*Result*: Neural underpinnings of internet gaming addiction tendency: The role of the limbic network in reward/punishment sensitivity and risky decision-making alterations.
Title:
Neural underpinnings of internet gaming addiction tendency: The role of the limbic network in reward/punishment sensitivity and risky decision-making alterations.
Authors:
He J; Faculty of Psychology, Ministry of Education (MOE) Key Laboratory of Cognition and Personality, Southwest University, Chongqing, China., Zhao H; Faculty of Psychology, Ministry of Education (MOE) Key Laboratory of Cognition and Personality, Southwest University, Chongqing, China., Turel O; School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia., Zhang S; Department of Psychology, Faculty of Education, Guangxi Normal University, Guilin, China., Lei X; Faculty of Psychology, Ministry of Education (MOE) Key Laboratory of Cognition and Personality, Southwest University, Chongqing, China., Qiu J; Faculty of Psychology, Ministry of Education (MOE) Key Laboratory of Cognition and Personality, Southwest University, Chongqing, China., Feng T; Faculty of Psychology, Ministry of Education (MOE) Key Laboratory of Cognition and Personality, Southwest University, Chongqing, China., Chen H; Faculty of Psychology, Ministry of Education (MOE) Key Laboratory of Cognition and Personality, Southwest University, Chongqing, China., He Q; Faculty of Psychology, Ministry of Education (MOE) Key Laboratory of Cognition and Personality, Southwest University, Chongqing, China.
Source:
Addiction (Abingdon, England) [Addiction] 2026 Mar; Vol. 121 (3), pp. 535-546. Date of Electronic Publication: 2025 Oct 25.
Publication Type:
Journal Article; Observational Study
Language:
English
Journal Info:
Publisher: Wiley-Blackwell Country of Publication: England NLM ID: 9304118 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1360-0443 (Electronic) Linking ISSN: 09652140 NLM ISO Abbreviation: Addiction Subsets: MEDLINE
Imprint Name(s):
Publication: Oxford : Wiley-Blackwell
Original Publication: Abingdon, Oxfordshire, UK : Carfax Pub. Co., c1993-
Original Publication: Abingdon, Oxfordshire, UK : Carfax Pub. Co., c1993-
MeSH Terms:
Internet Addiction Disorder*/physiopathology , Internet Addiction Disorder*/psychology , Internet Addiction Disorder*/diagnostic imaging , Punishment*/psychology , Decision Making*/physiology , Limbic System*/physiopathology , Limbic System*/diagnostic imaging , Behavior, Addictive*/physiopathology , Reward* , Risk-Taking* , Video Games*, Humans ; Male ; Female ; Adolescent ; Cross-Sectional Studies ; Young Adult ; Magnetic Resonance Imaging ; Adult ; Surveys and Questionnaires ; Internet
References:
Turel O. Videogames and guns in adolescents: preliminary tests of a bipartite association. Comput Hum Behav. 2020;109(paper 106355):1–8. https://doi.org/10.1016/j.chb.2020.106355.
He Q, Turel O, Wei L, Bechara A. Structural brain differences associated with extensive massively‐multiplayer video gaming. Brain Imaging Behav. 2021;15(1):364–374. https://doi.org/10.1007/s11682-020-00263-0.
Wei L, Zhang S, Turel O, Bechara A, He Q. A tripartite neurocognitive model of internet gaming disorder. Front Psych. 2017;8(285):1–11. https://doi.org/10.3389/fpsyt.2017.00285.
Young KS. Internet addiction: the emergence of a new clinical disorder. Cyberpsychol Behav. 1998;1(3):237–244. https://doi.org/10.1089/cpb.1998.1.237.
Griffiths M. A ‘components’ model of addiction within a biopsychosocial framework. J Subst Abuse. 2005;10(4):191–197. https://doi.org/10.1080/14659890500114359.
Xu ZC, Turel O, Yuan YF. Online game addiction among adolescents: motivation and prevention factors. Eur J Inf Syst. 2012;21(3):321–340. https://doi.org/10.1057/ejis.2011.56.
Mehroof M, Griffiths MD. Online gaming addiction: the role of sensation seeking, self‐control, neuroticism, aggression, state anxiety, and trait anxiety. Cyberpsychol Behav Soc Netw. 2010;13(3):313–316. https://doi.org/10.1089/cyber.2009.0229.
Hyun GJ, Han DH, Lee YS, Kang KD, Yoo SK, Chung U‐S, et al. Risk factors associated with online game addiction: a hierarchical model. Comput Hum Behav. 2015;48:706–713. https://doi.org/10.1016/j.chb.2015.02.008.
Pan N, Yang Y, Du X, Qi X, Du G, Zhang Y, et al. Brain structures associated with internet addiction tendency in adolescent online game players. Front Psych. 2018;9:67. https://doi.org/10.3389/fpsyt.2018.00067.
Kuss DJ, Griffiths MD. Internet and gaming addiction: a systematic literature review of neuroimaging studies. Brain Sci. 2012;2(3):347–374. https://doi.org/10.3390/brainsci2030347.
Kuss DJ, Griffiths MD. Internet gaming addiction: A systematic review of empirical research. Int J Ment Health Addict. 2012;10(2):278–296. https://doi.org/10.1007/s11469-011-9318-5.
Pan N, Yang Y, Du X, Qi X, Du G, Zhang Y, et al. Brain structures associated with internet addiction tendency in adolescent online game players. Front Psych. 2018;9:291270. https://doi.org/10.3389/fpsyt.2018.00067.
Dong G, Wang M, Liu X, Liang Q, Du X, Potenza MN. Cue‐elicited craving–related lentiform activation during gaming deprivation is associated with the emergence of internet gaming disorder. Addict Biol. 2020;25(1):e12713. https://doi.org/10.1111/adb.12713.
He W, Qi A, Wang Q, Wu H, Zhang Z, Gu R, et al. Abnormal reward and punishment sensitivity associated with internet addicts. Comput Hum Behav. 2017;75:678–683. https://doi.org/10.1016/j.chb.2017.06.017.
Meerkerk G‐J, van den Eijnden RJ, Franken IH, Garretsen H. Is compulsive internet use related to sensitivity to reward and punishment, and impulsivity? Comput Hum Behav. 2010;26(4):729–735. https://doi.org/10.1016/j.chb.2010.01.009.
Gray JA. The psychophysiological basis of introversion‐extraversion. Behav Res Ther. 1970;8(3):249–266. https://doi.org/10.1016/0005-7967(70)90069-0.
Torrubia R, Avila C, Moltó J, Caseras X. The sensitivity to punishment and sensitivity to reward questionnaire (SPSRQ) as a measure of Gray's anxiety and impulsivity dimensions. Personal Individ Differ. 2001;31(6):837–862. https://doi.org/10.1016/S0191-8869(00)00183-5.
Kahneman D, Tversky A. Prospect theory: an analysis of decision under risk. In: Handbook of the fundamentals of financial decision making: Part I World Scientific; 2013. p. 99–127. https://doi.org/10.1017/cbo9780511609220.014.
Balogh KN, Mayes LC, Potenza MN. Risk‐taking and decision‐making in youth: relationships to addiction vulnerability. J Behav Addict. 2013;2(1):1–9. https://doi.org/10.1556/JBA.2.2013.1.1.
Gray JA. Précis of the neuropsychology of anxiety: an enquiry into the functions of the septo‐hippocampal system. Behav Brain Sci. 1982;5(3):469–484. https://doi.org/10.1017/S0140525X00013066.
Gray JA. Perspectives on anxiety and impulsivity: A commentary. J Res Pers. 1987;21(4):493–509. https://doi.org/10.1016/0092-6566(87)90036-5.
Dong G, Potenza MN. A cognitive‐behavioral model of internet gaming disorder: theoretical underpinnings and clinical implications. J Psychiatr Res. 2014;58:7–11. https://doi.org/10.1016/j.jpsychires.2014.07.005.
Dong G, Hu Y, Lin X. Reward/punishment sensitivities among internet addicts: implications for their addictive behaviors. Prog Neuro‐Psychopharmacol Biol Psychiatry. 2013;46:139–145. https://doi.org/10.1016/j.pnpbp.2013.07.007.
Ahmed YB, Al‐Bzour AN, Alzghoul SM, Ibrahim RB, Al‐Khalili AA, Al‐Majali GN, et al. Limbic and cortical regions as functional biomarkers associated with emotion regulation in bipolar disorder: A meta‐analysis of neuroimaging studies. J Affect Disord. 2023;323:506–513. https://doi.org/10.1016/j.jad.2022.11.071.
Brand M, Young KS, Laier C, Wölfling K, Potenza MN. Integrating psychological and neurobiological considerations regarding the development and maintenance of specific internet‐use disorders: an interaction of person‐affect‐cognition‐execution (I‐PACE) model. Neurosci Biobehav Rev. 2016;71:252–266. https://doi.org/10.1016/j.neubiorev.2016.08.033.
Ko C‐H, Liu G‐C, Hsiao S, Yen J‐Y, Yang M‐J, Lin W‐C, et al. Brain activities associated with gaming urge of online gaming addiction. J Psychiatr Res. 2009;43(7):739–747. https://doi.org/10.1016/j.jpsychires.2008.09.012.
Morgane PJ, Galler JR, Mokler DJ. A review of systems and networks of the limbic forebrain/limbic midbrain. Prog Neurobiol. 2005;75(2):143–160. https://doi.org/10.1016/j.pneurobio.2005.01.001.
Rolls ET. Limbic systems for emotion and for memory, but no single limbic system. Cortex. 2015;62:119–157. https://doi.org/10.1016/j.cortex.2013.12.005.
Weinstein A, Lejoyeux M. Neurobiological mechanisms underlying internet gaming disorder. Dialogues Clin Neurosci. 2020;22(2):113–126. https://doi.org/10.31887/DCNS.2020.22.2/aweinstein.
Seymour B, Daw N, Dayan P, Singer T, Dolan R. Differential encoding of losses and gains in the human striatum. J Neurosci. 2007;27(18):4826–4831. https://doi.org/10.1523/JNEUROSCI.0400-07.2007.
Pessiglione M, Schmidt L, Draganski B, Kalisch R, Lau H, Dolan RJ, et al. How the brain translates money into force: a neuroimaging study of subliminal motivation. Science. 2007;316(5826):904–906. https://doi.org/10.1126/science.1140459.
Menon V. Large‐scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci. 2011;15(10):483–506. https://doi.org/10.1016/j.tics.2011.08.003.
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. https://doi.org/10.1016/j.eurpsy.2017.03.004.
Yang WJ, Zhou ZJ. The relationship between the type of Internet addiction and the personality trait of college students. Int J Psychol. 2004;39(5‐6):277. https://doi.org/10.19648/j.cnki.jhustss1980.2004.03.009.
Bechara A, Damasio AR, Damasio H, Anderson SW. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition. 1994;50(1–3):7–15. https://doi.org/10.1016/0010-0277(94)90018-3.
Buelow MT, Blaine AL. The assessment of risky decision making: a factor analysis of performance on the Iowa gambling task, balloon analogue risk task, and Columbia card task. Psychol Assess. 2015;27(3):777–785. https://doi.org/10.1037/a0038622.
Friston KJ, Williams S, Howard R, Frackowiak RS, Turner R. Movement‐related effects in fMRI time‐series. Magn Reson Med. 1996;35(3):346–355. https://doi.org/10.1002/mrm.1910350312.
Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage. 2012;59(3):2142–2154. https://doi.org/10.1016/j.neuroimage.2011.10.018.
Muschelli J, Nebel MB, Caffo BS, Barber AD, Pekar JJ, Mostofsky SH. Reduction of motion‐related artifacts in resting state fMRI using aCompCor. Neuroimage. 2014;96:22–35. https://doi.org/10.1016/j.neuroimage.2014.03.028.
Hu LT, Bentler PM. Cut off criteria of fit indices in co‐variance structure analysis; Conventional criteria versus new alternatives. Struct Equ Modeling. 1999;6(1):1–55. https://doi.org/10.1080/10705519909540118.
Wang J, Wang X, Xia M, Liao X, Evans A, He Y. GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci. 2015;9:386. https://doi.org/10.3389/fnhum.2015.00386.
Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106(3):1125–1165. https://doi.org/10.1152/jn.00338.2011.
Corr PJ. Reinforcement sensitivity theory and personality. Neurosci Biobehav Rev. 2004;28(3):317–332. https://doi.org/10.1016/j.neubiorev.2004.01.005.
Corr PJ, Perkins AM. The role of theory in the psychophysiology of personality: from Ivan Pavlov to Jeffrey Gray. Int J Psychophysiol. 2006;62(3):367–376. https://doi.org/10.1016/j.ijpsycho.2006.01.005.
Bechara A, Damasio H, Tranel D, Damasio AR. The Iowa gambling task and the somatic marker hypothesis: some questions and answers. Trends Cogn Sci. 2005;9(4):159–162. https://doi.org/10.1016/j.tics.2005.02.002.
Wang Y, Wu L, Zhou H, Lin X, Zhang Y, Du X, 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. https://doi.org/10.1007/s00406-016-0721-6.
Gentile DA, Choo H, Liau A, Sim T, Li D, Fung D, et al. Pathological video game use among youths: a two‐year longitudinal study. Pediatrics. 2011;127(2):e319–e329. https://doi.org/10.1542/peds.2010-1353.
Brand M, Wegmann E, Stark R, Müller A, Wölfling K, Robbins TW, et al. The interaction of person‐affect‐cognition‐execution (I‐PACE) model for addictive behaviors: update, generalization to addictive behaviors beyond internet‐use disorders, and specification of the process character of addictive behaviors. Neurosci Biobehav Rev. 2019;104:1–10. https://doi.org/10.1016/j.neubiorev.2019.06.032.
Schultz W. Predictive reward signal of dopamine neurons. J Neurophysiol. 1998;80(1):1–27. https://doi.org/10.1152/jn.1998.80.1.1.
Peters J, Büchel C. Neural representations of subjective reward value. Behav Brain Res. 2010;213(2):135–141. https://doi.org/10.1016/j.bbr.2010.04.031.
Steinberg L. A dual systems model of adolescent risk‐taking. Dev Psychobiol. 2010;52(3):216–224. https://doi.org/10.1002/dev.20445.
Menon V, Uddin LQ. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct. 2010;214(5‐6):655–667. https://doi.org/10.1007/s00429-010-0262-0.
Gerlach KD, Spreng RN, Madore KP, Schacter DL. Future planning: default network activity couples with frontoparietal control network and reward‐processing regions during process and outcome simulations. Soc Cogn Affect Neurosci. 2014;9(12):1942–1951. https://doi.org/10.1093/scan/nsu001.
Schettler L, Thomasius R, Paschke K. Neural correlates of problematic gaming in adolescents: a systematic review of structural and functional magnetic resonance imaging studies. Addict Biol. 2022;27(1):e13093. https://doi.org/10.1111/adb.13093.
He Q, Turel O, Wei L, Bechara A. Structural brain differences associated with extensive massively‐multiplayer video gaming. Brain Imaging Behav. 2021;15(1):364–374. https://doi.org/10.1007/s11682-020-00263-0.
Wei L, Zhang S, Turel O, Bechara A, He Q. A tripartite neurocognitive model of internet gaming disorder. Front Psych. 2017;8(285):1–11. https://doi.org/10.3389/fpsyt.2017.00285.
Young KS. Internet addiction: the emergence of a new clinical disorder. Cyberpsychol Behav. 1998;1(3):237–244. https://doi.org/10.1089/cpb.1998.1.237.
Griffiths M. A ‘components’ model of addiction within a biopsychosocial framework. J Subst Abuse. 2005;10(4):191–197. https://doi.org/10.1080/14659890500114359.
Xu ZC, Turel O, Yuan YF. Online game addiction among adolescents: motivation and prevention factors. Eur J Inf Syst. 2012;21(3):321–340. https://doi.org/10.1057/ejis.2011.56.
Mehroof M, Griffiths MD. Online gaming addiction: the role of sensation seeking, self‐control, neuroticism, aggression, state anxiety, and trait anxiety. Cyberpsychol Behav Soc Netw. 2010;13(3):313–316. https://doi.org/10.1089/cyber.2009.0229.
Hyun GJ, Han DH, Lee YS, Kang KD, Yoo SK, Chung U‐S, et al. Risk factors associated with online game addiction: a hierarchical model. Comput Hum Behav. 2015;48:706–713. https://doi.org/10.1016/j.chb.2015.02.008.
Pan N, Yang Y, Du X, Qi X, Du G, Zhang Y, et al. Brain structures associated with internet addiction tendency in adolescent online game players. Front Psych. 2018;9:67. https://doi.org/10.3389/fpsyt.2018.00067.
Kuss DJ, Griffiths MD. Internet and gaming addiction: a systematic literature review of neuroimaging studies. Brain Sci. 2012;2(3):347–374. https://doi.org/10.3390/brainsci2030347.
Kuss DJ, Griffiths MD. Internet gaming addiction: A systematic review of empirical research. Int J Ment Health Addict. 2012;10(2):278–296. https://doi.org/10.1007/s11469-011-9318-5.
Pan N, Yang Y, Du X, Qi X, Du G, Zhang Y, et al. Brain structures associated with internet addiction tendency in adolescent online game players. Front Psych. 2018;9:291270. https://doi.org/10.3389/fpsyt.2018.00067.
Dong G, Wang M, Liu X, Liang Q, Du X, Potenza MN. Cue‐elicited craving–related lentiform activation during gaming deprivation is associated with the emergence of internet gaming disorder. Addict Biol. 2020;25(1):e12713. https://doi.org/10.1111/adb.12713.
He W, Qi A, Wang Q, Wu H, Zhang Z, Gu R, et al. Abnormal reward and punishment sensitivity associated with internet addicts. Comput Hum Behav. 2017;75:678–683. https://doi.org/10.1016/j.chb.2017.06.017.
Meerkerk G‐J, van den Eijnden RJ, Franken IH, Garretsen H. Is compulsive internet use related to sensitivity to reward and punishment, and impulsivity? Comput Hum Behav. 2010;26(4):729–735. https://doi.org/10.1016/j.chb.2010.01.009.
Gray JA. The psychophysiological basis of introversion‐extraversion. Behav Res Ther. 1970;8(3):249–266. https://doi.org/10.1016/0005-7967(70)90069-0.
Torrubia R, Avila C, Moltó J, Caseras X. The sensitivity to punishment and sensitivity to reward questionnaire (SPSRQ) as a measure of Gray's anxiety and impulsivity dimensions. Personal Individ Differ. 2001;31(6):837–862. https://doi.org/10.1016/S0191-8869(00)00183-5.
Kahneman D, Tversky A. Prospect theory: an analysis of decision under risk. In: Handbook of the fundamentals of financial decision making: Part I World Scientific; 2013. p. 99–127. https://doi.org/10.1017/cbo9780511609220.014.
Balogh KN, Mayes LC, Potenza MN. Risk‐taking and decision‐making in youth: relationships to addiction vulnerability. J Behav Addict. 2013;2(1):1–9. https://doi.org/10.1556/JBA.2.2013.1.1.
Gray JA. Précis of the neuropsychology of anxiety: an enquiry into the functions of the septo‐hippocampal system. Behav Brain Sci. 1982;5(3):469–484. https://doi.org/10.1017/S0140525X00013066.
Gray JA. Perspectives on anxiety and impulsivity: A commentary. J Res Pers. 1987;21(4):493–509. https://doi.org/10.1016/0092-6566(87)90036-5.
Dong G, Potenza MN. A cognitive‐behavioral model of internet gaming disorder: theoretical underpinnings and clinical implications. J Psychiatr Res. 2014;58:7–11. https://doi.org/10.1016/j.jpsychires.2014.07.005.
Dong G, Hu Y, Lin X. Reward/punishment sensitivities among internet addicts: implications for their addictive behaviors. Prog Neuro‐Psychopharmacol Biol Psychiatry. 2013;46:139–145. https://doi.org/10.1016/j.pnpbp.2013.07.007.
Ahmed YB, Al‐Bzour AN, Alzghoul SM, Ibrahim RB, Al‐Khalili AA, Al‐Majali GN, et al. Limbic and cortical regions as functional biomarkers associated with emotion regulation in bipolar disorder: A meta‐analysis of neuroimaging studies. J Affect Disord. 2023;323:506–513. https://doi.org/10.1016/j.jad.2022.11.071.
Brand M, Young KS, Laier C, Wölfling K, Potenza MN. Integrating psychological and neurobiological considerations regarding the development and maintenance of specific internet‐use disorders: an interaction of person‐affect‐cognition‐execution (I‐PACE) model. Neurosci Biobehav Rev. 2016;71:252–266. https://doi.org/10.1016/j.neubiorev.2016.08.033.
Ko C‐H, Liu G‐C, Hsiao S, Yen J‐Y, Yang M‐J, Lin W‐C, et al. Brain activities associated with gaming urge of online gaming addiction. J Psychiatr Res. 2009;43(7):739–747. https://doi.org/10.1016/j.jpsychires.2008.09.012.
Morgane PJ, Galler JR, Mokler DJ. A review of systems and networks of the limbic forebrain/limbic midbrain. Prog Neurobiol. 2005;75(2):143–160. https://doi.org/10.1016/j.pneurobio.2005.01.001.
Rolls ET. Limbic systems for emotion and for memory, but no single limbic system. Cortex. 2015;62:119–157. https://doi.org/10.1016/j.cortex.2013.12.005.
Weinstein A, Lejoyeux M. Neurobiological mechanisms underlying internet gaming disorder. Dialogues Clin Neurosci. 2020;22(2):113–126. https://doi.org/10.31887/DCNS.2020.22.2/aweinstein.
Seymour B, Daw N, Dayan P, Singer T, Dolan R. Differential encoding of losses and gains in the human striatum. J Neurosci. 2007;27(18):4826–4831. https://doi.org/10.1523/JNEUROSCI.0400-07.2007.
Pessiglione M, Schmidt L, Draganski B, Kalisch R, Lau H, Dolan RJ, et al. How the brain translates money into force: a neuroimaging study of subliminal motivation. Science. 2007;316(5826):904–906. https://doi.org/10.1126/science.1140459.
Menon V. Large‐scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci. 2011;15(10):483–506. https://doi.org/10.1016/j.tics.2011.08.003.
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. https://doi.org/10.1016/j.eurpsy.2017.03.004.
Yang WJ, Zhou ZJ. The relationship between the type of Internet addiction and the personality trait of college students. Int J Psychol. 2004;39(5‐6):277. https://doi.org/10.19648/j.cnki.jhustss1980.2004.03.009.
Bechara A, Damasio AR, Damasio H, Anderson SW. Insensitivity to future consequences following damage to human prefrontal cortex. Cognition. 1994;50(1–3):7–15. https://doi.org/10.1016/0010-0277(94)90018-3.
Buelow MT, Blaine AL. The assessment of risky decision making: a factor analysis of performance on the Iowa gambling task, balloon analogue risk task, and Columbia card task. Psychol Assess. 2015;27(3):777–785. https://doi.org/10.1037/a0038622.
Friston KJ, Williams S, Howard R, Frackowiak RS, Turner R. Movement‐related effects in fMRI time‐series. Magn Reson Med. 1996;35(3):346–355. https://doi.org/10.1002/mrm.1910350312.
Power JD, Barnes KA, Snyder AZ, Schlaggar BL, Petersen SE. Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage. 2012;59(3):2142–2154. https://doi.org/10.1016/j.neuroimage.2011.10.018.
Muschelli J, Nebel MB, Caffo BS, Barber AD, Pekar JJ, Mostofsky SH. Reduction of motion‐related artifacts in resting state fMRI using aCompCor. Neuroimage. 2014;96:22–35. https://doi.org/10.1016/j.neuroimage.2014.03.028.
Hu LT, Bentler PM. Cut off criteria of fit indices in co‐variance structure analysis; Conventional criteria versus new alternatives. Struct Equ Modeling. 1999;6(1):1–55. https://doi.org/10.1080/10705519909540118.
Wang J, Wang X, Xia M, Liao X, Evans A, He Y. GRETNA: a graph theoretical network analysis toolbox for imaging connectomics. Front Hum Neurosci. 2015;9:386. https://doi.org/10.3389/fnhum.2015.00386.
Yeo BT, Krienen FM, Sepulcre J, Sabuncu MR, Lashkari D, Hollinshead M, et al. The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol. 2011;106(3):1125–1165. https://doi.org/10.1152/jn.00338.2011.
Corr PJ. Reinforcement sensitivity theory and personality. Neurosci Biobehav Rev. 2004;28(3):317–332. https://doi.org/10.1016/j.neubiorev.2004.01.005.
Corr PJ, Perkins AM. The role of theory in the psychophysiology of personality: from Ivan Pavlov to Jeffrey Gray. Int J Psychophysiol. 2006;62(3):367–376. https://doi.org/10.1016/j.ijpsycho.2006.01.005.
Bechara A, Damasio H, Tranel D, Damasio AR. The Iowa gambling task and the somatic marker hypothesis: some questions and answers. Trends Cogn Sci. 2005;9(4):159–162. https://doi.org/10.1016/j.tics.2005.02.002.
Wang Y, Wu L, Zhou H, Lin X, Zhang Y, Du X, 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. https://doi.org/10.1007/s00406-016-0721-6.
Gentile DA, Choo H, Liau A, Sim T, Li D, Fung D, et al. Pathological video game use among youths: a two‐year longitudinal study. Pediatrics. 2011;127(2):e319–e329. https://doi.org/10.1542/peds.2010-1353.
Brand M, Wegmann E, Stark R, Müller A, Wölfling K, Robbins TW, et al. The interaction of person‐affect‐cognition‐execution (I‐PACE) model for addictive behaviors: update, generalization to addictive behaviors beyond internet‐use disorders, and specification of the process character of addictive behaviors. Neurosci Biobehav Rev. 2019;104:1–10. https://doi.org/10.1016/j.neubiorev.2019.06.032.
Schultz W. Predictive reward signal of dopamine neurons. J Neurophysiol. 1998;80(1):1–27. https://doi.org/10.1152/jn.1998.80.1.1.
Peters J, Büchel C. Neural representations of subjective reward value. Behav Brain Res. 2010;213(2):135–141. https://doi.org/10.1016/j.bbr.2010.04.031.
Steinberg L. A dual systems model of adolescent risk‐taking. Dev Psychobiol. 2010;52(3):216–224. https://doi.org/10.1002/dev.20445.
Menon V, Uddin LQ. Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct. 2010;214(5‐6):655–667. https://doi.org/10.1007/s00429-010-0262-0.
Gerlach KD, Spreng RN, Madore KP, Schacter DL. Future planning: default network activity couples with frontoparietal control network and reward‐processing regions during process and outcome simulations. Soc Cogn Affect Neurosci. 2014;9(12):1942–1951. https://doi.org/10.1093/scan/nsu001.
Schettler L, Thomasius R, Paschke K. Neural correlates of problematic gaming in adolescents: a systematic review of structural and functional magnetic resonance imaging studies. Addict Biol. 2022;27(1):e13093. https://doi.org/10.1111/adb.13093.
Grant Information:
31972906 National Natural Science Foundation of China; SWU2209235 Fundamental Research Funds for the Central Universities; SWUPilotPlan006 Innovation Research 2035 Pilot Plan of Southwest University
Contributed Indexing:
Keywords: functional magnetic resonance imaging (fMRI); internet gaming addiction tendency; limbic network; punishment sensitivity; reward sensitivity; risky decision‐making
Entry Date(s):
Date Created: 20251025 Date Completed: 20260210 Latest Revision: 20260210
Update Code:
20260210
DOI:
10.1111/add.70219
PMID:
41137797
Database:
MEDLINE
*Further Information*
*Background and Aims: Internet gaming addiction (IGA) is associated with altered reward/punishment sensitivity and risky decision-making. Nevertheless, the underlying neural mechanisms of such changes remain poorly understood. This study examined behavioral and neural predictors of IGA tendency with multiple datasets.
Design: Observational study.
Setting and Participants: A total of 1142 university students [360 males and 782 females, mean (standard deviation) age of 18.75 (1.67) years] participated in the behavior-brain cross-sectional dataset (BBC). A subset of 303 BBC participants [71 males and 232 females, baseline mean age of 18.84 (1.72) years] participated in the behavior longitudinal dataset (BL).
Measurements: The Sensitivity to Punishment and Sensitivity to Reward Questionnaire (SPSRQ) assessed sensitivity to reward and punishment stimuli. The Internet Game Addiction Questionnaire assessed levels of addiction symptoms in the context of internet games. The Iowa Gambling Task (IGT) assessed risky decision-making behavior. Resting-state functional magnetic resonance imaging (MRI) data were preprocessed using standard pipelines and analyzed based on Yeo's seven-network parcellation template, with particular focus on the Limbic Network (LN) and its functional connectivity patterns. Statistical analyses included Spearman correlation, structural equation modeling and cross-lagged panel models.
Findings: Cross-sectional analyses revealed that the IGT net score (NS) was negatively associated with reward sensitivity (RS, rho = -0.181, P = 0.022), which was positively associated with punishment sensitivity (PS, rho = 0.125, P < 0.001). PS positively predicted IGA tendency (β = 0.180, P < 0.001). Additionally, LN strength exhibited a positive correlation with RS (rho = 0.077, P < 0.001) and a negative correlation with PS (rho = -0.045, P = 0.090). Moreover, the functional connectivity strength between LN and other functional networks was positively associated with RS. Longitudinal analyses demonstrated that (1) the IGT net score at the first time point (T1) negatively predicted RS at the second time point (T2, β = -0.123, P = 0.031), (2) RS at T1 positively predicted IGA tendency at T2 (β = 0.100, P = 0.019), (3) PS at T1 negatively predicted RS at T2 (β = 0.085, P = 0.056) and (4) LN strength at T1 directly predicted RS and PS at T1 (RS: β = 0.126, P = 0.027; PS: β = -0.104, P = 0.064), as well as RS at T2 (β = 0.079, P = 0.080).
Conclusion: Internet gaming activity net score appears to be negatively correlated with reward sensitivity. Punishment sensitivity appears to be positively correlated with tendency toward internet gaming activity. There appears to be a positive correlation between reward sensitivity and punishment sensitivity.
(© 2025 Society for the Study of Addiction.)*