Disruption of Reward Processing in Addiction : An Image-Based Meta-analysis of Functional Magnetic Resonance Imaging Studies

doi:10.1001/JAMAPSYCHIATRY.2016.3084

Disruption of Reward Processing in Addiction

An Image-Based Meta-analysis of Functional Magnetic

Resonance Imaging Studies

Maartje Luijten, PhD; Arnt F. Schellekens, MD, PhD; Simone Kühn, PhD; Marise W. J. Machielse, MD, PhD; Guillaume Sescousse, PhD

IMPORTANCE

Disrupted reward processing, mainly driven by striatal dysfunction, is a key

characteristic of addictive behaviors. However, functional magnetic resonance imaging (fMRI)

studies have reported conflicting results, with both hypoactivations and hyperactivations

during anticipation and outcome notification of monetary rewards in addiction.

OBJECTIVE To determine the nature and direction of reward-processing disruptions during

anticipation and outcome notification of monetary rewards in individuals with addiction using

image-based meta-analyses of fMRI studies.

DATA SOURCES Relevant publications were identified searching PubMed (inclusion until

March 2015) using the following terms: reward, fMRI, substance use, cocaine, cannabis,

opiates, alcohol, nicotine, smokers, gambling, gamblers, gaming, and gamers. Authors of

included articles were contacted to obtain statistical fMRI maps.

STUDY SELECTION Inclusion criteria: reward task involving monetary reward anticipation

and/or outcome; participants showing addictive behaviors; and healthy control group.

Exclusion criteria: participants aged younger than 18 years; recreational substance use or

gambling; participants at risk for addictive behaviors; and studies using the same patient data

as other included studies.

DATA EXTRACTION AND SYNTHESIS Study procedures were conducted in accordance with the

Meta-analysis of Observational Studies in Epidemiology guidelines. Using Seed-based d

Mapping software, meta-analyses were performed using random-effect nonparametric

statistics with group whole brain T-maps from individual studies as input. Analyses were

performed across all addictions and for substance and gambling addictions separately.

MAIN OUTCOMES AND MEASURES Group differences (individuals with addiction vs control

individuals) in reward-related brain activation during reward anticipation and outcome using

fMRI (planned before data collection).

RESULTS Twenty-five studies were included in the meta-analysis, representing 643

individuals with addictive behaviors and 609 healthy control individuals. During reward

anticipation, individuals with substance and gambling addictions showed decreased striatal

activation compared with healthy control individuals. During reward outcome, individuals

with substance addiction showed increased activation in the ventral striatum, whereas

individuals with gambling addiction showed decreased activation in the dorsal striatum

compared with healthy control individuals.

CONCLUSIONS AND RELEVANCE Striatal hypoactivation in individuals with addiction during

reward anticipation and in individuals with gambling addiction during reward outcome is in

line with the reward-deficiency theory of addiction. However, the combination of

hypoactivation during reward anticipation and hyperactivation during reward outcome in the

striatum of individuals with substance addiction may be explained using learning-deficit

theory.

JAMA Psychiatry. 2017;74(4):387-398. doi:10.1001/jamapsychiatry.2016.3084

Published online February 1, 2017.

Supplemental content

Author Affiliations: Behavioural

Science Institute, Radboud

University, Nijmegen, the

Netherlands (Luijten); Radboud

University Medical Centre,

Department of Psychiatry, Nijmegen

Institute for Scientist Practitioners in

Addiction, Nijmegen, the

Netherlands (Schellekens);

Department of Psychiatry and

Psychotherapy, University Medical

Center Hamburg-Eppendorf,

Hamburg, Germany (Kühn);

Department of Psychiatry, Academic

Medical Center, Amsterdam, the

Netherlands (Machielse); Donders

Institute for Brain, Cognition, and

Behaviour, Radboud University,

Nijmegen, the Netherlands

(Sescousse).

Corresponding Author: Maartje

Luijten, PhD, Behavioural Science

Institute, Radboud University, PO Box

9104, 6500 HE Nijmegen, the

Netherlands (m.luijten@bsi.ru.nl).

Research

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A

ddictive behaviors, including substance use disorders

(SUD) and gambling disorder (GD), are among the most

common and devastating psychopathologies.

1

Sub-

stance use disorders have a prevalence of about 4% to 5% in

the general population

2

and account for about 20% of all dis-

ability-adjusted life years attributed to mental conditions

worldwide.

1

Additionally, 21% of the world population uses

tobacco products, which are associated with many serious

health risks including dependence.

3

The prevalence of GD in

developed countries ranges between 1% and 3% and has been

rising in college students in the past decade.

4,5

Despite the

growing number of evidence-based treatments for SUD, their

efficacy remains moderate, with relapse rates for SUD of

about 50% to 60% within 1 year.

6,7

For GD, only few

evidence-based treatments are available, and their efficacy

has been mostly disappointing.

8

Insight into the neural

mechanisms of addictive behaviors is crucial to target the

pathophysiology of addiction and develop more effective

treatments.

One of the key findings on the pathophysiology of addic-

tive behaviors is a dysfunction of so-called cortico-striatal re-

ward pathways, including the ventral striatum (VS) and the me-

dial prefrontal cortex (mPFC).

9-14

In the past decade, reward

processing in individuals with addictive behaviors has been

extensively studied by measuring brain reactivity to nondrug

rewards (mostly monetary) using functional magnetic reso-

nance imaging (fMRI). However, the nature and direction of

reward-processing dysfunction remain unclear because some

studies show hypoactivations and others hyperactivations (or

nondeviant activations) in the VS of individuals with addic-

tive behaviors compared with healthy control individuals (for

reviews, see Balodis et al,

9

Hommer et al,

10

Diekhof et al,

12

and

Leyton et al

14

).

These conflicting results have been interpreted in the

context of 3 dominant but largely incompatible theories,

known as the reward deficiency syndrome (RDS) theory,

impulsivity theory, and incentive sensitization theory. The

RDS theory

15,16

posits that individuals with addictive behav-

iors have a general deficit in recruiting brain reward path-

ways, resulting in chronic hypoactivation of these circuits

and supposedly reduced pleasurable experience from

rewards. Addictive behaviors, such as substance use or gam-

bling, are consequently initiated to compensate for this

reward deficiency and stimulate brain reward centers,

including the VS. In contrast, the impulsivity theory

17-19

sug-

gests that addictive behaviors are the result of a generally

hyperactive brain reward system. Individuals with a hyperre-

active brain reward system may show a strong response to

cues predicting potential rewards, thereby explaining novelty

seeking, impulsivity, and the continuous drive and motiva-

tion to obtain substances of abuse or other potentially

rewarding stimuli. Finally, the incentive sensitization

theory

13

proposes that individuals with addictive behaviors

show a bias toward addiction-related cues owing to acquired

incentive salience of these cues. As a result, addictive behav-

iors and associated cues hijack the brain-reward system,

resulting in relatively increased VS responses to drug cues

and relatively reduced VS responses to nondrug cues in

SUD.

20

Note that in the case of GD, where money is the

reward of interest, the incentive sensitization theory would

predict an increased response to gambling/monetary cues in

the brain reward pathways.

Several issues may explain inconsistencies in fMRI find-

ings on reward-processing deficits in addiction. First, differ-

ent studies report activity during reward anticipation vs re-

ward outcome phases, which reflect distinct processes with

different relevance for addictive behaviors.

21

For example, re-

ward anticipation often results from the perception of appe-

titive cues whose incentive value is innate or has been learned

by association with positive outcomes, thus reflecting moti-

vational processes.

22

In contrast, reward outcome is more rel-

evant for learning processes and signaling the salience of new

stimuli.

21,23

While reward anticipation and reward outcome

processes can be separately investigated using fMRI, the domi-

nant addiction theories remain relatively vague in specifying

which of these processes are specifically affected in the course

of addiction. A second reason for previously reported incon-

sistencies relates to the fact that reward-processing deficits in

addicted individuals are likely phase dependent. For ex-

ample, normalization of structural abnormalities within the

reward neuro-circuitry has been described after prolonged

abstinence.

24,25

Furthermore, often co-occurring psychiatric

disorders, such as attention-deficit/hyperactivity disorder, also

affect reward processing and have been associated with reward-

anticipation deficits.

26,27

To unravel contradictory findings on reward-processing

dysfunction in addiction, we performed a quantitative image-

based meta-analysis of the fMRI literature, using whole-

brain group T maps from individual studies as input. This ap-

proach is more powerful than traditional coordinate-based

meta-analyses in that it uses the full image information, in-

cluding effect sizes and between-subject variance, in addi-

tion to activation localization.

28,29

Moreover, image-based

meta-analyses partly address issues of limited sample sizes in

individual studies and variability in data analysis. We per-

formed separate analyses focusing on reward anticipation vs

outcome processing in both individuals with SUD and indi-

viduals with GD. Substance use disorders and GD were both

included to test the hypothesis of shared neurobiological

Key Points

Question What are the nature and direction of pathophysiologic

reward-processing disruptions in the brain during anticipation and

outcome notification of monetary rewards in individuals with

addiction to substances and gambling?

Findings In this image-based meta-analysis of neuroimaging

studies, striatal activation during reward anticipation was

decreased among individuals with addiction compared with those

in control groups. During reward outcome, substance-addicted

individuals showed increased activation in the ventral striatum,

whereas gambling-addicted individuals showed decreased

activation in the dorsal striatum, compared with controls.

Meaning These findings provide evidence for both reward

deficiency and learning-deficit theories in addiction.

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mechanisms underlying substance and behavioral addic-

tions. Finally, we explored the influence of psychiatric comor-

bidities and different stages of the addiction cycle on reward

processing.

9,10,30

We hypothesized that a blunted reward re-

sponse (mainly in VS and mPFC) in both SUD and GD sup-

ports the RDS theory, while an increased reward response sup-

ports the impulsivity theory. In contrast, a blunted reward

response in SUD individuals, together with an increased re-

sponse in GD individuals, supports incentive sensitization

theory, given the monetary nature of the task paradigms.

Methods

Inclusion of Studies

A literature search was performed using PubMed. Search terms

“reward” and “fMRI” had to co-occur with 1 of the following

keywords: substance use, cocaine, cannabis, opiates, alco-

hol, nicotine, smokers, gambling, gamblers, gaming, and gam-

ers. Reference lists of included studies were screened to iden-

tify additional relevant articles. Inclusion criteria were inclusion

in PubMed before March 1, 2015, participants showing addic-

tive behavior, reward task involving monetary reward antici-

pation and/or outcome, and inclusion of a healthy control

group. Exclusion criteria were inclusion of adolescents (aged

younger than 18 years); target population with recreational sub-

stance use, gambling or gaming, or at-risk groups (eg, ad-

dicted family members); and studies using the same patient

data as other included studies. Meta-analysis of Observa-

tional Studies in Epidemiology guidelines for meta-analyses

of observational studies

31

were followed in this study. Next,

corresponding authors were contacted via e-mail requesting

to share their group-level fMRI data, ie, T maps representing

monetary reward anticipation and/or outcome contrasts, both

within groups (SUD, GD, and healthy control) and between-

groups (SUD vs healthy control and GD vs healthy control).

Analyses

Seed-Based d Mapping

Our image-based meta-analyses were performed using the soft-

ware Seed-Based d Mapping (SDM, version 4.31, http://www

.sdmproject.com, formerly “Signed Differential Mapping”).

Seed-Based d Mapping allows us to perform voxel-based meta-

analyses using full statistical images as input

29,32

and has been

extensively validated in previous meta-analyses.

33-36

Seed-Based d Mapping recreates voxel-level maps of ef-

fect sizes (Hedge’s d) and their variances based on input T maps,

thereby substantially increasing the sensitivity of voxel-

based meta-analyses compared with coordinate-based

methods.

28,29

Additionally, SDM offers the possibility to have

both positive and negative values in the same map; this pro-

vides richer information and allows the computation of

standard meta-analytic measures such as between-study

heterogeneity. To evaluate cross-study convergence, a random-

effect model is applied in which each study is weighted by the

inverse of the sum of its variance plus the between-study vari-

ance. Thus, studies with larger sample sizes or lower variabil-

ity have a stronger contribution. Meta-analytic effect sizes are

then divided by their standard errors across studies, eventu-

ally leading to SDM Z maps. Given that the distribution of these

Z values typically deviates from normality, a null distribution

is empirically estimated using permutation statistics (ie, ran-

domizations of effect sizes across voxels). Previous simula-

tions have shown that a number as low as 20 permutations al-

ready leads to highly stable estimates.

29

To be on the safe side,

all the analyses reported were based on 50 permutations.

Finally, statistical thresholding is based on a voxel-wise

uncorrected P value less than .005. It has been shown that in

the context of presently used permutation statistics, such a

threshold is equivalent to a corrected P value of .05 and

provides an optimal balance between sensitivity and

specificity.

29

This threshold was complemented by a required

minimum cluster size of 10 voxels (approximately 80 mm

3

)

and a peak-level threshold of Z greater than 1 to further

reduce the possibility of false positives.

Whole-Brain Analyses

Primary analyses examined brain activity during reward an-

ticipation and reward outcome in all individuals showing ad-

dictive behaviors vs healthy control individuals. For reward

anticipation and reward outcome, included T maps were be-

tween-group comparisons of contrasts representing the an-

ticipation/outcome of a monetary win vs the anticipation/

outcome of a neutral, negative, or alternative event (see eTable

1intheSupplement for specific contrasts included per study).

To increase consistency across studies, some of the contrasts

included in the analyses are not those reported in the original

publication but additional contrasts obtained from the au-

thors. Secondary analyses examined the same contrasts sepa-

rately in individuals with SUD and individuals with GD (vs

healthy control individuals). Statistical analyses were re-

stricted to a custom gray-matter mask, following SDM recom-

mendations. Although some of the collected T maps did not

have full brain coverage (n = 5), all of them covered the key

areas of interest including the striatum and mPFC.

The robustness of whole-brain results was examined using

a jack-knife procedure, consisting of systematic repetitions of

the meta-analyses described in previous paragraphs after ex-

cluding 1 study at a time. In the Results section, we report the

number of overlapping jack-knife analyses as an index of rep-

licability of the results.

Region of Interest Analyses

Region of interest analyses were performed to examine the

contributions of individual studies and potential publication

biases more closely. Regions of interest were defined func-

tionally from whole brain maps and regional mean effect sizes,

and variances for individual studies were extracted using the

option integrated in SDM. These data were used to make for-

est plots and funnel plots illustrating striatal activation pat-

terns during reward anticipation and outcome. In the forest

plots, we categorized studies as a function of psychiatric co-

morbidities and phase of addiction to qualitatively assess

whether the results might be potentially driven by some of

these subgroups (see eMaterials in the Supplement for deci-

sion criteria regarding the categorization of studies).

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Results

Included Studies and Sample Characteristics

The search resulted in 31 studies to be included in the meta-

analyses (Figure 1). We received data from 26 studies (84%).

One of these studies was excluded from the analyses because

of technical problems. The remaining 25 studies

17,37-60

were

included in the analyses; of these, 17 reported brain activa-

tion both during reward anticipation and outcome phases.

Combined, these studies included 643 individuals with ad-

dictive behaviors (mean (SD) age, 33.5 (8) years; 527 men

[82%]) and 609 unique healthy control individuals (mean

(SD) age, 33.2 (8.5) years; 469 men [77%]). Across studies, in-

dividuals with addictive behaviors and healthy control indi-

viduals did not differ in terms of age and sex. Study-specific

details including demographic information and type of

addicted populations are shown in the Table.

In total, 20 studies were included in the analyses of brain

activation during reward anticipation. Two of these studies in-

cluded 2 addicted populations that were each compared with

1 healthy control group, leading to a final number of 22 data

sets available for meta-analyses on reward anticipation. Twenty

studies were included in the analyses of brain activation dur-

ing reward outcome. Three of these studies included 2 ad-

dicted populations that were each compared with 1 healthy

control group, leading to a final number of 23 data sets avail-

able for meta-analyses on reward outcome. See eTable 2 in the

Supplement for the included studies and the number of par-

ticipants per analysis specifically.

Meta-analytic Brain Imaging Results

Whole-brain maps of the main results are available online at

http://neurovault.org/collections/1501.

Reward Anticipation Phase

We first examined group differences across all included stud-

ies, ie, across all addictions. The most striking group differ-

ence was observed in the bilateral striatum, in which individu-

als with addictive behaviors showed decreased responses

during reward anticipation compared with healthy control in-

dividuals (Figure 2A). Further whole-brain analyses revealed

that this pattern was present in individuals with SUD and in

individuals with GD compared with their respective control

groups when analyzed separately (Figure 2A). The extraction

of effect sizes within significant striatal voxels revealed no gross

abnormalities (Figure 3) except for 2 data sets presenting

relatively large effect sizes (see the Sensitivity Analyses sub-

section). Other group differences were visible outside of the

striatum including in the mPFC, anterior cingulate cortex,

amygdala, orbitofrontal cortex, and dorsolateral prefrontal

cortex (see eTables 3 and eTable 4 in the Supplement). Z

maps showing brain activations separately for addicted and

matched healthy control groups are reported in eFigure 1 in

the Supplement.

Reward Outcome Phase

In contrast to the anticipation phase, individuals with addic-

tive behaviors showed enhanced responses in the ventral stria-

tum during reward outcome compared with healthy controls

(Figure 2B). The extraction of effect sizes revealed no gross ab-

normalities (Figure 4). Importantly, further analyses showed

that this pattern was mostly driven by individuals with SUD,

who showed markedly enhanced activity in the ventral stria-

tum (Figure 2B). In contrast, individuals with GD showed no

significant differences in the ventral striatum compared with

healthy control individuals, but decreased responses in the bi-

lateral dorsal striatum (Figure 2B). Other whole-brain group

differences, including findings in the orbitofrontal cortex, in-

sula, and dorsolateral prefrontal cortex, are reported in eTable

5 and eTable 6 in the Supplement. Z maps showing brain ac-

tivations separately for addicted and matched healthy con-

trol groups are reported in eFigure 2 in the Supplement.

Sensitivity Analyses

Using a jackknife procedure, we found that the striatal group

differences observed during reward anticipation and out-

come were replicated in virtually all jack-knife analyses, dem-

onstrating the robustness of our results (eFigure 3 in the Supple-

ment). We also reran our analyses after excluding the 2 data

sets

57

with the largest striatal effect sizes (Figures 3 and 4), but

the results remained qualitatively similar, suggesting that these

Figure 1. Flowchart Outlining the Selec tion Procedure of Studies

452 Studies identified

88 Full articles checked

31 Data requested

26 Data received

Included in reward

anticipation analyses

20 Studies

22 Datasets

526 Patients

505 Control

individuals

b

Included in reward

outcome analyses

20 Studies

23 Datasets

506 Patients

475 Control

individuals

b

1 Excluded because of problems

with data analyses

408 Excluded because title/

abstract unrelated to objective

58 Excluded for following reasons

a

30 Task design and analyses

9 No control group

6 Nondependent users

14 Age <18 y

8 At-risk group

1 Review article

2 Same dataset as in other

included study

1 Inclusion based

on reference

list of eligible

articles

a

Studies could be excluded for multiple reasons.

b

This number of control individuals reflects the number of unique control

individuals. Some studies included 2 different addicted groups (eg, cannabis

users and smokers) and 1 reference group of control individuals. Therefore, the

number of datasets is higher than the number of studies.

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Table. Participant Characteristics

Source

Type of

Participants,

No. Age, Mean (SD)

Male Sex,

No. (%)

Addiction Diagnosis

and Severity

Active, Initial

Abstinence,

Prolonged Abstinence

Psychiatric

Comorbidity

a

Balodis et al,

37

2012

GD (14); HC

(14)

PG: 35.8 (11.7); HC:

37.1 (11.3)

PG: 10 (71); HC:

10 (71)

DSM-IV diagnosis GD Active gambling No current psychiatric

comorbidity (self-report)

Beck et al,

38

2009

ADI (19); HC

(19)

ADI: 41.8 (6.8); HC:

41.7 (9.0)

ADI: 19 (100); HC:

19 (100)

DSM-IV and ICD-10

diagnoses alcohol

dependence

Initial abstinence

alcohol

No current psychiatric

comorbidity (SCID)

Bjork et al,

39

2008

ADI (23); HC

(23)

ADI: 33.8 (9.1); HC:

34.7 (9.7)

ADI: 12 (52) ; HC:

12 (52)

DSM-IV diagnosis alcohol

dependence

Initial abstinence

alcohol

Psychiatric comorbidity

present (interview)

Bjork et al,

17

2012

ADI (29); HC

(23)

ADI: 30.9 (8.2); HC:

30.1 (5.9)

ADI: 15 (52); HC:

15 (52)

DSM-IV diagnosis alcohol

dependence

Initial abstinence

alcohol

Psychiatric comorbidity

present (interview)

Bustamante et

al,

40

2014

CoDI (17); HC

(18)

CoDI: 37.4 (8.2); HC:

37.5 (5.9)

CoDI: 17 (100);

HC: 18 (100)

DSM-IV diagnosis cocaine

dependence

Prolonged abstinence

cocaine

No current psychiatric

comorbidity (SCID)

Choi et al,

41

2012

GD (15); HC

(15)

GD: 27.9 (6.9); HC:

26.6 (4.3)

GD: 15 (100); HC:

15 (100)

DSM-IV diagnosis GD Active gambling No current psychiatric

comorbidity (SCID)

Cousijn et al,

42

2012

CaDI (32); HC

(41)

CaDI: 21.4 (2.3); HC:

22.2 (2.4)

CaDI: 21 (66); HC:

26 (63)

Heavy cannabis use ≥10

d/mo for ≥2 y

Active cannabis No current psychiatric

comorbidity (MINI)

Fauth-Bühler et

al,

43

2014

GD (80); HC

(89)

GD: 37.4 (9.1); HC:

36.2 (9.4)

GD: 80 (100); HC:

89 (100)

DSM-IV diagnosis GD Mix gambling Psychiatric comorbidity

not excluded (SCID)

Filbey et al,

44

2013

CaDI (59); HC

(27)

CaDI: 23.5 (6.4); HC:

30.3 (10.1)

CaDI:46 (78); HC:

5 (18)

DSM-IV diagnosis cannabis

dependence (58%);

DSM-IV diagnosis cannabis

abuse (10%); all: heavy

cannabis use; ≥4 d/wk for

≥6 mo

Initial abstinence

cannabis

Psychiatric comorbidity

not excluded (SCID)

Goldstein et

al,

45

2007

CoDI (16); HC

(13)

CoDI: 42.8 (4.6); HC:

37.6 (6.8)

CoDI: 12 (75); HC:

9 (69)

DSM-IV diagnosis cocaine

dependence (56%);

DSM-IV diagnosis cocaine

abuse (38%); DSM-IV

diagnosis past

poly-substance abuse (6%)

Mix cocaine Psychiatric comorbidity

not excluded (interview)

Hägele et al,

46

2014

ADI (26); HC

(54)

ADI: 43.3 (7.0); HC:

37.7 (11.1)

ADI: 25 (96); HC:

41 (76)

DSM-IV and ICD-10

diagnoses alcohol

dependence

Mix alcohol No current psychiatric

comorbidity (SCID)

Jansma et al,

47

2013

NDI (10); HC

(11)

NDI: 21.2 (2.5); HC:

25.6 (7)

NDI: 10 (100); HC:

11 (100)

At least 10 cigarettes per

d; FTND mean (SD):

4.3 (0.95)

Active nicotine, initial

abstinence cannabis

No current psychiatric

comorbidity (MINI)

Jia et al,

48

2011 CoDI (20); HC

(20)

CoDI: 38.6 (9.3); HC:

35.3 (10.2)

CoDI: 12 (60); HC:

12 (60)

DSM-IV diagnosis cocaine

dependence

Mix cocaine Psychiatric comorbidity

present (SCID)

Martin et al,

49

2014

NDI (16); HC

(17)

NDI: 31.4 (9.8); HC:

33.7 (10.3)

NDI: 6 (38); HC:

8 (47)

At least 10 cigarettes per

d; FTND mean (SD):

3.6 (1.9)

Active nicotine No current psychiatric

comorbidity (self-report)

Miedl et al,

50

2010

GD (12); OG

(12)

GD: 39.5 (9.3); OG:

33.4 (8.0)

GD: 12 (100); OG:

12 (100)

DSM-IV diagnosis problem

gambling (42%); DSM-IV

diagnosis GD (58%)

Active gambling No current psychiatric

comorbidity (assessment

unknown)

Nestor et al,

51

2010

CaDI (14); HC

(14)

CaDI: 22.1 (4.5); HC:

23.1 (4.5)

CaDI: 12 (86); HC:

11 (79)

Heavy cannabis use 5-7

d/wk for ≥2 y

Mix cannabis No current psychiatric

comorbidity (assessment

unknown)

Patel et al,

52

2013

CoDI (42); HC

(47)

CoDI: 38.5 (7.1); HC:

34.6 (9.0)

CoDI: 24 (58); HC:

26 (56)

DSM-IV diagnosis cocaine

dependence

Active cocaine No current psychiatric

comorbidity (SCID)

Romanczuk-

Seiferth et al,

53

2015

GD (18); ADI

(15); HC (17)

GD: 33.6 (9.5); ADI:

45.4 (10.2); HC:

37.4 (11.8)

GD: 18 (100); ADI:

15 (100); HC:

17 (100)

GD: DSM-IV and ICD-10

diagnosis GD; ADI: DSM-IV

and ICD-10 diagnosis

alcohol dependence

Active gambling,

initial abstinence

alcohol

No current psychiatric

comorbidity (interview)

Rose et al,

54

2014

NDI (28); HC

(28)

NDI: 32.7 (10.0);

HC: 30.1 (7.8)

NDI: 13 (46); HC:

16 (57)

18-40 cigarettes per d;

FTND mean (range):

5.9 (3-9)

Active nicotine No current psychiatric

comorbidity (assessment

unknown)

De Ruiter et al,

55

2009

GD (19); NDI

(19); HC (19)

GD: 34.3 (9.4); NDI:

34.8 (9.8); HC:

34.1 (9.3)

GD: 19 (100); NDI:

19 (100); HC:

19 (100)

GD: All in treatment for

gambling problems; 79%

lifetime DSM-IV diagnosis

GD; NDI: at least 15

cigarettes per d; FTND

mean (SD): 5.1 (1.5)

Initial abstinence

gambling, initial

abstinence nicotine

GD: Psychiatric

comorbidity present

NDI: No current

psychiatric comorbidity

(DIS)

Sescousse et

al,

56

2013

GD (18); HC

(20)

GD: 34.1 (11.6); HC:

31.0 (7.3)

GD: 18 (100); HC:

20 (100)

DSM-IV diagnosis GD Active gambling No current psychiatric

comorbidity (structured

interview)

Van Hell et al,

57

2010

CaDI (14); NDI

(14); HC (13)

CaDI: 24 (4.4); NDI:

25 (4.5); HC:

24 (2.7)

CaDI: 13 (93);

NDI: 11 (79); HC:

11 (85)

CaDI: Heavy cannabis use

≥150 joints last year and

≥1500 joints lifetime; NDI:

≥5 cigarettes per d

(mean = 13)

Initial abstinence

cannabis, active

nicotine

No current psychiatric

comorbidity (interview)

(continued)

Disruption of Reward Processing in Addiction Original Investigation Research

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Disruption of Reward Processing in Addiction : An Image-Based Meta-analysis of Functional Magnetic Resonance Imaging Studies

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