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. 2017 Mar 29;14:741–749. doi: 10.1016/j.nicl.2017.03.010

Dissociable neural processes during risky decision-making in individuals with Internet-gaming disorder☆

Lu Liu a, Gui Xue b,c, Marc N Potenza d,e, Jin-Tao Zhang b,⁎, Yuan-Wei Yao b, Cui-Cui Xia a,f, Jing Lan a, Shan-Shan Ma b, Xiao-Yi Fang a,⁎
PMCID: PMC5385591  PMID: 28413776

Abstract

Risk-taking is purported to be central to addictive behaviors. However, for Internet gaming disorder (IGD), a condition conceptualized as a behavioral addiction, the neural processes underlying impaired decision-making (risk evaluation and outcome processing) related to gains and losses have not been systematically investigated. Forty-one males with IGD and 27 healthy comparison (HC) male participants were recruited, and the cups task was used to identify neural processes associated with gain- and loss-related risk- and outcome-processing in IGD. During risk evaluation, the IGD group, compared to the HC participants, showed weaker modulation for experienced risk within the bilateral dorsolateral prefrontal cortex (DLPFC) (t = − 4.07; t = − 3.94; PFWE < 0.05) and inferior parietal lobule (IPL) (t = − 4.08; t = − 4.08; PFWE < 0.05) for potential losses. The modulation of the left DLPFC and bilateral IPL activation were negatively related to addiction severity within the IGD group (r = − 0.55; r = − 0.61; r = − 0.51; PFWE < 0.05). During outcome processing, the IGD group presented greater responses for the experienced reward within the ventral striatum, ventromedial prefrontal cortex, and orbitofrontal cortex (OFC) (t = 5.04, PFWE < 0.05) for potential gains, as compared to HC participants. Within the IGD group, the increased reward-related activity in the right OFC was positively associated with severity of IGD (r = 0.51, PFWE < 0.05). These results provide a neurobiological foundation for decision-making deficits in individuals with IGD and suggest an imbalance between hypersensitivity for reward and weaker risk experience and self-control for loss. The findings suggest a biological mechanism for why individuals with IGD may persist in game-seeking behavior despite negative consequences, and treatment development strategies may focus on targeting these neural pathways in this population.

Keywords: fMRI, Internet gaming disorder, Outcome processing, Risky decision-making, Risk evaluation

Highlights

  • •

    IGD subjects showed greater activation in reward-related areas for reward outcomes.

  • •

    IGD subjects showed weaker modulation for risk processing in bilateral DLPFC and IPL.

  • •

    The reward-related activity in the right OFC was positively related to severity of IGD.

  • •

    The modulation of the DLPFC and IPL activation were negatively related to severity of IGD.

1. Introduction

Internet gaming disorder (IGD) is characterized by persistent gaming online despite negative consequences (e.g., poor academic performance, impaired social interaction, insomnia) (King and Delfabbro, 2014, Petry et al., 2014). This particular feature is a criterion of IGD that has been considered central to the disorder (Petry et al., 2014), and one reflecting maladaptive risky decision-making in real life. Behavioral studies have shown that individuals with IGD select risky options more often than do healthy participants (Dong and Potenza, 2015, Yao et al., 2015a, Yao et al., 2015b), Risk-taking also has been associated with substance-use and gambling disorders (Noël et al., 2013). These observations together suggest that impairments in risky decision-making may be core features shared by different kinds of addictions.

Risky decision-making, as a complex process, involves both risk evaluation and outcome processing (Gowin et al., 2013, Xue et al., 2009). Previous studies of IGD or Internet addiction disorder (IAD) have revealed neural alterations underlying reward/outcome processing including ones involving the medial prefrontal cortex (MPFC), orbitofrontal cortex (OFC), anterior cingulate cortex (ACC), and the posterior cingulate cortex (PCC); these regions have been implicated in affective/motivational responses, reward-value processing, aversive processing and attentional/motor control, respectively (Dong et al., 2013b, Dong et al., 2011, Lorenz et al., 2013). Studies have also identified altered neural function related to risk evaluation and impulse control including the inferior frontal gyrus (IFG), anterior insula, ACC and the inferior parietal lobule (IPL) individuals with IGD as compared to those without (Dong et al., 2013a, Dong and Potenza, 2015, Lin et al., 2015), with the ACC along with the anterior insula implicated in conflict monitoring and aversive processing, the IFG in inhibition processing, and the IPL in uncertainty processing. However, few studies have assessed dysfunction of specific facets of decision-making to investigate systematically decision-making deficits and their neural correlates in IGD.

Risk evaluation and outcome processing underlying decision-making may require different processes for assessing potential gains versus losses (Levin et al., 2011, Weller et al., 2007). Individuals with substance-use disorders (SUDs) have been shown to make more disadvantageous choices for potential rewards related to impaired risk evaluation (Brevers et al., 2014, Brevers et al., 2012), to demonstrate greater reward sensitivity associated with hyper-activation of reward circuitry (Jia et al., 2011), and to show hypersensitivity to loss outcomes (Gowin et al., 2016). Moreover, individuals with IGD made more disadvantageous choices for potential losses due to insensitivity to levels of uncertainties and outcome magnitudes (Yao et al., 2015a). In addition, neural studies suggest enhanced reward sensitivity and decreased neural response to monetary loss in IGD (Dong et al., 2013b, Dong et al., 2011). However, since previous decision-making tasks mostly framed mixed prospects (combining gain and loss within one trial as potential outcomes), such as a reality-simulated guessing task, the balloon analog risk task (BART) and the game of dice task (GDT) (Dong et al., 2013b, Qi et al., 2015, Yao et al., 2014), they do not provide optimal insight into risk perceptions and outcome processes under separate valences (potential gains versus losses) in IGD. Thus, it is promising to further distinguish features of these two processes in potential gains versus losses, which may contribute to an improved understanding of the pathophysiology of IGD and ultimately to the development of more effective interventions for IGD.

Previous work has identified several neural regions involved in processes of risky decision-making (Goldstein and Volkow, 2002, Schonberg et al., 2011). Specifically, the ventromedial prefrontal cortex (vmPFC), OFC and ventral striatum (VS), within the mesolimbic dopaminergic (DA) system, are neural structures involved in reward anticipation and outcome processing (Gowin et al., 2013, McClure et al., 2004, Xue et al., 2009). Data suggest altered responsiveness within these regions to reward- versus loss- or control-outcomes in individuals with SUDs and behavioral addictions (Balodis and Potenza, 2015). Meanwhile, the DLPFC, related to risk-taking and self-control, functions differently during risky decision-making in individuals with and without SUDs (Kohno et al., 2014, Paulus et al., 2003). Similarly, the IPL, implicated in processing uncertainty (Vickery and Jiang, 2009), has been found to function differently during decision-making in individuals with and without IGD (Dong and Potenza, 2015). In addition, the insula, OFC and ACC have been implicated in risk representation (Naqvi et al., 2014, Schonberg et al., 2011), the abnormal activation of which has been related to impaired risk-taking in addicted populations (Brevers et al., 2015, Gowin et al., 2014, Paulus et al., 2008). Taken together, these central brain structures may contribute importantly to impaired decision-making in IGD.

In the current study, we used a decision-making task, the cups task, which can separately assess gain and loss domains and has been used in studies of lesions and addictions (Levin et al., 2011, Weller et al., 2007), as well as in healthy samples (Xue et al., 2009), to systematically investigate behavioral and neural alterations underlying impaired risky decision-making in individuals with IGD. Based on previous findings summarized above, we hypothesized that IGD participants, compared to healthy comparison (HC) participants, would: (1) choose more risky options in both gain and loss trials; and, (2) show greater responses in mesocorticolimbic regions for gain-related outcome processing, and decreased activation within risk- and control-related regions for risk evaluation. We also hypothesized that within IGD participants, the observed between-group differences in behavior and brain activations would relate to individual risk preferences and severity of IGD.

2. Materials and methods

2.1. Participants

Forty-five individuals with IGD and 30 HC participants were recruited via the Internet and advertisements posted at local universities and selected through an online questionnaire and telephone screening.

Participants were recruited according to their weekly Internet gaming time and scores on the Chinese Internet Addiction Scale (CIAS) (Chen et al., 2003), which consists of 26 items on a 4-point Likert scale. The reliability and validity of the CIAS among college students has been demonstrated previously (Chen et al., 2003). Inclusion criteria for the IGD group were: (1) a score > 67 on the CIAS (Ko et al., 2009); (2) > 20 h per week engaged in Internet gaming, for a minimum of one year; and, (3) endorsement of Internet gaming as their primary Internet activity. Inclusion criteria for HC participants were: (1) a score of 60 or lower on the CIAS; and, (2) never having spent > 2 h per week engaged in Internet gaming. Participants who reported current or a history of use of illegal substances and any gambling experience (including online gambling) were excluded given the illegality of gambling in China. Additional exclusion criteria were assessed through a semi-structured personal interview, consistent with previous studies in IGD (Yao et al., 2015b, Zhang et al., 2016). Exclusion criteria included: (1) any self-reported history of any psychiatric or neurological illness; and, (2) current use of any psychotropic medication.

Given the higher prevalence of IGD in males versus females (Ko et al., 2009, Meng et al., 2014), only male participants were included. Four IGD and three HC subjects were excluded due to excessive head motion during scanning (defined as motion in excess of one voxel); thus, the final dataset was comprised of 41 individuals with IGD and 27 HC individuals.

2.2. Questionnaires

Current status of depression and anxiety was assessed using the Beck Depression Inventory (Beck et al., 1961) and the Beck Anxiety Inventory (Beck et al., 1988), respectively. Cigarettes and alcohol use was recorded, and the Fagerstrom Test for Nicotine Dependence (FTND) (Fagerström, 1978) and alcohol consumption questions from the Alcohol Use Disorders Identification Test (AUDIT-C) (Bush et al., 1998) were used to assess tobacco and alcohol use disorders, respectively. Exclusion criteria are detailed in the Methods section of Supplementary Materials.

2.3. The cups task

To assess the neural mechanisms of risky decision-making in gain and loss domains, we used a computerized version of the cups task (Xue et al., 2009). The cups task includes a gain domain and a loss domain. Subjects were instructed to win as much money as possible in the gain domain and to lose as little money as possible in the loss domain. For both gain-domain trials and loss-domain trials, subjects were required to choose between a risky option and a safe option. The safe option is to win or lose $1 for sure, whereas the risky option could lead to a probability (0.20, 0.33, or 0.50) of a larger win ($2, $3, or $5) or winning nothing otherwise in the gain domain, and to a possibility of losing more ($2, $3, or $5) or losing nothing otherwise in the loss domain (Fig. 1). Within each domain, probability and outcome magnitude of the risky option were manipulated such that some combinations of probability and outcome magnitude create equal expected value (EQEV) for the risky and safe options: 0.20 × 5, 0.33 × 3, and 0.50 × 2 on both gain and loss trials. This approach provides a measure of participants' risk preference. Some combinations are slightly risk advantageous (RA), meaning that the expected value (EV) is more favorable for the risky option than for the safe option: 0.33 × 5, 0.50 × 3 in the gain domain; 0.20 × 3, 0.33 × 2 in the loss domain. Some combinations are slightly risk-disadvantageous (RD), meaning that the EV is more favorable for the safe option: 0.20 × 3, 0.33 × 2 in the gain domain; 0.33 × 5, 0.50 × 3 in the loss domain. The 2 combinations with the biggest differences in EV between risk and safe options (i.e., 0.20 × 2 and 0.50 × 5) were excluded in the current study because of their insensitivity to individuals' attitude toward risk, evident from healthy young adults (Xue et al., 2009) (see the Methods section of Supplementary Materials for further details on task and experimental methods).

Fig. 1.

Fig. 1

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The cups task and behavioral performance among IGD and HC participants. The cups includes a gain domain (A) and a loss domain (B). Each trial consists of a safe option with $1 in one cup, and a risky option with a probability of 1/2–1/5 (as determined by the number of cups) of larger gain or loss (±$2 to ±$5). In some trials, the EV of the safe option is equal to that of the risky choice (i.e., EQEV), whereas other combinations could be RA or RD (see Methods section). Mean percentage of risky choices made in the gain domain (C) and loss domain (D), as a function of EV level and group, are displayed. Mean response times (RTs) during decision-making in the gain domain (E) and loss domain (F) are also displayed. IGD = Internet gaming disorder; HC = healthy comparison; EV = expected value; RA = risk advantageous; EQEV = equal expected value; RD = risk disadvantageous.

2.4. Behavioral data analysis

Between-group comparisons of demographic, clinical characteristics, and task performance measures were conducted using mixed-model ANOVAs, independent-sample t-tests or Chi-square tests as appropriate in SPSS 20.0.

2.5. fMRI acquisition and analysis

Data were acquired using a SIEMENS Trio 3.0 T scanner in the Beijing Normal University Imaging Center for Brain Research. Image acquisition and analysis methods are detailed in Supplementary Materials. Imaging data were preprocessed using SPM8 (Welcome Department of Cognitive Neurology, London, UK, http://www.fil.ion.ucl.ac.uk/spm/spm8). Functional data were slice-timed, realigned, coregistered with the structural images, segmented for normalization to the standard MNI Space (ICBM152), and smoothed with a 5-mm Gaussian kernel at full width at half maximum (FWHM). To remove low-frequency signal drift, a high-pass filter (280 Hz) was applied.

Statistical analysis of individual participant imaging data was performed using both categorical analysis and parametric analysis of a general linear model (GLM) (Friston et al., 1995). For the category analysis, the following events were modeled based on participants' responses: Risky Loss_Gain-domain, Risky Win_Gain-domain, NoRisk_Gain-domain, Risky Loss_Loss-domain, Risky Win_Loss-domain, and NoRisk_Loss-domain. To quantify the brain activation for each type of choice and outcome, we sorted the trials into 6 different categories according to the task domain (gain vs. loss), subjects' choice (risky vs. safe) and the outcome (win vs. loss). To reveal the domain-related differences, contrast images for decision (subjects' choices) and outcome were created separately in gain and loss trials.

The parametric analysis was used to further quantitatively describe the relationship between brain activation and decision parameters. The following 5 parameters were generated for each trial and entered into a GLM model: the magnitude (Mag, the possible outcome of the risky choice), the probability (Prob, the number of cups), the relative EV of the risky option, the experienced risk and the experienced reward. The relative EV of the risky option is calculated by subtracting the EV of the safe choice from that of the risky choice ($1 or −$1 for the Win and Loss domain, respectively). As previously (Preuschoff et al., 2006, Tobler et al., 2007, Xue et al., 2009), risk was defined as the variance of the outcome and calculated as follows: Risk = [(1 − Prob) × (0 − EV)2 + Prob × (Mag − EV)2]. The experienced risk reflects the outcome uncertainty that is generated only by taking a risky choice. In order to examine the experienced risk, we multiply the decision risk parameter by the subject's response (coded 1 for the risky choice and 0 for the safe). We mainly focused on the 2 parameters associated with experienced risk and experienced reward which due to the factorial design and nature of the task, were largely orthogonal, and the use of a GLM allowed us to examine their unique contributions.

A fixed effect model was used to create cross-run contrasts for each subject for a set of contrast images. These contrast images were entered into a second-level random-effects analysis using a two-sample t-test design to investigate between-group effects. We report significant brain activations at the whole-brain level corrected by means of Gaussian Random Field Theory (GRFT) with voxel-level P < 0.005 and cluster-level P < 0.05 to result in a family-wise error rate of 5%. For confirmatory purposes, a more stringent criterion (corrected by means of GRFT with voxel-level P < 0.001 and cluster-level P < 0.05) was also conducted in whole-brain analyses (see Table S1). The results were visualized using DPABI (http://rfmri.org/dpabi).

2.6. Exploratory analysis: voxel-wise regressions

In order to probe whether group differences in risk evaluation and outcome processing were related to addiction severity or altered risk preference behavior, we conducted voxel-wise regression analyses for the IGD participants of brain responses within voxels, finding significant differences between IGD and HC participants in risk-experience and outcome-processing (masks with clusters survived at voxel-level P < 0.005 and cluster-level P < 0.05) and of the self-report CIAS scores, rate of risk-taking as well as response time for decision-making under the EQEV trials (potential gains and losses separately). According to previous studies on risky decision-making (Tom et al., 2007, Xue et al., 2009), risk-taking under the EQEV trials provides a sensitive estimation of individuals' risk preferences. For exploratory purpose, regions of interests (ROIs) analyses were also conducted including the vmPFC, VS, and OFC using bilateral masks derived from the Harvard-Oxford atlas (http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Atlases; we applied the Harvard-Oxford atlas to MNI space with DPABI). We report significant brain activations within the ROIs that corrected by means of GRFT with voxel-level P < 0.001 and cluster-level P < 0.05 (PSVC-FWE < 0.05).

3. Results

3.1. Demographic and behavioral characteristics

Demographic characteristics of IGD and HC participants are shown in Table 1. IGD and HC groups did not differ significantly in age or years of education. Consistent with the inclusion criteria, IGD participants had significantly higher CIAS scores, with 26.94 (SD = 9.81) hours of time on Internet games weekly. Thirty of the 41 IGD participants and 15 of the 27 HC participants were occasional alcohol drinkers (non-dependent drinkers), with no group difference in AUDIT-C scores. Three IGD and no HC participants reported current cigarette smoking. Participants with IGD scored higher than HC participants on the BDI, with a trend toward higher scores on the BAI.

Table 1.

Demographic characteristics between IGD and HC subjects.

Variable IGD (n = 41) HC (n = 27) Test statistics
Mean (SD) Mean (SD)
Age 21.93 (1.88) 22.74 (2.35) t = − 1.58, P = 0.12
Years of education 15.73 (1.86) 16.22 (1.93) t = − 1.05, P = 0.30
Time spent on Internet gaming (hours per week) 26.94(9.81) 1.33 (0.58)a t = 16.32, P < 0.001
CIAS score 79.66 (8.45) 40.22 (9.44) t = 17.98, P < 0.001
Alcohol use (at least once per month) 30 15 χ2 = 2.26; P = 0.13
AUDIT-C score 3.20 (1.90)b 2.33 (1.29)c t = 1.58, P = 0.12
Cigarette use (at least once per month) 3 0
FTND score 2 (0.00)d –
Depression severity (BDI score) 8.63 (4.90) 3.41 (5.43) t = 4.12, P < 0.001
Anxiety severity (BAI score) 4.41 (4.20) 2.63 (3.62) t = 1.81, P = 0.08
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SD = standard deviation; IGD = Internet gaming disorder; HC = healthy comparison; CIAS = Chen Internet addiction scale; AUDIT-C = alcohol use disorder identification test; FTND = Fagerstrom test for nicotine dependence; BDI = Beck Depression Inventory; BAI = Back Anxiety Inventory.

a

n = 3.

b

n = 30.

c

n = 15.

d

n = 3.

3.2. Risk-taking preference

Risky preference reflects an individual's tendency to choose the risky option over the safe option at each of the three EV levels (RA, EQEV, RD) calculated separately for the gain and loss domains. A 2–by-3-by-2 repeated-measures ANOVA involving domain (gain, loss), EV level (RA, EQEV, RD) and group (IGD participants, HC participants) was conducted. There were significant main effects of domain (F1, 66 = 19.07, P < 0.001, partial η2 = 0.22) and EV level (F2, 132 = 244.59, P < 0.001 partial η2 = 0.79), but no significant main effect of group (F1, 66 = 0.02, P = 0.89, partial η2 < 0.001) or interactions (Fig. 1C, D).

Although individual tendencies to choose the risky option at each EV level did not vary significantly between groups, interesting differences were found in response times for decision-making. In the gain domain, responses took longer for RD and EQEV trials than for RA trials within the IGD group (F2, 132 = 17.66, P < 0.001), and this effect was not found in HC participants (F2, 132 = 1.97, P = 0.14). In the loss domain, responses took longer for RD and EQEV trials than for RA trials in both the IGD (F2, 132 = 16.94, P < 0.001) and HC (F2, 132 = 18.61, P < 0.001) groups. In addition, there were a significant domain-by-EV-level interaction (F2, 132 = 14.55, P < 0.001, partial η2 = 0.18) and a trend toward a domain-by-group interaction (F2, 132 = 3.59, P = 0.06, partial η2 = 0.05) (Fig. 1E, F).

3.3. fMRI results

3.3.1. Alterations in IGD participants processing risks

3.3.1.1. Gain domain

No group differences were found in the gain domain.

3.3.1.2. Loss domain

Whole-brain-corrected group comparisons of the parametric analysis for experienced risk revealed decreased modulation of bilateral DLPFC (MNI: − 42, 54, 18, t = − 4.07; MNI: 33, 21, 51, t = − 3.94) and IPL (MNI: − 45, − 54, 48, t = − 4.08; MNI: 33, − 63, 57, t = − 4.08) in IGD participants compared to the HC group (Fig. 2A and Table 2). That is, there was a weaker relationship between activation of the identified brain regions (DLPFC, IPL) and the experimental parameter (experienced risk) in the IGD as compared to the HC group.

Fig. 2.

Fig. 2

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Group differences in fMRI findings using parametric (panel A and B) and categorical analysis (panel C and D). Group differences of sensitivity to experienced risk under loss conditions (A) and experienced reward (B) are displayed. Group differences of participants' responses to risky versus safe for potential losses (C) and to win versus loss for potential gains (D) are displayed. The color bars reflect t values. The 2D activation maps are overlaid on a T1 image using DPABI. (voxel-level P < 0.005 and cluster-level of P < 0.05, whole-brain corrected). (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Table 2.

Whole-brain analyses comparing risk-related BOLD responses between IGD and HC participants (voxel-level P < 0.005 and cluster-level of P < 0.05).

Hemisphere Brain regions BA Cluster size Peak MNI (mm)
 Peak t value
X Y Z
IGD > HC
 Loss domain - parametric analysis
 L MFG/IFG 46, 47, 10 172 − 42 54 18 − 4.07
 L IPL/Precuneus/SPL/Angular 40, 39, 7 492 − 45 − 54 48 − 4.08
 R MFG/SFG 8, 9, 46 144 33 21 51 − 3.94
 R IPL/Angular/Precuneus/SOG 7, 39, 40 270 33 − 63 57 − 4.08
 R IOG/Fusiform Gyrus/MOG 19 132 39 − 75 − 15 − 3.82
 Loss domain - categorical analysis
 L Insula/OFC 47, 38 122 − 30 21 − 15 4.49
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BOLD = blood oxygen level dependent; IGD = Internet gaming disorder; HC = healthy comparison; BA = Brodmann's area; R = right; L = left; MFG = middle frontal gyrus; IFG = inferior frontal gyrus; SFG = superior frontal gyrus; IPL = inferior parietal lobule; SPL = superior parietal lobule; SOG = superior occipital gyrus; IOG = inferior occipital gyrus; MOG = middle occipital gyrus; OFC = orbitofrontal cortex.

Whole-brain-corrected group comparisons of risky choices > safe choices showed higher brain activations in the left insula extending into the lateral OFC (MNI: − 30, 21, − 15, t = 4.49) in the IGD group compared to the HC group (Fig. 2C and Table 2).

3.3.2. Alterations in IGD participants processing outcomes

3.3.2.1. Gain domain

Whole-brain-corrected group comparisons of the parametric analysis for the experienced outcome revealed increased modulation of the VS, which extended to the vmPFC and OFC (MNI: − 9, 3, − 9, t = 3.94) in IGD participants compared to the HC group (Fig. 2B and Table 3).

Table 3.

Whole-brain analyses comparing outcome-related BOLD responses between IGD and HC participants (voxel-level P < 0.005 and cluster-level of P < 0.05).

Hemisphere Brain regions BA Cluster size Peak MNI (mm)
 Peak t value
X Y Z
IGD > HC
 Gain domain - parametric analysis
 L VS/ACC/vmPFC/OFC/Rectus 25, 11 115 − 9 3 − 9 3.94
 Gain domain - categorical analysis
 L/R VS/vmPFC/Caudate/ACC/Putamen/OFC 11, 25, 38, 47 276 − 15 24 − 9 5.04
 R OFC/VS/Caudate/vmPFC/Rectus 11, 48, 25 216 27 24 − 21 4.54
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BOLD = blood oxygen level dependent; IGD = Internet gaming disorder; HC = healthy comparison; BA = Brodmann's area; R = right; L = left; VS = ventral striatum; vmPFC = ventromedial prefrontal cortex; ACC = anterior cingulate cortex; OFC = orbitofrontal cortex.

Similarly, categorical analysis of win- > loss-outcomes showed higher brain activations in the bilateral VS (MNI: − 15, 24, − 9, t = 5.04) and the right OFC (MNI: 27, 24, − 21, t = 4.54) extending into the vmPFC (Fig. 2D and Table 3).

3.3.2.2. Loss domain

In the loss domain, no group differences were found.

3.3.3. Relationship between brain activities, individual risk preference and IGD characteristics

3.3.3.1. Risk-processing in the loss domain

Among the IGD group, whole-brain regression analyses revealed significant negative associations between severity of IGD and modulation of the left DLPFC-activation-experienced risk (MNI: − 42, 45, 6, r = − 0.55; small-volume-corrected (SVC)) and the modulation of bilateral IPL (MNI: − 42, − 54, 57, r = − 0.61; MNI: 45, − 57, 45, r = − 0.51; SVC) (Fig. 3A).

Fig. 3.

Fig. 3

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fMRI correlations of IGD participants addiction severity with regions surviving in group comparisons. Regions show significant negative correlation between Internet-addiction severity of IGD and modulation of the left DLPFC and IPL activation for experienced risk (A); and a significant positive association between severity of IGD and the right OFC activation in the risky vs. safe choices contrast (B). Scatterplots are shown of correlations between addiction severity and beta values for each cluster surviving in the left DLPFC, IPL, and right OFC, respectively. (voxel-level P < 0.001 and cluster-level of P < 0.05; small-volume corrected).

Small-volume regression analyses revealed a significant negative association between HC participants' risk preference and risk-related activity within the left IFG (risk > safe contrast, MNI: − 45, 12, 24, r = − 0.68; SVC).

3.3.3.2. Outcome-processing in the gain domain

Among IGD participants, ROI-based regression analyses revealed a positive association between severity of IGD and right OFC activation in win vs. loss outcomes (MNI: 39, 21, − 21, r = 0.51; SVC) (Fig. 3B). The HC group showed a positive association between individual risk preference and this reward-related activity within the right vmPFC (MNI: 9, 51, − 15, r = 0.63; SVC).

3.3.4. Functional dissociations of risk and outcome processing among HC participants

When contrasting the risky choices with the safe ones and the win trials with the loss ones for whole trials, this study replicated results among HC participants for differentially modulated dorsal and ventral MPFC activation by risk estimation and outcome processing (Xue et al., 2009) (see Supplementary Results).

4. Discussion

By combining functional MRI and a task which isolates decision-making for gain and loss domains as well as separates two decision-making processes that support the evaluation of risk versus the evaluation of outcomes, our study detected different mechanisms for processing potential gains versus losses during risky decision-making in individuals with IGD. Specifically, our results suggest that individuals with IGD may be overly sensitive to reward-outcomes in gain conditions, with relatively increased activations within the vmPFC, VS and OFC; in contrast, this population showed less activation in the DLPFC and IPL during risk perception and evaluation of potential losses. Consistent with theories in individuals with substance-use (Goldstein and Volkow, 2011) and Internet-use (Brand et al., 2016, Dong and Potenza, 2014) disorders displaying enhanced reward-related drives and affective-related sensitivities in conjunction with reduced executive function/inhibitory, our data further suggest such an imbalance underlying impaired risky decision-making in individuals with IGD. Such an imbalance may promote persistent gaming behavior despite negative consequences.

4.1. Risk processing for potential losses

Consistent with findings from substance-using populations (Kohno et al., 2014, Paulus et al., 2003, Paulus et al., 2008), and from IGD subjects (Qi et al., 2015), between-group differences identified weaker modulation of activity in the DLPFC for risk evaluation for potential losses. Given its role in risk avoidance and self-control (Schonberg et al., 2011), blunted DLPFC sensitivity to risk in IGD subjects may reflect impaired risk processing for potential losses, which may further explain previous behavioral findings regarding IGD participants' insensitivity to uncertainty (Yao et al., 2015a). The IPL, implicated in uncertainty processing and risk-taking in decision-making (Krain et al., 2006, Vickery and Jiang, 2009), showed weaker modulation in this region among IGD subjects, further supporting a characteristic linked to insensitivity regarding uncertainty within this population. Consistently, previous studies showed decreased response within the IPL for decision-making among IGD subjects (Dong and Potenza, 2015). Moreover, the negative associations between modulation in the DLPFC and IPL and Internet-addiction severity further link possible dysregulation of these brain structures to risk sensitization in IGD.

Increased activations of the left anterior insula and lateral OFC among IGD versus HC participants were found in the loss condition. Similarly, previous studies using different decision-making tasks showed increased activation of the insula in individuals with IGD (Dong et al., 2013a) and substance addictions (DeVito et al., 2013, Gowin et al., 2014); and increased activation of the lateral OFC in seen in individuals with pathological gambling (Power et al., 2012). In healthy participants, the anterior insula and lateral OFC contribute to risk representation and risk-taking (Bechara, 2005, Naqvi et al., 2014, Schonberg et al., 2011). Further, the insula has been implicated in anticipatory and affective/motivational processes, as well as during harm avoidance (Craig, 2009, Ernst and Paulus, 2005). The insula is also hypothesized to exhibit alterations in psychiatric disorders at particular stages of decision-making (Ernst and Paulus, 2005). In the loss domain of the study, taking risky options could give subjects a chance to avoid losing even though it might be only $1 for the safe option. Individuals with IGD may experience higher anticipation to loss avoidance by exhibiting risk-taking in loss situations. In addition, given models of addiction emphasizing roles of the OFC in affective “hot” function and DLPFC in cognitive “cool” executive function in decision-making (Goldstein and Volkow, 2011), our findings suggest that abnormal risk preference mediated by OFC and insula function, combined with reduced sensitivity to risk relating to DLPFC and IPL function during loss conditions, may be a core deficit in IGD that underlies continued gaming behavior despite negative consequences.

4.2. Outcome processing for potential gains

Group comparisons using contrasts from parametric and categorical analyses both identified a greater outcome-related neural activation for gain trials in the vmPFC, which extended to the VS and OFC in IGD participants. These findings are consistent with studies that individuals with IGD show increased activation in the vmPFC/OFC during gain trials in a reality-simulated guessing task (Dong et al., 2011), and that heavy cannabis users demonstrated higher activity in the OFC during win versus loss evaluation during the Iowa gambling task (Cousijn et al., 2013), and that individuals with cocaine dependence than those without cocaine dependence show a greater response for rewarding outcome in the bilateral VS during a monetary incentive delay task (Jia et al., 2011). The vmPFC, OFC and VS are linked to reward anticipation and outcome processing during decision-making (Brand et al., 2014, Preuschoff et al., 2006) and may help encode both wins and losses by tuning up and down activations (Tom et al., 2007, Xue et al., 2009). Consistent with the theory proposed by Robinson and Berridge (2003) that posits that the possibility of drug-enhanced reward sensitivity may spillover to nondrug rewards (e.g., money rewards), our results, together with previous findings, suggest a generalized hypersensitivity in brain reward circuit among different addiction subtypes including IGD. Furthermore, the activity in the right OFC was positively associated with Internet-addiction severity within the IGD group, which is consistent with studies in IGD and heavy cannabis users (Cousijn et al., 2013, Dong et al., 2011). The increased response within these reward-related regions for rewarding outcomes may reflect hypersensitivity to reward finally leading to elevated reward-seeking (e.g., maladaptive gaming) despite negative consequences, although this interpretation remains speculative.

In addition, enhanced reward sensitivity is suggested by findings from response times (RTs) during decision-making. That is, the significantly increased RTs at RD and EQ levels for gain trials in IGD participants suggest that there may be more deliberation, conflict or hesitation when IGD participants, compared with HC participants, make decisions during trials when subjects' optimal actions are risk-averse versus risk-seeking.

Partially consistent with theoretical models of IGD and other types of Internet addictions (Brand et al., 2016, Dong and Potenza, 2014), findings in this study indicate that a dysfunctional interaction between poor executive control and enhanced generalized reward-seeking, perhaps as a result of addictive behaviors, may promote disadvantageous decision-making. This may reflect a bias toward affective and reward responses over executive control during decision-making in IGD that may promote this population's persistent gaming behavior despite negative consequences. These models, based on theories of substance addictions (Goldstein and Volkow, 2011), may apply to non-substance addictions in non-addiction-related situations, or at least to IGD in monetary risky decision-making, although further investigation is needed to verify this possibility.

In seeming contrast to previous studies of IGD (Yao et al., 2015a, Yao et al., 2015b), no group differences on risk-taking preference were observed in this study. Contrary to previous studies with larger amounts of money for winning or losing (possible outcomes: 200 yuan, 300 yuan, or 400 yuan), the smaller amounts of money and the exclusion of options with the biggest differences in EV (see Methods section of Supplementary Materials) in this study may have blunted the detection of group differences. This version of the cups task, in return, may be more sensitive to individuals' attitude toward risk and the detection of group differences in brain responses.

4.3. Limitations and future directions

Since our data are cross-sectional, it is not possible to make causal conclusions related to altered brain activations and risky decision-making. Longitudinal (including intervention) research is needed to further characterize the relationship between altered brain activations in impaired decision-making and the course of IGD. The extent to which the identified behavioral and neural differences in IGD relate to other clinically relevant measures (e.g., individual differences in treatment outcome that may subsequently be helpful for developing and targeting interventions) warrants additional study. Given that theoretical models of IGD consider decision-making impairments in combination with reward sensitivity and executive functioning, it would be important in future studies to investigate such interactions of cognitive functions (e.g., level of reward sensitivity and inhibitory control) and brain activity (e.g., activation in the OFC and DLPFC) in IGD. In addition, IGD participants were oversampled in the current study. Thus, there exists an imbalance in the IGD and HC groups. Despite these limitations, the use of a task that parses specific aspects of decision-making is strength and provides insight into the neural correlates of these processes in IGD.

5. Conclusions

This study provides new insights into the neurobiological foundation for decision-making deficits in individuals with IGD. By combining fMRI and a task which isolates decision-making for gain and loss domains as well as risk- and outcome-processing, the results of our study suggest that individuals with IGD may be hypersensitive to reward-outcomes in gain conditions with relatively increased activations within the vmPFC, VS and OFC; and this population may be weaker in risk experience and self-control of potential losses relating to less activation in the DLPFC and IPL. Such findings implicate a biological mechanism for why individuals with IGD may persist in game-seeking behavior despite negative consequences.

Authors contribution

XYF and JTZ were responsible for the study concept and design. LL, YWY, CCX, JL, and SSM contributed to the acquisition of MRI data. LL, JTZ, MNP and GX assisted with data analysis and interpretation of findings. LL drafted the manuscript. JTZ, MNP and XYF provided critical revision of the manuscript for intellectual content. All authors critically reviewed and approved the final version of the manuscript submitted for publication.

Financial disclosures

The authors report that they have no financial conflicts of interest with respect to the content of this manuscript. Dr. Potenza has received financial support or compensation for the following: Dr. Potenza has consulted for and advised Boehringer Ingelheim, Ironwood, Lundbeck, INSYS, Shire, RiverMend Health, Opiant/Lakelight Therapeutics and Jazz Pharmaceuticals; has received research support from the NIH, Veteran's Administration, Mohegan Sun Casino, the National Center for Responsible Gaming, and Pfizer, Forest Laboratories, Ortho-McNeil, Psyadon, Oy-Control/Biotie and Glaxo-SmithKline pharmaceuticals; has participated in surveys, mailings or telephone consultations related to drug addiction, impulse control disorders or other health topics; has consulted for law offices and the federal public defender's office in issues related to impulse control disorders; provides clinical care in the Connecticut Department of Mental Health and Addiction Services Problem Gambling Services Program; has performed grant reviews for the NIH and other agencies; has edited journals and journal sections; has given academic lectures in grand rounds, CME events and other clinical or scientific venues; and has generated books or book chapters for publishers of mental health texts. The other authors reported no biomedical financial interests or other conflicts of interest.

Acknowledgements

This work was supported by the National Natural Science Foundation of China (No. 31170990 to X.-Y.F and No. 81100992 to J.-T.Z), Project of Humanities and Social Sciences supported by Ministry of Education in China (No. 15YJC190035), Fundamental Research Funds for the Central Universities (No. 2015KJJCA13 to J.-T.Z); Dr. Potenza received support from the National Center for Responsible Gaming and the National Center on Addiction and Substance Abuse (CASAColumbia). The contents of the manuscript are solely the responsibility of the authors and do not necessarily represent the official view of any of the funding agencies.

Footnotes

☆

The address where the data were collected: 3.0 T Siemens Trio scanner at State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University.

Appendix A

Supplementary data to this article can be found online at http://dx.doi.org/10.1016/j.nicl.2017.03.010.

Contributor Information

Jin-Tao Zhang, Email: zhangjintao@bnu.edu.cn.

Xiao-Yi Fang, Email: fangxy@bnu.edu.cn.

Appendix A. Supplementary data

Supplementary material

mmc1.docx (1.1MB, docx)

References

  1. Balodis I.M., Potenza M.N. Anticipatory reward processing in addicted populations: a focus on the monetary incentive delay task. Biol. Psychiatry. 2015;77:434–444. doi: 10.1016/j.biopsych.2014.08.020. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Bechara A. Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nat. Neurosci. 2005;8:1458–1463. doi: 10.1038/nn1584. [DOI] [PubMed] [Google Scholar]
  3. Beck A.T., Ward C., Mendelson M. Beck depression inventory (BDI) Arch. Gen. Psychiatry. 1961;4:561–571. doi: 10.1001/archpsyc.1961.01710120031004. [DOI] [PubMed] [Google Scholar]
  4. Beck A.T., Epstein N., Brown G., Steer R.A. An inventory for measuring clinical anxiety: psychometric properties. J. Consult. Clin. Psychol. 1988;56:893–897. doi: 10.1037//0022-006x.56.6.893. [DOI] [PubMed] [Google Scholar]
  5. Brand M., Young K.S., Laier C. Prefrontal control and Internet addiction: a theoretical model and review of neuropsychological and neuroimaging findings. Front. Hum. Neurosci. 2014;8:1–13. doi: 10.3389/fnhum.2014.00375. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Brand M., Young K., Laier C., Wölfling K., Potenza M.N. Integrating psychological and neurobiological considerations regarding the development and maintenance of specific Internet-use disorders: an Interaction of Person-Affect-Cognition-Execution (I-PACE) model. Neurosci. Biobehav. Rev. 2016;71:252–266. doi: 10.1016/j.neubiorev.2016.08.033. [DOI] [PubMed] [Google Scholar]
  7. Brevers D., Cleeremans A., Goudriaan A.E., Bechara A., Kornreich C., Verbanck P., Noël X. Decision making under ambiguity but not under risk is related to problem gambling severity. Psychiatry Res. 2012;200:2–3. doi: 10.1016/j.psychres.2012.03.053. [DOI] [PubMed] [Google Scholar]
  8. Brevers D., Bechara A., Cleeremans A., Kornreich C., Verbanck P., Noël X. Impaired decision-making under risk in individuals with alcohol dependence. Alcohol. Clin. Exp. Res. 2014;38:1924–1931. doi: 10.1111/acer.12447. [DOI] [PMC free article] [PubMed] [Google Scholar]
  9. Brevers D., Noël X., He Q., Melrose J.A., Bechara A. Increased ventral-striatal activity during monetary decision making is a marker of problem poker gambling severity. Addict. Biol. 2015;21:5328–5332. doi: 10.1111/adb.12239. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Bush K., Kivlahan D.R., McDonell M.B., Fihn S.D., Bradley K.A. The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Arch. Intern. Med. 1998;158:1789–1795. doi: 10.1001/archinte.158.16.1789. [DOI] [PubMed] [Google Scholar]
  11. Chen S., Weng L., Su Y., Wu H., Yang P. Development of a Chinese Internet addiction scale and its psychometric study. Chin. J. Psychol. 2003;45:279–294. [Google Scholar]
  12. Cousijn J., Wiers R.W., Ridderinkhof K.R., Brink W.V.D., Veltman D.J., Porrino L.J., Goudriaan A.E. Individual differences in decision making and reward processing predict changes in cannabis use: a prospective functional magnetic resonance imaging study. Addict. Biol. 2013;18:1013–1023. doi: 10.1111/j.1369-1600.2012.00498.x. [DOI] [PubMed] [Google Scholar]
  13. Craig A.D. How do you feel—now? The anterior insula and human awareness. Nat. Rev. Neurosci. 2009;10:59–70. doi: 10.1038/nrn2555. [DOI] [PubMed] [Google Scholar]
  14. DeVito E.E., Meda S.A., Jiantonio R., Potenza M.N., Krystal J.H., Pearlson G.D. Neural correlates of impulsivity in healthy males and females with family histories of alcoholism. Neuropsychopharmacology. 2013;38:1854–1863. doi: 10.1038/npp.2013.92. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Dong G., Potenza M.N. A cognitive-behavioral model of Internet gaming disorder: theoretical underpinnings and clinical implications. J. Psychiatr. Res. 2014;58:7–11. doi: 10.1016/j.jpsychires.2014.07.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  16. Dong G., Potenza M.N. Risk-taking and risky decision-making in Internet gaming disorder: implications regarding online gaming in the setting of negative consequences. J. Psychiatr. Res. 2015;73:1–8. doi: 10.1016/j.jpsychires.2015.11.011. [DOI] [PubMed] [Google Scholar]
  17. Dong G., Huang J., Du X. Enhanced reward sensitivity and decreased loss sensitivity in Internet addicts: an fMRI study during a guessing task. J. Psychiatr. Res. 2011;45:1525–1529. doi: 10.1016/j.jpsychires.2011.06.017. [DOI] [PubMed] [Google Scholar]
  18. Dong G., Hu Y., Lin X., Lu Q. What makes Internet addicts continue playing online even when faced by severe negative consequences? Possible explanations from an fMRI study. Biol. Psychol. 2013;94:282–289. doi: 10.1016/j.biopsycho.2013.07.009. [DOI] [PubMed] [Google Scholar]
  19. Dong G., Hu Y., Xiao L. Reward/punishment sensitivities among internet addicts: implications for their addictive behaviors. Prog. Neuro-Psychopharmacol. Biol. Psychiatry. 2013;46:139–145. doi: 10.1016/j.pnpbp.2013.07.007. [DOI] [PubMed] [Google Scholar]
  20. Ernst M., Paulus M.P. Neurobiology of decision making: a selective review from a neurocognitive and clinical perspective. Biol. Psychiatry. 2005;58:597–604. doi: 10.1016/j.biopsych.2005.06.004. [DOI] [PubMed] [Google Scholar]
  21. Fagerström K.-O. Measuring degree of physical dependence to tobacco smoking with reference to individualization of treatment. Addict. Behav. 1978;3:235–241. doi: 10.1016/0306-4603(78)90024-2. [DOI] [PubMed] [Google Scholar]
  22. Friston K.J., Holmes A.P., Poline J., Grasby P., Williams S., Frackowiak R.S., Turner R. Analysis of fMRI time-series revisited. NeuroImage. 1995;2:45–53. doi: 10.1006/nimg.1995.1007. [DOI] [PubMed] [Google Scholar]
  23. Goldstein R.Z., Volkow N.D. Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. Am. J. Psychiatry. 2002;159:1642–1652. doi: 10.1176/appi.ajp.159.10.1642. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Goldstein R.Z., Volkow N.D. Dysfunction of the prefrontal cortex in addiction: neuroimaging findings and clinical implications. Nat. Rev. Neurosci. 2011;12:652–669. doi: 10.1038/nrn3119. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Gowin J.L., Mackey S., Paulus M.P. Altered risk-related processing in substance users: imbalance of pain and gain. Drug Alcohol Depend. 2013;132:13–21. doi: 10.1016/j.drugalcdep.2013.03.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  26. Gowin J.L., Stewart J.L., May A.C., Ball T.M., Wittmann M., Tapert S.F., Paulus M.P. Altered cingulate and insular cortex activation during risk-taking in methamphetamine dependence: losses lose impact. Addiction. 2014;109:237–247. doi: 10.1111/add.12354. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Gowin J.L., May A.C., Wittmann M., Tapert S.F., Paulus M.P. Doubling down: increased risk-taking behavior following a loss by individuals with cocaine use disorder is associated with striatal and anterior cingulate dysfunction. Biol. Psychiatry Cogn. Neurosci. Neuroimaging. 2016;2:94–103. doi: 10.1016/j.bpsc.2016.02.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Jia Z., Worhunsky P.D., Carroll K.M., Rounsaville B.J., Stevens M.C., Pearlson G.D., Potenza M.N. An initial study of neural responses to monetary incentives as related to treatment outcome in cocaine dependence. Biol. Psychiatry. 2011;70:553–560. doi: 10.1016/j.biopsych.2011.05.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. King D.L., Delfabbro P.H. The cognitive psychology of Internet gaming disorder. Clin. Psychol. Rev. 2014;34:298–308. doi: 10.1016/j.cpr.2014.03.006. [DOI] [PubMed] [Google Scholar]
  30. Ko C.-H., Yen J.-Y., Chen S.-H., Yang M.-J., Lin H.-C., Yen C.-F. Proposed diagnostic criteria and the screening and diagnosing tool of Internet addiction in college students. Compr. Psychiatry. 2009;50:378–384. doi: 10.1016/j.comppsych.2007.05.019. [DOI] [PubMed] [Google Scholar]
  31. Kohno M., Morales A.M., Ghahremani D.G., Hellemann G., London E.D. Risky decision making, prefrontal cortex, and mesocorticolimbic functional connectivity in methamphetamine dependence. Jama Psychiatry. 2014;71:812–820. doi: 10.1001/jamapsychiatry.2014.399. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Krain A.L., Wilson A.M., Arbuckle R., Castellanos F.X., Milham M.P. Distinct neural mechanisms of risk and ambiguity: a meta-analysis of decision-making. NeuroImage. 2006;32:477–484. doi: 10.1016/j.neuroimage.2006.02.047. [DOI] [PubMed] [Google Scholar]
  33. Levin I.P., Xue G., Weller J.A., Reimann M., Lauriola M., Bechara A. A neuropsychological approach to understanding risk-taking for potential gains and losses. Front. Neurosci. 2011;6:15. doi: 10.3389/fnins.2012.00015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Lin X., Zhou H., Dong G., Du X. Impaired risk evaluation in people with Internet gaming disorder: fMRI evidence from a probability discounting task. Prog. Neuro-Psychopharmacol. Biol. Psychiatry. 2015;56:142–148. doi: 10.1016/j.pnpbp.2014.08.016. [DOI] [PubMed] [Google Scholar]
  35. Lorenz R.C., Krüger J.K., Neumann B., Schott B.H., Kaufmann C., Heinz A., Wüstenberg T. Cue reactivity and its inhibition in pathological computer game players. Addict. Biol. 2013;18:134–146. doi: 10.1111/j.1369-1600.2012.00491.x. [DOI] [PubMed] [Google Scholar]
  36. McClure S.M., York M.K., Montague P.R. The neural substrates of reward processing in humans: the modern role of fMRI. Neuroscientist. 2004;10:260–268. doi: 10.1177/1073858404263526. [DOI] [PubMed] [Google Scholar]
  37. Meng Y., Deng W., Wang H., Guo W., Li T. The prefrontal dysfunction in individuals with Internet gaming disorder: a meta-analysis of functional magnetic resonance imaging studies. Addict. Biol. 2014;20:799–808. doi: 10.1111/adb.12154. [DOI] [PubMed] [Google Scholar]
  38. Naqvi N.H., Gaznick N., Tranel D., Bechara A. The insula: a critical neural substrate for craving and drug seeking under conflict and risk. NY Acad. Sci. 2014;1316:53–70. doi: 10.1111/nyas.12415. [DOI] [PMC free article] [PubMed] [Google Scholar]
  39. Noël X., Brevers D., Bechara A. A neurocognitive approach to understanding the neurobiology of addiction. Curr. Opin. Neurobiol. 2013;23:632–638. doi: 10.1016/j.conb.2013.01.018. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Paulus M.P., Hozack N., Frank L. Decision making by methamphetamine-dependent subjects is associated with error-rate. Biol. Psychiatry. 2003;53:65–74. doi: 10.1016/s0006-3223(02)01442-7. [DOI] [PubMed] [Google Scholar]
  41. Paulus M.P., Lovero K.L., Wittmann M., Leland D.S. Reduced behavioral and neural activation in stimulant users to different error rates during decision making. Biol. Psychiatry. 2008;63:1054–1060. doi: 10.1016/j.biopsych.2007.09.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Petry N.M., Rehbein F., Gentile D.A., Lemmens J.S., Rumpf H.J., Mößle T., Bischof G., Tao R., Fung D.S., Borges G. An international consensus for assessing internet gaming disorder using the new DSM-5 approach. Addiction. 2014;109:1399–1406. doi: 10.1111/add.12457. [DOI] [PubMed] [Google Scholar]
  43. Power Y., Goodyear B., Crockford D. Neural correlates of pathological gamblers preference for immediate rewards during the iowa gambling task: an fMRI study. J. Gambl. Behav. 2012;28:623–636. doi: 10.1007/s10899-011-9278-5. [DOI] [PubMed] [Google Scholar]
  44. Preuschoff K., Bossaerts P., Quartz S.R. Neural differentiation of expected reward and risk in human subcortical structures. Neuron. 2006;51:381–390. doi: 10.1016/j.neuron.2006.06.024. [DOI] [PubMed] [Google Scholar]
  45. Qi X., Du X., Yang Y.-X., Du G.J., Gao P.-H., Zhang Y., Qin W., Li X.-D., Zhang Q. Decreased modulation by the risk level on the brain activation during decision making in adolescents with internet gaming disorder. Front. Behav. Neurosci. 2015;9:1–8. doi: 10.3389/fnbeh.2015.00296. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Robinson T.E., Berridge K.C. Addiction. Annu. Rev. Psychol. 2003;26:507–513. doi: 10.1146/annurev.psych.54.101601.145237. [DOI] [PubMed] [Google Scholar]
  47. Schonberg T., Fox C.R., Poldrack R.A. Mind the gap: bridging economic and naturalistic risk-taking with cognitive neuroscience. Trends Cogn. Sci. 2011;15:11–19. doi: 10.1016/j.tics.2010.10.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Tobler, P., O'Doherty, J., Dolan RJ, Schultz, W., 2007. Reward value coding distinct from risk attitude-related uncertainty coding in human reward systems. J. Neurophysiol. 97, 1621–1632. [DOI] [PMC free article] [PubMed]
  49. Tom S.M., Fox C.R., Trepel C., Poldrack R.A. The neural basis of loss aversion in decision-making under risk. Science. 2007;315:515–518. doi: 10.1126/science.1134239. [DOI] [PubMed] [Google Scholar]
  50. Vickery T.J., Jiang Y.V. Inferior parietal lobule supports decision making under uncertainty in humans. Cereb. Cortex. 2009;19:916–925. doi: 10.1093/cercor/bhn140. [DOI] [PubMed] [Google Scholar]
  51. Weller J.A., Levin I.P., Shiv B., Bechara A. Neural correlates of adaptive decision making for risky gains and losses. Psychol. Sci. 2007;18:958–964. doi: 10.1111/j.1467-9280.2007.02009.x. [DOI] [PubMed] [Google Scholar]
  52. Xue G., Lu Z., Levin I.P., Weller J.A., Li X., Bechara A. Functional dissociations of risk and reward processing in the medial prefrontal cortex. Cereb. Cortex. 2009;19:1019–1027. doi: 10.1093/cercor/bhn147. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Yao Y.-W., Chen P.-R., Chen C., Wang L.-J., Zhang J.-T., Xue G., Deng L.-Y., Liu Q.-X., Yip S.W., Fang X.-Y. Failure to utilize feedback causes decision-making deficits among excessive Internet gamers. Psychiatry Res. 2014;219:583–588. doi: 10.1016/j.psychres.2014.06.033. [DOI] [PubMed] [Google Scholar]
  54. Yao Y.-W., Chen P.-R., Li S., Wang L.-J., Zhang J.-T., Yip S.W., Chen G., Deng L.-Y., Liu Q.-X., Fang X.-Y. Decision-making for risky gains and losses among college students with Internet gaming disorder. PLoS One. 2015;10 doi: 10.1371/journal.pone.0116471. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Yao Y.-W., Wang L.-J., Yip S.W., Chen P.-R., Li S., Xu J., Zhang J.-T., Deng L.-Y., Liu Q.-X., Fang X.-Y. Impaired decision-making under risk is associated with gaming-specific inhibition deficits among college students with Internet gaming disorder. Psychiatry Res. 2015;229:302–309. doi: 10.1016/j.psychres.2015.07.004. [DOI] [PubMed] [Google Scholar]
  56. Zhang J.-T., Yao Y.-W., Potenza M.N., Xia C.-C., Lan J., Liu L., Wang L.-J., Liu B., Ma S.-S., Fang X.-Y. Altered resting-state neural activity and changes following a craving behavioral intervention for Internet gaming disorder. Sci. Rep. 2016;6:28109. doi: 10.1038/srep28109. [DOI] [PMC free article] [PubMed] [Google Scholar]

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