Psychiatry and Clinical Neurosciences 2014
doi:10.1111/pcn.12224
Regular Article
Brain correlates of response inhibition in Internet gaming disorder Chiao-Yun Chen, MD,1,3 Mei-Feng Huang, MD,2 Ju-Yu Yen, MD, PhD,2,4,6 Cheng-Sheng Chen, MD, PhD,2,4 Gin-Chung Liu, MD,1,3 Cheng-Fang Yen, MD, PhD2,4 and Chih-Hung Ko, MD, PhD2,4,5* Departments of 1Medical Imaging and 2Psychiatry, Kaohsiung Medical University Hospital, Departments of 3Radiology and 4 Psychiatry, Faculty of Medicine, College of Medicine, 5Department of Psychiatry, Kaohsiung Municipal Hsiao-Kang Hospital, Kaohsiung Medical University and 6Department of Psychiatry, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung, Taiwan
Aims: The present study aimed to evaluate the brain correlates of response inhibition among subjects with Internet gaming disorder (IGD). Methods: For this purpose, 15 men with IGD for at least 1 year, and 15 controls with no history of IGD were recruited to perform the Go/Nogo task under functional magnetic resonance imaging investigation. Prior to scanning, the subjects were assessed using the Chen Internet Addiction Scale and the Barrett Impulsivity Scale.
prefrontal cortex, and caudate for response inhibition. However, the IGD group had a higher impulsivity and lower activity of the right SMA/pre-SMA in comparison to the control group.
Conclusions: The results obtained suggest that dysfunctional activation of the SMA for response inhibition is one of the candidate mechanisms of IGD. Key words: impulsivity, Internet gaming disorder, response inhibition, supplement motor area.
Results: The control group exhibited activation of the right supplement motor area (SMA), dorsolateral
HE INTERNET IS a major communication medium in modern life. However, loss of control regarding Internet use has resulted in negative psychosocial consequences.1 With the growth in popularity of the Internet worldwide, Internet addiction has become prevalent not only in Western but also in Eastern societies.2 Internet gaming is a popular online activity that has been reported to predict the risk of Internet addiction.3 Internet gaming disorder (IGD) is one of the more popular subtypes (57.5%) of Internet addiction in college students.4 There are behavioral similarities of Internet gaming, such as loss of
T
*Correspondence: Chih-Hung Ko, MD, PhD, Department of Psychiatry, Kaohsiung Medical University Hospital, 100 Tzyou 1stRoad, Kaohsiung City 807, Taiwan. Email: [email protected] Received 14 March 2014; revised 11 June 2014; accepted 14 July 2014.
control, withdrawal, and persisting despite negative consequences, with gambling disorder or substance use disorder, such as preoccupation, tolerance, and withdrawal. As Internet gaming is of significant public importance, IGD was classified into section III, conditions for future study, of the DSM-5.5 It is necessary to clarify the mechanisms of IGD in order to provide adequate evidence to support the theory that Internet gaming disorder has merit as an independent disorder. The proposed DSM-5 suggested nine criteria for IGD as follows: (i) preoccupation; (ii) withdrawal symptoms; (iii) tolerance; (iv) unsuccessful control; (v) impaired decision-making; (vi) rewarding deficit; (vii) escape; (viii) deceit about Internet gaming; and (ix) impaired function. These criteria show a similarity in symptoms between IGD and other addictive behavior, such as substance use disorder or gambling disorder.5 However, the general characteristics of the
© 2014 The Authors Psychiatry and Clinical Neurosciences © 2014 Japanese Society of Psychiatry and Neurology
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Internet, such as anonymity, high availability, or information and efficiency,6 might contribute to addiction to online games, but not to other addictive behavior or to off-line games. Further, there are factors, such as motivation factors or structure characteristics of individual Internet activities,7 associated with addiction to specific Internet activities. Thus, there is a significant differerece between generlized Internet addiciton and IGD.8 It would suggest that IGD should be diagnosed as an independent disorder to be further researched.9,10
Internet gaming disorder and impulsivity Subjects with IGD may persist in online gaming in spite of awareness that the habit is directly harmful to life functioning. Further, subjects may also attempt, but fail, to control online gaming. It has been suggested that loss of control regarding Internet use is an important core symptom of IGD that requires greater understanding. Impulsivity is one of the most important factors resulting in loss of control of addictive behavior.11 Previous studies of adolescents and adults have reported higher impulsivity in subjects with Internet addiction.12,13 Further, impulsivity was also noted to predict Internet use disorder in a longitudinal study.14,15 Neuropsychological study has established that adolescents with IGD have an increased error rate on the Go/Nogo task.12 These results support an association between impulsivity and IGD. Brain-imaging studies of addictive behaviors have identified a key involvement of the prefrontal cortex (PFC) through its regulation of the limbic reward regions as well as its involvement in a higher-order executive function.16 Further, a weakened inhibitory control of the fronto-striatal circuit is one of the core mechanisms of addictive behavior.17 The weaker connectivity between putamen and insula, anterior cingulate, and medical prefrontal lobe had been demonstrated to represent the impairment of selfcontrol in alcohol dependence.18 These studies suggest that a loss of control of addictive behavior can be explained by a deficit in inhibitory control over a response that provides immediate reinforcement. Thus, we expect that the deficit in inhibitory control may play a similar role involving the mechanism of IGD. A deficit in suppressing prepotent motor responses is one of the important dimensions of impulsivity and has been repeatedly tested with the Go/Nogo task to represent response inhibition.19 The Go/Nogo
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task requires subjects to press a button as quickly as possible when observing Go stimuli and to refrain from doing so when observing Nogo stimuli. Response inhibition in the Go/Nogo task has been well evaluated by functional magnetic resonance imaging (fMRI) studies to represent the role of the frontostriatal circuit, such as the cingulate, supplementary motor area, and basal ganglion, in the mechanism of response inhibition.20,21 It has been used to evaluate impulsivity in substance use disorder and demonstrated impaired activation over insula, orbital frontal lobe, and anterior cingulate.22,23 Thus, we expect that deficit in frontostriatal circuit might contribute to the impaired response inhibition among subjects with IGD. The present study aimed to evaluate the brain correlates of response inhibition among subjects with IGD. Thus, the brain activations for response inhibition in the Go/Nogo task were investigated with fMRI among subjects with IGD, and control subjects.
METHODS Participants In the present study, right-handed participants were recruited by an advertisement posted on campus. As male subjects are more likely to be addicted to online gaming, this study recruited only male subjects.24 All the subjects in the IGD group were interviewed by a psychiatrist to confirm the diagnosis of IGD for more than 1 year in accordance with the diagnostic criteria for Internet addiction (DCIA).25 Six or more criteria should be fulfilled of the nine criteria A of the DCIA. Further, criteria B, related to functional impairment, and criteria C, which are exclusion criteria, should also be fulfilled. The subjects were currently addicted to the same online game, World of Warcraft, and had spent an average of 4 or more h/day on weekdays and an average of 8 or more h/day on weekends on online gaming. In the control group, psychiatric interviews and reviews of history were performed to confirm that none of the participants had ever fulfilled the criteria of IGD. The exclusion criteria included: 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 to MRI. After receiving a detailed explanation of the study, all subjects provided written informed consent. A total
© 2014 The Authors Psychiatry and Clinical Neurosciences © 2014 Japanese Society of Psychiatry and Neurology
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of 15 subjects with IGD and 15 controls were recruited in this study. Two subjects of IGD had nicotine dependence. All participants had no other concurrent substance use disorder. The study was approved by the Kaohsiung Medical University Institutional Review Board.
Image acquisition The fMRI experiments were performed using a 3 Tesla General Electric MR scanner (Signa VH/I 4.0, General Electric, New York, NY, USA). The magnetic resonance (MR) sequence for functional imaging was a gradient-recalled echo planar imaging (EPI) sequence (64 × 64 matrix; 24-cm field of view, echo time [TE] = 35 ms; repetition time [TR] = 2.5 s; 3-mmthick slices with a 0-mm gap). Thirty-five image planes were collected parallel to the anterior commissure and posterior commissure (AC–PC) line with the aid of sagittal localizer images. Head motion was corrected by post-processing using SPM5 (Wellcome Department of Cognitive Neurology, London, UK).
Process All the invited participants were interviewed by a psychiatrist to confirm the diagnosis of IGD and screened for the exclusion criteria based on the Mini International Neuropsychiatric Interview.26 Prior to scanning, all subjects completed the Chen Internet Addiction Scale (CIAS)25,27 and the Barrett Impulsivity Scale 11 (BIS-11).28,29 The CIAS indicates the severity of IGD, while the BIS-11 indicates the severity of impulsivity. Subsequently, functional MR images were acquired during the Go/Nogo task using a block design applied in an earlier study, with some modifications as mentioned below.30
Behavior task The Go/Nogo task had two conditions: In the Go condition (Block A), a numeral from 1 to 5 was shown 20 times in a white font on a black background. In the Nogo condition (Block B), a nontarget (number 0) was shown 10 times, and a target (a numeral from 1 to 5) was shown 10 times in a pseudorandom sequence. In the introduction section, the participants were told to press the button as quickly as possible for all numbers except for the number 0. The duration of the number presentation was 0.2 s and the inter-trial interval was 1.3 s. Within
Response inhibition of IGD 3
each block of 30 s, trials were presented in a pseudorandomized sequence. Block sections were presented in the order ABABABABABAB, which resulted in a total of 240 trials in each section. The performance on the Go/Nogo task was represented by the sum of the correct responses for the Nogo trial, successful holding of button pushing. Prior to the fMRI scan, all participants had practiced the Go/Nogo task in order to become familiar with the task.
Data analysis All the time series data exported from the GE system were converted into the SPM format using SPM DICOM import. Next, image preprocessing and statistical analysis were performed using SPM5. Each image was realigned for motion correction. Each structural image was co-registered to the mean motioncorrected functional image for each participant. The realigned datasets were normalized to Montreal Neurological Institute space and sliced again at a 2 × 2 × 2-mm3 voxel size. An 8-mm full-width-halfmaximum Gaussian kernel was used to smooth the data. One image per contrast was computed for each participant, whereby the data were subjected to a high pass filter of 128 s with no global scaling. For the statistical analysis, the Nogo block versus the Go block (Nogo block – Go block) variables were modeled as the explanatory variables within the context of a general linear model on a voxel-by-voxel basis with first level analysis using SPM5. Voxel-wise t-statistics were computed and normalized to Z scores to provide a statistical measure of activation. Z-maps of activation for the contrast (Nogo-Go) were computed for each participant. For group level analysis, the contrasts (Nogo-Go) of the individual subjects were combined into a group analysis. After including the contrasts of all the participants in the IGD or control groups, the activations for the ‘Nogo-Go’ contrast were demonstrated for each group by one sample t-test with a threshold of P < 0.001 uncorrected at the voxel level in combination with a cluster-extent threshold of contiguously significant voxels that permits inference of significant clusters at a level of P < 0.05 family-wise error (FWE) corrected. The differences between the IGD and control groups were determined using two-sample t-test with a threshold of P < 0.001 uncorrected at the voxel level in combination with a level of P < 0.05 with FWE small volume correction in regions of interest (ROI).
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ROI: These predefined ROI within a binary mask were created from the Anatomical Automatic Labeling (AAL) library as found in WFU Pickatlas 2.4 (Wake Forest University, NC, USA).31 The brain areas of the AAL library were selected based on significant brain activation areas for response inhibition in the control.
group. The IGD group exhibited activation of the right insula for response inhibition on the voxel level, but FWE was not significant. The control group showed significant activation of the right SMA, dorsolateral prefrontal cortex (DLPFC), and insula for response inhibition on the cluster level.
Comparison of IGD and control groups by two-sample t-test
RESULTS Results of the behavior task The demographic data in Table 1 indicate that the IGD and control groups did not differ significantly in terms of either age or education level. The IGD group had higher scores on the CIAS in comparison to the control group. Further, the IGD group had higher scores on the BIS-11 in comparison to the control group. In the behavior task, there were no significant differences in the performance of Go/Nogo task (successfully inhibited responses in Nogo trails) or reaction time between the IGD group and the control group. In addition, the score on the BIS-11 was positively correlated with the CIAS score (r = 0.33, P = 0.03) and negatively correlated to the performance of Go/Nogo task (r = −0.54, P < 0.001).
Activation of response inhibition among the IGD and control groups by one-sample t-test Table 2 and Figure 1 depict the results of the onesample t-test between the IGD group and the control
Based on significant activation areas in the control group, the right SMA, DLPFC, and insula from the AAL library were selected as ROI for small volume correction in between-group comparisons. The results shown in Table 2 and Figure 1 demonstrate that the control group exhibited significantly higher activation of the right SMA/preSMA in comparison to the IGD group (FWE corrected P = 0.014 in small volume correction in SMA). There was no higher activation for the IGD group in comparison to the control.
DISCUSSION Deficit in the frontal striatal circuit for response inhibition among the IGD group in comparison to the control group CIAS score was positively correlated with BIS-11 score, an indicator of impulsivity. The higher impulsivity score observed in the IGD group in comparison
Table 1. Age, educational level, performance on the Go/Nogo task, severity of IGD, and impulsivity of the IGD and control groups Variable
IGD group† Mean ± SD
Control group Mean ± SD
Z
Age Educational level Go/Nogo performance‡ Reaction time IGD severity§ Impulsivity¶
24.67 ± 3.12 15.47 ± 1.55 53.67 ± 7.55/60 0.38 ± 0.06 76.00 ± 12.09 74.33 ± 6.56
24.47 ± 2.83 16.00 ± 1.13 56.13 ± 3.81/60 0.36 ± 0.04 26.00 ± 0.00 62.00 ± 9.66
0.084 1.039 0.53 0.64 4.99*** 3.84***
***P < 0.001. Z: Wilcoxon rank sum test. † IGD: subjects with IGD for more than one year. ‡ Performance of Go/Nogo task: the corrected response in Go/Nogo task. ¶ Impulsivity: Score of Barrett Impulsivity Scale 11. § Severity of IGD, Score of Chen Internet Addiction Scale. IGD, Internet gaming disorder.
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Table 2. Brain areas activated for response inhibition† in the IGD and control groups Region of activation
MNI coordinates
IGD group Insula Control group SMA SMA Middle frontal lobe Middle frontal lobe Middle frontal lobe Insula Insula Control group – IGD group SMA/Superior medial frontal lobe (preSMA) IGD group – control group No activation
‡
R/L R
BA
X 36
Y 16
Z −6
Voxels 126
Z§ 3.47
P 0.000
P¶ 0.781
R R R R R R R
0/0 32/32 46/46 46/46 46/46 47/47 0/0
4 10 36 34 38 36 46
24 10 40 46 34 18 18
50 48 22 14 30 −8 −10
773
5.69 3.71 4.37 4.19 3.94 3.97 3.89
0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000
R
8/8
6
24
52
319
401
254
5.15
0.000
0.020
0.008 P§§ 0.014
†
BOLD signal subtracting the ‘Go’ block from the ‘Nogo block’. The activation area was on the right (R) or left (L) side. § Z score values are depicted, representing a P-value with a threshold of 0.05 with small volume correction. The number of voxels in a cluster of contiguous 2 × 2 × 2 mm voxels is depicted, with a cluster size threshold of 20 voxels. ¶ P: Family-wise-error-corrected P-value at the cluster level. §§ P: small volume FWE corrected P-value in the ROI. BA, Brodmann’s area; IGD, Internet gaming disorder; MNI, Montreal Neurological Institute; SMA, supplement motor area. ‡
with the controls indicated a significant deficiency in impulse control in the IGD group. The impulsivity score is also negatively correlated with the performance of Go/Nogo task in this study. The IGD group had poorer performance of Go/Nogo task than the control group, but this was not significant. This result is in line with previous reports of fMRI study of inhibitory control in IGD.32 However, the IGD and control group responded correctly to more than 85% of the Nogo trials. This revealed that all the subjects had executed the Go/Nogo task as guided. The fronto-striatal circuit has been suggested to contribute to the function of response inhibition.33 The results obtained from the control group supported this hypothesis and demonstrated significant activation of the supplement motor area (SMA), DLPFC, orbital frontal cortex, and insula. However, none of the above-mentioned areas was significantly activated in the IGD group. Further analysis revealed that the IGD group had lower activation of the SMA/preSMA, a member of the fronto-striatal network, for response inhibition in comparison to the control group. Lesion study,34,35 fMRI study,36 and transcranial magnetic stimulation37
have all demonstrated that the SMA is important for response inhibition. It has been suggested that the preSMA circuits are critical for the selection of appropriate behavior, including selecting to engage appropriate motor responses as well as selecting to withhold inappropriate motor responses.38 In the present study, the brain activation deficit over the SMA/preSMA might indicate impairment of inhibitory and selective control over behavior among the subjects with IGD. This might explain why subjects with IGD persisted in heavy Internet use in spite of an awareness of its harmful effects. On the other hand, SMA had been repeatedly reported to encode reward expectancy.39,40 Although the monetary reward was not provided according to their performance in this study, the lower activation over SMA might suggest that subjects with IGD had lower reward response to corrected inhibition in Nogo trails. The reward deficiency syndrome is an insufficiency of usual feelings of satisfaction.41 Those suffering from reward deficiency syndrome are unable to produce an adequate feeling of well-being and consequently often self-medicate with substances or behaviors that help raise the levels of ‘feel
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(a) Internet gaming disorder group (one-sample t-test) Z = –6 3 2 1 0 (b) Control group (one-sample t-test) Z = 50 Z = –8 Z = 22
X=0 6 4 2
0 (c) Brain activation higher in control group than that in IGD group (two-sample t-test) Y = 26 Z = 52 X=4 6 4 2 0 Figure 1. Brain activation for response inhibition among the Internet gaming disorder (IGD) group and control group. Brain activation for response inhibition was determined by subtracting the contrast of the Go block from the contrast of the Nogo block. The significant threshold is P < 0.001 uncorrected on the voxel level in combination with P < 0.05 with family-wise error (FWE) correction on the cluster level. (a) The IGD group exhibited activation of the right insula for response inhibition on the voxel level, but FWE was not significant. (b) The control group showed activation of the right supplement motor area (SMA), middle frontal lobe, and insula. (c) The differences in brain activation were determined by two-sample t-test. Brain activations were seen in a threshold of P < 0.001 uncorrected on the voxel level in combination with P < 0.05 with small volume correction over the SMA, middle frontal lobe, and insula. The control group had a significantly higher activation over the right SMA in comparison to the IGD group.
good’ chemicals.42 Thus, it had been suggested to explain addictive behaviors, such as substance use, gaming, or gambling. In DSM-5, loss of interest is one of the criteria to define IGD.5 Further, the reward sensitivity had been reported to decrease among ado-
lescents with persistent addiction to the Internet.43 Thus, the lower SMA activation might also indicate the altered response to nature reward and support the role of reward deficiency syndrome involving the mechanism of IGD.
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Impaired response inhibition and salience attribution syndrome Drug addiction can be defined as an impaired response inhibition and salience attribution (I-RISA) syndrome.44 The salience attributed to online gaming is supported by fMRI studies of cue-induced gaming craving.4 In line with previous results regarding drug addiction,44,45 the results obtained in this study support the finding that subjects with IGD did not exhibit activation of the fronto-striatal network, particularly the SMA, for response inhibition. Subjects with impaired SMA function, inhibitory and selective control, had difficulty in regulating Internet use when their salience was attributed to the gaming cue in the environment. Thus, the results indicate that the function of the fronto-striatal circuit for response inhibition plays an important role in the mechanism of IGD as well as that of drug addiction.45 Based on these results, further attention should be paid to the deficit of inhibitory control when treating Internet use disorder. Chinese students experience a higher rate of Internet addiction than their US counterparts.46 This indicates that, aside from impulsivity, environmental factors, such as cultural factors or availability of other healthy recreational activities, might contribute to IGD. Environmental intervention, such as promoting other real-life healthy activities, or environmental control, such as limiting the availability of the online gaming service, may help subjects with IGD to control their excessive online gaming. Lastly, the preventive schedule for IGD should pay more attention to subjects with impulsivity or inadequate prefrontal function for inhibitory control, such as adolescents.47
Limitations There are several limitations to the present study. First, only male subjects were included in the study. Second, subjects with comorbid substance use and other major psychiatric disorders were excluded from the study. Thus, there is a limitation in generalizing the results to IGD subjects with other substance use disorders or major psychiatric disorders. Further, we did not match the Internet use time between the three groups. However, the matching may result in a biasing selection of the control group. Third, the number of subjects was limited. The limited case number might be associated with the insignificant
Response inhibition of IGD 7
differences in performance on the Go/Nogo task between the IGD and control groups. Fourth, there is a limitation in the block design of the Go/Nogo task. The between-group differences in subtraction of the ‘Go’ block from the ‘Nogo block’ was possibly biased by the difference in ‘Go’ block. However, this is the standard way to analyze the block design of the Go/Nogo task. Fifth, since several comorbid disorders, such as the attention deficit and hyperactivity disorder, anxiety disorder, and insomnia, were not assessed in this study, we could not exclude the possible effect of these comorbidities in the present results. Sixth, 12 subjects of IGD reported to sleep less than 4 h/night and 13 subjects of IGD felt tired during the daytime because of late-night gaming in the self-reported questionnaire, the CIAS.25,27 However, the sleep problems were not routinely evaluated in diagnostic interviewing. The exact percentages of sleep problems among subjects with IGD were not confirmed in the present study. Lastly, the limitation in subject number might contribute to the limitation to detect all BOLD signal change on all brain areas responsible for response inhibition. Further, only significant activations of response inhibition were selected as ROI to evaluate the difference between IGD subjects and controls. Thus, the difference in some essential brain areas of response inhibition, such as orbital frontal lobe, could have been missed in the present study.
Conclusion The present study demonstrated the neurobiological mechanism of deficient response inhibition in subjects with IGD. The results obtained demonstrated that subjects with IGD have higher impulsivity and have impaired activation of the SMA when processing response inhibition. The results obtained support the finding that the impaired SMA function in response inhibition is an important mechanism not only in substance use disorder, but also in IGD.
ACKNOWLEDGMENTS The present study was supported by grants from the Kaohsiung Municipal Hsiao-Kang Hospital (KMHK98-001), Kaohsiung Medical University Hospital (KMUH99-9R50) and the National Science Council (NSC99-2321-B-037-004-) in Taiwan. There is no conflict of interest to be declared.
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REFERENCES 1. Young KS. Internet addiction: The emergence of a new clinical disorder. CyberPsychol. Behav. 1998; 1: 237–244. 2. Yen CF, Yen JY, Ko CH. Internet addiction: Ongoing research in Asia. World Psychiatry 2010; 9: 97. 3. Ko CH, Yen JY, Yen CF, Lin HC, Yang MJ. Factors predictive for incidence and remission of internet addiction in young adolescents: A prospective study. CyberPsychol. Behav. 2007; 10: 545–551. 4. Ko CH, Liu GC, Hsiao S et al. Brain activities associated with gaming urge of online gaming addiction. J. Psychiatr. Res. 2009; 43: 739–747. 5. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 4th edn. American Psychiatric Association, Arlington, 2013. 6. Chiang IP, Su YH. Measuring and analyzing the causes of problematic Internet use. Cyberpsychol. Behav. Soc. Netw. 2012; 15: 591–596. 7. Billieux J. Problematic use of the mobile phone: A literature review and a pathways model. Curr. Psychiatry Rev. 2012; 8: 299–307. 8. Montag C, Bey K, Sha P et al. Is it meaningful to distinguish between generalized and specific Internet addiction? Evidence from a cross-cultural study from Germany, Sweden, Taiwan and China. Asia Pac. Psychiatry 2014. doi: 10.1111/appy.12122 9. Petry NM, O’Brien CP. Internet gaming disorder and the DSM-5. Addiction 2013; 108: 1186–1187. 10. Starcevic V. Is Internet addiction a useful concept? Aust. N. Z. J. Psychiatry 2013; 47: 16–19. 11. Winstanley CA. The orbitofrontal cortex, impulsivity, and addiction: Probing orbitofrontal dysfunction at the neural, neurochemical, and molecular level. Ann. N. Y. Acad. Sci. 2007; 1121: 639–655. 12. Cao F, Su L, Liu T, Gao X. The relationship between impulsivity and internet addiction in a sample of Chinese adolescents. Eur. Psychiatry 2007; 22: 466–471. 13. Lee HW, Choi JS, Shin YC, Lee JY, Jung HY, Kwon JS. Impulsivity in internet addiction: A comparison with pathological gambling. Cyberpsychol. Behav. Soc. Netw. 2012; 15: 373–377. 14. Billieux J, Chanal J, Khazaal Y et al. Psychological predictors of problematic involvement in massively multiplayer online role-playing games: Illustration in a sample of male cybercafe players. Psychopathology 2011; 44: 165–171. 15. Gentile DA, Choo H, Liau A et al. Pathological video game use among youths: A two-year longitudinal study. Pediatrics 2011; 127: e319–e329. 16. Goldstein RZ, Volkow ND. Dysfunction of the prefrontal cortex in addiction: Neuroimaging findings and clinical implications. Nat. Rev. Neurosci. 2011; 12: 652–669. 17. Volkow ND, Wang GJ, Fowler JS, Tomasi D, Telang F, Baler R. Addiction: Decreased reward sensitivity and increased expectation sensitivity conspire to overwhelm
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
the brain’s control circuit. Bioessays 2010; 32: 748– 755. Courtney KE, Ghahremani DG, Ray LA. Fronto-striatal functional connectivity during response inhibition in alcohol dependence. Addict. Biol. 2013; 18: 593–604. Torregrossa MM, Quinn JJ, Taylor JR. Impulsivity, compulsivity, and habit: The role of orbitofrontal cortex revisited. Biol. Psychiatry 2008; 63: 253–255. Horn NR, Dolan M, Elliott R, Deakin JF, Woodruff PW. Response inhibition and impulsivity: An fMRI study. Neuropsychologia 2003; 41: 1959–1966. Vollm B, Richardson P, Stirling J et al. Neurobiological substrates of antisocial and borderline personality disorder: Preliminary results of a functional fMRI study. Crim. Behav. Ment. Health 2004; 14: 39–54. Claus ED, Feldstein Ewing SW, Filbey FM, Hutchison KE. Behavioral control in alcohol use disorders: Relationships with severity. J. Stud. Alcohol Drugs 2013; 74: 141–151. Kaufman JN, Ross TJ, Stein EA, Garavan H. Cingulate hypoactivity in cocaine users during a GO-NOGO task as revealed by event-related functional magnetic resonance imaging. J. Neurosci. 2003; 23: 7839–7843. Ko CH, Yen JY, Chen CC, Chen SH, Yen CF. Gender differences and related factors affecting online gaming addiction among Taiwanese adolescents. J. Nerv. Ment. Dis. 2005; 193: 273–277. Ko CH, Yen JY, Chen SH, Yang MJ, Lin HC, Yen CF. Proposed diagnostic criteria and the screening and diagnosing tool of Internet addiction in college students. Compr. Psychiatry 2009; 50: 378–384. Sheehan DV, Lecrubier Y, Sheehan KH et al. The MiniInternational Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J. Clin. Psychiatry 1998; 59: 22–33. Chen SH, Weng LC, Su YJ, Wu HM, Yang PF. Development of Chinese internet addiction scale and its psychometric study. Chinese J. Psychol. 2003; 45: 279–294. Patton JH, Stanford MS, Barratt ES. Factor structure of the Barratt impulsiveness scale. J. Clin. Psychiatry 1995; 51: 768–774. Li CS, Chen SH. Obsessive-compulsiveness and impulsivity in a non-clinical population of adolescent males and females. Psychiatry Res. 2007; 149: 129–138. Mobbs D, Eckert MA, Mills D et al. Frontostriatal dysfunction during response inhibition in Williams syndrome. Biol. Psychiatry 2007; 62: 256–261. Tzourio-Mazoyer N, Landeau B, Papathanassiou D et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002; 15: 273–289. Dong G, Devito EE, Du X, Cui Z. Impaired inhibitory control in ‘internet addiction disorder’: A functional magnetic resonance imaging study. Psychiatry Res. 2012; 203: 153–158.
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33. Chambers CD, Garavan H, Bellgrove MA. Insights into the neural basis of response inhibition from cognitive and clinical neuroscience. Neurosci. Biobehav. Rev. 2009; 33: 631–646. 34. Floden D, Stuss DT. Inhibitory control is slowed in patients with right superior medial frontal damage. J. Cogn. Neurosci. 2006; 18: 1843–1849. 35. Picton TW, Stuss DT, Alexander MP, Shallice T, Binns MA, Gillingham S. Effects of focal frontal lesions on response inhibition. Cereb. Cortex 2007; 17: 826–838. 36. Coxon JP, Stinear CM, Byblow WD. Stop and go: The neural basis of selective movement prevention. J. Cogn. Neurosci. 2009; 21: 1193–1203. 37. Chen CY, Muggleton NG, Tzeng OJ, Hung DL, Juan CH. Control of prepotent responses by the superior medial frontal cortex. Neuroimage 2009; 44: 537–545. 38. Mostofsky SH, Simmonds DJ. Response inhibition and response selection: Two sides of the same coin. J. Cogn. Neurosci. 2008; 20: 751–761. 39. Campos M, Breznen B, Bernheim K, Andersen RA. Supplementary motor area encodes reward expectancy in eye-movement tasks. J. Neurophysiol. 2005; 94: 1325– 1335. 40. Lee D. Behavioral context and coherent oscillations in the supplementary motor area. J. Neurosci. 2004; 24: 4453– 4459.
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41. Blum K, Gardner E, Oscar-Berman M, Gold M. ‘Liking’ and ‘wanting’ linked to Reward Deficiency Syndrome (RDS): Hypothesizing differential responsivity in brain reward circuitry. Curr. Pharm. Des. 2012; 18: 113–118. 42. Blum K, Liu Y, Shriner R, Gold MS. Reward circuitry dopaminergic activation regulates food and drug craving behavior. Curr. Pharm. Des. 2011; 17: 1158–1167. 43. Yen JY, Yen CF, Chen CS, Chang YH, Yeh YC, Ko CH. The bidirectional interactions between addiction, behaviour approach and behaviour inhibition systems among adolescents in a prospective study. Psychiatry Res. 2012; 200: 588–592. 44. Goldstein RZ, Volkow ND. Drug addiction and its underlying neurobiological basis: Neuroimaging evidence for the involvement of the frontal cortex. Am. J. Psychiatry 2002; 159: 1642–1652. 45. Crews FT, Boettiger CA. Impulsivity, frontal lobes and risk for addiction. Pharmacol. Biochem. Behav. 2009; 93: 237– 247. 46. Zhang L, Amos C, McDowell WC. A comparative study of Internet addiction between the United States and China. Cyberpsychol. Behav. 2008; 11: 727–729. 47. Andrews-Hanna JR, Mackiewicz Seghete KL, Claus ED, Burgess GC, Ruzic L, Banich MT. Cognitive control in adolescence: Neural underpinnings and relation to selfreport behaviors. PLoS ONE 2011; 6: e21598.
© 2014 The Authors Psychiatry and Clinical Neurosciences © 2014 Japanese Society of Psychiatry and Neurology
doi:10.1111/pcn.12224
Regular Article
Brain correlates of response inhibition in Internet gaming disorder Chiao-Yun Chen, MD,1,3 Mei-Feng Huang, MD,2 Ju-Yu Yen, MD, PhD,2,4,6 Cheng-Sheng Chen, MD, PhD,2,4 Gin-Chung Liu, MD,1,3 Cheng-Fang Yen, MD, PhD2,4 and Chih-Hung Ko, MD, PhD2,4,5* Departments of 1Medical Imaging and 2Psychiatry, Kaohsiung Medical University Hospital, Departments of 3Radiology and 4 Psychiatry, Faculty of Medicine, College of Medicine, 5Department of Psychiatry, Kaohsiung Municipal Hsiao-Kang Hospital, Kaohsiung Medical University and 6Department of Psychiatry, Kaohsiung Municipal Ta-Tung Hospital, Kaohsiung, Taiwan
Aims: The present study aimed to evaluate the brain correlates of response inhibition among subjects with Internet gaming disorder (IGD). Methods: For this purpose, 15 men with IGD for at least 1 year, and 15 controls with no history of IGD were recruited to perform the Go/Nogo task under functional magnetic resonance imaging investigation. Prior to scanning, the subjects were assessed using the Chen Internet Addiction Scale and the Barrett Impulsivity Scale.
prefrontal cortex, and caudate for response inhibition. However, the IGD group had a higher impulsivity and lower activity of the right SMA/pre-SMA in comparison to the control group.
Conclusions: The results obtained suggest that dysfunctional activation of the SMA for response inhibition is one of the candidate mechanisms of IGD. Key words: impulsivity, Internet gaming disorder, response inhibition, supplement motor area.
Results: The control group exhibited activation of the right supplement motor area (SMA), dorsolateral
HE INTERNET IS a major communication medium in modern life. However, loss of control regarding Internet use has resulted in negative psychosocial consequences.1 With the growth in popularity of the Internet worldwide, Internet addiction has become prevalent not only in Western but also in Eastern societies.2 Internet gaming is a popular online activity that has been reported to predict the risk of Internet addiction.3 Internet gaming disorder (IGD) is one of the more popular subtypes (57.5%) of Internet addiction in college students.4 There are behavioral similarities of Internet gaming, such as loss of
T
*Correspondence: Chih-Hung Ko, MD, PhD, Department of Psychiatry, Kaohsiung Medical University Hospital, 100 Tzyou 1stRoad, Kaohsiung City 807, Taiwan. Email: [email protected] Received 14 March 2014; revised 11 June 2014; accepted 14 July 2014.
control, withdrawal, and persisting despite negative consequences, with gambling disorder or substance use disorder, such as preoccupation, tolerance, and withdrawal. As Internet gaming is of significant public importance, IGD was classified into section III, conditions for future study, of the DSM-5.5 It is necessary to clarify the mechanisms of IGD in order to provide adequate evidence to support the theory that Internet gaming disorder has merit as an independent disorder. The proposed DSM-5 suggested nine criteria for IGD as follows: (i) preoccupation; (ii) withdrawal symptoms; (iii) tolerance; (iv) unsuccessful control; (v) impaired decision-making; (vi) rewarding deficit; (vii) escape; (viii) deceit about Internet gaming; and (ix) impaired function. These criteria show a similarity in symptoms between IGD and other addictive behavior, such as substance use disorder or gambling disorder.5 However, the general characteristics of the
© 2014 The Authors Psychiatry and Clinical Neurosciences © 2014 Japanese Society of Psychiatry and Neurology
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Internet, such as anonymity, high availability, or information and efficiency,6 might contribute to addiction to online games, but not to other addictive behavior or to off-line games. Further, there are factors, such as motivation factors or structure characteristics of individual Internet activities,7 associated with addiction to specific Internet activities. Thus, there is a significant differerece between generlized Internet addiciton and IGD.8 It would suggest that IGD should be diagnosed as an independent disorder to be further researched.9,10
Internet gaming disorder and impulsivity Subjects with IGD may persist in online gaming in spite of awareness that the habit is directly harmful to life functioning. Further, subjects may also attempt, but fail, to control online gaming. It has been suggested that loss of control regarding Internet use is an important core symptom of IGD that requires greater understanding. Impulsivity is one of the most important factors resulting in loss of control of addictive behavior.11 Previous studies of adolescents and adults have reported higher impulsivity in subjects with Internet addiction.12,13 Further, impulsivity was also noted to predict Internet use disorder in a longitudinal study.14,15 Neuropsychological study has established that adolescents with IGD have an increased error rate on the Go/Nogo task.12 These results support an association between impulsivity and IGD. Brain-imaging studies of addictive behaviors have identified a key involvement of the prefrontal cortex (PFC) through its regulation of the limbic reward regions as well as its involvement in a higher-order executive function.16 Further, a weakened inhibitory control of the fronto-striatal circuit is one of the core mechanisms of addictive behavior.17 The weaker connectivity between putamen and insula, anterior cingulate, and medical prefrontal lobe had been demonstrated to represent the impairment of selfcontrol in alcohol dependence.18 These studies suggest that a loss of control of addictive behavior can be explained by a deficit in inhibitory control over a response that provides immediate reinforcement. Thus, we expect that the deficit in inhibitory control may play a similar role involving the mechanism of IGD. A deficit in suppressing prepotent motor responses is one of the important dimensions of impulsivity and has been repeatedly tested with the Go/Nogo task to represent response inhibition.19 The Go/Nogo
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task requires subjects to press a button as quickly as possible when observing Go stimuli and to refrain from doing so when observing Nogo stimuli. Response inhibition in the Go/Nogo task has been well evaluated by functional magnetic resonance imaging (fMRI) studies to represent the role of the frontostriatal circuit, such as the cingulate, supplementary motor area, and basal ganglion, in the mechanism of response inhibition.20,21 It has been used to evaluate impulsivity in substance use disorder and demonstrated impaired activation over insula, orbital frontal lobe, and anterior cingulate.22,23 Thus, we expect that deficit in frontostriatal circuit might contribute to the impaired response inhibition among subjects with IGD. The present study aimed to evaluate the brain correlates of response inhibition among subjects with IGD. Thus, the brain activations for response inhibition in the Go/Nogo task were investigated with fMRI among subjects with IGD, and control subjects.
METHODS Participants In the present study, right-handed participants were recruited by an advertisement posted on campus. As male subjects are more likely to be addicted to online gaming, this study recruited only male subjects.24 All the subjects in the IGD group were interviewed by a psychiatrist to confirm the diagnosis of IGD for more than 1 year in accordance with the diagnostic criteria for Internet addiction (DCIA).25 Six or more criteria should be fulfilled of the nine criteria A of the DCIA. Further, criteria B, related to functional impairment, and criteria C, which are exclusion criteria, should also be fulfilled. The subjects were currently addicted to the same online game, World of Warcraft, and had spent an average of 4 or more h/day on weekdays and an average of 8 or more h/day on weekends on online gaming. In the control group, psychiatric interviews and reviews of history were performed to confirm that none of the participants had ever fulfilled the criteria of IGD. The exclusion criteria included: 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 to MRI. After receiving a detailed explanation of the study, all subjects provided written informed consent. A total
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of 15 subjects with IGD and 15 controls were recruited in this study. Two subjects of IGD had nicotine dependence. All participants had no other concurrent substance use disorder. The study was approved by the Kaohsiung Medical University Institutional Review Board.
Image acquisition The fMRI experiments were performed using a 3 Tesla General Electric MR scanner (Signa VH/I 4.0, General Electric, New York, NY, USA). The magnetic resonance (MR) sequence for functional imaging was a gradient-recalled echo planar imaging (EPI) sequence (64 × 64 matrix; 24-cm field of view, echo time [TE] = 35 ms; repetition time [TR] = 2.5 s; 3-mmthick slices with a 0-mm gap). Thirty-five image planes were collected parallel to the anterior commissure and posterior commissure (AC–PC) line with the aid of sagittal localizer images. Head motion was corrected by post-processing using SPM5 (Wellcome Department of Cognitive Neurology, London, UK).
Process All the invited participants were interviewed by a psychiatrist to confirm the diagnosis of IGD and screened for the exclusion criteria based on the Mini International Neuropsychiatric Interview.26 Prior to scanning, all subjects completed the Chen Internet Addiction Scale (CIAS)25,27 and the Barrett Impulsivity Scale 11 (BIS-11).28,29 The CIAS indicates the severity of IGD, while the BIS-11 indicates the severity of impulsivity. Subsequently, functional MR images were acquired during the Go/Nogo task using a block design applied in an earlier study, with some modifications as mentioned below.30
Behavior task The Go/Nogo task had two conditions: In the Go condition (Block A), a numeral from 1 to 5 was shown 20 times in a white font on a black background. In the Nogo condition (Block B), a nontarget (number 0) was shown 10 times, and a target (a numeral from 1 to 5) was shown 10 times in a pseudorandom sequence. In the introduction section, the participants were told to press the button as quickly as possible for all numbers except for the number 0. The duration of the number presentation was 0.2 s and the inter-trial interval was 1.3 s. Within
Response inhibition of IGD 3
each block of 30 s, trials were presented in a pseudorandomized sequence. Block sections were presented in the order ABABABABABAB, which resulted in a total of 240 trials in each section. The performance on the Go/Nogo task was represented by the sum of the correct responses for the Nogo trial, successful holding of button pushing. Prior to the fMRI scan, all participants had practiced the Go/Nogo task in order to become familiar with the task.
Data analysis All the time series data exported from the GE system were converted into the SPM format using SPM DICOM import. Next, image preprocessing and statistical analysis were performed using SPM5. Each image was realigned for motion correction. Each structural image was co-registered to the mean motioncorrected functional image for each participant. The realigned datasets were normalized to Montreal Neurological Institute space and sliced again at a 2 × 2 × 2-mm3 voxel size. An 8-mm full-width-halfmaximum Gaussian kernel was used to smooth the data. One image per contrast was computed for each participant, whereby the data were subjected to a high pass filter of 128 s with no global scaling. For the statistical analysis, the Nogo block versus the Go block (Nogo block – Go block) variables were modeled as the explanatory variables within the context of a general linear model on a voxel-by-voxel basis with first level analysis using SPM5. Voxel-wise t-statistics were computed and normalized to Z scores to provide a statistical measure of activation. Z-maps of activation for the contrast (Nogo-Go) were computed for each participant. For group level analysis, the contrasts (Nogo-Go) of the individual subjects were combined into a group analysis. After including the contrasts of all the participants in the IGD or control groups, the activations for the ‘Nogo-Go’ contrast were demonstrated for each group by one sample t-test with a threshold of P < 0.001 uncorrected at the voxel level in combination with a cluster-extent threshold of contiguously significant voxels that permits inference of significant clusters at a level of P < 0.05 family-wise error (FWE) corrected. The differences between the IGD and control groups were determined using two-sample t-test with a threshold of P < 0.001 uncorrected at the voxel level in combination with a level of P < 0.05 with FWE small volume correction in regions of interest (ROI).
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ROI: These predefined ROI within a binary mask were created from the Anatomical Automatic Labeling (AAL) library as found in WFU Pickatlas 2.4 (Wake Forest University, NC, USA).31 The brain areas of the AAL library were selected based on significant brain activation areas for response inhibition in the control.
group. The IGD group exhibited activation of the right insula for response inhibition on the voxel level, but FWE was not significant. The control group showed significant activation of the right SMA, dorsolateral prefrontal cortex (DLPFC), and insula for response inhibition on the cluster level.
Comparison of IGD and control groups by two-sample t-test
RESULTS Results of the behavior task The demographic data in Table 1 indicate that the IGD and control groups did not differ significantly in terms of either age or education level. The IGD group had higher scores on the CIAS in comparison to the control group. Further, the IGD group had higher scores on the BIS-11 in comparison to the control group. In the behavior task, there were no significant differences in the performance of Go/Nogo task (successfully inhibited responses in Nogo trails) or reaction time between the IGD group and the control group. In addition, the score on the BIS-11 was positively correlated with the CIAS score (r = 0.33, P = 0.03) and negatively correlated to the performance of Go/Nogo task (r = −0.54, P < 0.001).
Activation of response inhibition among the IGD and control groups by one-sample t-test Table 2 and Figure 1 depict the results of the onesample t-test between the IGD group and the control
Based on significant activation areas in the control group, the right SMA, DLPFC, and insula from the AAL library were selected as ROI for small volume correction in between-group comparisons. The results shown in Table 2 and Figure 1 demonstrate that the control group exhibited significantly higher activation of the right SMA/preSMA in comparison to the IGD group (FWE corrected P = 0.014 in small volume correction in SMA). There was no higher activation for the IGD group in comparison to the control.
DISCUSSION Deficit in the frontal striatal circuit for response inhibition among the IGD group in comparison to the control group CIAS score was positively correlated with BIS-11 score, an indicator of impulsivity. The higher impulsivity score observed in the IGD group in comparison
Table 1. Age, educational level, performance on the Go/Nogo task, severity of IGD, and impulsivity of the IGD and control groups Variable
IGD group† Mean ± SD
Control group Mean ± SD
Z
Age Educational level Go/Nogo performance‡ Reaction time IGD severity§ Impulsivity¶
24.67 ± 3.12 15.47 ± 1.55 53.67 ± 7.55/60 0.38 ± 0.06 76.00 ± 12.09 74.33 ± 6.56
24.47 ± 2.83 16.00 ± 1.13 56.13 ± 3.81/60 0.36 ± 0.04 26.00 ± 0.00 62.00 ± 9.66
0.084 1.039 0.53 0.64 4.99*** 3.84***
***P < 0.001. Z: Wilcoxon rank sum test. † IGD: subjects with IGD for more than one year. ‡ Performance of Go/Nogo task: the corrected response in Go/Nogo task. ¶ Impulsivity: Score of Barrett Impulsivity Scale 11. § Severity of IGD, Score of Chen Internet Addiction Scale. IGD, Internet gaming disorder.
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Table 2. Brain areas activated for response inhibition† in the IGD and control groups Region of activation
MNI coordinates
IGD group Insula Control group SMA SMA Middle frontal lobe Middle frontal lobe Middle frontal lobe Insula Insula Control group – IGD group SMA/Superior medial frontal lobe (preSMA) IGD group – control group No activation
‡
R/L R
BA
X 36
Y 16
Z −6
Voxels 126
Z§ 3.47
P 0.000
P¶ 0.781
R R R R R R R
0/0 32/32 46/46 46/46 46/46 47/47 0/0
4 10 36 34 38 36 46
24 10 40 46 34 18 18
50 48 22 14 30 −8 −10
773
5.69 3.71 4.37 4.19 3.94 3.97 3.89
0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.000
R
8/8
6
24
52
319
401
254
5.15
0.000
0.020
0.008 P§§ 0.014
†
BOLD signal subtracting the ‘Go’ block from the ‘Nogo block’. The activation area was on the right (R) or left (L) side. § Z score values are depicted, representing a P-value with a threshold of 0.05 with small volume correction. The number of voxels in a cluster of contiguous 2 × 2 × 2 mm voxels is depicted, with a cluster size threshold of 20 voxels. ¶ P: Family-wise-error-corrected P-value at the cluster level. §§ P: small volume FWE corrected P-value in the ROI. BA, Brodmann’s area; IGD, Internet gaming disorder; MNI, Montreal Neurological Institute; SMA, supplement motor area. ‡
with the controls indicated a significant deficiency in impulse control in the IGD group. The impulsivity score is also negatively correlated with the performance of Go/Nogo task in this study. The IGD group had poorer performance of Go/Nogo task than the control group, but this was not significant. This result is in line with previous reports of fMRI study of inhibitory control in IGD.32 However, the IGD and control group responded correctly to more than 85% of the Nogo trials. This revealed that all the subjects had executed the Go/Nogo task as guided. The fronto-striatal circuit has been suggested to contribute to the function of response inhibition.33 The results obtained from the control group supported this hypothesis and demonstrated significant activation of the supplement motor area (SMA), DLPFC, orbital frontal cortex, and insula. However, none of the above-mentioned areas was significantly activated in the IGD group. Further analysis revealed that the IGD group had lower activation of the SMA/preSMA, a member of the fronto-striatal network, for response inhibition in comparison to the control group. Lesion study,34,35 fMRI study,36 and transcranial magnetic stimulation37
have all demonstrated that the SMA is important for response inhibition. It has been suggested that the preSMA circuits are critical for the selection of appropriate behavior, including selecting to engage appropriate motor responses as well as selecting to withhold inappropriate motor responses.38 In the present study, the brain activation deficit over the SMA/preSMA might indicate impairment of inhibitory and selective control over behavior among the subjects with IGD. This might explain why subjects with IGD persisted in heavy Internet use in spite of an awareness of its harmful effects. On the other hand, SMA had been repeatedly reported to encode reward expectancy.39,40 Although the monetary reward was not provided according to their performance in this study, the lower activation over SMA might suggest that subjects with IGD had lower reward response to corrected inhibition in Nogo trails. The reward deficiency syndrome is an insufficiency of usual feelings of satisfaction.41 Those suffering from reward deficiency syndrome are unable to produce an adequate feeling of well-being and consequently often self-medicate with substances or behaviors that help raise the levels of ‘feel
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(a) Internet gaming disorder group (one-sample t-test) Z = –6 3 2 1 0 (b) Control group (one-sample t-test) Z = 50 Z = –8 Z = 22
X=0 6 4 2
0 (c) Brain activation higher in control group than that in IGD group (two-sample t-test) Y = 26 Z = 52 X=4 6 4 2 0 Figure 1. Brain activation for response inhibition among the Internet gaming disorder (IGD) group and control group. Brain activation for response inhibition was determined by subtracting the contrast of the Go block from the contrast of the Nogo block. The significant threshold is P < 0.001 uncorrected on the voxel level in combination with P < 0.05 with family-wise error (FWE) correction on the cluster level. (a) The IGD group exhibited activation of the right insula for response inhibition on the voxel level, but FWE was not significant. (b) The control group showed activation of the right supplement motor area (SMA), middle frontal lobe, and insula. (c) The differences in brain activation were determined by two-sample t-test. Brain activations were seen in a threshold of P < 0.001 uncorrected on the voxel level in combination with P < 0.05 with small volume correction over the SMA, middle frontal lobe, and insula. The control group had a significantly higher activation over the right SMA in comparison to the IGD group.
good’ chemicals.42 Thus, it had been suggested to explain addictive behaviors, such as substance use, gaming, or gambling. In DSM-5, loss of interest is one of the criteria to define IGD.5 Further, the reward sensitivity had been reported to decrease among ado-
lescents with persistent addiction to the Internet.43 Thus, the lower SMA activation might also indicate the altered response to nature reward and support the role of reward deficiency syndrome involving the mechanism of IGD.
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Impaired response inhibition and salience attribution syndrome Drug addiction can be defined as an impaired response inhibition and salience attribution (I-RISA) syndrome.44 The salience attributed to online gaming is supported by fMRI studies of cue-induced gaming craving.4 In line with previous results regarding drug addiction,44,45 the results obtained in this study support the finding that subjects with IGD did not exhibit activation of the fronto-striatal network, particularly the SMA, for response inhibition. Subjects with impaired SMA function, inhibitory and selective control, had difficulty in regulating Internet use when their salience was attributed to the gaming cue in the environment. Thus, the results indicate that the function of the fronto-striatal circuit for response inhibition plays an important role in the mechanism of IGD as well as that of drug addiction.45 Based on these results, further attention should be paid to the deficit of inhibitory control when treating Internet use disorder. Chinese students experience a higher rate of Internet addiction than their US counterparts.46 This indicates that, aside from impulsivity, environmental factors, such as cultural factors or availability of other healthy recreational activities, might contribute to IGD. Environmental intervention, such as promoting other real-life healthy activities, or environmental control, such as limiting the availability of the online gaming service, may help subjects with IGD to control their excessive online gaming. Lastly, the preventive schedule for IGD should pay more attention to subjects with impulsivity or inadequate prefrontal function for inhibitory control, such as adolescents.47
Limitations There are several limitations to the present study. First, only male subjects were included in the study. Second, subjects with comorbid substance use and other major psychiatric disorders were excluded from the study. Thus, there is a limitation in generalizing the results to IGD subjects with other substance use disorders or major psychiatric disorders. Further, we did not match the Internet use time between the three groups. However, the matching may result in a biasing selection of the control group. Third, the number of subjects was limited. The limited case number might be associated with the insignificant
Response inhibition of IGD 7
differences in performance on the Go/Nogo task between the IGD and control groups. Fourth, there is a limitation in the block design of the Go/Nogo task. The between-group differences in subtraction of the ‘Go’ block from the ‘Nogo block’ was possibly biased by the difference in ‘Go’ block. However, this is the standard way to analyze the block design of the Go/Nogo task. Fifth, since several comorbid disorders, such as the attention deficit and hyperactivity disorder, anxiety disorder, and insomnia, were not assessed in this study, we could not exclude the possible effect of these comorbidities in the present results. Sixth, 12 subjects of IGD reported to sleep less than 4 h/night and 13 subjects of IGD felt tired during the daytime because of late-night gaming in the self-reported questionnaire, the CIAS.25,27 However, the sleep problems were not routinely evaluated in diagnostic interviewing. The exact percentages of sleep problems among subjects with IGD were not confirmed in the present study. Lastly, the limitation in subject number might contribute to the limitation to detect all BOLD signal change on all brain areas responsible for response inhibition. Further, only significant activations of response inhibition were selected as ROI to evaluate the difference between IGD subjects and controls. Thus, the difference in some essential brain areas of response inhibition, such as orbital frontal lobe, could have been missed in the present study.
Conclusion The present study demonstrated the neurobiological mechanism of deficient response inhibition in subjects with IGD. The results obtained demonstrated that subjects with IGD have higher impulsivity and have impaired activation of the SMA when processing response inhibition. The results obtained support the finding that the impaired SMA function in response inhibition is an important mechanism not only in substance use disorder, but also in IGD.
ACKNOWLEDGMENTS The present study was supported by grants from the Kaohsiung Municipal Hsiao-Kang Hospital (KMHK98-001), Kaohsiung Medical University Hospital (KMUH99-9R50) and the National Science Council (NSC99-2321-B-037-004-) in Taiwan. There is no conflict of interest to be declared.
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REFERENCES 1. Young KS. Internet addiction: The emergence of a new clinical disorder. CyberPsychol. Behav. 1998; 1: 237–244. 2. Yen CF, Yen JY, Ko CH. Internet addiction: Ongoing research in Asia. World Psychiatry 2010; 9: 97. 3. Ko CH, Yen JY, Yen CF, Lin HC, Yang MJ. Factors predictive for incidence and remission of internet addiction in young adolescents: A prospective study. CyberPsychol. Behav. 2007; 10: 545–551. 4. Ko CH, Liu GC, Hsiao S et al. Brain activities associated with gaming urge of online gaming addiction. J. Psychiatr. Res. 2009; 43: 739–747. 5. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, 4th edn. American Psychiatric Association, Arlington, 2013. 6. Chiang IP, Su YH. Measuring and analyzing the causes of problematic Internet use. Cyberpsychol. Behav. Soc. Netw. 2012; 15: 591–596. 7. Billieux J. Problematic use of the mobile phone: A literature review and a pathways model. Curr. Psychiatry Rev. 2012; 8: 299–307. 8. Montag C, Bey K, Sha P et al. Is it meaningful to distinguish between generalized and specific Internet addiction? Evidence from a cross-cultural study from Germany, Sweden, Taiwan and China. Asia Pac. Psychiatry 2014. doi: 10.1111/appy.12122 9. Petry NM, O’Brien CP. Internet gaming disorder and the DSM-5. Addiction 2013; 108: 1186–1187. 10. Starcevic V. Is Internet addiction a useful concept? Aust. N. Z. J. Psychiatry 2013; 47: 16–19. 11. Winstanley CA. The orbitofrontal cortex, impulsivity, and addiction: Probing orbitofrontal dysfunction at the neural, neurochemical, and molecular level. Ann. N. Y. Acad. Sci. 2007; 1121: 639–655. 12. Cao F, Su L, Liu T, Gao X. The relationship between impulsivity and internet addiction in a sample of Chinese adolescents. Eur. Psychiatry 2007; 22: 466–471. 13. Lee HW, Choi JS, Shin YC, Lee JY, Jung HY, Kwon JS. Impulsivity in internet addiction: A comparison with pathological gambling. Cyberpsychol. Behav. Soc. Netw. 2012; 15: 373–377. 14. Billieux J, Chanal J, Khazaal Y et al. Psychological predictors of problematic involvement in massively multiplayer online role-playing games: Illustration in a sample of male cybercafe players. Psychopathology 2011; 44: 165–171. 15. Gentile DA, Choo H, Liau A et al. Pathological video game use among youths: A two-year longitudinal study. Pediatrics 2011; 127: e319–e329. 16. Goldstein RZ, Volkow ND. Dysfunction of the prefrontal cortex in addiction: Neuroimaging findings and clinical implications. Nat. Rev. Neurosci. 2011; 12: 652–669. 17. Volkow ND, Wang GJ, Fowler JS, Tomasi D, Telang F, Baler R. Addiction: Decreased reward sensitivity and increased expectation sensitivity conspire to overwhelm
18.
19.
20.
21.
22.
23.
24.
25.
26.
27.
28.
29.
30.
31.
32.
the brain’s control circuit. Bioessays 2010; 32: 748– 755. Courtney KE, Ghahremani DG, Ray LA. Fronto-striatal functional connectivity during response inhibition in alcohol dependence. Addict. Biol. 2013; 18: 593–604. Torregrossa MM, Quinn JJ, Taylor JR. Impulsivity, compulsivity, and habit: The role of orbitofrontal cortex revisited. Biol. Psychiatry 2008; 63: 253–255. Horn NR, Dolan M, Elliott R, Deakin JF, Woodruff PW. Response inhibition and impulsivity: An fMRI study. Neuropsychologia 2003; 41: 1959–1966. Vollm B, Richardson P, Stirling J et al. Neurobiological substrates of antisocial and borderline personality disorder: Preliminary results of a functional fMRI study. Crim. Behav. Ment. Health 2004; 14: 39–54. Claus ED, Feldstein Ewing SW, Filbey FM, Hutchison KE. Behavioral control in alcohol use disorders: Relationships with severity. J. Stud. Alcohol Drugs 2013; 74: 141–151. Kaufman JN, Ross TJ, Stein EA, Garavan H. Cingulate hypoactivity in cocaine users during a GO-NOGO task as revealed by event-related functional magnetic resonance imaging. J. Neurosci. 2003; 23: 7839–7843. Ko CH, Yen JY, Chen CC, Chen SH, Yen CF. Gender differences and related factors affecting online gaming addiction among Taiwanese adolescents. J. Nerv. Ment. Dis. 2005; 193: 273–277. Ko CH, Yen JY, Chen SH, Yang MJ, Lin HC, Yen CF. Proposed diagnostic criteria and the screening and diagnosing tool of Internet addiction in college students. Compr. Psychiatry 2009; 50: 378–384. Sheehan DV, Lecrubier Y, Sheehan KH et al. The MiniInternational Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J. Clin. Psychiatry 1998; 59: 22–33. Chen SH, Weng LC, Su YJ, Wu HM, Yang PF. Development of Chinese internet addiction scale and its psychometric study. Chinese J. Psychol. 2003; 45: 279–294. Patton JH, Stanford MS, Barratt ES. Factor structure of the Barratt impulsiveness scale. J. Clin. Psychiatry 1995; 51: 768–774. Li CS, Chen SH. Obsessive-compulsiveness and impulsivity in a non-clinical population of adolescent males and females. Psychiatry Res. 2007; 149: 129–138. Mobbs D, Eckert MA, Mills D et al. Frontostriatal dysfunction during response inhibition in Williams syndrome. Biol. Psychiatry 2007; 62: 256–261. Tzourio-Mazoyer N, Landeau B, Papathanassiou D et al. Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage 2002; 15: 273–289. Dong G, Devito EE, Du X, Cui Z. Impaired inhibitory control in ‘internet addiction disorder’: A functional magnetic resonance imaging study. Psychiatry Res. 2012; 203: 153–158.
© 2014 The Authors Psychiatry and Clinical Neurosciences © 2014 Japanese Society of Psychiatry and Neurology
Psychiatry and Clinical Neurosciences 2014
33. Chambers CD, Garavan H, Bellgrove MA. Insights into the neural basis of response inhibition from cognitive and clinical neuroscience. Neurosci. Biobehav. Rev. 2009; 33: 631–646. 34. Floden D, Stuss DT. Inhibitory control is slowed in patients with right superior medial frontal damage. J. Cogn. Neurosci. 2006; 18: 1843–1849. 35. Picton TW, Stuss DT, Alexander MP, Shallice T, Binns MA, Gillingham S. Effects of focal frontal lesions on response inhibition. Cereb. Cortex 2007; 17: 826–838. 36. Coxon JP, Stinear CM, Byblow WD. Stop and go: The neural basis of selective movement prevention. J. Cogn. Neurosci. 2009; 21: 1193–1203. 37. Chen CY, Muggleton NG, Tzeng OJ, Hung DL, Juan CH. Control of prepotent responses by the superior medial frontal cortex. Neuroimage 2009; 44: 537–545. 38. Mostofsky SH, Simmonds DJ. Response inhibition and response selection: Two sides of the same coin. J. Cogn. Neurosci. 2008; 20: 751–761. 39. Campos M, Breznen B, Bernheim K, Andersen RA. Supplementary motor area encodes reward expectancy in eye-movement tasks. J. Neurophysiol. 2005; 94: 1325– 1335. 40. Lee D. Behavioral context and coherent oscillations in the supplementary motor area. J. Neurosci. 2004; 24: 4453– 4459.
Response inhibition of IGD 9
41. Blum K, Gardner E, Oscar-Berman M, Gold M. ‘Liking’ and ‘wanting’ linked to Reward Deficiency Syndrome (RDS): Hypothesizing differential responsivity in brain reward circuitry. Curr. Pharm. Des. 2012; 18: 113–118. 42. Blum K, Liu Y, Shriner R, Gold MS. Reward circuitry dopaminergic activation regulates food and drug craving behavior. Curr. Pharm. Des. 2011; 17: 1158–1167. 43. Yen JY, Yen CF, Chen CS, Chang YH, Yeh YC, Ko CH. The bidirectional interactions between addiction, behaviour approach and behaviour inhibition systems among adolescents in a prospective study. Psychiatry Res. 2012; 200: 588–592. 44. Goldstein RZ, Volkow ND. Drug addiction and its underlying neurobiological basis: Neuroimaging evidence for the involvement of the frontal cortex. Am. J. Psychiatry 2002; 159: 1642–1652. 45. Crews FT, Boettiger CA. Impulsivity, frontal lobes and risk for addiction. Pharmacol. Biochem. Behav. 2009; 93: 237– 247. 46. Zhang L, Amos C, McDowell WC. A comparative study of Internet addiction between the United States and China. Cyberpsychol. Behav. 2008; 11: 727–729. 47. Andrews-Hanna JR, Mackiewicz Seghete KL, Claus ED, Burgess GC, Ruzic L, Banich MT. Cognitive control in adolescence: Neural underpinnings and relation to selfreport behaviors. PLoS ONE 2011; 6: e21598.
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