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Gaming Increases Craving to Gaming-Related Stimuli in Individuals With Internet Gaming Disorder
Biological Psychiatry: CNNI
Archival Report Gaming Increases Craving to Gaming-Related Stimuli in Individuals With Internet Gaming Disorder Guangheng Dong, Lingxiao Wang, Xiaoxia Du, and Marc N. Potenza
ABSTRACT BACKGROUND: Internet gaming disorder (IGD) has been proposed as a behavioral addiction warranting additional investigation. Craving is considered a core component of addictions. However, few studies to date have investigated craving in IGD. In the current study, we investigated how gaming was associated with changes in response to gaming-related stimuli in subjects with IGD and those with recreational game use (RGU). METHODS: Behavioral and functional magnetic resonance imaging data were collected from 27 individuals with IGD and 43 individuals with RGU. Subjects’ craving responses to gaming-related stimuli were measured before and after 30 minutes of gaming. RESULTS: The comparison between post- and pregaming measures showed that for IGD, gaming was associated with increased craving and increased brain activation of the lateral and prefrontal cortex, the striatum, and the precuneus when exposed to gaming-related stimuli. In individuals with RGU, no enhanced brain activity was observed. CONCLUSIONS: These results suggest that gaming behavior enhances craving responses in subjects with IGD but not in subjects with RGU, provide insight into potential mechanisms underlying IGD, and suggest behavioral and neurobiological targets for IGD-related interventions. Keywords: Craving, fMRI, Internet gaming disorder, Prefrontal cortex, Recreational gaming use, Striatum http://dx.doi.org/10.1016/j.bpsc.2017.01.002
Craving is an important feature of addictions and has recently been included as inclusionary criteria for substance use disorders (1). Craving reflects a motivational state that promotes seeking drugs in drug addiction (2,3). Craving also appears to be relevant to behavioral addictions like gambling disorder (4) and is thus a target for behavioral therapies like cognitive behavioral therapy (5) and pharmacotherapies like naltrexone (6). Craving may impair subjects’ cognitive processes and promote self-regulation failures (2,7). For example, craving may shift attentional and monitoring processes toward drug-related cues (2,8) and interfere with abilities to notice these changes (9). In addition, craving may also be associated with evaluation of drug-related information; it may influence decision-making processes, shifting choices toward the pursuit of immediate satisfaction rather than long-term rewards (10–12). In 2013, the DSM-5 committee considering substance use and addictive disorders generated criteria for Internet gaming disorder (IGD), and this condition is included in the section of the DSM-5 containing disorders warranting additional study (1,13). IGD has been associated with negative health measures and Internet café–related deaths, prompting a need for further investigation to develop treatments for this disorder (14). Brain reactivity to gaming-related cues may provide important
insight into IGD. Enhanced craving and impaired control over urges for gaming (15,16) have been associated with disruptions in brain pathways in IGD, as occur in drug and alcohol addictions (7). Subjects with IGD demonstrate greater cognitive biases toward gaming-related stimuli (16,17). For example, subjects with IGD, as compared with those without IGD, show greater cue-induced activations in the striatum when exposed to gaming-related pictures, and they show cognitive and shifting biases toward them (15,18). Existing findings suggest that craving may operate similarly across IGD and drug addictions. In drug addictions, drug cues (e.g., photos of drugs or paraphernalia or videotapes or audiotapes of experiences associated with drug use) may induce craving, and exposure to such cues and the subsequent craving may lead to more drug use (19–21). However, studies to date have not directly investigated how gaming may influence craving responses to gaming-related stimuli in individuals with and without IGD. In drug addictions, drugs may influence brain reward circuits, including corticostriatolimbic circuitry underlying motivated behaviors (22), and this circuitry has been implicated in drug craving responses (20). People with substance use disorders may engage in addictive behaviors to compensate for hypofunctioning reward signals in the mesolimbic
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dopamine pathway (23,24), or vice versa, with drug deprivation enhancing craving and drug taking relieving craving. The extent to which similar mechanisms may operate in IGD has yet to be examined directly. In craving studies, familiarity with cues is an important consideration. In some studies of alcohol craving (25,26) or cocaine/stimulant craving (20,27), a social drinking/recreationally using group has been used to contrast with groups with substance addictions to provide a meaningful comparison. Such groups have had experience with substances without having developed problems. Thus, when examining gamingrelated craving in the current study, we used a comparison group with recreational game use (RGU). Gaming is enjoyable for many people, and many individuals, particularly young male individuals, report gaming (28); however, only a small group of individuals who play online games develop IGD, with the majority demonstrating the ability to control gaming behaviors without developing problems (29–31). The inclusion of an RGU comparison group can overcome some limitations of a nongaming group by having similar familiarities with gaming-related cues and gaming exposure. Craving is an important factor that may trigger and maintain addiction. A meta-analysis of 41 addiction studies indicated that people suffering from substance addiction show higher subjective and physiological reactions when encountering substance-related cues (19). Similar findings have been observed in behavioral addictions such as pathological gambling (20), IGD (17), and compulsive shopping (32). In addition, the physiological and neural reactions in individuals with addictions appear to be associated with relapse (33). A recent meta-analysis showed overlapping neuronal substrates of reactivity to drug, gambling, food, and sexual cues in individuals with corresponding conditions (34). With respect to brain regions underlying cue-induced substance-related craving, several meta-analyses suggest that the ventral striatum, dorsal and medial prefrontal cortex (MPFC), and anterior cingulate cortex were more active during the presentation of substance-related cues as compared with non-substance-related cues (7,35–37). However, additional brain regions (e.g., lateral PFC [LPFC]) have also been observed with respect to drug cue–induced activations, particularly in men (20). Individuals with IGD also show greater activation of the PFC to gaming-related words (17) and of the dorsal and ventral striatum to gaming-related pictures (18). The MPFC has been previously implicated in craving processes. Meta-analyses have indicated that deprived smokers show higher cue activation in the MPFC than nondeprived smokers (7,38). Another meta-analysis of drug users found that the left PFC, especially the dorsolateral PFC, was activated to drug cues in drug users, suggesting that craving might be associated with the expectancy of drug-taking (36). The MPFC is a final projection site of the reward system and is directly activated by drugs or other substances (39,40). The MPFC may thus contribute to drug-seeking behaviors in response to drug cues. In addition, the frontal cortex, especially the dorsolateral PFC, is important in executive functions, for example, in inhibitory control (20,41,42). More robust brain responses in these areas to relevant cues may suggest an attempt to inhibit urges in response to cues, although this possibility is speculative. Together, studies suggest that
craving is positively associated with brain activations in the MPFC and LPFC in individuals with addictions. In this study, we investigated in individuals with IGD and those with RGU and without IGD how gaming related to changes in craving (subjective and brain responses) following exposure to gaming-related stimuli. Previous studies of substance addictions have shown that deprivation may enhance craving and that substance use may decrease craving (2,43). Based on these findings, we hypothesized that gaming would decrease subjective craving responses to gaming-related stimuli in subjects with IGD. We also hypothesized that gaming in IGD would be related to decreased brain responses in cue reactivity–related brain regions such as the MPFC, anterior cingulate cortex, and striatum. We also hypothesized that these findings would not be observed in individuals with RGU, with group 3 gaming period (pre/post) interactions observed.
METHODS AND MATERIALS Participant Selection The experiment conformed to the Code of Ethics of the World Medical Association (Declaration of Helsinki). The Human Investigations Committee of Zhejiang Normal University approved this research. All subjects were university students from Shanghai and were recruited through advertisements. Data were collected from 27 subjects with IGD and 43 subjects with RGU. There were no significant between-group differences in age (Table 1). All participants were right-handed, had normal or corrected-to-normal vision, provided written informed consent, and completed a structured psychiatric interview (Mini-International Neuropsychiatric Interview) (44) that was performed by an experienced psychiatrist and lasted approximately 15 minutes after the subjects had completed written portions of the Mini-International Neuropsychiatric Interview. All participants were free of Axis I psychiatric disorders assessed via the Mini-International Neuropsychiatric Interview. We further assessed depression with the Beck Depression Inventory (45), and only participants scoring less than 5 were included (Table 1). Thus, participants with IGD or RGU did not fulfill criteria for a non-IGD mental disorder, which was an inclusion criterion for the study. All participants were instructed not to use any substances of abuse, including tobacco and caffeinated drinks, on the day of scanning. No participants reported previous use of illicit drugs. IGD was determined based on scores of 50 or more on Young’s online Internet addiction test (www.netaddiction.com) (46) and also based on meeting the proposed nine-item DSM5 IGD diagnosis (meeting at least five of nine inclusionary criteria) (47) (Table 1). The Internet addiction test is a valid and reliable instrument that can be used in classifying Internet addiction (48,49). To control for types for gaming, we recruited only subjects (IGD and RGU) who regularly played League of Legends (Riot Games, Santa Monica, CA) and had played the game for at least 1 year.
Definition and Selection of the RGU Group Because the definition of RGU is an important component of our study, we reviewed the extant literature and interviewed 14
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Table 1. Demographic Information and Group Differences IGD (n 5 27) RGU (n 5 43) Age BDI Score IAT Score
21.00 6 1.33
21.47 6 1.32
2.30 6 0.82
2.02 6 0.83
64.56 6 11.89 32.23 6 10.74 5.89 6 1.12
2.65 6 1.38
Game Playing, Hours/Week 19.33 6 9.40
20.70 6 9.57
DSM-5 Score
t
p
0.43 .561 1.34 .184 8.12 .000a 10.25 .000a 20.58 .561
Values are presented as mean (6 SD). BDI, Beck Depression Inventory; IAT, Internet addiction test; IGD, Internet gaming disorder; RGU, recreational game use. a p , .001.
individuals who described themselves as RGU. Based on the findings, we used the following criteria for RGU. First, RGU participants should not demonstrate significant interference in life functioning and thus should meet fewer than four (of nine) of the proposed DSM-5 criteria for IGD and score less than 50 on Young’s Internet addiction test. Second, individuals with RGU should have been playing online games for a minimum of 2 years without meeting any symptoms of physical or psychological dependence (to ensure that they had been playing online games for a sufficient period of time to provide an opportunity to develop IGD). Third, individuals with RGU were required to play online games for least 5 of 7 days in a week (a frequency to define regular use). Fourth, individuals with RGU should play online games for more than 14 hours per week (an amount to define regular use). In addition, individuals with RGU should report no feelings of guilt or remorse about playing online games and state that their regular use does not interfere with work, school, family, or social obligations, which was assessed by the question “Do you feel guilt or remorse about your game playing behavior?”
pictures, background imagery was similar except that somebody is typing rather than gaming.
Controlling for Picture Complexity All pictures were generated in the same indoor setting (a white wall background, a white desk, and a black screen). Only male individuals were included in the pictures. The pictures showed only single sides of faces to diminish subjects’ abilities to distinguish emotions. No accessories were contained in the images of the hands or faces. The only differences between gaming-related stimuli and typing-related stimuli involved the on-screen material.
Task Performance Motivation Participants were told that they would be paid a guaranteed 50 Yuan (E $8 U.S.) for participation and, to encourage their motivation to respond accurately, were told that they would be rewarded with an additional 0 to 50 Yuan based on their task performance (accuracy rates 3 50). Accuracy rates (for identification of pictures with faces or no faces) were expected to approach/reach ceiling effects for the two groups.
Controlling for Task Repetition The functional magnetic resonance imaging tasks in the preand posttests were of the same types but differed in content to avoid/minimize possible repetition effects. We designed two copies of the task with different items (Copy A and Copy B). Half of each group of participants participated in an A–B sequence, and the other half received a B–A sequence, in their pre- and postgaming scans.
Gaming in the Scanner Pre- and Posttests The cue reactivity task was used in pre- and postgaming scans (Figure 1A). In each trial, a fixation was presented first for 500 ms, and then a stimulus was presented with a response needed; this stage lasted for up to 4000 ms. During this period, participants were asked to answer whether there is a face in the picture and to select yes or no via a button press (1 for yes and 2 for no, with counterbalancing between subjects). The stimulus turned black after key pressing and lasted for (4000 – response time) ms. A black screen was presented for a duration of 1500 to 3500 ms, with the duration jittered. No feedback was given about correct or incorrect responses. In the subsequent evaluation stage, subjects were asked to evaluate the level of their craving (on a scale from 1 [low] to 5 [high]) for the relevant stimuli. This process lasted for up to 4000 ms and was terminated by a button press. After another jitter ranging from 1500 to 3500 ms, the next trial ensued. We focused analyses on the response stage in the current study. The task used 160 pictures divided into two categories: gaming-related and typing-related pictures (neutral baseline). Half (50%) of all pictures within each category contained a face, and the other half contained a hand. As shown in Figure 1B, in gaming-related stimuli somebody is displayed playing a game on a computer, with some stimuli showing faces and others showing hands. In the typing-related
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In the scanner, subjects were asked to play League of Legends for one round. In general, one round of League of Legends will take about 20 to 40 minutes. To control for time spent in the scanner, subjects were instructed to try to finish the game in 25 minutes, with notifications provided at 20 and 25 minutes and termination of the game at 30 minutes. All subjects finished and won the game in 30 minutes. To promote subjects’ familiarity with their gaming experiences, participants were asked to log into the game with their own account.
Data Collection Structural images were collected using a T1-weighted threedimensional spoiled gradient-recalled sequence covering the whole brain (176 slices, repetition time 5 1700 ms, echo time 5 3.93 ms, slice thickness 5 1.0 mm, skip 5 0 mm, flip angle 5 15, inversion time 5 1100 ms, field of view 5 240 3 240 mm, in-plane resolution 5 256 3 256). Functional magnetic resonance imaging was performed on a 3T scanner (Siemens Trio, Malvern, PA) with a gradient-echo planar imaging T2*weighted-sensitive pulse sequence in 33 slices (interleaved sequence, 3 mm thickness, repetition time 5 2000 ms, echo time 5 30 ms, flip angle 5 901, field of view 5 220 3 220 mm2, matrix 5 64 3 64) (50). Stimuli were presented using an Invivo synchronous system (http://www.invivocorp.com) through a screen in the head coil, enabling participants to
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Figure 1. The timeline of one trial in the functional magnetic resonance imaging task. (A) In one trial of the task, a fixation was presented first for 500 ms, and then a response stage lasted for up to 4000 ms. Participants were asked to answer whether there was a face in the picture and to press the relevant keys. A jittered black screen was presented for 1500 to 3500 ms. After this, the evaluation stage followed, during which subjects were asked to evaluate the level of their craving (0–5) for the relevant behaviors (playing games or typing). This process lasted for up to 4000 ms and was terminated by a button press. After another jitter ranging from 1500 to 3500 ms, the next trial ensued. (B) Examples of the four types of trials used in the current study. The face typing (FT) and face gaming (FG) types reflect pictures with faces; the hands typing (HT) and hands gaming (HG) types reflect pictures with hands. For data analysis, the FT and HT types were considered neutral stimuli (typing-related stimuli), and the FG and HG types were considered gaming-related stimuli. Headphones were also presented in half of the pictures of each type to make the figures more engaging.
view the stimuli. The whole experiment lasted for more than 1 hour: pre- and posttest (14 minutes each); T1 structural (6 minutes); prepare for scanning and the time between different tasks (6 minutes); gaming ( 30 minutes).
Data Preprocessing The functional data were analyzed using SPM8 (http://www.fil. ion.ucl.ac.uk/spm) and NeuroElf (http://neuroelf.net), as described previously (51,52). Images were slice timed, reoriented, and realigned to the first volume, with T1-coregistered volumes being used to correct for head movements. Images were then normalized to Montreal Neurological Institute space and spatially smoothed using a 6-mm full width at half maximum Gaussian kernel. No subjects were removed from analysis because of head motion (the exclusion criteria were 2 mm in directional movement or 21 in rotational movement). A general linear model (GLM) was applied to identify blood oxygen level–dependent activation in relation to brain activities. Different types of trials (gaming related, typing related, incorrect, or missed) were separately convolved with a canonical hemodynamic response function to form task regressors. The duration of each trial was 4000 ms. The GLMs included a constant term per run. Six head movement parameters derived from the realignment stage were included to exclude motionrelated variances. A GLM approach was used to identify voxels that were significantly activated for each event during the response stage.
Post/Pre Comparisons Given that craving has been associated with activations in other brain regions (e.g., LPFC), whole-brain analyses were
conducted to examine our hypotheses. First, within-group (IGD and RGU) comparisons were made to examine postgaming versus pregaming differences in brain activations: post (IGDgaming-related stimuli 2 IGDtyping-related stimuli) 2 pre (IGDgamingrelated stimuli 2 IGDtyping-related stimuli); post (RGUgaming-related stimuli 2 RGUtyping-related stimuli) 2 pre (RGUgaming-related stimuli 2 RGUtyping-related stimuli). Second, a voxelwise 2 3 2 (factor 1: IGD, RGU; factor 2: post- and pretests) repeated-measures analysis was administered to examine statistically significant between-group differences across pre-/postgaming exposure: post (IGDgaming-related stimuli 2 IGDtyping-related stimuli) 2 post (RGUgaming-related stimuli 2 RGUtyping-related stimuli); pre (IGDgamingrelated stimuli 2 IGDtyping-related stimuli) 2 pre (RGUgaming-related stimuli 2 RGUtyping-related stimuli). Familywise error thresholds were determined using AlphaSim, and all comparisons were corrected using AlphaSim (https://afni.nimh.nih.gov/pub/dist/ doc/program_help/AlphaSim.html). Significant clusters (familywise error corrected, p , .01) were thresholded at p , .01, two tailed, uncorrected, with an extent of at least 125 voxels, based on the unresliced voxel size (3 3 3 3 3). All of these steps were performed using NeuroElf. The smoothing kernel used during simulating false-positive (noise) maps using AlphaSim was 6 mm and was estimated from the residual fields of the contrast maps being entered into the one-sample t test.
Correlation Analyses We first compared the brain activation between pre- and postgaming scans and then took the surviving cluster as a region of interest. We also explored relationships with the PFC, precuneus, and striatal regions found to differ in the IGD
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group from pre- to postscan given the findings and the relevance of these to addictive processes like craving. For each region of interest, a representative blood oxygen level– dependent beta value was obtained by averaging the signal of all the voxels within it. We performed the correlations between the self-reported craving to gaming-related stimuli and brain activations in the regions of interest at the pre- and postgaming tests separately.
RESULTS
craving in the RGU group at posttest versus pretest (Supplemental Figure S2B).
Correlation Results Correlations (at p , .05 or p , .10) were found in all subjects between brain activation in the striatum and subjective craving to game playing at pre- and postgaming tests, although none survived correction for multiple comparisons (Supplemental Figure S3). Correlations between brain activation in the PFC and precuneus and subjective craving were not significant.
Behavioral Performance Comparisons from pre- to postgaming in the separate groups indicated that gaming was associated with increased craving to gaming-related cues in the IGD group (t = 2.322, p = .028) but not in the RGU group (t = 0.509, p = .613) (Figure 2). However, GLM results (using repeated measures) showed that the self-reported craving to stimuli in pre- and posttests in the scanner showed a main effect (F 5 27.402, p , .001), with no interaction observed (F 5 0.735, p 5 .692). Thus, the betweengroup differences in the increases in craving were statistically similar across groups in the GLM. With respect to in-scanner performance, there were few mistakes in either group, reflecting the relative simplicity of performance of the task (IGD, 99.6%; RGU, 100%). With respect to response times, subjects with IGD took longer to respond to gaming-related stimuli (mean 6 SD: IGD, 1249.55 6 326.53 ms; RGU, 1152.93 6 328.72 ms; t 5 4.524, p , .01). No difference was found between these two groups in their response times to typing-related stimuli (IGD, 1314.49 6 316.55 ms; RGU, 1297.35 6 316.27 ms; t 5 0.304, p . .05).
Imaging Results: Post/Pre 3 Group First, we compared the post- and pregaming findings in the two groups separately. In postgaming versus pregaming comparisons in the IGD group, increased brain activations were observed in the PFC, striatum, and precuneus; no decreased brain regions were observed (Figure 3). In the RGU group, no difference was observed in the comparison.
Imaging Results: Analysis of Variance Results The differences in the neural correlates of craving between the IGD and RGU groups were calculated (Supplemental Figure S1). The 2 3 2 (group 3 post/pre) results showed that gaming was associated with increased brain activations in the LPFC (Supplemental Figure S2A and Table 2). The extracted beta weights from this cluster showed that the difference was related to increased craving in the IGD group and decreased
DISCUSSION Gaming Increased Craving for Gaming-Related Cues in Subjects With IGD The findings suggest that gaming may affect craving differently in the IGD and RGU groups, although the findings across groups were arguably more similar than dissimilar statistically. Contrary to our hypothesis, gaming was associated with subjective increases in craving in the IGD group but not in the RGU group, suggesting a potential priming effect. However, this finding did not survive a more robust analysis of a group 3 gaming exposure (pre/post) interaction, rather showing a main effect, indicating that craving may have increased across groups following gaming. Also contrary to our hypothesis, posttest versus pretest comparison within each gaming group showed that gaming increased brain activations in the left PFC, right striatum, and left precuneus in the IGD group. However, in the RGU group, no difference was found when comparing activations at postgaming versus pregaming times. However, arguably the most robust statistical difference (a group 3 gaming exposure interaction) suggested that gaming was associated with increased brain activation in the left LPFC at posttest in the IGD group but not in the RGU group. As described in the introductory paragraphs, the results suggest that gaming enhanced regional brain activations in a craving-related area. Although the LPFC or MPFC findings did not relate to subjective craving, extracted beta weights from the striatum, another structure implicated in craving, suggest that this structure may be relevant to subjective craving in IGD. Specifically, the subjects with IGD showed higher craving for gaming-related cues following gaming, and suggestive correlations between brain activations in the striatum and subjective craving to gaming-related cues were observed pre- and postgaming, although these were relatively weak and would not survive correction for multiple comparisons. Data suggest that phasic dopamine release in the ventral striatum motivates goal-directed behavior (2,53). The striatum Figure 2. Self-reported mean craving ratings for gaming-related stimuli at pre- and postgaming times in the Internet gaming disorder (IGD) and recreational game use (RGU) groups.
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Figure 3. Brain features in postgaming versus pregaming comparisons in the Internet gaming disorder group. In the Internet gaming disorder group, when comparing postgaming and pregaming task activations, higher brain activations were observed in the left medial prefrontal cortex, striatum, and precuneus (whole brain corrected at p , .01).
has also been implicated in reward processing, reinforcement and reward-based learning (23,54), and addictions, in both impulsive and compulsive aspects (55) and when individuals
are exposed to addictive stimuli (56,57). The involvement of the striatum has been previously observed in IGD studies, with participants with IGD exhibiting higher cue-induced and
Table 2. Regional Brain Activity Changes to Gaming-Related Cues Cluster Number
x, y, za
Peak Intensity
Cluster Sizeb
Regionc
Brodmann Area
Post/Pre Comparison in IGD Group 1
26, 33, 42
4.515
252
L Precuneus
2
27, 212, 215
6.787
293
R Striatum (nucleus accumbens, putamen, caudate)
3
212, 60, 24
4.454
139
L Medial prefrontal cortex, medial frontal cortex
4.356
186
L Lateral prefrontal cortex
5 10,11
Post/Pre Comparison in RGU Group No brain regions survived GLM Repeated Measures (Group 3 Pre/Post) 1
242, 18, 21
9,10
FWE, familywise error; GLM, general linear model; IGD, Internet gaming disorder; L, left; R, right; RGU, recreational game use. a Peak Montreal Neurological Institute coordinates. b Number of voxels. We first identified clusters of contiguously significant voxels at an uncorrected threshold (p , .01), as also used for display purposes in the figures. We then tested these clusters for cluster-level FWE correction (p , .01), and the AlphaSim estimation indicated that clusters with 125 contiguous voxels would achieve an effective FWE threshold (p , .01). Voxel size 5 3 3 3 3 3. c The brain regions were referenced to the software xjView (http://www.alivelearn.net/xjview8) and verified through comparisons with a brain atlas.
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reward-related activations within both the ventral and dorsal striatum when compared with individuals without IGD (18,58,59). Thus, the current findings are consistent with and extend prior studies in that they suggest that the striatum is activated in response to gaming-related cues in IGD and that gaming may enhance gaming cue–related striatal activation in a manner linked to subjective craving responses. Another brain area showing higher activation at postgaming versus pregaming in the IGD group was the precuneus. A meta-analysis observed higher activations in the precuneus in response to smoking-related cues in smokers (7). The precuneus has been implicated in craving and in multiple cognitive functions, including attentional tracking of stimuli and the preparation of motor behaviors to stimuli (7,60). Thus, the higher responses to game-related cues in the precuneus may potentially reflect attentional biases toward gamingrelated cues or preparation for potential gaming behaviors, although this possibility remains speculative. In the current study, subjects with IGD showed increased precuneus activation at postgaming scanning, which suggests that gaming may have enhanced their attention to gaming-related cues that may have contributed to craving responses, although this line of reasoning warrants additional investigation.
Craving in Response to Gaming-Related Cues in Subjects With RGU In contrast to the results in subjects with IGD, self-reported craving to gaming stimuli was not increased significantly at posttest (in comparison with pretest) in subjects with RGU. Nonetheless, the main effect of craving changes observed across the RGU/IGD groups does not entirely exclude some impact of gaming on craving in RGU. In imaging analyses, no brain regions were identified in subjects with RGU as differing in postgaming as compared with pregaming in whole-brain analyses. The findings suggest that gaming did not substantially affect craving or gaming cue–related activations in subjects with RGU.
Summary The current results suggest that gaming increased gaming cue–related craving in subjects with IGD, which may differ from some studies indicating that drug use may decrease craving in drug addiction. In substance addiction, deprivation may enhance craving responses, and the intake of substances may lead to decreased craving (2,43,61). However, for IGD, gaming increased craving in response to gaming-related stimuli. It is possible that the amount of gaming performed in the current study was not sufficient to fully satisfy a craving for gaming, akin to a priming dose of alcohol in alcohol dependence. The current finding suggests a possible mechanism by which gaming may promote more gaming in individuals with IGD.
investigate the extent to which the findings extend to other groups. Strengths include the involvement of subjects with RGU and comparisons made pre- and postgaming. The data are valuable in understanding mechanisms underlying IGD. The results suggest a mechanism for why some individuals with IGD may continue gaming for excessive periods of time, whereas others with RGU might not.
ACKNOWLEDGMENTS AND DISCLOSURES GD was supported by the National Science Foundation of China (Grant No. 31371023) and by the Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning (Grant No. CNLYB 1207). MNP’s involvement was supported by the National Center on Addiction and Substance Abuse and the National Center for Responsible Gaming. The funding agencies did not contribute to the experimental design or conclusions, and the views presented in the article are those of the authors and might not reflect those of the funding agencies. GD designed the task and wrote the first draft of the manuscript. LW collected and analyzed the data and prepared the figures. XD collected the data. MNP contributed in the editing, interpretation, and revision processes. All authors contributed to and approved the final manuscript. MNP has consulted for and advised Ironwood, Lundbeck, INSYS, Shire, RiverMend Health, Opiant/Lakelight Therapeutics, Jazz Pharmaceuticals, and Pfizer; has received research support from the Mohegan Sun Casino, the National Center for Responsible Gaming, and Pfizer; has participated in surveys, mailings, and telephone consultations related to drug addiction, impulse control disorders, and other health topics; has consulted for gambling and legal entities on issues related to impulse control and addictive disorders; provides clinical care in the Connecticut Department of Mental Health and Addiction Services Problem Gambling Services Program; has performed grant reviews for the National Institutes of Health and other agencies; has edited journals or journal sections; has given academic lectures in grand rounds, Continuing Medical Education events, and other clinical or scientific venues; and has generated books and book chapters for publishers of mental health texts. All other authors report no biomedical financial interests or potential conflicts of interest.
ARTICLE INFORMATION From the Department of Psychology (GD, LW) and Institute of Psychological and Brain Sciences (GD), Zhejiang Normal University, Jinhua, Zhejiang Province; and Department of Physics (XD), Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People’s Republic of China; Department of Psychiatry (MNP), Department of Neuroscience, Child Study Center, and National Center on Addiction and Substance Abuse, Yale University School of Medicine; and Connecticut Mental Health Center (MNP), New Haven, Connecticut. Address correspondence to Guangheng Dong, Ph.D., Department of Psychology, Zhejiang Normal University, 688 Yingbin Road, Jinhua, Zhejiang Province, P.R. China; E-mail: [email protected]. Received Nov 6, 2016; revised Jan 13, 2017; accepted Jan 18, 2017. Supplementary material cited in this article is available online at http:// dx.doi.org/10.1016/j.bpsc.2017.01.002.
REFERENCES 1.
2.
Limitations and Strengths The current findings indicate that gaming enhanced craving for gaming-related cues in subjects with IGD. Limitations are detailed in the Supplement and include exclusively young male Asian participants, and additional studies should
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3.
4.
American Psychiatric Association (2013): Diagnostic and Statistical Manual of Mental Disorders (5th ed.). Arlington, VA: American Psychiatric Publishing. Sayette MA (2016): The role of craving in substance use disorders: Theoretical and methodological issues. Annu Rev Clin Psychol 12: 407–433. Sinha R, Li CS (2007): Imaging stress- and cue-induced drug and alcohol craving: Association with relapse and clinical implications. Drug Alcohol Rev 26:25–31. Potenza MN, Steinberg MA, Skudlarski P, Fulbright RK, Lacadie CM, Wilber MK, et al. (2003): Gambling urges in pathological gambling:
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5.
6.
7.
8.
9.
10.
11.
12. 13. 14.
15.
16.
17.
18.
19. 20.
21.
22. 23.
24.
25.
26.
A functional magnetic resonance imaging study. Arch Gen Psychiatry 60:828–836. Potenza MN, Balodis IM, Franco CA, Bullock S, Xu J, Chung T, et al. (2013): Neurobiological considerations in understanding behavioral treatments for pathological gambling. Psychol Addict Behav 27: 380–392. Grant JE, Kim SW, Hollander E, Potenza MN (2008): Predicting response to opiate antagonists and placebo in the treatment of pathological gambling. Psychopharmacology (Berl) 200:521–527. Engelmann JM, Versace F, Robinson JD, Minnix JA, Lam CY, Cui Y, et al. (2012): Neural substrates of smoking cue reactivity: A metaanalysis of fMRI studies. NeuroImage 60:252–262. Tiffany ST (1990): A cognitive model of drug urges and drug-use behavior: Role of automatic and nonautomatic processes. Psychol Rev 97:147–168. Sayette MA, Schooler JW, Reichle ED (2010): Out for a smoke: The impact of cigarette craving on zoning out during reading. Psychol Sci 21:26–30. Dong G, Potenza MN (2016): Risk-taking and risky decision-making in Internet gaming disorder: Implications regarding online gaming in the setting of negative consequences. J Psychiatr Res 73:1–8. Wilson SJ, Delgado MR, McKee SA, Grigson PS, MacLean RR, Nichols TT, et al. (2014): Weak ventral striatal responses to monetary outcomes predict an unwillingness to resist cigarette smoking. Cogn Affect Behav Neurosci 14:1196–1207. Piper ME (2015): Withdrawal: Expanding a key addiction construct. Nicotine Tob Res 17:1405–1415. Petry NM, O’Brien CP (2013): Internet gaming disorder and the DSM5. Addiction 108:1186–1187. Banz BC, Yip SW, Yau YH, Potenza MN (2016): Behavioral addictions in addiction medicine: From mechanisms to practical considerations. Prog Brain Res 223:311–328. Zhou ZH, Yuan GZ, Yao JJ (2012): Cognitive biases toward Internet game-related pictures and executive deficits in individuals with an Internet game addiction. PLoS One 7:e48961. Decker SA, Gay JN (2011): Cognitive-bias toward gaming-related words and disinhibition in World of Warcraft gamers. Comput Hum Behav 27:798–810. Zhang Y, Lin X, Zhou H, Xu J, Du X, Dong G (2016): Brain activity toward gaming-related cues in Internet gaming disorder during an addiction Stroop task. Front Psychol 7:714. Liu L, Yip SW, Zhang JT, Wang LJ, Shen ZJ, Liu B, et al. (2017): Activation of the ventral and dorsal striatum during cue reactivity in Internet gaming disorder. Addict Biol 22:791–801. Carter BL, Tiffany ST (1999): Meta-analysis of cue-reactivity in addiction research. Addiction 94:327–340. Potenza MN, Hong KI, Lacadie CM, Fulbright RK, Tuit KL, Sinha R (2012): Neural correlates of stress-induced and cue-induced drug craving: influences of sex and cocaine dependence. The American journal of psychiatry 169:406–414. Kosten TR, Scanley BE, Tucker KA, Oliveto A, Prince C, Sinha R, et al. (2006): Cue-induced brain activity changes and relapse in cocainedependent patients. Neuropsychopharmacology 31:644–650. Volkow ND, Li TK (2004): Drug addiction: The neurobiology of behaviour gone awry. Nat Rev Neurosci 5:963–970. Balodis IM, Potenza MN (2015): Anticipatory reward processing in addicted populations: A focus on the monetary incentive delay task. Biol Psychiatry 77:434–444. Blum K, Sheridan PJ, Wood RC, Braverman E, Chen TJ, Cull J, et al. (1996): The D2 dopamine receptor gene as a determinant of reward deficiency syndrome. J R Soc Med 89:396–400. Trick L, Kempton MJ, Williams SCR, Duka T (2014): Impaired fear recognition and attentional set-shifting is associated with brain structural changes in alcoholic patients. Addict Biol 19: 1041–1054. Chaplin TM, Hong K, Fox HC, Siedlarz KM, Bergquist K, Sinha R (2010): Behavioral arousal in response to stress and drug cue in alcohol and cocaine addicted individuals versus healthy controls. Hum Psychopharmacol 25:368–376.
27.
28.
29. 30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45. 46.
47.
48. 49.
Smith DG, Simon Jones P, Bullmore ET, Robbins TW, Ersche KD (2014): Enhanced orbitofrontal cortex function and lack of attentional bias to cocaine cues in recreational stimulant users. Biol Psychiatry 75:124–131. Desai RA, Krishnan-Sarin S, Cavallo D, Potenza MN (2010): Videogaming among high school students: Health correlates, gender differences, and problematic gaming. Pediatrics 126:E1414–E1424. Kuss DJ, Griffiths MD (2012): Internet gaming addiction: A systematic review of empirical research. Int J Ment Health Addict 10:278–296. Griffiths MD (2010): The role of context in online gaming excess and addiction: Some case study evidence. Int J Ment Health Addict 8: 119–125. King DL, Delfabbro PH, Zajac IT (2011): Preliminary validation of a new clinical tool for identifying problem video game playing. Int J Ment Health Addict 9:72–87. Starcke K, Schlereth B, Domass D, Scholer T, Brand M (2013): Cue reactivity towards shopping cues in female participants. J Behav Addict 2:17–22. Courtney KE, Schacht JP, Hutchison K, Roche DJ, Ray LA (2016): Neural substrates of cue reactivity: Association with treatment outcomes and relapse. Addict Biol 21:3–22. Noori HR, Cosa Linan A, Spanagel R (2016): Largely overlapping neuronal substrates of reactivity to drug, gambling, food and sexual cues: A comprehensive meta-analysis. Eur Neuropsychopharmacol 26:1419–1430. Kuhn S, Gallinat J (2011): Common biology of craving across legal and illegal drugs—A quantitative meta-analysis of cue-reactivity brain response. Eur J Neurosci 33:1318–1326. Chase HW, Eickhoff SB, Laird AR, Hogarth L (2011): The neural basis of drug stimulus processing and craving: An activation likelihood estimation meta-analysis. Biol Psychiatry 70:785–793. Boswell RG, Kober H (2016): Food cue reactivity and craving predict eating and weight gain: A meta-analytic review. Obes Rev 17: 159–177. Kober H, Mende-Siedlecki P, Kross EF, Weber J, Mischel W, Hart CL, et al. (2010): Prefrontal-striatal pathway underlies cognitive regulation of craving. Proc Natl Acad Sci U S A 107:14811–14816. Goldstein RZ, Volkow ND (2002): Drug addiction and its underlying neurobiological basis: Neuroimaging evidence for the involvement of the frontal cortex. Am J Psychiatry 159:1642–1652. Jentsch JD, Taylor JR (1999): Impulsivity resulting from frontostriatal dysfunction in drug abuse: Implications for the control of behavior by reward-related stimuli. Psychopharmacology (Berl) 146:373–390. Hartwell KJ, Johnson KA, Li X, Myrick H, LeMatty T, George MS, et al. (2011): Neural correlates of craving and resisting craving for tobacco in nicotine dependent smokers. Addict Biol 16:654–666. Lubman DI, Yucel M, Pantelis C (2004): Addiction, a condition of compulsive behaviour? Neuroimaging and neuropsychological evidence of inhibitory dysregulation. Addiction 99:1491–1502. Subramaniyan M, Dani JA (2015): Dopaminergic and cholinergic learning mechanisms in nicotine addiction. Ann N Y Acad Sci 1349: 46–63. Lecrubier Y, Sheehan DV, Weiller E, Amorim P, Bonora I, Harnett Sheehan K, et al. (1997): The Mini International Neuropsychiatric Interview (MINI): A short diagnostic structured interview—Reliability and validity according to the CIDI. Eur Psychiatry 12:224–231. Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J (1961): An inventory for measuring depression. Arch Gen Psychiatry 4:561–571. Young KS (2011): Clinical assessment of Internet-addicted clients. In: Young KS, Abreu C, editors. Internet Addiction: A Handbook and Guide to Evaluation and Treatment. Hoboken, NJ: John Wiley, 19–34. Petry NM, Rehbein F, Gentile DA, Lemmens JS, Rumpf HJ, Mossle T, et al. (2014): An international consensus for assessing Internet gaming disorder using the new DSM-5 approach. Addiction 109:1399–1406. Widyanto L, McMurran M (2004): The psychometric properties of the Internet addiction test. Cyberpsychol Behav 7:443–450. Widyanto L, Griffiths MD, Brunsden V (2011): A psychometric comparison of the Internet Addiction Test, the Internet-Related
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging July 2017; 2:404–412 www.sobp.org/BPCNNI
411
Biological Psychiatry: CNNI
50.
51.
52.
53.
54.
412
Craving Changes in IGD
Problem Scale, and self-diagnosis. Cyberpsychol Behav Soc Netw 14: 141–149. Dong G, Hu Y, Lin X, Lu Q (2013): What makes Internet addicts continue playing online even when faced by severe negative consequences? Possible explanations from an fMRI study. Biol Psychol 94:282–289. DeVito EE, Worhunsky PD, Carroll KM, Rounsaville BJ, Kober H, Potenza MN (2012): A preliminary study of the neural effects of behavioral therapy for substance use disorders. Drug Alcohol Depend 122:228–235. Krishnan-Sarin S, Balodis IM, Kober H, Worhunsky PD, Liss T, Xu JS, et al. (2013): An exploratory pilot study of the relationship between neural correlates of cognitive control and reduction in cigarette use among treatment-seeking adolescent smokers. Psychol Addict Behav 27:526–532. Ikemoto S, Yang C, Tan A (2015): Basal ganglia circuit loops, dopamine and motivation: A review and enquiry. Behav Brain Res 290: 17–31. Cheng Y, Huang CC, Ma T, Wei X, Wang X, Lu J, Wang J (2017): Distinct synaptic strengthening of the striatal direct and indirect pathways drives alcohol consumption. Biol Psychiatry 81: 918–929.
55.
56.
57.
58.
59.
60. 61.
Fineberg NA, Chamberlain SR, Goudriaan AE, Stein DJ, Vanderschuren LJ, Gillan CM, et al. (2014): New developments in human neurocognition: Clinical, genetic, and brain imaging correlates of impulsivity and compulsivity. CNS Spectr 19:69–89. Worhunsky PD, Malison RT, Rogers RD, Potenza MN (2014): Altered neural correlates of reward and loss processing during simulated slotmachine fMRI in pathological gambling and cocaine dependence. Drug Alcohol Depend 145:77–86. Balodis IM, Kober H, Worhunsky PD, Stevens MC, Pearlson GD, Potenza MN (2012): Attending to striatal ups and downs in addictions. Biol Psychiatry 72:e25–e26. Ko CH, Liu GC, Hsiao S, Yen JY, Yang MJ, Lin WC, et al. (2009): Brain activities associated with gaming urge of online gaming addiction. J Psychiatr Res 43:739–747. Sun Y, Ying H, Seetohul RM, Xuemei W, Ya Z, Qian L, et al. (2012): Brain fMRI study of crave induced by cue pictures in online game addicts (male adolescents). Behav Brain Res 233:563–576. Cavanna AE, Trimble MR (2006): The precuneus: A review of its functional anatomy and behavioural correlates. Brain 129:564–583. Potenza MN, Sofuoglu M, Carroll KM, Rounsaville BJ (2011): Neuroscience of behavioral and pharmacological treatments for addictions. Neuron 69:695–712.
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Archival Report Gaming Increases Craving to Gaming-Related Stimuli in Individuals With Internet Gaming Disorder Guangheng Dong, Lingxiao Wang, Xiaoxia Du, and Marc N. Potenza
ABSTRACT BACKGROUND: Internet gaming disorder (IGD) has been proposed as a behavioral addiction warranting additional investigation. Craving is considered a core component of addictions. However, few studies to date have investigated craving in IGD. In the current study, we investigated how gaming was associated with changes in response to gaming-related stimuli in subjects with IGD and those with recreational game use (RGU). METHODS: Behavioral and functional magnetic resonance imaging data were collected from 27 individuals with IGD and 43 individuals with RGU. Subjects’ craving responses to gaming-related stimuli were measured before and after 30 minutes of gaming. RESULTS: The comparison between post- and pregaming measures showed that for IGD, gaming was associated with increased craving and increased brain activation of the lateral and prefrontal cortex, the striatum, and the precuneus when exposed to gaming-related stimuli. In individuals with RGU, no enhanced brain activity was observed. CONCLUSIONS: These results suggest that gaming behavior enhances craving responses in subjects with IGD but not in subjects with RGU, provide insight into potential mechanisms underlying IGD, and suggest behavioral and neurobiological targets for IGD-related interventions. Keywords: Craving, fMRI, Internet gaming disorder, Prefrontal cortex, Recreational gaming use, Striatum http://dx.doi.org/10.1016/j.bpsc.2017.01.002
Craving is an important feature of addictions and has recently been included as inclusionary criteria for substance use disorders (1). Craving reflects a motivational state that promotes seeking drugs in drug addiction (2,3). Craving also appears to be relevant to behavioral addictions like gambling disorder (4) and is thus a target for behavioral therapies like cognitive behavioral therapy (5) and pharmacotherapies like naltrexone (6). Craving may impair subjects’ cognitive processes and promote self-regulation failures (2,7). For example, craving may shift attentional and monitoring processes toward drug-related cues (2,8) and interfere with abilities to notice these changes (9). In addition, craving may also be associated with evaluation of drug-related information; it may influence decision-making processes, shifting choices toward the pursuit of immediate satisfaction rather than long-term rewards (10–12). In 2013, the DSM-5 committee considering substance use and addictive disorders generated criteria for Internet gaming disorder (IGD), and this condition is included in the section of the DSM-5 containing disorders warranting additional study (1,13). IGD has been associated with negative health measures and Internet café–related deaths, prompting a need for further investigation to develop treatments for this disorder (14). Brain reactivity to gaming-related cues may provide important
insight into IGD. Enhanced craving and impaired control over urges for gaming (15,16) have been associated with disruptions in brain pathways in IGD, as occur in drug and alcohol addictions (7). Subjects with IGD demonstrate greater cognitive biases toward gaming-related stimuli (16,17). For example, subjects with IGD, as compared with those without IGD, show greater cue-induced activations in the striatum when exposed to gaming-related pictures, and they show cognitive and shifting biases toward them (15,18). Existing findings suggest that craving may operate similarly across IGD and drug addictions. In drug addictions, drug cues (e.g., photos of drugs or paraphernalia or videotapes or audiotapes of experiences associated with drug use) may induce craving, and exposure to such cues and the subsequent craving may lead to more drug use (19–21). However, studies to date have not directly investigated how gaming may influence craving responses to gaming-related stimuli in individuals with and without IGD. In drug addictions, drugs may influence brain reward circuits, including corticostriatolimbic circuitry underlying motivated behaviors (22), and this circuitry has been implicated in drug craving responses (20). People with substance use disorders may engage in addictive behaviors to compensate for hypofunctioning reward signals in the mesolimbic
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dopamine pathway (23,24), or vice versa, with drug deprivation enhancing craving and drug taking relieving craving. The extent to which similar mechanisms may operate in IGD has yet to be examined directly. In craving studies, familiarity with cues is an important consideration. In some studies of alcohol craving (25,26) or cocaine/stimulant craving (20,27), a social drinking/recreationally using group has been used to contrast with groups with substance addictions to provide a meaningful comparison. Such groups have had experience with substances without having developed problems. Thus, when examining gamingrelated craving in the current study, we used a comparison group with recreational game use (RGU). Gaming is enjoyable for many people, and many individuals, particularly young male individuals, report gaming (28); however, only a small group of individuals who play online games develop IGD, with the majority demonstrating the ability to control gaming behaviors without developing problems (29–31). The inclusion of an RGU comparison group can overcome some limitations of a nongaming group by having similar familiarities with gaming-related cues and gaming exposure. Craving is an important factor that may trigger and maintain addiction. A meta-analysis of 41 addiction studies indicated that people suffering from substance addiction show higher subjective and physiological reactions when encountering substance-related cues (19). Similar findings have been observed in behavioral addictions such as pathological gambling (20), IGD (17), and compulsive shopping (32). In addition, the physiological and neural reactions in individuals with addictions appear to be associated with relapse (33). A recent meta-analysis showed overlapping neuronal substrates of reactivity to drug, gambling, food, and sexual cues in individuals with corresponding conditions (34). With respect to brain regions underlying cue-induced substance-related craving, several meta-analyses suggest that the ventral striatum, dorsal and medial prefrontal cortex (MPFC), and anterior cingulate cortex were more active during the presentation of substance-related cues as compared with non-substance-related cues (7,35–37). However, additional brain regions (e.g., lateral PFC [LPFC]) have also been observed with respect to drug cue–induced activations, particularly in men (20). Individuals with IGD also show greater activation of the PFC to gaming-related words (17) and of the dorsal and ventral striatum to gaming-related pictures (18). The MPFC has been previously implicated in craving processes. Meta-analyses have indicated that deprived smokers show higher cue activation in the MPFC than nondeprived smokers (7,38). Another meta-analysis of drug users found that the left PFC, especially the dorsolateral PFC, was activated to drug cues in drug users, suggesting that craving might be associated with the expectancy of drug-taking (36). The MPFC is a final projection site of the reward system and is directly activated by drugs or other substances (39,40). The MPFC may thus contribute to drug-seeking behaviors in response to drug cues. In addition, the frontal cortex, especially the dorsolateral PFC, is important in executive functions, for example, in inhibitory control (20,41,42). More robust brain responses in these areas to relevant cues may suggest an attempt to inhibit urges in response to cues, although this possibility is speculative. Together, studies suggest that
craving is positively associated with brain activations in the MPFC and LPFC in individuals with addictions. In this study, we investigated in individuals with IGD and those with RGU and without IGD how gaming related to changes in craving (subjective and brain responses) following exposure to gaming-related stimuli. Previous studies of substance addictions have shown that deprivation may enhance craving and that substance use may decrease craving (2,43). Based on these findings, we hypothesized that gaming would decrease subjective craving responses to gaming-related stimuli in subjects with IGD. We also hypothesized that gaming in IGD would be related to decreased brain responses in cue reactivity–related brain regions such as the MPFC, anterior cingulate cortex, and striatum. We also hypothesized that these findings would not be observed in individuals with RGU, with group 3 gaming period (pre/post) interactions observed.
METHODS AND MATERIALS Participant Selection The experiment conformed to the Code of Ethics of the World Medical Association (Declaration of Helsinki). The Human Investigations Committee of Zhejiang Normal University approved this research. All subjects were university students from Shanghai and were recruited through advertisements. Data were collected from 27 subjects with IGD and 43 subjects with RGU. There were no significant between-group differences in age (Table 1). All participants were right-handed, had normal or corrected-to-normal vision, provided written informed consent, and completed a structured psychiatric interview (Mini-International Neuropsychiatric Interview) (44) that was performed by an experienced psychiatrist and lasted approximately 15 minutes after the subjects had completed written portions of the Mini-International Neuropsychiatric Interview. All participants were free of Axis I psychiatric disorders assessed via the Mini-International Neuropsychiatric Interview. We further assessed depression with the Beck Depression Inventory (45), and only participants scoring less than 5 were included (Table 1). Thus, participants with IGD or RGU did not fulfill criteria for a non-IGD mental disorder, which was an inclusion criterion for the study. All participants were instructed not to use any substances of abuse, including tobacco and caffeinated drinks, on the day of scanning. No participants reported previous use of illicit drugs. IGD was determined based on scores of 50 or more on Young’s online Internet addiction test (www.netaddiction.com) (46) and also based on meeting the proposed nine-item DSM5 IGD diagnosis (meeting at least five of nine inclusionary criteria) (47) (Table 1). The Internet addiction test is a valid and reliable instrument that can be used in classifying Internet addiction (48,49). To control for types for gaming, we recruited only subjects (IGD and RGU) who regularly played League of Legends (Riot Games, Santa Monica, CA) and had played the game for at least 1 year.
Definition and Selection of the RGU Group Because the definition of RGU is an important component of our study, we reviewed the extant literature and interviewed 14
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Table 1. Demographic Information and Group Differences IGD (n 5 27) RGU (n 5 43) Age BDI Score IAT Score
21.00 6 1.33
21.47 6 1.32
2.30 6 0.82
2.02 6 0.83
64.56 6 11.89 32.23 6 10.74 5.89 6 1.12
2.65 6 1.38
Game Playing, Hours/Week 19.33 6 9.40
20.70 6 9.57
DSM-5 Score
t
p
0.43 .561 1.34 .184 8.12 .000a 10.25 .000a 20.58 .561
Values are presented as mean (6 SD). BDI, Beck Depression Inventory; IAT, Internet addiction test; IGD, Internet gaming disorder; RGU, recreational game use. a p , .001.
individuals who described themselves as RGU. Based on the findings, we used the following criteria for RGU. First, RGU participants should not demonstrate significant interference in life functioning and thus should meet fewer than four (of nine) of the proposed DSM-5 criteria for IGD and score less than 50 on Young’s Internet addiction test. Second, individuals with RGU should have been playing online games for a minimum of 2 years without meeting any symptoms of physical or psychological dependence (to ensure that they had been playing online games for a sufficient period of time to provide an opportunity to develop IGD). Third, individuals with RGU were required to play online games for least 5 of 7 days in a week (a frequency to define regular use). Fourth, individuals with RGU should play online games for more than 14 hours per week (an amount to define regular use). In addition, individuals with RGU should report no feelings of guilt or remorse about playing online games and state that their regular use does not interfere with work, school, family, or social obligations, which was assessed by the question “Do you feel guilt or remorse about your game playing behavior?”
pictures, background imagery was similar except that somebody is typing rather than gaming.
Controlling for Picture Complexity All pictures were generated in the same indoor setting (a white wall background, a white desk, and a black screen). Only male individuals were included in the pictures. The pictures showed only single sides of faces to diminish subjects’ abilities to distinguish emotions. No accessories were contained in the images of the hands or faces. The only differences between gaming-related stimuli and typing-related stimuli involved the on-screen material.
Task Performance Motivation Participants were told that they would be paid a guaranteed 50 Yuan (E $8 U.S.) for participation and, to encourage their motivation to respond accurately, were told that they would be rewarded with an additional 0 to 50 Yuan based on their task performance (accuracy rates 3 50). Accuracy rates (for identification of pictures with faces or no faces) were expected to approach/reach ceiling effects for the two groups.
Controlling for Task Repetition The functional magnetic resonance imaging tasks in the preand posttests were of the same types but differed in content to avoid/minimize possible repetition effects. We designed two copies of the task with different items (Copy A and Copy B). Half of each group of participants participated in an A–B sequence, and the other half received a B–A sequence, in their pre- and postgaming scans.
Gaming in the Scanner Pre- and Posttests The cue reactivity task was used in pre- and postgaming scans (Figure 1A). In each trial, a fixation was presented first for 500 ms, and then a stimulus was presented with a response needed; this stage lasted for up to 4000 ms. During this period, participants were asked to answer whether there is a face in the picture and to select yes or no via a button press (1 for yes and 2 for no, with counterbalancing between subjects). The stimulus turned black after key pressing and lasted for (4000 – response time) ms. A black screen was presented for a duration of 1500 to 3500 ms, with the duration jittered. No feedback was given about correct or incorrect responses. In the subsequent evaluation stage, subjects were asked to evaluate the level of their craving (on a scale from 1 [low] to 5 [high]) for the relevant stimuli. This process lasted for up to 4000 ms and was terminated by a button press. After another jitter ranging from 1500 to 3500 ms, the next trial ensued. We focused analyses on the response stage in the current study. The task used 160 pictures divided into two categories: gaming-related and typing-related pictures (neutral baseline). Half (50%) of all pictures within each category contained a face, and the other half contained a hand. As shown in Figure 1B, in gaming-related stimuli somebody is displayed playing a game on a computer, with some stimuli showing faces and others showing hands. In the typing-related
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In the scanner, subjects were asked to play League of Legends for one round. In general, one round of League of Legends will take about 20 to 40 minutes. To control for time spent in the scanner, subjects were instructed to try to finish the game in 25 minutes, with notifications provided at 20 and 25 minutes and termination of the game at 30 minutes. All subjects finished and won the game in 30 minutes. To promote subjects’ familiarity with their gaming experiences, participants were asked to log into the game with their own account.
Data Collection Structural images were collected using a T1-weighted threedimensional spoiled gradient-recalled sequence covering the whole brain (176 slices, repetition time 5 1700 ms, echo time 5 3.93 ms, slice thickness 5 1.0 mm, skip 5 0 mm, flip angle 5 15, inversion time 5 1100 ms, field of view 5 240 3 240 mm, in-plane resolution 5 256 3 256). Functional magnetic resonance imaging was performed on a 3T scanner (Siemens Trio, Malvern, PA) with a gradient-echo planar imaging T2*weighted-sensitive pulse sequence in 33 slices (interleaved sequence, 3 mm thickness, repetition time 5 2000 ms, echo time 5 30 ms, flip angle 5 901, field of view 5 220 3 220 mm2, matrix 5 64 3 64) (50). Stimuli were presented using an Invivo synchronous system (http://www.invivocorp.com) through a screen in the head coil, enabling participants to
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Figure 1. The timeline of one trial in the functional magnetic resonance imaging task. (A) In one trial of the task, a fixation was presented first for 500 ms, and then a response stage lasted for up to 4000 ms. Participants were asked to answer whether there was a face in the picture and to press the relevant keys. A jittered black screen was presented for 1500 to 3500 ms. After this, the evaluation stage followed, during which subjects were asked to evaluate the level of their craving (0–5) for the relevant behaviors (playing games or typing). This process lasted for up to 4000 ms and was terminated by a button press. After another jitter ranging from 1500 to 3500 ms, the next trial ensued. (B) Examples of the four types of trials used in the current study. The face typing (FT) and face gaming (FG) types reflect pictures with faces; the hands typing (HT) and hands gaming (HG) types reflect pictures with hands. For data analysis, the FT and HT types were considered neutral stimuli (typing-related stimuli), and the FG and HG types were considered gaming-related stimuli. Headphones were also presented in half of the pictures of each type to make the figures more engaging.
view the stimuli. The whole experiment lasted for more than 1 hour: pre- and posttest (14 minutes each); T1 structural (6 minutes); prepare for scanning and the time between different tasks (6 minutes); gaming ( 30 minutes).
Data Preprocessing The functional data were analyzed using SPM8 (http://www.fil. ion.ucl.ac.uk/spm) and NeuroElf (http://neuroelf.net), as described previously (51,52). Images were slice timed, reoriented, and realigned to the first volume, with T1-coregistered volumes being used to correct for head movements. Images were then normalized to Montreal Neurological Institute space and spatially smoothed using a 6-mm full width at half maximum Gaussian kernel. No subjects were removed from analysis because of head motion (the exclusion criteria were 2 mm in directional movement or 21 in rotational movement). A general linear model (GLM) was applied to identify blood oxygen level–dependent activation in relation to brain activities. Different types of trials (gaming related, typing related, incorrect, or missed) were separately convolved with a canonical hemodynamic response function to form task regressors. The duration of each trial was 4000 ms. The GLMs included a constant term per run. Six head movement parameters derived from the realignment stage were included to exclude motionrelated variances. A GLM approach was used to identify voxels that were significantly activated for each event during the response stage.
Post/Pre Comparisons Given that craving has been associated with activations in other brain regions (e.g., LPFC), whole-brain analyses were
conducted to examine our hypotheses. First, within-group (IGD and RGU) comparisons were made to examine postgaming versus pregaming differences in brain activations: post (IGDgaming-related stimuli 2 IGDtyping-related stimuli) 2 pre (IGDgamingrelated stimuli 2 IGDtyping-related stimuli); post (RGUgaming-related stimuli 2 RGUtyping-related stimuli) 2 pre (RGUgaming-related stimuli 2 RGUtyping-related stimuli). Second, a voxelwise 2 3 2 (factor 1: IGD, RGU; factor 2: post- and pretests) repeated-measures analysis was administered to examine statistically significant between-group differences across pre-/postgaming exposure: post (IGDgaming-related stimuli 2 IGDtyping-related stimuli) 2 post (RGUgaming-related stimuli 2 RGUtyping-related stimuli); pre (IGDgamingrelated stimuli 2 IGDtyping-related stimuli) 2 pre (RGUgaming-related stimuli 2 RGUtyping-related stimuli). Familywise error thresholds were determined using AlphaSim, and all comparisons were corrected using AlphaSim (https://afni.nimh.nih.gov/pub/dist/ doc/program_help/AlphaSim.html). Significant clusters (familywise error corrected, p , .01) were thresholded at p , .01, two tailed, uncorrected, with an extent of at least 125 voxels, based on the unresliced voxel size (3 3 3 3 3). All of these steps were performed using NeuroElf. The smoothing kernel used during simulating false-positive (noise) maps using AlphaSim was 6 mm and was estimated from the residual fields of the contrast maps being entered into the one-sample t test.
Correlation Analyses We first compared the brain activation between pre- and postgaming scans and then took the surviving cluster as a region of interest. We also explored relationships with the PFC, precuneus, and striatal regions found to differ in the IGD
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group from pre- to postscan given the findings and the relevance of these to addictive processes like craving. For each region of interest, a representative blood oxygen level– dependent beta value was obtained by averaging the signal of all the voxels within it. We performed the correlations between the self-reported craving to gaming-related stimuli and brain activations in the regions of interest at the pre- and postgaming tests separately.
RESULTS
craving in the RGU group at posttest versus pretest (Supplemental Figure S2B).
Correlation Results Correlations (at p , .05 or p , .10) were found in all subjects between brain activation in the striatum and subjective craving to game playing at pre- and postgaming tests, although none survived correction for multiple comparisons (Supplemental Figure S3). Correlations between brain activation in the PFC and precuneus and subjective craving were not significant.
Behavioral Performance Comparisons from pre- to postgaming in the separate groups indicated that gaming was associated with increased craving to gaming-related cues in the IGD group (t = 2.322, p = .028) but not in the RGU group (t = 0.509, p = .613) (Figure 2). However, GLM results (using repeated measures) showed that the self-reported craving to stimuli in pre- and posttests in the scanner showed a main effect (F 5 27.402, p , .001), with no interaction observed (F 5 0.735, p 5 .692). Thus, the betweengroup differences in the increases in craving were statistically similar across groups in the GLM. With respect to in-scanner performance, there were few mistakes in either group, reflecting the relative simplicity of performance of the task (IGD, 99.6%; RGU, 100%). With respect to response times, subjects with IGD took longer to respond to gaming-related stimuli (mean 6 SD: IGD, 1249.55 6 326.53 ms; RGU, 1152.93 6 328.72 ms; t 5 4.524, p , .01). No difference was found between these two groups in their response times to typing-related stimuli (IGD, 1314.49 6 316.55 ms; RGU, 1297.35 6 316.27 ms; t 5 0.304, p . .05).
Imaging Results: Post/Pre 3 Group First, we compared the post- and pregaming findings in the two groups separately. In postgaming versus pregaming comparisons in the IGD group, increased brain activations were observed in the PFC, striatum, and precuneus; no decreased brain regions were observed (Figure 3). In the RGU group, no difference was observed in the comparison.
Imaging Results: Analysis of Variance Results The differences in the neural correlates of craving between the IGD and RGU groups were calculated (Supplemental Figure S1). The 2 3 2 (group 3 post/pre) results showed that gaming was associated with increased brain activations in the LPFC (Supplemental Figure S2A and Table 2). The extracted beta weights from this cluster showed that the difference was related to increased craving in the IGD group and decreased
DISCUSSION Gaming Increased Craving for Gaming-Related Cues in Subjects With IGD The findings suggest that gaming may affect craving differently in the IGD and RGU groups, although the findings across groups were arguably more similar than dissimilar statistically. Contrary to our hypothesis, gaming was associated with subjective increases in craving in the IGD group but not in the RGU group, suggesting a potential priming effect. However, this finding did not survive a more robust analysis of a group 3 gaming exposure (pre/post) interaction, rather showing a main effect, indicating that craving may have increased across groups following gaming. Also contrary to our hypothesis, posttest versus pretest comparison within each gaming group showed that gaming increased brain activations in the left PFC, right striatum, and left precuneus in the IGD group. However, in the RGU group, no difference was found when comparing activations at postgaming versus pregaming times. However, arguably the most robust statistical difference (a group 3 gaming exposure interaction) suggested that gaming was associated with increased brain activation in the left LPFC at posttest in the IGD group but not in the RGU group. As described in the introductory paragraphs, the results suggest that gaming enhanced regional brain activations in a craving-related area. Although the LPFC or MPFC findings did not relate to subjective craving, extracted beta weights from the striatum, another structure implicated in craving, suggest that this structure may be relevant to subjective craving in IGD. Specifically, the subjects with IGD showed higher craving for gaming-related cues following gaming, and suggestive correlations between brain activations in the striatum and subjective craving to gaming-related cues were observed pre- and postgaming, although these were relatively weak and would not survive correction for multiple comparisons. Data suggest that phasic dopamine release in the ventral striatum motivates goal-directed behavior (2,53). The striatum Figure 2. Self-reported mean craving ratings for gaming-related stimuli at pre- and postgaming times in the Internet gaming disorder (IGD) and recreational game use (RGU) groups.
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Figure 3. Brain features in postgaming versus pregaming comparisons in the Internet gaming disorder group. In the Internet gaming disorder group, when comparing postgaming and pregaming task activations, higher brain activations were observed in the left medial prefrontal cortex, striatum, and precuneus (whole brain corrected at p , .01).
has also been implicated in reward processing, reinforcement and reward-based learning (23,54), and addictions, in both impulsive and compulsive aspects (55) and when individuals
are exposed to addictive stimuli (56,57). The involvement of the striatum has been previously observed in IGD studies, with participants with IGD exhibiting higher cue-induced and
Table 2. Regional Brain Activity Changes to Gaming-Related Cues Cluster Number
x, y, za
Peak Intensity
Cluster Sizeb
Regionc
Brodmann Area
Post/Pre Comparison in IGD Group 1
26, 33, 42
4.515
252
L Precuneus
2
27, 212, 215
6.787
293
R Striatum (nucleus accumbens, putamen, caudate)
3
212, 60, 24
4.454
139
L Medial prefrontal cortex, medial frontal cortex
4.356
186
L Lateral prefrontal cortex
5 10,11
Post/Pre Comparison in RGU Group No brain regions survived GLM Repeated Measures (Group 3 Pre/Post) 1
242, 18, 21
9,10
FWE, familywise error; GLM, general linear model; IGD, Internet gaming disorder; L, left; R, right; RGU, recreational game use. a Peak Montreal Neurological Institute coordinates. b Number of voxels. We first identified clusters of contiguously significant voxels at an uncorrected threshold (p , .01), as also used for display purposes in the figures. We then tested these clusters for cluster-level FWE correction (p , .01), and the AlphaSim estimation indicated that clusters with 125 contiguous voxels would achieve an effective FWE threshold (p , .01). Voxel size 5 3 3 3 3 3. c The brain regions were referenced to the software xjView (http://www.alivelearn.net/xjview8) and verified through comparisons with a brain atlas.
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reward-related activations within both the ventral and dorsal striatum when compared with individuals without IGD (18,58,59). Thus, the current findings are consistent with and extend prior studies in that they suggest that the striatum is activated in response to gaming-related cues in IGD and that gaming may enhance gaming cue–related striatal activation in a manner linked to subjective craving responses. Another brain area showing higher activation at postgaming versus pregaming in the IGD group was the precuneus. A meta-analysis observed higher activations in the precuneus in response to smoking-related cues in smokers (7). The precuneus has been implicated in craving and in multiple cognitive functions, including attentional tracking of stimuli and the preparation of motor behaviors to stimuli (7,60). Thus, the higher responses to game-related cues in the precuneus may potentially reflect attentional biases toward gamingrelated cues or preparation for potential gaming behaviors, although this possibility remains speculative. In the current study, subjects with IGD showed increased precuneus activation at postgaming scanning, which suggests that gaming may have enhanced their attention to gaming-related cues that may have contributed to craving responses, although this line of reasoning warrants additional investigation.
Craving in Response to Gaming-Related Cues in Subjects With RGU In contrast to the results in subjects with IGD, self-reported craving to gaming stimuli was not increased significantly at posttest (in comparison with pretest) in subjects with RGU. Nonetheless, the main effect of craving changes observed across the RGU/IGD groups does not entirely exclude some impact of gaming on craving in RGU. In imaging analyses, no brain regions were identified in subjects with RGU as differing in postgaming as compared with pregaming in whole-brain analyses. The findings suggest that gaming did not substantially affect craving or gaming cue–related activations in subjects with RGU.
Summary The current results suggest that gaming increased gaming cue–related craving in subjects with IGD, which may differ from some studies indicating that drug use may decrease craving in drug addiction. In substance addiction, deprivation may enhance craving responses, and the intake of substances may lead to decreased craving (2,43,61). However, for IGD, gaming increased craving in response to gaming-related stimuli. It is possible that the amount of gaming performed in the current study was not sufficient to fully satisfy a craving for gaming, akin to a priming dose of alcohol in alcohol dependence. The current finding suggests a possible mechanism by which gaming may promote more gaming in individuals with IGD.
investigate the extent to which the findings extend to other groups. Strengths include the involvement of subjects with RGU and comparisons made pre- and postgaming. The data are valuable in understanding mechanisms underlying IGD. The results suggest a mechanism for why some individuals with IGD may continue gaming for excessive periods of time, whereas others with RGU might not.
ACKNOWLEDGMENTS AND DISCLOSURES GD was supported by the National Science Foundation of China (Grant No. 31371023) and by the Open Research Fund of the State Key Laboratory of Cognitive Neuroscience and Learning (Grant No. CNLYB 1207). MNP’s involvement was supported by the National Center on Addiction and Substance Abuse and the National Center for Responsible Gaming. The funding agencies did not contribute to the experimental design or conclusions, and the views presented in the article are those of the authors and might not reflect those of the funding agencies. GD designed the task and wrote the first draft of the manuscript. LW collected and analyzed the data and prepared the figures. XD collected the data. MNP contributed in the editing, interpretation, and revision processes. All authors contributed to and approved the final manuscript. MNP has consulted for and advised Ironwood, Lundbeck, INSYS, Shire, RiverMend Health, Opiant/Lakelight Therapeutics, Jazz Pharmaceuticals, and Pfizer; has received research support from the Mohegan Sun Casino, the National Center for Responsible Gaming, and Pfizer; has participated in surveys, mailings, and telephone consultations related to drug addiction, impulse control disorders, and other health topics; has consulted for gambling and legal entities on issues related to impulse control and addictive disorders; provides clinical care in the Connecticut Department of Mental Health and Addiction Services Problem Gambling Services Program; has performed grant reviews for the National Institutes of Health and other agencies; has edited journals or journal sections; has given academic lectures in grand rounds, Continuing Medical Education events, and other clinical or scientific venues; and has generated books and book chapters for publishers of mental health texts. All other authors report no biomedical financial interests or potential conflicts of interest.
ARTICLE INFORMATION From the Department of Psychology (GD, LW) and Institute of Psychological and Brain Sciences (GD), Zhejiang Normal University, Jinhua, Zhejiang Province; and Department of Physics (XD), Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, People’s Republic of China; Department of Psychiatry (MNP), Department of Neuroscience, Child Study Center, and National Center on Addiction and Substance Abuse, Yale University School of Medicine; and Connecticut Mental Health Center (MNP), New Haven, Connecticut. Address correspondence to Guangheng Dong, Ph.D., Department of Psychology, Zhejiang Normal University, 688 Yingbin Road, Jinhua, Zhejiang Province, P.R. China; E-mail: [email protected]. Received Nov 6, 2016; revised Jan 13, 2017; accepted Jan 18, 2017. Supplementary material cited in this article is available online at http:// dx.doi.org/10.1016/j.bpsc.2017.01.002.
REFERENCES 1.
2.
Limitations and Strengths The current findings indicate that gaming enhanced craving for gaming-related cues in subjects with IGD. Limitations are detailed in the Supplement and include exclusively young male Asian participants, and additional studies should
410
3.
4.
American Psychiatric Association (2013): Diagnostic and Statistical Manual of Mental Disorders (5th ed.). Arlington, VA: American Psychiatric Publishing. Sayette MA (2016): The role of craving in substance use disorders: Theoretical and methodological issues. Annu Rev Clin Psychol 12: 407–433. Sinha R, Li CS (2007): Imaging stress- and cue-induced drug and alcohol craving: Association with relapse and clinical implications. Drug Alcohol Rev 26:25–31. Potenza MN, Steinberg MA, Skudlarski P, Fulbright RK, Lacadie CM, Wilber MK, et al. (2003): Gambling urges in pathological gambling:
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging July 2017; 2:404–412 www.sobp.org/BPCNNI
Biological Psychiatry: CNNI
Craving Changes in IGD
5.
6.
7.
8.
9.
10.
11.
12. 13. 14.
15.
16.
17.
18.
19. 20.
21.
22. 23.
24.
25.
26.
A functional magnetic resonance imaging study. Arch Gen Psychiatry 60:828–836. Potenza MN, Balodis IM, Franco CA, Bullock S, Xu J, Chung T, et al. (2013): Neurobiological considerations in understanding behavioral treatments for pathological gambling. Psychol Addict Behav 27: 380–392. Grant JE, Kim SW, Hollander E, Potenza MN (2008): Predicting response to opiate antagonists and placebo in the treatment of pathological gambling. Psychopharmacology (Berl) 200:521–527. Engelmann JM, Versace F, Robinson JD, Minnix JA, Lam CY, Cui Y, et al. (2012): Neural substrates of smoking cue reactivity: A metaanalysis of fMRI studies. NeuroImage 60:252–262. Tiffany ST (1990): A cognitive model of drug urges and drug-use behavior: Role of automatic and nonautomatic processes. Psychol Rev 97:147–168. Sayette MA, Schooler JW, Reichle ED (2010): Out for a smoke: The impact of cigarette craving on zoning out during reading. Psychol Sci 21:26–30. Dong G, Potenza MN (2016): Risk-taking and risky decision-making in Internet gaming disorder: Implications regarding online gaming in the setting of negative consequences. J Psychiatr Res 73:1–8. Wilson SJ, Delgado MR, McKee SA, Grigson PS, MacLean RR, Nichols TT, et al. (2014): Weak ventral striatal responses to monetary outcomes predict an unwillingness to resist cigarette smoking. Cogn Affect Behav Neurosci 14:1196–1207. Piper ME (2015): Withdrawal: Expanding a key addiction construct. Nicotine Tob Res 17:1405–1415. Petry NM, O’Brien CP (2013): Internet gaming disorder and the DSM5. Addiction 108:1186–1187. Banz BC, Yip SW, Yau YH, Potenza MN (2016): Behavioral addictions in addiction medicine: From mechanisms to practical considerations. Prog Brain Res 223:311–328. Zhou ZH, Yuan GZ, Yao JJ (2012): Cognitive biases toward Internet game-related pictures and executive deficits in individuals with an Internet game addiction. PLoS One 7:e48961. Decker SA, Gay JN (2011): Cognitive-bias toward gaming-related words and disinhibition in World of Warcraft gamers. Comput Hum Behav 27:798–810. Zhang Y, Lin X, Zhou H, Xu J, Du X, Dong G (2016): Brain activity toward gaming-related cues in Internet gaming disorder during an addiction Stroop task. Front Psychol 7:714. Liu L, Yip SW, Zhang JT, Wang LJ, Shen ZJ, Liu B, et al. (2017): Activation of the ventral and dorsal striatum during cue reactivity in Internet gaming disorder. Addict Biol 22:791–801. Carter BL, Tiffany ST (1999): Meta-analysis of cue-reactivity in addiction research. Addiction 94:327–340. Potenza MN, Hong KI, Lacadie CM, Fulbright RK, Tuit KL, Sinha R (2012): Neural correlates of stress-induced and cue-induced drug craving: influences of sex and cocaine dependence. The American journal of psychiatry 169:406–414. Kosten TR, Scanley BE, Tucker KA, Oliveto A, Prince C, Sinha R, et al. (2006): Cue-induced brain activity changes and relapse in cocainedependent patients. Neuropsychopharmacology 31:644–650. Volkow ND, Li TK (2004): Drug addiction: The neurobiology of behaviour gone awry. Nat Rev Neurosci 5:963–970. Balodis IM, Potenza MN (2015): Anticipatory reward processing in addicted populations: A focus on the monetary incentive delay task. Biol Psychiatry 77:434–444. Blum K, Sheridan PJ, Wood RC, Braverman E, Chen TJ, Cull J, et al. (1996): The D2 dopamine receptor gene as a determinant of reward deficiency syndrome. J R Soc Med 89:396–400. Trick L, Kempton MJ, Williams SCR, Duka T (2014): Impaired fear recognition and attentional set-shifting is associated with brain structural changes in alcoholic patients. Addict Biol 19: 1041–1054. Chaplin TM, Hong K, Fox HC, Siedlarz KM, Bergquist K, Sinha R (2010): Behavioral arousal in response to stress and drug cue in alcohol and cocaine addicted individuals versus healthy controls. Hum Psychopharmacol 25:368–376.
27.
28.
29. 30.
31.
32.
33.
34.
35.
36.
37.
38.
39.
40.
41.
42.
43.
44.
45. 46.
47.
48. 49.
Smith DG, Simon Jones P, Bullmore ET, Robbins TW, Ersche KD (2014): Enhanced orbitofrontal cortex function and lack of attentional bias to cocaine cues in recreational stimulant users. Biol Psychiatry 75:124–131. Desai RA, Krishnan-Sarin S, Cavallo D, Potenza MN (2010): Videogaming among high school students: Health correlates, gender differences, and problematic gaming. Pediatrics 126:E1414–E1424. Kuss DJ, Griffiths MD (2012): Internet gaming addiction: A systematic review of empirical research. Int J Ment Health Addict 10:278–296. Griffiths MD (2010): The role of context in online gaming excess and addiction: Some case study evidence. Int J Ment Health Addict 8: 119–125. King DL, Delfabbro PH, Zajac IT (2011): Preliminary validation of a new clinical tool for identifying problem video game playing. Int J Ment Health Addict 9:72–87. Starcke K, Schlereth B, Domass D, Scholer T, Brand M (2013): Cue reactivity towards shopping cues in female participants. J Behav Addict 2:17–22. Courtney KE, Schacht JP, Hutchison K, Roche DJ, Ray LA (2016): Neural substrates of cue reactivity: Association with treatment outcomes and relapse. Addict Biol 21:3–22. Noori HR, Cosa Linan A, Spanagel R (2016): Largely overlapping neuronal substrates of reactivity to drug, gambling, food and sexual cues: A comprehensive meta-analysis. Eur Neuropsychopharmacol 26:1419–1430. Kuhn S, Gallinat J (2011): Common biology of craving across legal and illegal drugs—A quantitative meta-analysis of cue-reactivity brain response. Eur J Neurosci 33:1318–1326. Chase HW, Eickhoff SB, Laird AR, Hogarth L (2011): The neural basis of drug stimulus processing and craving: An activation likelihood estimation meta-analysis. Biol Psychiatry 70:785–793. Boswell RG, Kober H (2016): Food cue reactivity and craving predict eating and weight gain: A meta-analytic review. Obes Rev 17: 159–177. Kober H, Mende-Siedlecki P, Kross EF, Weber J, Mischel W, Hart CL, et al. (2010): Prefrontal-striatal pathway underlies cognitive regulation of craving. Proc Natl Acad Sci U S A 107:14811–14816. Goldstein RZ, Volkow ND (2002): Drug addiction and its underlying neurobiological basis: Neuroimaging evidence for the involvement of the frontal cortex. Am J Psychiatry 159:1642–1652. Jentsch JD, Taylor JR (1999): Impulsivity resulting from frontostriatal dysfunction in drug abuse: Implications for the control of behavior by reward-related stimuli. Psychopharmacology (Berl) 146:373–390. Hartwell KJ, Johnson KA, Li X, Myrick H, LeMatty T, George MS, et al. (2011): Neural correlates of craving and resisting craving for tobacco in nicotine dependent smokers. Addict Biol 16:654–666. Lubman DI, Yucel M, Pantelis C (2004): Addiction, a condition of compulsive behaviour? Neuroimaging and neuropsychological evidence of inhibitory dysregulation. Addiction 99:1491–1502. Subramaniyan M, Dani JA (2015): Dopaminergic and cholinergic learning mechanisms in nicotine addiction. Ann N Y Acad Sci 1349: 46–63. Lecrubier Y, Sheehan DV, Weiller E, Amorim P, Bonora I, Harnett Sheehan K, et al. (1997): The Mini International Neuropsychiatric Interview (MINI): A short diagnostic structured interview—Reliability and validity according to the CIDI. Eur Psychiatry 12:224–231. Beck AT, Ward CH, Mendelson M, Mock J, Erbaugh J (1961): An inventory for measuring depression. Arch Gen Psychiatry 4:561–571. Young KS (2011): Clinical assessment of Internet-addicted clients. In: Young KS, Abreu C, editors. Internet Addiction: A Handbook and Guide to Evaluation and Treatment. Hoboken, NJ: John Wiley, 19–34. Petry NM, Rehbein F, Gentile DA, Lemmens JS, Rumpf HJ, Mossle T, et al. (2014): An international consensus for assessing Internet gaming disorder using the new DSM-5 approach. Addiction 109:1399–1406. Widyanto L, McMurran M (2004): The psychometric properties of the Internet addiction test. Cyberpsychol Behav 7:443–450. Widyanto L, Griffiths MD, Brunsden V (2011): A psychometric comparison of the Internet Addiction Test, the Internet-Related
Biological Psychiatry: Cognitive Neuroscience and Neuroimaging July 2017; 2:404–412 www.sobp.org/BPCNNI
411
Biological Psychiatry: CNNI
50.
51.
52.
53.
54.
412
Craving Changes in IGD
Problem Scale, and self-diagnosis. Cyberpsychol Behav Soc Netw 14: 141–149. Dong G, Hu Y, Lin X, Lu Q (2013): What makes Internet addicts continue playing online even when faced by severe negative consequences? Possible explanations from an fMRI study. Biol Psychol 94:282–289. DeVito EE, Worhunsky PD, Carroll KM, Rounsaville BJ, Kober H, Potenza MN (2012): A preliminary study of the neural effects of behavioral therapy for substance use disorders. Drug Alcohol Depend 122:228–235. Krishnan-Sarin S, Balodis IM, Kober H, Worhunsky PD, Liss T, Xu JS, et al. (2013): An exploratory pilot study of the relationship between neural correlates of cognitive control and reduction in cigarette use among treatment-seeking adolescent smokers. Psychol Addict Behav 27:526–532. Ikemoto S, Yang C, Tan A (2015): Basal ganglia circuit loops, dopamine and motivation: A review and enquiry. Behav Brain Res 290: 17–31. Cheng Y, Huang CC, Ma T, Wei X, Wang X, Lu J, Wang J (2017): Distinct synaptic strengthening of the striatal direct and indirect pathways drives alcohol consumption. Biol Psychiatry 81: 918–929.
55.
56.
57.
58.
59.
60. 61.
Fineberg NA, Chamberlain SR, Goudriaan AE, Stein DJ, Vanderschuren LJ, Gillan CM, et al. (2014): New developments in human neurocognition: Clinical, genetic, and brain imaging correlates of impulsivity and compulsivity. CNS Spectr 19:69–89. Worhunsky PD, Malison RT, Rogers RD, Potenza MN (2014): Altered neural correlates of reward and loss processing during simulated slotmachine fMRI in pathological gambling and cocaine dependence. Drug Alcohol Depend 145:77–86. Balodis IM, Kober H, Worhunsky PD, Stevens MC, Pearlson GD, Potenza MN (2012): Attending to striatal ups and downs in addictions. Biol Psychiatry 72:e25–e26. Ko CH, Liu GC, Hsiao S, Yen JY, Yang MJ, Lin WC, et al. (2009): Brain activities associated with gaming urge of online gaming addiction. J Psychiatr Res 43:739–747. Sun Y, Ying H, Seetohul RM, Xuemei W, Ya Z, Qian L, et al. (2012): Brain fMRI study of crave induced by cue pictures in online game addicts (male adolescents). Behav Brain Res 233:563–576. Cavanna AE, Trimble MR (2006): The precuneus: A review of its functional anatomy and behavioural correlates. Brain 129:564–583. Potenza MN, Sofuoglu M, Carroll KM, Rounsaville BJ (2011): Neuroscience of behavioral and pharmacological treatments for addictions. Neuron 69:695–712.
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