Reward pathway dysfunction in gambling disorder: A meta-analysis of functional magnetic resonance imaging studies

Behavioural Brain Research 275 (2014) 243–251

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Behavioural Brain Research journal homepage: www.elsevier.com/locate/bbr

Review

Reward pathway dysfunction in gambling disorder: A meta-analysis of functional magnetic resonance imaging studies Ya-jing Meng a,b,1 , Wei Deng a,b,1 , Hui-yao Wang a , Wan-jun Guo a,b,∗ , Tao Li a,b,∗∗ , Chaw Lam c , Xia Lin d,e,f a

Mental Health Center, West China Hospital, Sichuan University, PR China State Key Laboratory of Biotherapy, Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China c Department of Psychology, Illinois Institute of Technology, Chicago, IL, U.S.A d Institute of post-disaster reconstruction, Sichuan University, Chengdu, China e Department of Rehabilitation Sciences, The Hong Kong Polytechnic, Hong Kong, China f Department of forensic medicine, North Sichuan Medical University, Nanchong, China b

h i g h l i g h t s • • • •

We selected 13 qualified voxel-wise whole brain fMRI studies of gambling disorder. GD showed hyperactivity in right lentiform nucleus and left middle occipital gyrus. The SOGS of GD was related to hyperactivity in right lentiform nucleus and left ACC. The result was also found in GD subgroups (regardless of excluding or not excluding any kind of substance use disorder).

a r t i c l e

i n f o

Article history: Received 5 July 2014 Received in revised form 25 August 2014 Accepted 30 August 2014 Available online 6 September 2014 Keywords: Gambling disorder (GD) Effect size signed differential mapping (ES-SDM) Functional magnetic resonance imaging (FMRI) The frontostriatal cortical pathway

a b s t r a c t Recent emerging functional magnetic resonance imaging (fMRI) studies have identified many brain regions in which gambling cues or rewards elicit activation and may shed light upon the ongoing disputes regarding the diagnostic and neuroscientific issues of gambling disorder (GD). However, no studies to date have systemically reviewed fMRI studies of GD to analyze the brain areas activated by gamblingrelated cues and examine whether these areas were differentially activated between cases and healthy controls (HC). This study reviewed 62 candidate articles and ultimately selected 13 qualified voxel-wise whole brain analysis studies to perform a comprehensive series of meta-analyses using the effect sizesigned differential mapping approach. Compared with HC, GD patients showed significant activation in right lentiform nucleus and left middle occipital gyrus. The increased activities in the lentiform nucleus compared to HC were also found in both GD subgroups, regardless of excluding or not excluding any kind of substance use disorder. In addition, the South Oaks Gambling Screen scores were associated with hyperactivity in right lentiform nucleus and bilateral parahippocampus, but negatively related to right middle frontal gyrus. These results suggest dysfunction within the frontostriatal cortical pathway in GD, which could contribute to our understanding of the categories and definition of GD and provide evidence for the reclassification of GD as a behavioral addiction in the DSM-5. © 2014 Elsevier B.V. All rights reserved.

∗ Corresponding author at: Mental Health Center & State Key Laboratory of Biotherapy, Psychiatric Laboratory, West China Hospital, Sichuan University, Chengdu, PR China. Tel.: +86 28 85422629; fax: +86 28 85426118. ∗∗ Corresponding author at: Mental Health Center, West China Hospital, Sichuan University, Chengdu, Sichuan, PR China. Tel.: +86 28 85423561; fax: +86 28 85422632. E-mail addresses: [email protected] (W.-j. Guo), [email protected] (T. Li). 1 These authors contributed equally to this work. http://dx.doi.org/10.1016/j.bbr.2014.08.057 0166-4328/© 2014 Elsevier B.V. All rights reserved.

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Contents 1. 2.

3.

4.

Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.1. Study collection and inclusion and exclusion criteria . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2.2. Meta-analysis of studies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.1. Characteristics of the samples of the studies included in the meta-analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3.2. Changes in regional brain responses to cognitive tasks in GD studies as well as subgroup and meta-regression analyses . . . . . . . . . . . . . . . 3.3. Sensitivity and robustness analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Discussions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.1. Reward circuit of GD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.2. GD and comorbidities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.3. The cerebellum in GD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4.4. Limitations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Competing interests . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

1. Introduction Gambling disorder (GD) is recognized and characterized by persistent and uncontrolled gambling leading to deleterious psychosocial consequences [1]. It is formally classified as the sole non-substance-related disorder in the “Substance-Related and Addictive Disorders” chapter of the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [2], although it was termed “pathological gambling” in the “Impulse-Control Disorders Not Elsewhere Classified” chapter of the DSM-IV [3]. Epidemiological surveys have reported that GD has a prevalence of 0.5–3.0% [4–6] in adults and causes significant impairments in psychological and social functioning [7]. There are a number of similarities between GD and substance use disorders (SUDs), including genetic vulnerability [8], biomarkers [9], and poor cognitive performance on neurocognitive tasks [10–12], specifically with respect to impulsive choice and response tendencies and compulsive features. These findings from neuroimaging studies in GD suggest dysfunction involving similar brain regions, including the ventromedial prefrontal cortex (PFC) and striatum and similar neurotransmitter systems, including dopaminergic and serotonergic [13,14]. Therefore, recent studies have suggested that GD may be considered a behavioral addiction [15–19]. However, there are also some crucial differences between GD and SUD, such as toxic effects of exogenous substances on the brain and the expectation of gambling or drug use [20]. Brain imaging technologies have allowed neuroscientists to map out the neural landscape of GD in the human brain and start understanding how psychostimuli modify it. The reward deficiency hypothesis predicts that the susceptibility to addiction stems from an insensitive or ineffective dopaminergic system [21]. However, a contrasting model predicted that the addicted brain exists in a hyperdopaminergic state [22]. Some brain imaging studies found that dopamine (the key player in the “ventral frontostriatal reward circuit”) increased in cases of GD [20] or a “double deficit” function of dopamine in GD [23,24]. Meanwhile, an alternative theoretical model of addiction that stressed the involvement of both the brain reward pathways (the ventral striatum) and the regulatory system (the PFC) has been raised based on recent evidence from functional magnetic resonance imaging (fMRI) studies of GD investigating reward processing, craving, decision-making, delay discounting, and other cognitive processes [25–27,14,15,28–30]. Considering these hypotheses, this model highlighted the features of GD that make it a valuable experimental model for the addiction field as well as the leverage that may be afforded by this illness for resolving the nature of the dysregulation in reinforcement processing in GD.

244 244 244 246 247 247 247 248 248 249 249 250 250 250 250 250

As with studies of drug addiction, these papers in GD have also isolated the striatum and the prefrontal lobe regions as lying at the core of this disrupted network [28]. However, different studies have included relatively small numbers of subjects with GD of varying severity, employed a variety of different cue or reward reactivity paradigms, and reported many different areas of cueelicited activation [28], such as the dorsal and ventral striatum [31–33], PFC [10,12,34–36], middle occipital gyrus [30], insula [37], cuneus [38], and precuneus [34]. Although several qualitative reviews of neuroimaging studies of gambling cue or reward reactivity exist [20,39,40,28], no study to our knowledge has used a scientific statistical methodology such as meta-analysis to systemically review the fMRI studies of GD and systematically characterize the brain areas activated by cues across subject populations, cueexposure paradigms, and imaging modalities. In the present study, we herein first surveyed the whole-brain functional neuroimaging investigations of GD using the effect size-signed differential mapping (ES-SDM) approach for quantitative meta-analysis to synthesize the findings from fMRI studies of GD. Secondarily, we sought to characterize the states and traits related to this activation by systematically reviewing correlations between activation and behaviors. 2. Method 2.1. Study collection and inclusion and exclusion criteria Using PubMed (http://www.pubmed.org), Google Scholar (http://scholar.google.com), Embase (https://www.embase.com), and the Cochrane library (http://www.thecochranelibrary.com), we searched for English-language MRI studies of GD published between Jan 2000 and Dec 2013 using the keywords “gambling disorder” or “pathological gambling” or “problem gambling” in combination with a neuroimaging term (e.g. fMRI or neuroimaging). Abstracts of initially identified articles in English were first reviewed as the basis for selecting papers for full-text review. References cited in the selected articles were also reviewed. These searches initially identified 62 candidate articles for possible inclusion. Studies that included a direct comparison between GD groups with at least one control group of healthy controls (HCs) or subjects without a diagnosis of GD were included in the metaanalysis. Other criteria included studies that reported whole-brain analysis of tasking-state fMRI scans and reported the coordinates of the activation areas of a voxel-wise whole-brain analysis in stereotactic coordinates using t, Z, or P values. Subjects with a diagnosis of anxiety and/or depression were not excluded because of their considerable comorbidity rates with gambling. Studies of GD that had

Table 1 Description of the studies included in the meta-analysis. Study

GD

HC

Study

Age (years)

CoSOGS morbidity/treatment scores

Duration of illness (months)

Sample/female

Age (years)

Tasks

Testa Software

Statistical threshold

FWHM (mm)

14/4

35.8

6 (nicotine use)/0

12.6

–

14/0

37.1

3.0 T SPM5

Corr

6

15/0

27.9

0/0

15.9

2.2

15/0

26.2

1.5 T SPM8

FWE

4

10/0

39.3

1 (nicotine dependence)/8

13.9

14.1

10/0

39.2

Monetary Incentive Delay Task Monetary Incentive Delay Task Gambling-related video

3.0 T Stimulate

Corr

–

19/0

34.3

8.9

>12

19/0

34.1

Probabilistic reversal-learning and the Tower of London task

3.0 T SPM2

FDR

8

17/0 Goudriaan, 2010 (Goudriaan et al. 2010) 17/0 De Ruiter, 2012 (de Ruiter et al. 2012) Holst, 2012 (Holst et al. 16/0 2012)

35.3

5 (anxiety & depression)/19 (cognitive behavioral therapy) 4 (anxiety & depression)/0

9.6

>12

17/0

34.7

Event-related cue reactivity paradigm

3.0 T SPM2

FWE

8

9.6

>12

17/0

34.7

A stop signal task.

3.0 T SPM2

FDR

8

11.6

–

15/0

36.2

FDR

8

16/0

35.0

10.1

–

16/0

38.0

An affective Go-NoGo task with gambling related pictures A quasi-realistic blackjack game task

3.0 T SPM5

Miedl, 2012 (Miedl, Peters, and Büchel, 2012) Potenza, 2003 (Potenza, Steinberg, et al., 2003) Potenza, 2003 (Potenza, Leung, et al., 2003) Power, 2012 (Power, Goodyear, and Crockford, 2012) Reuter, 2005 (Reuter et al., 2005) Tanabe, 2007 (Tanabe et al., 2007)

4 (anxiety & depression)/0 0/16 (cognitive behavioral therapy) 10 (nicotine use)/0

3.0 T SPM8

uncorr

8

10/0

36.2

3 (nicotine dependence)/0

12.6

–

10/0

30.1

View happy, sad and gambling scenarios

1.5 T Yale

Uncorr

6.25

13/0

35.1

6 (nicotine dependence)/0

12.6

–

11/0

29.0

A Stroop task

1.5 T Yale

Uncorr

6.25

13/0

42.4

0/0

13

–

13/0

41.0

Iowa gambling task

3.0 T FSL

corr

8

12/0

37.3

3 (marijuana use)/0

160.4

12/0

32.3

A guessing game task

3.0 T SPM2

corr

10

20/8

35.0

20 (substance dependent)/-

NA

16/5

37.0

Iowa gambling and decision task

3.0 T SPM2

FWE

6

Balodis, 2012 (Balodis et al., 2012) Choi, 2012 (Choi et al. 2012) Crockford, 2005 (Crockford et al. 2005) De Ruiter, 2009 (de Ruiter et al. 2009)

35.3 34.4

– 10.7

Y.-j. Meng et al. / Behavioural Brain Research 275 (2014) 243–251

Sample/female

Corr: corrected for multiple comparisons; FDR: False Discovery Rate; FSL: FMRIB Software Library; FWE: Family Wise Error; GD: gambling disorder; NA: not applicable; SOGS: South Oaks Gambling Screen; SPM: Statistical Parametric Mapping; uncorr: uncorrected for multiple comparisons; ‘–’: not provided.

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not excluded participants with substance (mostly nicotine and/or marijuana) use disorder comorbidity were also included but were analyzed in a subgroup. Studies of GD in the context of other mental diseases, such as Parkinson’s disease (PD), obsessive-compulsive disorder (OCD), borderline personality disorder, bipolar disorder, schizophrenia, and other psychoses were excluded from the analysis. Theoretical and literature review papers, papers by non-English languages were also excluded. A total of 14 papers were identified as meeting initial inclusion criteria. When more than one paper was based on the same sample set, the latest and best-fit study was selected; when a study reported pre- and post-treatment data, only the former were included in the analysis. One study was not included in our analyses since it overlapped to a degree with the already published studies [41]. If there was both voxel-based analysis and partial coverage in addition to regions of interest (ROIs) or small volume correction (SVM), only the voxel-based analysis result was included. Ultimately, 13 studies were included in the meta-analysis (Table 1) [10,11,30,34,37,35,38,29,42,12,36,43,44].

2.2. Meta-analysis of studies The meta-analysis in the present study was performed to compare the functional brain responses of patients with GD to those of HCs. This meta-analysis was calculated using the mean and threshold probability procedures of ES-SDM software (http://www.sdmproject.com). This software enables investigators to combine peak coordinates and statistical parametric maps and uses standard effect size- and variance-based meta-analytic calculations (http://sdmproject.com/software/Tutorial.pdf) [45]. The

meta-analysis included six steps. First, the files were prepared for the SDM software to collect raw information and the main outcomes of the included studies to create the SDM table. Second, global gray matter volumes were analyzed to select covariates, indicators for subgroup comparisons, and a filter for subgroup analysis. Third, effect-size brain maps of the original studies were created to generate Monte Carlo brain maps by randomly permuting voxels from these brain maps. Fourth, a mean analysis of the studies was conducted, which was the main outcome. Fifth, other related analyses including subgroup analysis of the different samples (task type, comorbidity, and so on), jackknife sensitivity analysis, visual inspection of heterogeneity, and meta-regression by clinical syndromes were performed. And finally, the images and results were checked. The full-width at half-maximum (FWHM) in SDM, which had excellent control for false positives according to previous studies, was set at 25 mm. Meta-regression analyses by clinical syndromes were applied to examine the variability of the South Oaks Gambling Screen (SOGS) scores. Additionally, descriptive analyses of quartiles were conducted to assess the proportion of the studies that reported results within a particular brain region. Finally, we conducted subgroup analyses of those based on the use of 3.0 T scanners, statistical parametric mapping (SPM) software, and the use of typical smoothing kernels (FWHM = 7–8 mm, correcting for multiple comparisons). Male subjects, comorbidities, and cue- or reward-processing tasks were also analyzed separately as subgroups. Moreover, based on the comorbidity type of the GD samples, we performed a subgroup analysis by dividing the samples into two subgroups, regardless of excluding or not excluding any kind of substance use disorder. The authors of the published reports were contacted for further information whenever required.

Fig. 1. The flow diagram of the literature search in the meta-analysis study.

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3.2. Changes in regional brain responses to cognitive tasks in GD studies as well as subgroup and meta-regression analyses

3. Results 3.1. Characteristics of the samples of the studies included in the meta-analysis A total of 13 high-quality studies of GD met the meta-analysis inclusion criteria (Fig. 1). These studies included 192 subjects with GD and 185 HCs. The percentage of female subjects was 6.3% (12/192) in the GD group and 2.7% (5/185) in the HC group. The mean age was 35.4 years in the GD group and 34.7 years in the HC group. The mean SOGS score of the patients with GD was 13.2. Among the 13 GD studies, only one used samples from Asian countries (The Netherlands, four studies; U.S., four studies; Canada, two studies; Germany, two studies; Korea, one study). Cognitive tasks according to the task stimulus types and paradigms involved cue- or reward-processing gambling (11 studies), decision making (one study), and impulsivity (one study) in GD studies. All of the contrasts were consistent in pairwise comparison between patients and well-matched controls. Herein, seven studies [10,11,34,35,42,36,44] used samples that had not excluded the substance (mostly nicotine and/or marijuana) use disorder comorbidities (which formed the “SUD-not-excluded pairs” subgroup). Six studies used samples without substance use disorder or dependence formed the “SUD-excluded pairs” subgroup [30,37,38,29,12,43]. One study with “substance dependence (without any special substances)” was excluded from this subgroup analysis [36]. Consequently, There were 95 subjects with GD (mean age, 36.3 years; mean SOGS score, 12.8) and 89 HCs (mean age, 34.6 years) included in the “SUD-not-excluded pairs” subgroup. There were 97 subjects with GD (mean age, 34.8 years; mean SOGS score, 11.1) and 96 HCs (mean age, 34.1 years) included in the “SUD-excluded pairs” subgroup. Detailed demographic and clinical characteristics of the participants are presented in Table 1.

Functional abnormalities in individuals with GD were obtained from all 13 studies. As shown in Table 2 and Fig. 2, compared with HCs, the patients with GD had significant hyperactivity in the right lentiform nucleus (including the right medial globus pallidus) and left middle occipital gyrus (BA18/19). Because we noted the hyperactivity in the left cerebellar lingual (Talairach coordinates: 0, −42, −14; voxel number, 38; Z, −1.268; P = 0.000) and the right declive (Talairach coordinates: 34, −74, −20; voxel number, 522; Z, −1.443; P = 0.000) in patients with GD relative to HCs suggested by this meta-analysis was overwhelmed by sparse findings in two studies [37,38], we conservatively do not consider it a significant finding. Consequently, no decreased activation was found in any brain regions of subjects with GD in this case–control meta-analysis. We also performed a meta-regression analysis of symptom severity described in the SOGS scores and found that it was associated with hyperactivity in the right lentiform nucleus and bilateral parahippocampus but negatively related to the right middle frontal gyrus. The subgroup analysis revealed that both GD subgroups that had, or had not, excluded any kind of substance use disorder comorbidity showed hyperactivity in the right lentiform nucleus. The GD group that had not exclude the substance (mostly nicotine and/or marijuana) use disorder comorbidity showed increased activity in the right lentiform nucleus and left percuneus compared to the HC group (“SUD-not-excluded pairs” subgroup analysis), while the subgroup of GD that had excluded any kind of substance use disorder comorbidity showed hyperactivity in right anterior cingulate gyrus and middle frontal gyrus, and left middle occipital gyrus, in addition to the right lentiform nucleus (“SUD-excluded pairs” subgroup analysis) (Fig. 3).

Table 2 Cognitive tasks brain response abnormalities in GD compared with HCs and meta-regression, subgroup analyses. Label

Volume (mm3 )

Clusters activated from GD patients compared with HCs

608

1.939

0.000152087

16

−2

−6

334

1.829

0.000299550

−32

−82

6

522 38

−1.443 −1.268

0.000907386 0.002706230

34 0

−74 −42

−20 −14

424

2.331

0.000118176

12

0

−4

263

2.389

0.000090430

−20

−22

−14

44

1.678

0.002147206

20

−22

−10

30

−1.705

0.000527168

36

38

12

245

1.547

0.000569300

−36

−88

0

113

1.498

0.000965960

32

6

40

176

1.512

0.000867823

−16

0

16

32

1.343

0.002503789

6

22

18

413 59

2.012 1.623

0.000466538 0.002735003

−6 14

−60 -2

40 4

Effects of the scores of SOGS Specific hyperactivations in GD patients

“SUD-excluded pairs” subgroup

“SUD-not-excluded pairs” subgroup

Z

P

Talairach coordinates (x, y, z)

Anatomical regions

Brodmann area

Right lentiform nucleus Left middle occipital gyrus Right declive Left cerebellar lingual

Medial globus pallidus 19/18

Right lentiform nucleus Left parahippocampal gyrus Right parahippocampal gyrus Right middle frontal gyrus

Medial globus pallidus 35

Left middle occipital gyrus Right middle frontal gyrus Right lentiform nucleus Right anterior cingulate

18/19

Left precuneus Right lentiform nucleus

– –

35

10

6 Putamen 24 7 Lateral globus pallidus

SOGS: South Oaks Gambling Screen. “SUD-excluded pairs” subgroup: The subgroup that had excluded the substance use disorder comorbidities. “SUD-not-excluded pairs” subgroup: The subgroup that had not excluded the substance (mostly nicotine and/or marijuana) use disorder comorbidities.

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Fig. 2. Cognitive tasks brain response abnormalities in GD compared with HCs and meta-regression analyses. Patients with GD had significant hyperactivity in the right lentiform nucleus (red clusters: A), left middle occipital gyrus (red clusters: B), but deactivation in the bilateral cerebellum (blue clusters: C and D) compared with HCs in the meta-analyses. And the SOGS score of GD patients was positively correlated with activations in the right lentiform nucleus (red clusters: E), the bilateral parahippocampal gyrus (red clusters: F and G), but negatively correlated with activations in the right middle frontal gyrus (blue clusters: H) in the meta-regression analyses. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

3.3. Sensitivity and robustness analysis

4. Discussions

The systematic whole-brain jackknife sensitivity analysis revealed that all of these results of the GD studies were highly replicable with the possible exception of the abnormalities in the left cerebellar lingual (12/13) and the right declive (13/13) (Table 3).

To our knowledge, this is the first neuroimaging meta-analysis of existing whole-brain studies investigating the functional brain response to cognitive tasks in patients with GD. We used quantitative ES-SDM meta-analytic methods to synthesize findings from 13

Fig. 3. Brain response abnormalities to cognitive tasks in GD subgroups. Both GD subgroups, regardless of excluding or not excluding any kind of substance use disorder, showed hyperactivity in the right lentiform nucleus (green and orange clusters: A). The subgroup of GD that did not exclude the substance (mostly nicotine and/or marijuana) use disorder showed increased activity in left percuneus (green cluster: B) (“SUD-not-excluded pairs”), while the subgroup that excluded any kind of substance use disorder showed hyperactivity in left middle occipital gyrus (orange cluster: C), right middle frontal gyrus and anterior cingulate gyrus (orange clusters: D and E) (“SUD-excluded pairs”). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

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Table 3 Cognitive tasks brain response abnormalities in GD: robustness analyses. Positive

Jackknife analysis All studies but Balodis, 2012 All studies but Choi, 2012 All studies but Crockford, 2005 All studies but Holst, 2012 All studies but Miedl, 2012 All studies but Potenza, 2003a All studies but Power, 2012 All studies but Reuter, 2005 All studies but Ruiter, 2009 All studies but Ruiter, 2012 All studies but Tanabe, 2007 All studies but Potenza 2003b All studies but Goudriaan 2010 Subgroup analyses Studies cue or reward-gambling stimulus tasks (n = 11, 85%) Studies using all male subjects (n = 11, 85%) Studies using subjects (comorbidity) (n = 9, 69%) Studies using Western studies (n = 12, 92%) Studies using 3.0 T scanners (n = 10, 77%) Studies using SPM software (n = 9, 69%) Studies applying 7–8 mm FWHM (n = 6, 50%)a Studies correcting for multiple comparisons (n = 10, 77%)

Negative

R lentiform nucleus

L middle occipital gyrus

R declive

L cerebellar lingual

Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes 13 out of 13

Yes Yes Yes No Yes Yes Yes Yes Yes Yes Yes Yes Yes 12 out of 13

Yes Yes Yes Yes Yes Yes No Yes Yes Yes Yes Yes Yes 12 out of 13

Yes No Yes Yes Yes No Yes Yes No Yes Yes Yes Yes 10 out of 13

Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes Yes Yes Yes Yes Yes Yes

Yes Yes No Yes Yes Yes No Yes

Yes Yes No No No Yes Yes No

L: left; R: right. a There was only one study not present the value of FWHM (Crockford, 2005).

functional neuroimaging studies of GD. The results revealed reliable clusters of abnormal activation in GD within the regions comprising the right lentiform nucleus and the left middle occipital gyrus compared with HCs. The subgroup analysis revealed that the GD group, regardless of excluding or not excluding any kind of substance use disorder showed increased activity in the right lentiform nucleus compared to the HC group. We also found that the SOGS symptom severity was associated with hyperactivity in the right lentiform nucleus but negatively related to the right middle frontal gyrus. 4.1. Reward circuit of GD The main findings of the present study were that, compared with HCs, the patients with GD had significant hyperactivity in the lentiform nucleus (including the putamen and globus pallidus), a phenomenon that was consistent with most findings of previous MRI studies in GD [10,11,30,34,37,35,38,29,42,12,36,43]. As a part of the striatum, the putamen, along with the globus pallidus and caudate nucleus, interacts through the “unnatural” reward circuit in the limbic system [21]. Most reward processing and addiction studies [11,12,35,42] provided evidence to support that the striatum represented the subjectively discounted value of delayed rewards, and reduced activity was seen in patients with established substance dependence [46] as well as greater activity in anticipation of monetary rewards [47]. Our findings also revealed a positive correlation between SOGS score and activation in the right lentiform nucleus but a negative correlation between SOGS score and the left middle frontal gyrus, which suggests the potential importance of the frontostriatal cortical pathway in the clinical control of GD severity. Heatherton and Wagner [48] further presented a prefrontal–subcortical balance model to partly explain addiction behavior that is consistent with recent findings in the cognitive neuroscience of addictive behavior [49,50] and emphasizes a deficiency in the top-down inhibitory control of drug-taking with a shift in the underlying neuroanatomical focus from the striatum to the PFC [51].

The frontostriatal cortical circuit appears to play major roles in executive functioning [52] and inhibitory control [53] and involve the reward, control, and motor circuits [54]. That is, when patients with GD spent more time and money on indulging themselves in gambling activities, their clinical syndromes are more serious, their SOGS scores increased, and they would try to regulate gambling behavior with the inhibited PFC due to feedback regulation from the overactive striatum to the PFC. This finding sheds light on the neural mechanisms underlying self-regulation and the intensive eagerness seen in gambling behaviors to maximize reward [55] in the frontostriatal cortical circuit of GD. Therefore, the regression analysis results of positively correlated hyperactivation in the lentiform nucleus but negatively correlated hyperactivation in the PFC with the severity of gambling in patients with GD are consistent with the view of GD as a behavioral addiction and with the dysfunctional reward circuit in GD. 4.2. GD and comorbidities As shown in the above discussion, we found overlap in the hyperactive brain regions between the results of our meta-analyses of fMRI studies in GD and that of previous substance use disorders or addiction fMRI studies. It seems possible to show the results including mixing with the parts of studies that had not excluded substance (mostly nicotine and/or marijuana) use disorder comorbidities. For this reason, we further divided the GD group into two subgroups and performed a subgroup analyses to avoid the interference of substance use disorders. Both the “SUD-not-excluded pairs” and “SUD-excluded pairs” subgroups showed increased activities in the lentiform nucleus in patients with GD compared to HCs, though this activation in the lentiform nucleus belongs to different parts of the nucleus (the putamen and lateral globus pallidus). This provided the evidence of brain function changes of the lentiform nucleus in GD subjects. The latter has also been emphasized by recent findings of neuroimaging researches on substance use disorders. This region’s activities have been reported to be linked to increased reward-seeking behavior [56], which might also be a

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compensatory mechanism for existing reward deficiencies in GD. Beyond, we also found a dysfunctional frontostriatal cortical circuit (the right lentiform nucleus and middle frontal gyrus) of patients with GD in “SUD-excluded pairs” subgroup analysis. This finding shed light on the abnormally functional reward circuit in GD as a behavioral addiction, because similar findings have also been documented by studies on drug addictions [57]. In addition to these aforementioned brain regions, increased activities were noted in the right prefrontal lobe and left middle occipital gyrus of the “SUD-not-excluded pairs”. The hyperactivations or functional deficits of the middle occipital gyrus have been widely reported in GD studies. The occipital lobe is generally associated with motion and visual processing. Crockford et al. suggested that visual gambling sensory cues are preferentially recognized by patients with GD as being salient for attention, reward expectancy, and behavior planning for attaining rewards. Therefore, they proposed that the occipital cortex could also participate in the gambling activities and may be involved in the unnatural reward process. This perspective is supported by another fMRI study [58]. Moreover, the fMRI study with the face-matching condition reported that high-risk youth exhibited increased occipital cortex activation compared to HCs and the occipital activation correlated positively with parent-rated emotional regulation impairments in the high-risk group. It is speculated that the cortical activation deficits in the occipital lobe during facial emotion processing in youth could be a high risk for the development of substance use disorders [59]. However, these findings came from an indirect rather than a direct comparison of patients with GD who had a comorbidity with substance use disorder or not. Thus, a lack of sufficient original evidence indicating that individuals with GD and the comorbidity of substance use disorder might influence the neuromechanism of GD. The above findings together could thus contribute to our understanding of the categories and definition of GD and provide support for the proposed reclassification of GD as a behavioral addiction in the DSM-V. 4.3. The cerebellum in GD The meta-analyses findings showed hypoactivity of the cerebellum (including the left cerebellar lingual and the right declive) in GD relative to HCs, although we considered them sparse findings and rejected deliberately. It cannot be denied that convergent studies implicate involvement of the cerebellum in addiction [37,38,60]. A likely contribution of the cerebellum to the addiction process is also suggested by imaging studies implicating it in cognitive processes underlying the execution of goal-directed behaviors and their inhibition when they are perceived as disadvantageous [60]. Moreover, several recent functional neuroimaging studies had found alterations in prefrontal-thalamic-cerebellar networks being associated with cognitive dysfunction in schizophrenia and other mental disorders. There are currently few fMRI studies of patients with GD that reported cerebellar dysfunction in executive or other cognitive processes, so it would be difficult to interpret such findings or justify it solely based on the limited available literature in our meta-analyses. 4.4. Limitations Although these findings offer an important contribution to the relatively modest literature on the neuroimaging underpinnings of GD, several limitations should be considered. First, the algorithms for MRI pre-processing varied among the studies, which may affect the results of the voxel-wise whole-brain analyses (such as using SPM software, applying 7–8 mm FWHM and correcting for multiple comparisons) and meta-regression by clinical syndromes

(such as small sample sizes, different experimental paradigm or task design, and heterogeneity with regard to comorbidities in the GD group), although the subgroup analysis reported that most of these GD studies reported significant activity in the similar brain regions compared to HCs (Table 3). Second, the ES-SDM approach to meta-analysis is inherently limited by its reliance on stereotactic coordinates that represent clusters of varying spatial extent and statistical significance [61]. This approach is also limited by the fact that functional imaging studies, unlike other behavioral science studies, traditionally do not report results that did not survive statistical thresholding, precluding the inclusion of sub-threshold but consistent activations across studies [62]. Future studies in larger and more diverse samples are needed to determine whether this approach is justifiable. The development of a reliable and valid brief gambling screen would also be of great practical use. Additional psychometric testing of the diagnostic criteria themselves and the establishment of a “gold standard” diagnostic instrument would be useful. In conclusion, this meta-analysis using the ES-SDM method documented reliable findings that reliably exhibit dysfunction or hyperactivation with the frontostriatal cortical pathway in GD, which could contribute to our understanding of the categories and definition of GD and provide supportive evidence for the reclassification of GD as a behavioral addiction in the DSM-5.

Competing interests None declared.

Acknowledgements This research was partly supported by This research was partly supported by National Natural Science Foundation of China (81130024, 30530300 and 30125014, T.L.); the Ph.D. Programs Foundation of Ministry of Education of China (20110181110014, T.L.), National Key Technology R&D Program of the Ministry of Science and Technology of China during the 12th Five-Year Plan (2012BAI01B06, T.L.).

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