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Altered brain functional networks in Internet gaming disorder: independent component and graph theoretical analysis under a probability discounting task

Published online by Cambridge University Press:  10 April 2019

Ziliang Wang ,
Xiaoyue Liu ,
Yanbo Hu ,
Hui Zheng ,
Xiaoxia Du  and
Guangheng Dong Open the ORCID record for Guangheng Dong [Opens in a new window]
Ziliang Wang
Affiliation:
Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China
Xiaoyue Liu
Affiliation:
School of Mental Health, Wenzhou Medical University, Wenzhou, Zhejiang, China
Yanbo Hu
Affiliation:
Department of Psychology, London Metropolitan University, London, United Kingdom
Hui Zheng
Affiliation:
Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China
Xiaoxia Du
Affiliation:
Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
Guangheng Dong*
Affiliation:
Center for Cognition and Brain Disorders, Hangzhou Normal University, Hangzhou, Zhejiang Province 311121, China Institute of Psychological and Brain Sciences, Zhejiang Normal University, Jinhua, China
*
*Address correspondence to: Guangheng Dong, Ph.D., Professor, Department of Psychology, Zhejiang Normal University, 688 Yingbin Road, Jinhua, Zhejiang Province 311121, China. (Email: [email protected])
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Abstract

Objectives

Internet gaming disorder (IGD) is becoming a matter of concern around the world. However, the neural mechanism underlying IGD remains unclear. The purpose of this paper is to explore the differences between the neuronal network of IGD participants and that of recreational Internet game users (RGU).

Methods

Imaging and behavioral data were collected from 18 IGD participants and 20 RGU under a probability discounting task. The independent component analysis (ICA) and graph theoretical analysis (GTA) were used to analyze the data.

Results

Behavioral results showed the IGD participants, compared to RGU, prefer risky options to the fixed ones and spent less time in making risky decisions. In imaging results, the ICA analysis revealed that the IGD participants showed stronger functional connectivity (FC) in reward circuits and executive control network, as well as lower FC in anterior salience network (ASN) than RGU; for the GTA results, the IGD participants showed impaired FC in reward circuits and ASN when compared with RGU.

Conclusions

These results suggest that IGD participants were more sensitive to rewards, and they were more impulsive in decision-making as they could not control their impulsivity effectively. This might explain why IGD participants cannot stop their gaming behaviors even when facing severe negative consequences.

Keywords

ICAGTApossibility discount taskexecutive controlreward circuits
Type
Original Research
Information
CNS Spectrums , Volume 24 , Issue 5 , October 2019 , pp. 544 - 556
Copyright
© Cambridge University Press 2019 

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Footnotes

Ziliang Wang and Xiaoyue Liu contributed equally. Ziliang Wang and Xiaoyue Liu analyzed the data and wrote the first draft of the manuscript. Hui Zheng contributed to experimental programming and data preprocessing. Xiaoxia Du contributed to fMRI data collection. Guangheng Dong and Yanbo Hu designed the research and revised and improved the manuscript. All authors contributed to and had approved the final manuscript.

References

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.CrossRefGoogle ScholarPubMed
Meng, Y, Deng, W, Wang, H, Guo, W, Li, T. The prefrontal dysfunction in individuals with Internet gaming disorder: a meta-analysis of functional magnetic resonance imaging studies. Addict Biol. 2014; 20(4): 799.CrossRefGoogle ScholarPubMed
Király, O, Nagygyörgy, K, Griths, MD, Demetrovics, Z. Problematic Online Gaming. In: Rosenberg, KP, Feder, LC (eds). Behavioral Addictions: Criteria, Evidence, and Treatment. San Diego, CA; Academic Press 2014: 61–97.CrossRefGoogle Scholar
Julie, M, Frédéric, M, Magali, N, et al. Massively multiplayer online role-playing games: comparing characteristics of addict vs non-addict online recruited gamers in a French adult population. BMC Psychiatry. 2011; 11(1): 144.Google Scholar
Gentile, DA, Choo, H, Liau, A, et al. Pathological video game use among youths: a two-year longitudinal study. Pediatrics. 2011; 127(2): e319.CrossRefGoogle ScholarPubMed
American Psychiatric Association. Diagnostic and statistical manual of mental disorders. Washington DC: American Psychiatric Pub; 2013.Google Scholar
Montag, C, Reuter, M. Internet Addiction: Neuroscientific Approaches and Therapeutical Interventions. Springer Publishing Company, Incorporated; 2015.CrossRefGoogle Scholar
Viriyavejakul, C. Recreational Gaming Behavior of Undergraduate Students in Thailand. In McFerrin, K., Weber, R., Carlsen, R. & Willis, D. (Eds.), Proceedings of SITE 2008--Society for Information Technology & Teacher Education International Conference. Las Vegas, NV; Association for the Advancement of Computing in Education (AACE) 2008: 4948–4955.Google Scholar
Kuss, DJ, Griffiths, MD. Internet gaming addiction: a systematic review of empirical research. Int J Mental Health Addict. 2012; 10(2): 278–296.CrossRefGoogle Scholar
Wang, Y, Wu, L, Wang, L, Zhang, Y, Du, X, Dong, G. Impaired decision-making and impulse control in Internet gaming addicts: evidence from the comparison with recreational Internet game users. Addict Biol. 2016; 22(6): 1610–1621.CrossRefGoogle ScholarPubMed
Dong, G, Li, H, Wang, L, Potenza, MN. Cognitive control and reward/loss processing in internet gaming disorder: results from a comparison with recreational internet game-users. Eur Psychiatry. 2017; 44: 30–38.CrossRefGoogle ScholarPubMed
Bae, S, Hong, JS, Kim, SM, Han, DH. Bupropion shows different effects on brain functional connectivity in patients with internet-based gambling disorder and internet gaming disorder. Front Psychiatry. 2018; 10(9): 130.CrossRefGoogle Scholar
Petry, NM, Zajac, K, Ginley, MK. Behavioral addictions as mental disorders: to be or not to be? Annu Rev Clin Psychol. 2016; 14(1): 399–423.CrossRefGoogle Scholar
Ko, CH, Hsieh, TJ, Wang, PW, et al. Altered gray matter density and disrupted functional connectivity of the amygdala in adults with Internet gaming disorder. Prog Neuro-Psychopharmacol Biol Psychiatry. 2015; 57: 185.CrossRefGoogle ScholarPubMed
Dong, G, Potenza, MN. A cognitive-behavioral model of Internet gaming disorder: theoretical underpinnings and clinical implications. J Psychiatric Res. 2014; 58(8): 7–11.CrossRefGoogle ScholarPubMed
Dong, G, Potenza, MN. Risk-taking and risky decision-making in internet gaming disorder: implications regarding online gaming in the setting of negative consequences. J Psychiatric Res. 2016; 73(1): 1.CrossRefGoogle ScholarPubMed
Dong, G, Wang, L, Du, X, Potenza, MN. Gaming increases craving to gaming-related stimuli in individuals with internet gaming disorder. Biol Psychiatry Cog Neurosci Neuroimaging. 2017; 2(5): 404–412.Google ScholarPubMed
Dong, G, Hu, Y, Xiao, L. Reward/punishment sensitivities among internet addicts: implications for their addictive behaviors. Prog Neuro-Psychopharmacol Biol Psychiatry. 2013; 46(5): 139–145.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Gilman, JM, Calderon, V, Curran, MT, Evins, AE. Young adult cannabis users report greater propensity for risk-taking only in non-monetary domains. Drug Alcohol Depend. 2015; 147: 26–31.CrossRefGoogle ScholarPubMed
Schutter, DJLG, Bokhoven, IV, Vanderschuren, LJMJ, Lochman, JE, Matthys, W. Risky decision making in substance dependent adolescents with a disruptive behavior disorder. J Abnormal Child Psychol. 2011; 39(3): 333.CrossRefGoogle ScholarPubMed
Madden, GJ, Petry, NM, Johnson, PS. Pathological gamblers discount probabilistic rewards less steeply than matched controls. Exp Clin Psychopharmacol. 2009; 17(5): 283–290.CrossRefGoogle ScholarPubMed
Calhoun, VD, Adali, 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.CrossRefGoogle ScholarPubMed
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. 2017; 267(3): 245–255.CrossRefGoogle Scholar
He, Y, Evans, A. Graph theoretical modeling of brain connectivity. Curr Opin Neurol. 2010; 23(4): 341–350.Google ScholarPubMed
Newman, MEJ. The structure and function of complex networks. SIAM Rev 2003; 45(2): 167–256.CrossRefGoogle Scholar
Achard, S, Bullmore, E. Efficiency and cost of economical brain functional networks. PLoS Comput Biol. 2007; 3(2):e17.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
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 Neuroimaging. 2016; 254: 156.CrossRefGoogle ScholarPubMed
Ye, Z, Doñamayor, N, Münte TF. Brain network of semantic integration in sentence reading: insights from independent component analysis and graph theoretical analysis. Hum Brain Mapp.. 2014; 35(2): 367–376.CrossRefGoogle ScholarPubMed
Dong, G, Hu, Y, Lin, X. Reward/punishment sensitivities among internet addicts: implications for their addictive behaviors. Prog Neuro-Psychopharmacol Biol Psychiatry. 2013; 46(5): 139.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle Scholar
Young, K. Internet addiction: diagnosis and treatment considerations. J Contemp Psychother. 2009; 39(4): 241–246.CrossRefGoogle Scholar
Yi, R, Chase, WD, Bickel, WK. Probability discounting among cigarette smokers and nonsmokers: molecular analysis discerns group differences. Behav Pharmacol. 2007; 18(7): 633–639.CrossRefGoogle ScholarPubMed
Rachlin, H, Raineri, A, Cross, D. Subjective probability and delay. J Exp Anal Behav. 1991; 55(2): 233–244.CrossRefGoogle Scholar
Young, KS. Internet addiction: the emergence of a new clinical disorder. Cyberpsychol Behav. 1998; 1(3): 237–244.CrossRefGoogle Scholar
Mitchell, SH. Measures of impulsivity in cigarette smokers and non-smokers. Psychopharmacology. 1999; 146(4): 455–464.CrossRefGoogle ScholarPubMed
Reynolds, B, Richards, JB, Horn, K, Karraker, K. Delay discounting and probability discounting as related to cigarette smoking status in adults. Behav Process. 2004; 65(1): 35–42.CrossRefGoogle ScholarPubMed
Bell, AJ, Sejnowski, TJ. An information-maximization approach to blind separation and blind deconvolution. Neural Comput. 1995; 7(6): 1129–1159.CrossRefGoogle ScholarPubMed
Himberg, J, Hyvärinen, A, Esposito, F. Validating the independent components of neuroimaging time series via clustering and visualization. Neuroimage. 2004; 22(3): 1214.CrossRefGoogle ScholarPubMed
Meda, SA, Stevens, MC, Folley, BS, Calhoun, VD, Pearlson, GD. Evidence for anomalous network connectivity during working memory encoding in schizophrenia: an ICA based analysis. Plos One. 2009; 4(11):e7911.CrossRefGoogle ScholarPubMed
Salvador, R, Suckling, J, Coleman, MR, Pickard, JD, Menon, D, Bullmore, E. Neurophysiological architecture of functional magnetic resonance images of human brain. Cerebral Cortex. 2005; 15(9): 1332–1342.CrossRefGoogle ScholarPubMed
Fisher, R. On the ‘probable error’ of a coefficient of correlation deduced from a small sample. Metron. 1921; 1: 3–32.Google Scholar
Liu, Y, Liang, M, Zhou, Y, et al. Disrupted small-world networks in schizophrenia. Brain. 2008; 131(4): 945–961.CrossRefGoogle Scholar
Watts, DJ, Strogatz, SH. Collective dynamics of ‘small-world’ networks. Nature. 1998; 393(6684): 440–442.CrossRefGoogle ScholarPubMed
Zhang, J, Wang, J, Wu, Q, et al. Disrupted brain connectivity networks in drug-naive, first-episode major depressive disorder. Biol Psychiatry. 2011; 70(4): 334–342.CrossRefGoogle ScholarPubMed
Widyanto, L, Griffiths, MD, Brunsden, V. A psychometric comparison of the internet addiction test, the internet-related problem scale, and self-diagnosis. Cyberpsychol Behav Social Networking. 2011; 14(3): 141–149.CrossRefGoogle ScholarPubMed
Dai, Z, Harrow, SE, Song, X, Rucklidge, JJ, Grace, RC. Gambling, delay, and probability discounting in adults with and without ADHD. J Attent Disord. 2013; 20(11): 968.CrossRefGoogle ScholarPubMed
Reynolds, B, Richards, JB, Horn, K, Karraker, K. Delay discounting and probability discounting as related to cigarette smoking status in adults. Behav Process. 2004; 65(1): 35–42.CrossRefGoogle ScholarPubMed
Holt, DD, Green, L, Myerson, J. Is discounting impulsive?. Evidence from temporal and probability discounting in gambling and non-gambling college students. Behav Process. 2003; 64(3): 355–367.CrossRefGoogle ScholarPubMed
Shirer, W, Ryali, S, Rykhlevskaia, E, Menon, V, Greicius, M. Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cerebral Cortex. 2012; 22(1): 158–165.CrossRefGoogle ScholarPubMed
Krmpotich, TD, Tregellas, JR, Thompson, LL, Banich, MT, Klenk, AM, Tanabe, JL. Resting-state activity in the left executive control network is associated with behavioral approach and is increased in substance dependence. Drug Alcohol Depend. 2013; 129(1-2): 1.CrossRefGoogle ScholarPubMed
Sutherland, MT, Mchugh, MJ, Pariyadath, V, Stein, EA. Resting state functional connectivity in addiction: lessons learned and a road ahead. Neuroimage. 2012; 62(4): 2281–2295.CrossRefGoogle Scholar
Rui, N, Taki, Y, Takeuchi, H, et al. Brain training game boosts executive functions, working memory and processing speed in the young adults: a randomized controlled trial. Plos One. 2013; 8(2):e55518.Google Scholar
Rui, N, Yasuyuki, T, Hikaru, T, et al. Brain training game improves executive functions and processing speed in the elderly: a randomized controlled trial. Plos One. 2012; 7(1):e29676.Google Scholar
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. Sci Rep-Uk. 2015; 5(1): 97.Google ScholarPubMed
Bonnelle, V, Ham, TE, Leech, R, et al. Salience network integrity predicts default mode network function after traumatic brain injury. Proc Natl Acad Sci. 2012; 109(12): 4690.CrossRefGoogle ScholarPubMed
Sridharan, D, Levitin, DJ, Menon, V. A critical role for the right fronto-insular cortex in switching between central-executive and default-mode networks. Proc Natl Acad Sci. 2008; 105(34): 12569.CrossRefGoogle ScholarPubMed
Uddin, LQ, Supekar, KS, Ryali, S, Menon, V. Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development. J Neurosci Off J Soc Neurosci. 2011; 31(50): 18578–18589.CrossRefGoogle ScholarPubMed
Menon, V. Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cog Sci. 2011; 15(10): 483–506.CrossRefGoogle ScholarPubMed
Delbeuck, X, Linden, MVD, Collette, F. Alzheimer’ disease as a disconnection syndrome? Neuropsychol Rev. 2003; 13(2): 79–92.CrossRefGoogle Scholar
Wang, XB, Zhao, XH, Jiang, H, et al. The brain functional network efficiency in patients with mild cognitive impairment. Chin Comput Med Imaging. 2015; 21(1):1–5.Google Scholar
Power, Y, Goodyear, B, Crockford, D. Neural correlates of pathological gamblers preference for immediate rewards during the iowa gambling task: an fMRI study. J Gambl Stud. 2012; 28(4): 623–636.CrossRefGoogle Scholar
Dong, G, Huang, J, Du, X. Enhanced reward sensitivity and decreased loss sensitivity in Internet addicts: an fMRI study during a guessing task. J Psychiatric Res. 2011; 45(11): 1525.CrossRefGoogle ScholarPubMed
Zhao, Q, Tang, Y, Feng, H, Li, C, Sui, D. The effects of neuron heterogeneity and connection mechanism in cortical networks. Phys A Stat Mech Appl. 2008; 387(23): 5952–5957.CrossRefGoogle Scholar
Yuan, K, Wei, Q, Yu, D, et al. Core brain networks interactions and cognitive control in internet gaming disorder individuals in late adolescence/early adulthood. Brain Struct Funct. 2016; 221(3): 1427–1442.CrossRefGoogle ScholarPubMed
Xing, L, Yuan, K, Bi, Y, et al. Reduced fiber integrity and cognitive control in adolescents with internet gaming disorder. Brain Res. 2014; 1586: 109.CrossRefGoogle ScholarPubMed
Moussa, MN, Steen, MR, Laurienti, PJ, Hayasaka, S. Consistency of network modules in resting-state fMRI connectome data. Plos One. 2012; 7(8): 1036.CrossRefGoogle ScholarPubMed
Schmidt, A, Denier, N, Magon, S, et al. Increased functional connectivity in the resting-state basal ganglia network after acute heroin substitution. Transl Psychiatry. 2014; 5(3):e533.CrossRefGoogle Scholar
Meng, YJ, Deng, W, Wang, HY, et al. Reward pathway dysfunction in gambling disorder: a meta-analysis of functional magnetic resonance imaging studies. Behav Brain Res. 2014; 275: 243.CrossRefGoogle ScholarPubMed
Volkow, ND, Wang, GJ, Fowler, JS, Tomasi, D, Telang, F, Baler, R. Addiction: decreased reward sensitivity and increased expectation sensitivity conspire to overwhelm the brain’s control circuit. Bioessays News Rev Mol Cell Dev Biol. 2010; 32(9): 748–755.CrossRefGoogle ScholarPubMed
Dong, G, Huang, J, Du, X. Enhanced reward sensitivity and decreased loss sensitivity in internet addicts: an fMRI study during a guessing task. J Psychiatric Res. 2011; 45(11): 1525–1529.CrossRefGoogle ScholarPubMed
Dong, G, Wang, Y, Potenza, MN. The activation of the caudate is associated with correct recollections in a reward-based recollection task. Hum Brain Mapp. 2016; 37(11): 3999–4005.CrossRefGoogle Scholar
Lorenz, RC, Gleich, T, Gallinat, J, Kühn, S. Video game training and the reward system. Front Hum Neurosci. 2015; 9: 40.CrossRefGoogle ScholarPubMed
Kaiser, M, Hilgetag, CC. Modelling the development of cortical systems networks. Neurocomputing. 2004; 58–60(3): 297–302.CrossRefGoogle Scholar
Wee, CY, Zhao, Z, Yap, PT, et al. Disrupted brain functional network in internet addiction disorder: a resting-state functional magnetic resonance imaging study. Plos One. 2014; 9(9):e107306.CrossRefGoogle ScholarPubMed
Lynall, ME, Bassett, DS, Kerwin, R, et al. Functional connectivity and brain networks in schizophrenia. J Neurosci. 2010; 30(28): 9477–9487.CrossRefGoogle Scholar
Van Den Heuvel, MP, Hulshoff Pol, HE. Exploring the brain network: a review on resting-state fMRI functional connectivity. Eur Neuropsychopharmacol. 2010; 20(8): 519–534.CrossRefGoogle ScholarPubMed
Hayasaka, S, Laurienti, PJ. Comparison of characteristics between region-and voxel-based network analyses in resting-state fMRI data. Neuroimage. 2010; 50(2): 499.CrossRefGoogle ScholarPubMed