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Group independent component analysis reveals alternation of right executive control network in Internet gaming disorder

Published online by Cambridge University Press:  29 August 2017

Lingxiao Wang ,
Yifen Zhang ,
Xiao Lin ,
Hongli Zhou ,
Xiaoxia Du  and
Guangheng Dong
Lingxiao Wang
Affiliation:
Department of Psychology, Zhejiang Normal University, Jinhua, P.R. China
Yifen Zhang
Affiliation:
Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, P.R. China
Xiao Lin
Affiliation:
Department of Psychology, Zhejiang Normal University, Jinhua, P.R. China Peking-Tsinghua Center for Life Sciences, Peking University, Beijing, P.R. China
Hongli Zhou
Affiliation:
Department of Psychology, Zhejiang Normal University, Jinhua, P.R. China
Xiaoxia Du
Affiliation:
Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, P.R. China
Guangheng Dong*
Affiliation:
Department of Psychology, Zhejiang Normal University, Jinhua, P.R. China Institute of Psychological and Brain Researches, Zhejiang Normal University, Jinhua, P.R. China
*
*Address for correspondence: Guangheng Dong, Department of Psychology, Zhejiang Normal University, 688 Yingbin Road, Jinhua, Zhejiang Province 321004, P.R. China. Email: dongguangheng@zjnu.edu.cn
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Abstract

Objective

Previous studies have demonstrated that individuals with Internet gaming disorder (IGD) showed attentional bias toward gaming-related cues and exhibited impaired executive functions. The purpose of this study was to explore the alternations in related functional brain networks underlying attentional bias in IGD subjects.

Methods

Eighteen IGD subjects and 19 healthy controls (HC) were scanned with functional magnetic resonance imaging while they were performing an addiction Stroop task. Networks of functional connectivity were identified using group independent component analysis (ICA).

Results

ICA identified 4 functional networks that showed differences between the 2 groups, which were related to the right executive control network and visual related networks in our study. Within the right executive control network, in contrast to controls, IGD subjects showed increased functional connectivity in the temporal gyrus and frontal gyrus, and reduced functional connectivity in the posterior cingulate cortex, temporal gyrus, and frontal gyrus.

Conclusion

These findings suggest that IGD is related to abnormal functional connectivity of the right executive control network, and may be described as addiction-related abnormally increased cognitive control processing and diminished response inhibition during an addiction Stroop task. The results suggest that IGD subjects show increased susceptibility towards gaming-related cues but weakened strength of inhibitory control.

Keywords

Addiction Stroop taskattentional biasexecutive control networkindependent component analysisInternet gaming disorder

Information

Type
Original Research
Information
CNS Spectrums , Volume 23 , Special Issue 5: Theme: Obsessive-Compulsive Disorder , October 2018 , pp. 300 - 310
Copyright
© Cambridge University Press 2017 

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Footnotes

This research was approved by the Human Investigations Committee of Zhejiang Normal University. All participants provided written informed consent.

This research was supported by the National Science Foundation of China (31371023). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. We received no assistance from technical writers, language editors or writing agencies in preparing this manuscript.

References

1. Young, KS. Internet addiction over the decade: a personal look back. World Psychiatry. 2010; 9(2): 91.Google Scholar
2. Tao, R, Huang, X, Wang, J, Zhang, H, Zhang, Y, Li, M. Proposed diagnostic criteria for internet addiction. Addiction. 2010; 105(3): 556–564.Google Scholar
3. Shaw, M, Black, DW. Internet addiction: definition, assessment, epidemiology and clinical management. CNS Drugs. 2008; 22(5): 353–365.Google Scholar
4. Kuss, DJ, Griffiths, MD. Internet and gaming addiction: a systematic literature review of neuroimaging studies. Brain Sci. 2012; 2(3): 347–374.Google Scholar
5. Kuss, DJ, Griffiths, MD. Internet gaming addiction: a systematic review of empirical research. International Journal of Mental Health and Addiction. 2012; 10(2): 278–296.Google Scholar
6. Young, KS. Internet addiction: the emergence of a new clinical disorder. CyberPsychology & Behavior. 2009; 1(3): 237–244.Google Scholar
7. Petry, NM, O’Brien, CP. Internet gaming disorder and the DSM-5. Addiction. 2013; 108(7): 1186–1187.Google Scholar
8. Regier, DA, Kuhl, EA, Kupfer, DJ. The DSM-5: classification and criteria changes. World Psychiatry. 2013; 12(2): 92–98.Google Scholar
9. Marissen, MAE, Franken, IHA, Waters, AJ, Blanken, P, Van Den Brink, W, Hendriks, VM. Attentional bias predicts heroin relapse following treatment. Addiction. 2006; 101(9): 1306–1312.Google Scholar
10. Field, M, Cox, MW. Attentional bias in addictive behaviors: a review of its development, causes, and consequences. Drug Alcohol Depend. 2008; 97(1): 1–20.Google Scholar
11. Franken, IHA. Drug craving and addiction: integrating psychological and neuropsychopharmacological approaches. Prog Neuropsychopharmacol Biol Psychiatry. 2003; 27(4): 563–579.Google Scholar
12. Robbins, SJ, Ehrman, RN. The role of attentional bias in substance abuse. Behav Cogn Neurosci Rev. 2004; 3(4): 243–260.Google Scholar
13. Van Holst, RJ, Lemmens, JS, Valkenburg, PM, Peter, J, Veltman, DJ, Goudriaan, AE. Attentional bias and disinhibition toward gaming cues are related to problem gaming in male adolescents. J Adolesc Health. 2012; 50(6): 541–546.Google Scholar
14. Dong, G, Wang, L, Du, X, Potenza, MN. Gaming increases craving to gaming-related stimuli in individuals with Internet gaming disorder. Biol Psychiatry. In press. doi: 10.1016/j.bpsc.2017.01.002.Google Scholar
15. Amin, Z, Todd, CR, Canli, T. Attentional bias for valenced stimuli as a function of personality in the dot-probe task. Journal of Research in Personality. 2004; 38(1): 15–23.Google Scholar
16. Hester, R, Dixon, V, Garavan, H. A consistent attentional bias for drug-related material in active cocaine users across word and picture versions of the emotional Stroop task. Drug Alcohol Depend. 2006; 81(3): 251–257.Google Scholar
17. Metcalf, O, Pammer, K. Attentional bias in excessive massively multiplayer online role-playing gamers using a modified Stroop task. Computers in Human Behavior. 2011; 27(5): 1942–1947.Google Scholar
18. Ko, C-H, Liu, G-C, Hsiao, S, et al. Brain activities associated with gaming urge of online gaming addiction. J Psychiatr Res. 2009; 43(7): 739–747.Google Scholar
19. Wilson, SJ, Sayette, MA, Fiez, JA. Prefrontal responses to drug cues: a neurocognitive analysis. Nat Neurosci. 2004; 7(3): 211–214.Google Scholar
20. Miyake, A, Friedman, NP, Emerson, MJ, Witzki, AH, Howerter, A. The unity and diversity of executive functions and their contributions to complex ‘frontal lobe’ tasks: a latent variable analysis. Cogn Psychol. 2000; 41(1): 49–100.Google Scholar
21. Kim, DI, Manoach, DS, Mathalon, DH, et al. Dysregulation of working memory and default-mode networks in schizophrenia using independent component analysis, an fBIRN and MCIC study. Hum Brain Mapp. 2009; 30(11): 3795–3811.Google Scholar
22. Specht, K, Laeng, B. An independent component analysis of fMRI data of grapheme-colour synaesthesia. J Neuropsychol. 2011; 5(2): 203–213.Google Scholar
23. Chen, Z, Lei, X, Ding, C, Li, H, Chen, A. The neural mechanisms of semantic and response conflicts: an fMRI study of practice-related effects in the Stroop task. Neuroimage. 2013; 66(1): 577–584.Google Scholar
24. Van, VV, Carter, CS. Separating semantic conflict and response conflict in the Stroop task: a functional MRI study. Neuroimage. 2005; 27(3): 497–504.Google Scholar
25. Kane, MJ, Engle, RW. Working-memory capacity and the control of attention: the contributions of goal neglect, response competition, and task set to Stroop interference. J Exp Psychol Gen. 2003; 132(1): 47–70.Google Scholar
26. Dong, G, Zhou, H, Zhao, X. Male Internet addicts show impaired executive control ability: evidence from a color-word Stroop task. Neurosci Lett. 2011; 499(2): 114–118.Google Scholar
27. Dong, G, Potenza, MN. A cognitive-behavioral model of Internet gaming disorder: theoretical underpinnings and clinical implications. J Psychiatr Res. 2014; 58: 7–11.Google Scholar
28. MacLeod, CM. Half a century of research on the Stroop effect: an integrative review. Psychol Bull. 1991; 109(2): 163–203.Google Scholar
29. MacLeod, CM, MacDonald, PA. Interdimensional interference in the Stroop effect: uncovering the cognitive and neural anatomy of attention. Trends Cogn Sci. 2000; 4(10): 383–391.Google Scholar
30. Peterson, BS, Skudlarski, P, Gatenby, CJ, Zhang, H, Anderson, AW, Gore, JC. An fMRI study of Stroop word-color interference: evidence for cingulate subregions subserving multiple distributed attentional systems. Biol Psychiatry. 1999; 45(10): 1237–1258.Google Scholar
31. Adiele, I, Olatokun, W. Prevalence and determinants of Internet addiction among adolescents. Computers in Human Behavior. 2014; 31: 100–110.Google Scholar
32. Lecrubier, Y, Sheehan, DV, Weiller, E, et al. The Mini International Neuropsychiatric Interview (MINI). A short diagnostic structured interview: reliability and validity according to the CIDI. Eur Psychiatry. 1997; 12(5): 224–231.Google Scholar
33. Petry, NM, Rehbein, F, Gentile, DA, et al. An international consensus for assessing internet gaming disorder using the new DSM-5 approach. Addiction. 2014; 109(9): 1399–1406.Google Scholar
34. Widyanto, L, Griffiths, M. ‘Internet addiction’: a critical review. International Journal of Mental Health and Addiction. 2006; 4(1): 31–51.Google Scholar
35. Wang, Y, Wu, L, Zhou, H, et al. Impaired executive control and reward circuit in Internet gaming addicts under a delay discounting task: independent component analysis. Eur Arch Psychiatry Clin Neurosci. 2016; 267(3): 245–255.Google Scholar
36. Wang, L, Wu, L, Lin, X, et al. Dysfunctional default mode network and executive control network in people with Internet gaming disorder: independent component analysis under a probability discounting task. Eur Psychiatry. 2016; 34: 36–42.Google Scholar
37. Wang, L, Wu, L, Lin, X, et al. Altered brain functional networks in people with Internet gaming disorder: evidence from resting-state fMRI. Psychiatry Res. 2016; 254: 156–163.Google Scholar
38. Lin, X, Zhou, H, Dong, G, Du, X. Impaired risk evaluation in people with Internet gaming disorder: fMRI evidence from a probability discounting task. Prog Neuropsychopharmacol Biol Psychiatry. 2015; 56: 142–148.Google Scholar
39. Lin, X, Dong, G, Wang, Q, Du, X. Abnormal gray matter and white matter volume in ‘Internet gaming addicts’. Addict Behav. 2015; 40: 137–143.Google Scholar
40. Dong, G, Shen, Y, Huang, J, Du, X. Impaired error-monitoring function in people with Internet addiction disorder: an event-related fMRI study. Eur Addict Res. 2013; 19(5): 269–275.Google Scholar
41. Beckmann, CF, DeLuca, M, Devlin, JT, Smith, SM. Investigations into resting-state connectivity using independent component analysis. Philos Trans R Soc Lond B Biol Sci. 2005; 360(1457): 1001–1013.Google Scholar
42. Calhoun, VD, Adalı, T, Pearlson, GD, Pekar, JJ. A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp. 2001; 14(3): 140–151.Google Scholar
43. Himberg, J, Hyvärinen, A, Esposito, F. Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage. 2004; 22(3): 1214–1222.Google Scholar
44. Stevens, MC, Kiehl, KA, Pearlson, G, Calhoun, VD. Functional neural circuits for mental timekeeping. Hum Brain Mapp. 2007; 28(5): 394–408.Google Scholar
45. Greicius, MD, Flores, BH, Menon, V, et al. Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biol Psychiatry. 2007; 62(5): 429–437.Google Scholar
46. Calhoun, VD, Liu, J, Adalı, T. A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data. Neuroimage. 2009; 45(1): S163–S172.Google Scholar
47. Kilts, CD, Kennedy, A, Elton, AL, et al. Individual differences in attentional bias associated with cocaine dependence are related to varying engagement of neural processing networks. Neuropsychopharmacology. 2014; 39(5): 1135–1147.Google Scholar
48. Worhunsky, PD, Stevens, MC, Carroll, KM, et al. Functional brain networks associated with cognitive control, cocaine dependence, and treatment outcome. Psychol Addict Behav. 2013; 27(2): 477–488.Google Scholar
49. Rita, GZ, Nora, VD. Drug addiction and its underlying neurobiological basis: neuroimaging evidence for the involvement of the frontal cortex. Am J Psychiatry. 2002; 159(10): 1642–1652.Google Scholar
50. Verdejo-García, A, Bechara, A. A somatic marker theory of addiction. Neuropharmacology. 2009; 56(Suppl 1): 48–62.Google Scholar
51. Wiers, RW, Bartholow, BD, van den Wildenberg, E, et al. Automatic and controlled processes and the development of addictive behaviors in adolescents: a review and a model. Pharmacol Biochem Behav. 2007; 86(2): 263–283.Google Scholar
52. Wiers, RW, Bartholow, BD, van den Wildenberg, E, et al. Automatic and controlled processes and the development of addictive behaviors in adolescents: A review and a model. Pharmacol Biochem Behav. 2007; 86(2): 263–283.Google Scholar
53. Koob, GF, Volkow, ND. Neurocircuitry of addiction. Neuropsychopharmacology. 2010; 35(1): 217–238.Google Scholar
54. Everitt, BJ, Heberlein, U. Addiction. Curr Opin Neurobiol. 2013; 23(4): 463–466.Google Scholar
55. Dong, G, Lin, X, Hu, Y, Xie, C, Du, X. Imbalanced functional link between executive control network and reward network explain the online-game seeking behaviors in Internet gaming disorder. Scientific Reports. 2015; 5:article 9197.Google Scholar
56. Fillmore, MT, Vogel-Sprott, M. An alcohol model of impaired inhibitory control and its treatment in humans. Exp Clin Psychopharmacol. 1999; 7(1): 49–55.Google Scholar
57. Fillmore, MT, Vogel-Sprott, M. Response inhibition under alcohol: effects of cognitive and motivational conflict. J Stud Alcohol. 2000; 61(2): 239–246.Google Scholar
58. Dong, G, Shen, Y, Huang, J, Du, X. Impaired error-monitoring function in people with Internet addiction disorder: an event-related fMRI study. Eur Addict Res. 2013; 19(5): 269–275.Google Scholar
59. Li, CS, Luo, X, Yan, P, Bergquist, K, Sinha, R. Altered impulse control in alcohol dependence: neural measures of stop signal performance. Alcohol Clin Exp Res. 2009; 33(4): 740–750.Google Scholar
60. Dong, G, Lin, X, Potenza, MN. Decreased functional connectivity in an executive control network is related to impaired executive function in Internet gaming disorder. Prog Neuropsychopharmacol Biol Psychiatry. 2015; 57: 76–85.Google Scholar
61. Verdejo-García, A, Pérez-García, M. Profile of executive deficits in cocaine and heroin polysubstance user: common and differential effects on separate executive components. Psychopharmacology (Berl). 2007; 190(4): 517–530.Google Scholar
62. Kerns, JG, Cohen, JD, MacDonald, AW 3rd, Cho, RY, Stenger, VA, Carter, CS. Anterior cingulate conflict monitoring and adjustments in control. Science. 2004; 303(5660): 1023–1026.Google Scholar
63. Garavan, H, Ross, TJ, Murphy, K, Roche, RAP, Stein, EA. Dissociable executive functions in the dynamic control of behavior: inhibition, error detection, and correction. Neuroimage. 2002; 17(4): 1820–1829.Google Scholar
64. Wagner, AD, Maril, A, Bjork, RA, Schacter, DL. Prefrontal contributions to executive control: fMRI evidence for functional distinctions within lateral prefrontal cortex. Neuroimage. 2001; 14(6): 1337–1347.Google Scholar
65. Reuben, E, Sapienza, P, Zingales, L. Time discounting for primary and monetary rewards. Economics Letters. 2010; 106(2): 125–127.Google Scholar
66. Monterosso, J, Piray, P, Luo, S. Neuroeconomics and the study of addiction. Biol Psychiatry. 2012; 72(2): 107–112.Google Scholar
67. Barrós-Loscertales, A, Bustamante, J-C, Ventura-Campos, N, Llopis, J-J, Parcet, M-A, Ávila, C. Lower activation in the right frontoparietal network during a counting Stroop task in a cocaine-dependent group. Psychiatry Res. 2011; 194(2): 111–118.Google Scholar
68. Zhang, S, Li, CS. Functional networks for cognitive control in a stop signal task: independent component analysis. Hum Brain Mapp. 2012; 33(1): 89–104.Google Scholar
69. Lin, X, Dong, G, Wang, Q, Du, X. Abnormal gray matter and white matter volume in ‘Internet gaming addicts’. Addict Behav. 2015; 40: 137–143.Google Scholar
70. Zhou, Z, Li, C, Zhu, H. An error-related negativity potential investigation of response monitoring function in individuals with internet addiction disorder. Front Behav Neurosci. 2013; 7: 131.Google Scholar
71. Ruff, CC, Woodward, TS, Laurens, KR, Liddle, PF. The role of the anterior cingulate cortex in conflict processing: evidence from reverse stroop interference. Neuroimage. 2001; 14(5): 1150–1158.Google Scholar
72. Hester, R, Garavan, H. Executive dysfunction in cocaine addiction: evidence for discordant frontal, cingulate, and cerebellar activity. J Neurosci. 2004; 24(49): 11017–11022.Google Scholar
73. Elton, A, Tripathi, SP, Mletzko, T, et al. Childhood maltreatment is associated with a sex-dependent functional reorganization of a brain inhibitory control network. Hum Brain Mapp. 2014; 35(4): 1654–1667.Google Scholar
74. Pearson, JM, Hayden, BY, Raghavachari, S, Platt, ML. Neurons in posterior cingulate cortex signal exploratory decisions in a dynamic multioption choice task. Curr Biol. 2009; 19(18): 1532–1537.Google Scholar
75. Ding, W-N, Sun, J-H, Sun, Y-W, et al. Altered default network resting-state functional connectivity in adolescents with internet gaming addiction. PLoS One. 2013; 8(3): e59902.Google Scholar
76. Dong, G, DeVito, E, Huang, J, Du, X. Diffusion tensor imaging reveals thalamus and posterior cingulate cortex abnormalities in internet gaming addicts. J Psychiatr Res. 2012; 46(9): 1212–1216.Google Scholar