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Addiction Biology

HUMAN NEUROIMAGING STUDY

doi:10.1111/j.1369-1600.2011.00405.x

Brain correlates of craving for online gaming under cue exposure in subjects with Internet gaming addiction and in remitted subjects Chih-Hung Ko1,3,5, Gin-Chung Liu2,4, Ju-Yu Yen1,3, Chiao-Yun Chen2,4, Cheng-Fang Yen1,3 & Cheng-Sheng Chen1,3 Departments of Psychiatry1 and Medical Imaging2, Kaohsiung Medical University Hospital, Kaohsiung Medical University, Taiwan, Departments of Psychiatry3 and Medical Imaging4, Faculty of Medicine, College of Medicine, Kaohsiung Medical University,Taiwan and Department of Psychiatry, Kaohsiung Municipal Hsiao-Kang Hospital, Kaohsiung Medical University, Taiwan5

ABSTRACT

adb_405

559..569

This study aimed to evaluate brain correlates of cue-induced craving to play online games in subjects with Internet gaming addiction (IGA), subjects in remission from IGA and controls. The craving response was assessed by eventrelated design of functional magnetic resonance images (fMRIs). Fifteen subjects with IGA, 15 in remission from IGA and 15 controls were recruited in this study. The subjects were arranged to view the gaming screenshots and neutral images under investigation of fMRIs. The results showed that bilateral dorsolateral prefrontal cortex (DLPFC), precuneus, left parahippocampus, posterior cingulate and right anterior cingulate were activated in response to gaming cues in the IGA group and their activation was stronger in the IGA group than those in the control group. Their region-ofinterest was also positively correlated with subjective gaming urge under cue exposure. These activated brain areas represent the brain circuit corresponding to the mechanism of substance use disorder. Thus, it would suggest that the mechanism of IGA is similar to substance use disorder. Furthermore, the IGA group had stronger activation over right DLPFC and left parahippocampus than did the remission group. The two areas would be candidate markers for current addiction to online gaming and should be investigated in future studies. Keywords

Craving for online gaming, cue reactivity, Internet gaming addiction, parahippocampus, remission.

Correspondence to: Gin-Chung Liu, Department of Medical Imaging, Kaohsiung Medical University Hospital, 100 Tzyou 1st Rd., Kaohsiung City 807, Taiwan. E-mail: [email protected]

INTRODUCTION Internet addiction is a newly identified condition associated with loss of control over Internet use, leading to negative psychosocial results (Ko et al. in press). This addiction, is referred to in the literature as Internet addiction (Ko et al. 2009b). Although the precise definition of Internet addiction varies, its prevalence is reportedly to be 2.4–17.9% (Ko et al. in press). The neurobiological mechanisms of Internet addiction should be identified as quickly as possible to develop effective interventions. Emerging neurobiological, behavioral and genetic findings suggest shared vulnerabilities underlying the pathological pursuit of substance and non-substance rewards (Frascella et al. 2010). According to the

diagnostic criteria for Internet addiction (DCIA) (Ko et al. 2009b), the core symptoms of Internet addiction are identical to diagnostic criteria for substance dependence (American Psychiatric Assn 2000). Internet addiction has also been associated with harmful use of alcohol and substances (Ko et al. 2006; Yen et al. 2009), and these studies revealed that these two disorders are both characterized by similar personality characteristics such as high novelty-seeking and behavior activation (Ko et al. 2006; Yen et al. 2009). Lastly, the brain activation for gaming urge among subjects with Internet gaming addiction (IGA) has been found to be similar to that for craving responses of substance use disorder (Ko et al. 2009a). The similarity of symptoms and associative factors between Internet addiction and substance use disorder suggests a possible shared vulnerability mechanism.

© 2011 The Authors, Addiction Biology © 2011 Society for the Study of Addiction

Addiction Biology, 18, 559–569

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Craving has been defined as a desire of any intensity to act out an addictive behavior (Skinner & Aubin 2010). The mechanisms of uncontrolled desire are multiple dimensional and complex. The conditioned model suggests that craving is a conditioned withdrawal response to condition stimuli, such as substance-related cues. The cognitive model suggests that the alcoholrelated cue would trigger positive expectations, which drives the addictive behavior. The incentive sensitization theory, one theory of psychobiological mode, claims that long-term substance use causes neurobiological change. When consumption stops, craving occurs to reacquire homeostasis in an imbalanced brain (Skinner & Aubin 2010). Based on the different models, multiple response domains, including emotional, cognitive, behavior and psychophysiological experiences, have been assessed to represent the concept of craving (Rosenberg 2009). Selfreported single-item scale could assess craving with ease of administration and scoring, suitability for frequent and repeated measurement, and apparent sensitivity to rapid changes in the psychological state being assessed (McCormack, Horne & Sheather 1988). On the other hand, self-reported multiple-item scales could represent the presumably multi-dimensional nature of craving (Tiffany et al. 1993). However, the self-report itself has limitations such as desirability biases and tendency to limit the amount of effort that subjects exert when reading questionnaires (Schwartz 1999). Furthermore, the responses of craving might involve some mechanisms that are out of self-awareness and could therefore not be well-assessed by the self-report questionnaire. According to the four models mentioned previously, cue reactivity provides an important model to provoke positive memory, positive expectation, imbalance of motivational state, withdrawal syndrome, rewarding drive and lastly craving response (Sinha & Li 2007; Skinner & Aubin 2010). Thus, the cue-induced craving is one of the most important mechanisms to explain the craving response for substance use. This mechanism may also cause drug-seeking behavior or relapse from abstinence status (Heinz et al. 2009). The neurobiological mechanism of cue-induced craving has been studied by using functional magnetic resonance imaging (fMRI) to identify associated brain activations (Sinha et al. 2005; McClernon et al. 2009). Such studies suggest that the drug-induced craving response mainly originates in the limbic–corticostriatal pathway (Heinz et al. 2009; Volkow et al. 2010). In recent years, the cue-induced craving paradigm of fMRI has also been used to investigate the craving response in addiction behavior (Crockford et al. 2005). Goudriaan and colleagues applied this paradigm to reveal that gambling picture activates posterior

cingulate and parahippocampus in problem gamblers (Goudriaan et al. 2010). Ko and colleagues demonstrated that gaming cues activate the anterior cingulate, orbital frontal lobe, nucleus accumbens, dorsal striatum and dorsolateral prefrontal cortex (DLPFC) in IGA subjects (Ko et al. 2009a). Another study further revealed that the DLPFC and parahippocampus are activated by exposure to gaming cues (Han, Hwang & Renshaw 2010). However, the cue stimuli in the previous fMRI of Internet addiction were shown in a block design. They analyzed brain activation during the block of time (e.g. 20–30 seconds) under repeated gaming cue exposure. Please do not indicate the immediate ‘response’ to gaming cues. Therefore, event-related fMRI studies are needed to determine the immediate reaction to gaming cues and to clarify the nature of the cue-induced craving response. Subjects in remission state of substance use disorder were not further directly affected by substance use. However, they also have a memory of the substance exposure experience as current drug abuser. Comparison between case and remission group might demonstrate the marker for addictive state or remission state. Previous studies have demonstrated that abstinent smokers and alcoholics had decreased activation in the amygdala and hippocampus for cue reactivity in comparison with current smokers and alcoholics, respectively (Schneider et al. 2001; McClernon et al. 2007). On the other hand, exposure to stress, substance-related cues or the substance itself can each reinstate drug-seeking behavior and increase relapse susceptibility (Sinha & Li 2007). Previous study had found that substance cue could activate the medial prefrontal, anterior and posterior cingulate, and the striatal and posterior insula regions among subjects under abstinence from substance. Activities of these areas were also known to predict relapse outcomes (Sinha & Li 2007; Heinz et al. 2009). According to the incentive sensitization theory, persistent incentive sensitization causes pathological desire for drugs that can last for years, even after discontinued drug use (Robinson & Berridge 2008). Thus, cue-induced reactivity in subjects who are in remission from substance use disorder may play an important role in relapse of the disorder, even after longterm remission. Thus, the persistence of brain activation associated with the gaming craving response requires further study in subjects of IGA who are in the remission state. Volkow et al. (2010) proposed a model of addiction, suggesting that, the substance-related cue might provoke enhanced value of substance in the reward, motivation and memory circuits. The intense desire overcomes the inhibitory control exerted by prefrontal lobe and then drives the motivation to take substance. Based on the model proposed by Volkow et al. (2010), the mechanism



Online gaming craving

of cue-induced gaming craving is hypothesized to be involved in (1) visual processing areas, such as occipital lobe or precuneus, to link the gaming cues to their internal information; (2) memory system, such as hippocampus, parahippocampus or amygdala, to provide emotional memory and context information of gaming cues; (3) the reward system, such as limbic system or posterior cingulate, to evaluate the gaming related information and provide expectation and rewarding significance; (4) the motivation system, such as anterior cingulate or orbital frontal lobe, to determine desire for gaming; and (5) the executive system, such as DLPFC or prefrontal cortex, to make the plan to get online for gaming. Thus, the aims of the study were to perform fMRI studies of the brain activation associated with cue-induced craving for online gaming in current and remitted cases of IGA to test the hypothesis.

561

sequence for functional imaging was a gradient-recalled echo planar imaging (EPI) sequence (64 ¥ 64 matrix; 24-cm field of view, echo time = 35 milliseconds; repetition time = 2.5 seconds; 3-mm thick slices with 0-mm gap). Thirty-five image planes were collected parallel to the anterior commissure and posterior commissure lines with the aid of sagittal localizer images. The head motion was corrected by post-processing using SPM5. Process

Male right-hand participants were recruited by an advertisement posted in campus. All subjects in the IGA group were interviewed by a psychiatrist to confirm the diagnoses of IGA for more than 1 year according to the DCIA. Because subjects with IGA are highly variable and heterogenic, all addicted subjects recruited for this study were currently addicted to the same online game. All subjects in the IGA group had spent an average of 4 or more hours/day on weekdays and an average of 8 or more hours/day on weekends on online gaming. All subjects in the remission group had a history of IGA, all were addicted to the same online game, and all were currently in remission from IGA for more than half-a-year according to the DCIA. In the control group, psychiatric interviews and reviews of history were performed to confirm that none had ever been diagnosed with Internet addiction. Exclusion criteria were current use of psychotropic medication and any history of substance use disorder (excluding nicotine dependence), major depressive episode, bipolar I disorder, psychotic disorder, neurological illness or injury, mental retardation or poor tolerance of magnetic resonance (MR) imaging. After receiving a detailed explanation of the study, all subjects gave written informed consent. The study was approved by the Kaohsiung Medical University Institutional Review Board.

Before scanning, subjects completed the Chen Internet Addiction Scale (CIAS), visual analog scale of perceived gaming urge (PGU; scoring 0–10) and Questionnaire on Gaming Urge Brief (QGU-B). The CIAS developed by Chen contains 26 items on a 4-point Likert scale. Its total score ranges from 26 to 104 to represent the severity of Internet addiction. The internal reliability of the scale and the subscales in the original study ranged from 0.79 to 0.93 (Chen et al. 2003). PGU represents the severity of subjective gaming urge. The QGU-B was a modification of the Questionnaire on Smoking Urges Brief (QSU-B) (Cox, Tiffany & Christen 2001), which was developed to assess the craving response for smoking. It contains 10 questions such as ‘I have a desire for online gaming right now’ or ‘Nothing would be better than online gaming right now’. Every questionnaire scores from 1 to 7 to represent gaming craving. Its total score was significantly correlated with CIAS. The internal reliability of the scale was 0.99, and test–re-test reliability was 0.96. Then, functional MR images were acquired as bloodoxygen-level dependence (BOLD) signal by using an event-related design of cue-induced craving paradigm. Two online game players who had not been enrolled as participants in this study were asked to select 60 screenshots of major events in the selected online game. The 60 images were highly modified by inverting them and then converting them to mosaic images so that the participants could not identify the original image. The 60 mosaics were used as the neutral stimulation. The subjects then viewed the 120 images in a pseudo-random sequence under fMRI scanning. Each image was shown for 2 seconds. The interstimuli intervals were jittered and maintained within the range of 3.2–8.3 seconds. The paradigm was run for 712.5 seconds after four dummy scans (10 seconds) and introduction (10 seconds). A total of 289 volumes of data were collected for analysis.

Image acquisition

Post-scanning test

The fMRI experiments were performed with a 3 T General Electric MR scanner (Sigma VH/I, software: version 4.0, General Electric Company, Fairfield, CT, USA). Liquid crystal display goggles were placed over the eyes of each participant after fixing the head inside a head coil. The MR

The subjects were asked to complete PGU and QGU-B to recall their gaming urge and craving under gaming cue exposure. The introduction of PGU and QGU-B was revised as ‘Please response to these questions according to the subjective feeling when you viewed the gaming

METHODS AND MATERIALS Participants



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Chih-Hung Ko et al.

screenshots in scan’. Another original PGU was also completed to demonstrate the gaming urge after scanning. Data analysis All time series data exported from the GE system were converted to statistical parametric mapping (SPM) format using MRIcro (Rorden & Brett 2000). The subsequent image preprocessing and statistical analysis was performed using spm5 package (Wellcome Department of Cognitive Neurology, London, UK). Each image was realigned for motion correction. The realigned datasets were normalized to Montreal Neurological Institute (MNI) space. An 8-mm full-width-half-maximum Gaussian kernel was used for data smoothing. Statistical analysis was conducted with spm5. For each participant, the t values computed for maps of contrast (Gaming–Neutral) activations were computed, and the t values were normalized to Z scores. The contrast (Gaming–Neutral) for individual subjects was combined into contrast in group for each of the IGA, remission and control groups. The activations for the ‘Gaming–Neutral’ contrast were then demonstrated for each group by one sample t-test (one-tailed) in the first-level analysis of spm5 with threshold P < 0.005 with false discovery rate (FDR) correction and cluster size > 20 voxels. Group differences for ‘Gaming–Neutral’ were assessed by second-level analysis of individual contrast images. Differences between the IGA, remission and control groups for the ‘Gaming–Neutral’ contrast were analyzed by two sample t-tests with threshold P < 0.001 and cluster size > 10 voxels. After using a linear algorithm to convert MNIs to Talairach coordinates (Talairach & Tournoux 1998),

Brodmann’s areas of significant brain activations were identified by the Talairach Daemon (Lancaster et al. 2000). For region-of-interest (ROI) analysis, activations for the ‘Gaming–Neutral’ contrast were calculated by MarsBaR (http://marsbar.sourceforge.net/) (Brett et al. 2002). Person’s correlation was used to test ROIs for correlations with PGU, and QGU-B under gaming cue exposure and CIAS. A P value of less than 0.05 was considered significant. The behavior data were analyzed by non-parametric analysis such as Kruskal–Wallis test and Wilcoxon signed ranks test. A P value of less than 0.05 was considered significant. The P value for significance in post hoc analysis was corrected to be 0.017.

RESULTS The final analysis of those who had completed all evaluations included 15 subjects in the IGA, remission and control groups. The three groups did not significantly differ in age or education level (Table 1). For the CIAS score, craving to play online games (assessed with QGU-B) before scanning or under gaming cue exposure, and gaming urge (assessed by PGU) before or after scanning or under gaming cue exposure, the IGA group was higher than the remission group, and the remission group was higher than the control group (Table 1). Lastly, Wilcoxon signed ranks test for the score of PGU in IGA group demonstrated that the gaming urge was significantly higher when viewing the gaming screenshots than that before scanning (Z = 2.51, P = 0.01). However,

Table 1 Demographic data and results of behavior assessment for the Internet gaming addiction (IGA) group, remission (R) group and control (C) group. c2a

Post hocb

Variables

IGA group

Remission group

Control group

Age Education level

24.67 ⫾ 3.11 15.47 ⫾ 1.56

24.80 ⫾ 2.68 15.87 ⫾ 1.41

24.47 ⫾ 2.83 16.00 ⫾ 1.13

0.18 0.14

Gaming urge: visual analog scale of perceived gaming urge Pre-scan 6.27 ⫾ 2.34 1.20 ⫾ 1.32 Under gaming cue 7.60 ⫾ 2.10 2.20 ⫾ 2.43 Z–1.93 (P = 0.05)c Z = 2.51c,* Post-scan 6.60 ⫾ 2.61 1.40 ⫾ 1.99

0.07 ⫾ 0.26 0.00 ⫾ 0.00 Z = 1.0c 0.00 ⫾ 0.00

34.12*** 34.63***

IGA>R >C IGA>R >C

33.68***

IGA>R >C

Gaming craving: questionnaire on gaming urge brief Pre-scan 44.27 ⫾ 15.48 15.60 ⫾ 6.65 Under gaming cue 46.13 ⫾ 17.08 16.87 ⫾ 7.39 Z = 1.38d Z = 1.14d CIAS 76.00 ⫾ 12.09 52.33 ⫾ 13.91

10.00 ⫾ 0.00 10.00 ⫾ 0.00 Z = 0d 26.00 ⫾ 0.00

35.32*** 33.10***

IGA>R >C IGA>R >C

37.35***

IGA>R >C

*P < 0.05, ***P < 0.001. ac2 = non-parametric analysis with Kruskal–Wallis test. bPost hoc = the P value for significance in post hoc analysis is corrected to be 0.017. cZ = the result of Wilcoxon signed ranks test for difference between gaming urge before scanning and that under gaming cue exposure. d Z = the result of Wilcoxon signed ranks test for difference between gaming craving before scanning and that under gaming cue exposure. C = control group; CIAS = Chen Internet addiction scale; IGA = Internet gaming addiction group; Post-scan = after functional magnetic resonance scanning; Pre-scan = before functional magnetic resonance scanning; R = remission group. © 2011 The Authors, Addiction Biology © 2011 Society for the Study of Addiction


Online gaming craving Table 2 The first-level analysisa of brain areas activated in response to gaming cues in Internet gaming addiction (IGA) group, remission groupb and control group [P < 0.005 with false discovery rate (FDR) correction, voxels > 20].

563

IGA group

Talairach coordinates

Activated region

Lc/Rd

BAe

X

Y

Z

Voxels

Z

Occipital lobe, lingual gyrus Precuneus Superior parietal lobule Posterior cingulate Precuneus Parahippocampus Thalamus Subthalamic nucleus DLPFCg Anterior cingulate Thalamus, ventral lateral nucleus DLPFC Posterior cingulate

Bf R L L L L L R L Bf L R R

17 7 7 30 7 19

-18 30 -24 -10 -14 -28 -8 8 -42 4 -16 46 6

-87 -74 -64 -54 -64 -49 -6 -12 13 -1 -17 28 -37

-1 30 44 10 50 -6 0 -3 27 28 14 15 31

15577

5.35 4.71 4.21 3.50 3.82 3.30 4.18 3.54 4.18 3.84 3.77 3.64 3.51

Remission group


Activated region

L/R

BAa

X

Y

Z

Voxels

Z

Sub-lobar, lentiform nucleus Occipital lobe, cuneus Superior parietal lobule DLPFC DLPFC

L R L R L

-26 22 -24 38 -38

-9 -79 -64 9 5

-6 9 46 33 29

18139

17 7 9 9

5.34 5.26 6.67 4.34 3.63

Control group


Activated region

L/R

BAa

X

Y

Z

Voxels

Z

Inferior occipital gyrus DLPFC Middle frontal gyrus Inferior frontal gyrus Frontal lobe, precentral gyrus DLPFC Middle frontal gyrus DLPFC Limbic lobe, uncus

R R R L R R R L L

18 9 8 47 6 46 11 9

28 40 53 -30 40 55 28 -38 -24

-87 5 15 30 -7 27 32 9 -1

-1 27 38 -18 56 26 -15 29 -24

20034 569

6.19 4.50 3.95 4.35 4.07 3.99 3.94 3.69 3.67

9 24 46 31

149 212 89 117 101 23

338 190 165

274 72 177 58 51 24

Note: Z score values are depicted, representing a P value with a threshold of 0.005 with FDR correction. The number of voxels in a cluster of contiguous 3.75 ¥ 3.75 ¥ 3 mm voxels is depicted, with a cluster size threshold of 20 voxels. Boldface entries represent local maximal within a cluster. a The first-level analysis = one sample t-test for contrast between blood-oxygen-level dependence signal of responsiveness to gaming picture and mosaic picture with event-related design. bRemission group = Subjects with history of Internet gaming addiction have not fulfilled the criteria of Internet gaming addiction now. cR = the activation area was on the right side. dL = the activation area was on the left side. eBrodmann’s area. fB = the activation area was located on both sides. g DLPFC = dorsolateral prefrontal cortex.

the difference between the score of QGU-B under gaming cue exposure and that before scanning was not significant (Z = 1.14, P = 0.25). There is no significant difference between gaming urge or craving under gaming cue exposure and that before scanning among both remission and control group. Yet, the gaming urge under gaming has a trend of going higher than that before scanning among remission group (Z = 1.93, P = 0.05).

Table 2 and Fig. 1 present the results of the first-level analysis. Analysis of the IGA group showed that compared with activation in response to neutral cues, activation in response to gaming cues was higher over bilateral occipital lobe, DLPFC (BA 9, BA 46), anterior cingulate (BA24), posterior cingulate (BA 30 and 31), and precuneus (BA7), right subthalamic nucleus, and left parahippocampus (BA19), superior parietal lobe (BA7),



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Chih-Hung Ko et al.

Figure 1 The brain activation for ‘Gaming–Neutral’ contrast in Internet gaming addiction (IGA), remission and control groups. [Significant threshold: P < 0.005 with false discovery rate (FDR) correction, voxels > 20.] Legends: for each participant, the t values computed for maps of contrast (Gaming–Neutral) activations were computed, and the t values were normalized to Z scores.The contrast (Gaming– Neutral) for individual subjects were combined into contrast in group for each of the IGA, remission and control groups. The activations for the ‘Gaming–Neutral’ contrast were then demonstrated for each group by one sample t-test (one-tailed) in the first-level analysis of SPM5 with threshold P < 0.005 with FDR correction and cluster size > 20 voxels

thalamus and its ventral lateral nucleus. The results for the second-level analysis (Table 3, Fig. 2) revealed that compared with the control group, the IGA group had higher activation over bilateral DLPFC (BA9; BA 45), precuneus (BA7, 19), and occipital lobe (BA 19), right anterior cerebellum, and anterior cingulate (BA 24), and left posterior cingulate (BA 31), and parahippocampus (BA 19) response to gaming cues (for the ‘Gaming–Neutral’

contrast). Bilateral DLPFC, precuneus, left posterior cingulate, parahippocampus and right anterior cingulate activate under gaming cue exposure among IGA group and activate higher among IGA group than control group. These brain areas were suggested to be associated with gaming craving under cue exposure. The ROI of bilateral DLPFC and right precuneus was significantly positively associated with both scores of PGU



Online gaming craving Table 3 Second-level analysisa of brain areas activated by cue-induced craving to play online games: Internet gaming addiction (IGA) group versus control group, remissionb group versus control group and IGA group versus remission group (P < 0.001, voxels > 10).

565

IGA group–control group


Activated region

Lc/Rd

BAe

X

Y

Z

Voxels

Z

Occipital lobe, cuneus Parietal lobe, precuneus Superior occipital gyrus DLPFCf Posterior cingulate Anterior cingulate Cerebellum, anterior lobe, culmen DLPFC Parietal lobe, precuneus Parahippocampus

L R R L L R R R L L

19 19 19 9 30 24

-28 32 38 -46 -10 4 -10 44 -16 -28

-86 -72 -80 9 -56 -1 -49 24 -64 -49

37 33 32 27 10 26 3 15 49 -6

144 277

3.93 3.83 3.59 3.71 3.49 3.47 3.39 3.35 3.33 3.32

Remission group–control group


Activated region

L/R

BAa

X

Y

Z

Voxels

Z

Superior parietal lobule,

L

7

-28

-63

53

18

3.35

IGA group–Remission group


Activated region

L/R

BAa

X

Y

Z

Voxels

Z

DLPFC Middle temporal gyrus Parahippocampus

R L L

46 39 19

46 -34 -36

32 -67 -51

15 25 -4

26 31 18

3.42 3.42 3.36

45 7 19

57 90 21 42 17 21 20

Note: Z score values are depicted, representing an uncorrected P value with a threshold of 0.001. The number of voxels in a cluster of contiguous 3.75 ¥ 3.75 ¥ 3 mm voxels is depicted, with a cluster size threshold of 10 voxels. Boldface entries represent local maximal within a cluster. aThe second-level analysis = two sample t-test for groups differences for ‘Gaming–Neutral’ contrast. b Remission group = subjects with history of Internet gaming addiction have not fulfilled the criteria of Internet gaming addiction now. cR = the activation area was on the right side. dL = the activation area was on the left side. eBrodmann’s area. fDLPFC = dorsolateral prefrontal cortex.

and QGU-B under gaming cue exposure. Furthermore, the ROI of left parahippocampus was positively correlated with the score of PGU under gaming cue exposure. Lastly, the CIAS score was positively associated with the ROI of left posterior cingulate, right anterior cingulate and bilateral DLPFC (Table 4). The results of the first-level analysis (Table 2 and Fig. 1) showed that the remission group had higher activation over the bilateral DLPFC, left lenticular nucleus, superior parietal lobe, and right occipital lobe in response to gaming cues in comparison with the response to neutral images. Furthermore, the second-level analysis of the remission group revealed higher activation of left superior parietal lobe (BA7) in response to gaming cues (for the ‘Gaming–Neutral’ contrast) compared with the control group (Table 3 and Fig. 2). Thus, left superior parietal lobe activated for gaming cue among the remission group and activated higher among the remission group than it did among the control group. Finally, the results of the second-level analysis (Table 3 and Fig. 2) showed that compared with the

remission group, the IGA group had higher activation over the right DLPFC (BA46), left parahippocampus (BA 19) and left middle temporal gyrus (BA 39).

DISCUSSION The current event-related study showed that bilateral DLPFC, precuneus, left parahippocampus, posterior cingulate and right anterior cingulate were activated in response to gaming cues in the IGA group and their activations were stronger in the IGA group than those in the control group. Furthermore, the ROIs of DLPFC and the right precuneus were associated with the gaming craving, gaming urge and CIAS score. The ROI of the left parahippocampus was associated with gaming urge. The ROIs of the posterior cingulate and anterior cingulate were associated with the severity of Internet addiction. These results suggest that bilateral DLPFC, precuneus, left parahippocampus, posterior cingulate and right anterior cingulate are associated with the mechanism of gaming craving under cue exposure.



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Chih-Hung Ko et al.

Figure 2 The difference in brain activation for ‘Gaming–Neutral’ contrast within internet gaming addiction (IGA), remission and control groups (significant threshold: P < 0.001, voxels > 10). Legends: differences between the IGA, remission and control groups for the ‘Gaming–Neutral’ contrast were analyzed by two sample t-tests with threshold P < 0.001 and cluster size > 10 voxels

Moreover, the remission group scored a lower score on gaming urge and craving than the IGA group. In this study, the IGA group had higher activation over right DLPFC, left parahippocampus and left middle temporal gyrus under gaming cue exposure than the remission group. These activated areas are the candidate brain activation markers for addiction state to online gaming. Thus, the more robust result of this event-related study is that DLPFC and the parahippocampus are involved not only in gaming craving under cue exposure but also possibly represent brain activation corresponding to current addiction state to online gaming. DLPFC (Skinner & Aubin 2010) and parahippocampus (Smolka et al. 2006; Park et al. 2007) have been repeatedly reported to be associated with cue-induced craving in substance use disorder. Furthermore, both DLPFC and the parahippocampus have also been reported to activate cue reactivity for pathological gambling and Internet addiction (Crockford et al. 2005; Han et al. 2010). The parahippocampus receives input from the nucleus accumbens and amygdala, evaluate the behavioral significance of

sensory information (Salzmann, Vidyasagar & Creutzfeldt 1993) and contribute to automatic emotion process (Lorberbaum et al. 2004). It also provides a contextual representation function and is an important afferent pathway to hippocampus (Rudy 2009). Thus, it might play a role in the recollection of a previous gaming experience, especially the emotional experience elicited by gaming screenshots. When the gaming cue was exposure, our results might support that the hippocampus processes the emotional significance of gaming cue and represents previous contextual emotion memory. This function of integrating context representation and emotional significance might contribute then to motivate the desire for online gaming. This might explain why its ROI was correlated with gaming urge under cue exposure. The DLPFC evaluates and integrates emotionalrelated information from sensory input and affective information from amygdala and nucleus accumbens (Skinner & Aubin 2010). Furthermore, it also contributes to decision making based on emotional information such as rewarding (Mitchell 2011). Thus, it connects with




Table 4 Associations between regions-of-interest in significantly activated areas and gaming urge (GU) and craving (GC) to play online games under gaming cue exposure, and score on Chen internet addiction scale (CIAS) among all subjects. All subjects The ROI (L/R; BA; X,Y,Z)

GC

GU

CIAS

DLPFC (BA9:L; -42,13,27) DLPFC (BA9:L; -46,9,27) DLPFC (BA45:R; 44,24,15) DLPFC (BA46:R; 46,28,15) Parahippocampus (BA19:L; -28,-49,-6) Parahippocampus (BA19:L; -36,-51,-4) Precuneus (BA7:R; 32,-72,33) Posterior cingulate (BA30:L; -10,-56,10) Anterior cingulate (BA24:R; 4,-1,26)

0.37* 0.35* 0.39** 0.41** 0.16

0.39** 0.39** 0.44** 0.42** 0.30*

0.39** 0.37* 0.40** 0.37* 0.23

0.24

0.30*

0.16

0.35*

0.32*

0.36*

0.13

0.28

0.33*

0.20

0.28

0.31*

*P < 0.05, **P < 0.01. DLPFC = dorsolateral prefrontal cortex; GC = gaming craving assessed by questionnaire on gaming urge brief; GU = gaming urge assessed by visual analog scale of perceived gaming urge.

other cortical areas and serves to link the present sensory experience to memory of past experiences to direct and generate appropriate goal-directed action (GoldmanRakic & Leung 2002). As DLPFC plays a more complex role in different dimensions such as emotion, memory and decision of craving, its ROIs were associated not only with gaming urge but also gaming craving and severity of Internet addiction. Thus, in line with our previous reports (Ko et al. 2009a), this result supports that DLPFC participates in making plan to get online for gaming when expose to gaming cue. The precuneus is associated with visual imagery, attention and memory retrievals. It participates in the visual process and integrates the related memory. (Cavanna & Trimble 2006) In this study, it activated high for gaming cue reactivity among IGA group and its ROI was correlated with gaming urge, craving and severity of Internet addiction. This result suggests that the precuneus activates to process the gaming cue, integrate retrieved memory and contribute to cue-induced craving for online gaming. The posterior cingulate has a prominent role in the processing of emotionally salient stimuli and the modulation of memory by emotionally arousing stimuli (Maddock 1999). Furthermore, it also integrates the reward outcome and motivational information of visual stimuli (Pearson et al. 2011). Thus, our result suggests that posterior cingulate might be involved in processing the emotional significance of gaming cues and provide their rewarding significance.

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The anterior cingulate contributes to functional brain activation associated with craving for alcohol and cocaine (Kilts et al. 2001; Heinz et al. 2009). It participates in attention and memory processes by encoding the motivational value of stimuli (Heinz et al. 2009). Anterior cingulate is also associated with salience to emotional, motivational information and regulatory control over reward-seeking behavior (Chiamulera 2005; Risinger et al. 2005). Once appetitive cues are identified, the anterior cingulate contributes to whether behavior response is emitted and the intensity of such response (Kalivas & Volkow 2005). Thus, our result suggests that when the emotional response is activated under gaming cues exposure, the anterior cingulate might evaluate the reward significance and involve in determining the intensity of desire for online gaming. Thus, our results support our hypothesis according to addiction model proposed by Volkow et al. (2010). The hypothesis suggests that when subjects with IGA view a gaming cue, the visual information might be processed by precuneus and linked to previous memory. The emotional significance of gaming cue based on gaming experience would be provided by parahippocampus in memory system. Then, posterior cingulate in reward system may integrate the motivational information (pleasure in gaming) and provide expectation and reward significance for gaming behaviors (to get satisfaction in gaming). After that, anterior cingulate in motivation system may determine the intensity of motivation to gaming (wanting online gaming). Lastly, the intense desire will drive the DLPFC to make a plan for online gaming and the craving for online gaming was generated. Thus, our results would support the addiction model for substance use disorder, which could also be used to understand the mechanism of IGA. The severity of Internet addiction and craving to play online games was less intense in the remission group than in the case group. However, they also had higher gaming urge under cue exposure than did the control group. Despite non-significance, they also had a trend of having higher gaming urge under cue exposure than that before scanning, with P value = 0.05. This outcome supports the incentive sensitization theory (Robinson & Berridge 2008) in Internet gaming addiction, which suggests that submitted IGA case persists to attribute incentive salience to gaming cue even after long-term abstinence. Further analysis for brain activation demonstrates higher activation over the superior parietal cortex among the remission group than those of the control group. The superior parietal cortex has been reported to activate for cue activity among smokers (McClernon et al. 2009). It also contributes to visual attention (Salmi et al. 2007) and attention bias to smoking cue (Luijten et al. 2011). Thus, the gaming cue might still be highly attractive for



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the remission group and provoke higher visual attention over the superior parietal lobe. Furthermore, the superior parietal lobe also activated for smoking cue among subjects with long abstinence (Janes et al. 2009). Thus, its activity for cue might represent the remitted state for both substance and behavior addiction. However, its role on relapse cannot be confirmed in this present study and needs to be investigated by a follow-up study. Several limitations of this study are noted. First, only men were analyzed. Second, because this study excluded subjects with multiple addictions and those with major psychiatric disorders, the results should be generalized cautiously to these groups. Third, the number of subjects was limited because of the difficulty in recruiting subjects for the remission group. Fourth, we did not use any other task to monitor the attention of subjects to the pictures in scanning because it might disturb brain reactivity to gaming cue. Thus, we could not confirm whether they had kept attention toward the picture.

CONCLUSION The event-related design of fMRI study has demonstrated that bilateral DLPFC, precuneus, left parahippocampus, posterior cingulate and right anterior cingulate were the brain correlates of gaming urge under cue exposure. This activated brain circuit represents the model for substance use disorder proposed by Volkow et al. (2010). Furthermore, DLPFC and parahippocampus were the possible brain activation markers for a current addiction state for online gaming. In the remission group, their activations were higher on the left superior parietal lobe. This present study would suggest that IGA might share a similar mechanism on cue-induced craving response with substance use disorder. Acknowledgement This study was supported by a grant from the National Science Council of Taiwan (NSC96-2413-H-037–003MY2). Authors Contribution CHK was responsible for the study concept and design, and drafted the manuscript. GCL supervised the study. CFY and CSC contributed to recruiting the subjects. JYY and CYC assisted with data analysis and interpretation of findings. All authors critically reviewed the content and approved the final version for publication. References American Psychiatric Assn (2000) Diagnostic and Statistical Manual of Mental Disorders, 4th edn. Text Revision. Washington, DC: American Psychiatric Association.

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