a laboratory test of decision making.  Neuropsychologia 2000; 38:1180–1187
[CrossRef] | [PubMed]
 
Bechara  A;  Dolan  S;  Denburg  N;  Hindes  A;  Anderson  SW;  Nathan  PE:  Decision-making deficits, linked to a dysfunctional ventromedial prefrontal cortex, revealed in alcohol and stimulant abusers.  Neuropsychologia 2001; 39:376–389
[CrossRef] | [PubMed]
 
Bechara  A;  Dolan  S;  Hindes  A:  Decision-making and addiction (part II): myopia for the future or hypersensitivity to reward? Neuropsychologia 2002; 40:1690–1705
[CrossRef] | [PubMed]
 
Yechiam  E;  Busemeyer  JR;  Stout  JC;  Bechara  A:  Using cognitive models to map relations between neuropsychological disorders and human decision-making deficits.  Psychol Sci 2005; 16:973–978
[CrossRef] | [PubMed]
 
Ernst  M;  Bolla  K;  Mouratidis  M;  Contoreggi  C;  Matochik  JA;  Kurian  V;  Cadet  JL;  Kimes  AS;  London  ED:  Decision-making in a risk-taking task: a PET study.  Neuropsychopharmacology 2002; 26:682–691
[CrossRef] | [PubMed]
 
Fukui  H;  Murai  T;  Fukuyama  H;  Hayashi  T;  Hanakawa  T:  Functional activity related to risk anticipation during performance of the Iowa Gambling Task.  Neuroimage 2005; 24:253–259
[CrossRef] | [PubMed]
 
Tanabe  J;  Thompson  LL;  Claus  ED;  Dalwani  M;  Hutchison  KE;  Banich  MT:  Prefrontal cortex activity is reduced in gambling and nongambling substance users during decision-making.  Hum Brain Mapp 2007; 28:1276–1286
[CrossRef] | [PubMed]
 
Li  X;  Lu  ZL;  D’Argembeau  A;  Ng  M;  Bechara  A:  The Iowa Gambling Task in fMRI images.  Hum Brain Mapp 2010; 31:410–423
[PubMed]
 
Lawrence  NS;  Jollant  F;  O’Daly  O;  Zelaya  F;  Phillips  ML:  Distinct roles of prefrontal cortical subregions in the Iowa Gambling Task.  Cereb Cortex 2009; 19:1134–1143
[CrossRef] | [PubMed]
 
Wesley  MJ;  Hanlon  CA;  Porrino  LJ:  Poor decision-making by chronic marijuana users is associated with decreased functional responsiveness to negative consequences.  Psychiatry Res 2011; 191:51–59
[CrossRef] | [PubMed]
 
O’Doherty  JP;  Hampton  A;  Kim  H:  Model-based fMRI and its application to reward learning and decision making.  Ann NY Acad Sci 2007; 1104:35–53
[CrossRef] | [PubMed]
 
d’Acremont  M;  Lu  ZL;  Li  X;  van der Linden  M;  Bechara  A:  Neural correlates of risk prediction error during reinforcement learning in humans.  Neuroimage 2009; 47:1929–1939
[CrossRef] | [PubMed]
 
Christakou  A;  Brammer  M;  Giampietro  V;  Rubia  K:  Right ventromedial and dorsolateral prefrontal cortices mediate adaptive decisions under ambiguity by integrating choice utility and outcome evaluation.  J Neurosci 2009; 29:11020–11028
[CrossRef] | [PubMed]
 
Thompson  LL;  Claus  ED;  Mikulich-Gilbertson  SK;  Banich  MT;  Crowley  T;  Krmpotich  T;  Miller  D;  Tanabe  J:  Negative reinforcement learning is affected in substance dependence.  Drug Alcohol Depend 2012; 123:84–90
[CrossRef] | [PubMed]
 
Cottler  LB;  Robins  LN;  Helzer  JE:  The reliability of the CIDI-SAM: a comprehensive substance abuse interview.  Br J Addict 1989; 84:801–814
[CrossRef] | [PubMed]
 
Patton  JH;  Stanford  MS;  Barratt  ES:  Factor structure of the Barratt Impulsiveness Scale.  J Clin Psychol 1995; 51:768–774
[CrossRef] | [PubMed]
 
Bechara  A;  Damasio  AR;  Damasio  H;  Anderson  SW:  Insensitivity to future consequences following damage to human prefrontal cortex.  Cognition 1994; 50:7–15
[CrossRef] | [PubMed]
 
Stout  JC;  Busemeyer  JR;  Lin  A;  Grant  SJ;  Bonson  KR:  Cognitive modeling analysis of decision-making processes in cocaine abusers.  Psychon Bull Rev 2004; 11:742–747
[CrossRef] | [PubMed]
 
Du  YP;  Dalwani  M;  Wylie  K;  Claus  ED;  Tregellas  JR:  Reducing susceptibility artifacts in fMRI using volume-selective z-shim compensation.  Magn Reson Med 2007; 57:396–404
[CrossRef] | [PubMed]
 
Mawlawi  O;  Martinez  D;  Slifstein  M;  Broft  A;  Chatterjee  R;  Hwang  DR;  Huang  Y;  Simpson  N;  Ngo  K;  Van Heertum  R;  Laruelle  M:  Imaging human mesolimbic dopamine transmission with positron emission tomography, I: accuracy and precision of D(2) receptor parameter measurements in ventral striatum.  J Cereb Blood Flow Metab 2001; 21:1034–1057
[CrossRef] | [PubMed]
 
Volkow  ND;  Wang  GJ;  Telang  F;  Fowler  JS;  Logan  J;  Childress  AR;  Jayne  M;  Ma  Y;  Wong  C:  Cocaine cues and dopamine in dorsal striatum: mechanism of craving in cocaine addiction.  J Neurosci 2006; 26:6583–6588
[CrossRef] | [PubMed]
 
Takahashi  Y;  Schoenbaum  G;  Niv  Y:  Silencing the critics: understanding the effects of cocaine sensitization on dorsolateral and ventral striatum in the context of an actor/critic model.  Front Neurosci 2008; 2:86–99
[CrossRef] | [PubMed]
 
Thorpe  SJ;  Rolls  ET;  Maddison  S:  The orbitofrontal cortex: neuronal activity in the behaving monkey.  Exp Brain Res 1983; 49:93–115
[CrossRef] | [PubMed]
 
Schoenbaum  G;  Roesch  MR;  Stalnaker  TA:  Orbitofrontal cortex, decision-making, and drug addiction.  Trends Neurosci 2006; 29:116–124
[CrossRef] | [PubMed]
 
Stalnaker  TA;  Roesch  MR;  Franz  TM;  Burke  KA;  Schoenbaum  G:  Abnormal associative encoding in orbitofrontal neurons in cocaine-experienced rats during decision-making.  Eur J Neurosci 2006; 24:2643–2653
[CrossRef] | [PubMed]
 
Seymour  B;  O’Doherty  JP;  Koltzenburg  M;  Wiech  K;  Frackowiak  R;  Friston  K;  Dolan  R:  Opponent appetitive-aversive neural processes underlie predictive learning of pain relief.  Nat Neurosci 2005; 8:1234–1240
[CrossRef] | [PubMed]
 
Elliott  R;  Dolan  RJ;  Frith  CD:  Dissociable functions in the medial and lateral orbitofrontal cortex: evidence from human neuroimaging studies.  Cereb Cortex 2000; 10:308–317
[CrossRef] | [PubMed]
 
Wallis  JD:  Orbitofrontal cortex and its contribution to decision-making.  Annu Rev Neurosci 2007; 30:31–56
[CrossRef] | [PubMed]
 
Stout  JC;  Rock  SL;  Campbell  MC;  Busemeyer  JR;  Finn  PR:  Psychological processes underlying risky decisions in drug abusers.  Psychol Addict Behav 2005; 19:148–157
[CrossRef] | [PubMed]
 
Petry  NM:  Substance abuse, pathological gambling, and impulsiveness.  Drug Alcohol Depend 2001; 63:29–38
[CrossRef] | [PubMed]
 
Fridberg  DJ;  Queller  S;  Ahn  WY;  Kim  W;  Bishara  AJ;  Busemeyer  JR;  Porrino  L;  Stout  JC:  Cognitive mechanisms underlying risky decision-making in chronic cannabis users.  J Math Psychol 2010; 54:28–38
[CrossRef] | [PubMed]
 
References Container
+
+

Self-Assessment Quiz

Did you know? You can add a subscription now to earn CME Credits!

1.
Which of the following brain regions demonstrated weaker tracking of prediction error in substance-dependent individuals?
2.
Computational fMRI allows for stronger inferences of biological mechanisms compared to condition-based fMRI because computational fMRI has which of the following features?
3.
Why are prediction error signals thought to be an important mechanism of drug addiction?
Submit a Comments
Please read the other comments before you post yours. Contributors must reveal any conflict of interest.
Comments are moderated and will appear on the site at the discertion of APA editorial staff.

* = Required Field
(if multiple authors, separate names by comma)
Example: John Doe



Related Content
Articles
Books
The American Psychiatric Publishing Textbook of Psychopharmacology, 4th Edition > Chapter 49.  >
The American Psychiatric Publishing Textbook of Psychopharmacology, 4th Edition > Chapter 51.  >
The American Psychiatric Publishing Textbook of Substance Abuse Treatment, 4th Edition > Chapter 10.  >
The American Psychiatric Publishing Textbook of Psychopharmacology, 4th Edition > Chapter 49.  >
The American Psychiatric Publishing Textbook of Psychopharmacology, 4th Edition > Chapter 49.  >
Topic Collections
Psychiatric News