Hostname: page-component-745bb68f8f-l4dxg Total loading time: 0 Render date: 2025-01-11T18:44:27.840Z Has data issue: false hasContentIssue false
  • English
  • Français

Blunted reward prediction error signals in internet gaming disorder

Published online by Cambridge University Press:  04 November 2020

Wei Lei ,
Kezhi Liu ,
Guangxiang Chen ,
Serenella Tolomeo ,
Cuizhen Liu ,
Zhenlei Peng ,
Boya Liu ,
Xuemei Liang ,
Chaohua Huang  and
Bo Xiang
...Show all authors
Wei Lei
Affiliation:
Department of Psychiatry, The Affiliated Hospital of Southwest Medical University, Luzhou, China Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China
Kezhi Liu
Affiliation:
Department of Psychiatry, The Affiliated Hospital of Southwest Medical University, Luzhou, China Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China
Guangxiang Chen
Affiliation:
Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China Radiology Department, The Affiliated Hospital of Southwest Medical University, Luzhou, China
Serenella Tolomeo
Affiliation:
Department of Psychology, National University of Singapore, Singapore, Singapore
Cuizhen Liu
Affiliation:
Department of Psychology, National University of Singapore, Singapore, Singapore
Zhenlei Peng
Affiliation:
Department of Psychiatry, The Affiliated Hospital of Southwest Medical University, Luzhou, China
Boya Liu
Affiliation:
Department of Psychiatry, The Affiliated Hospital of Southwest Medical University, Luzhou, China
Xuemei Liang
Affiliation:
Department of Psychiatry, The Affiliated Hospital of Southwest Medical University, Luzhou, China
Chaohua Huang
Affiliation:
Department of Psychiatry, The Affiliated Hospital of Southwest Medical University, Luzhou, China
Bo Xiang
Affiliation:
Department of Psychiatry, The Affiliated Hospital of Southwest Medical University, Luzhou, China
Jia Zhou
Affiliation:
School of Humanities and Management Science, Southwest Medical University, Luzhou, China
Fulin Zhao
Affiliation:
Department of Medical Imaging, Southwest Medical University, Luzhou, China
Rongjun Yu*
Affiliation:
Department of Psychology, National University of Singapore, Singapore, Singapore
Jing Chen*
Affiliation:
Department of Psychiatry, The Affiliated Hospital of Southwest Medical University, Luzhou, China Laboratory of Neurological Diseases and Brain Function, The Affiliated Hospital of Southwest Medical University, Luzhou, China Nuclear Medicine and Molecular Imaging Key Laboratory of Sichuan Province, Luzhou, China
*
Author for correspondence: Rongjun Yu, E-mail: psyyr@nus.edu.sg; Jing Chen, E-mail: chenjing_fy@swmu.edu.cn
Author for correspondence: Rongjun Yu, E-mail: psyyr@nus.edu.sg; Jing Chen, E-mail: chenjing_fy@swmu.edu.cn
Get access Rights & Permissions [Opens in a new window]

Abstract

Background

Internet gaming disorder (IGD) is a type of behavioural addictions. One of the key features of addiction is the excessive exposure to addictive objectives (e.g. drugs) reduces the sensitivity of the brain reward system to daily rewards (e.g. money). This is thought to be mediated via the signals expressed as dopaminergic reward prediction error (RPE). Emerging evidence highlights blunted RPE signals in drug addictions. However, no study has examined whether IGD also involves alterations in RPE signals that are observed in other types of addictions.

Methods

To fill this gap, we used functional magnetic resonance imaging data from 45 IGD and 42 healthy controls (HCs) during a reward-related prediction-error task and utilised a psychophysiological interaction (PPI) analysis to characterise the underlying neural correlates of RPE and related functional connectivity.

Results

Relative to HCs, IGD individuals showed impaired reinforcement learning, blunted RPE signals in multiple regions of the brain reward system, including the right caudate, left orbitofrontal cortex (OFC), and right dorsolateral prefrontal cortex (DLPFC). Moreover, the PPI analysis revealed a pattern of hyperconnectivity between the right caudate, right putamen, bilateral DLPFC, and right dorsal anterior cingulate cortex (dACC) in the IGD group. Finally, linear regression suggested that the connection between the right DLPFC and right dACC could significantly predict the variation of RPE signals in the left OFC.

Conclusions

These results highlight disrupted RPE signalling and hyperconnectivity between regions of the brain reward system in IGD. Reinforcement learning deficits may be crucial underlying characteristics of IGD pathophysiology.

Keywords

Addictionbrain reward systemfMRIinternet gaming disorderreinforcement learningreward prediction error
Type
Original Article
Information
Psychological Medicine , Volume 52 , Issue 11 , August 2022 , pp. 2124 - 2133
Copyright
Copyright © The Author(s), 2020. Published by Cambridge University Press

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

Wei Lei, Kezhi Liu, Guangxiang Chen, and Serenella Tolomeo contributed equally to this study.

References

Allison, S. E., Von Wahlde, L., Shockley, T., & Gabbard, G. O. (2006). The development of the self in the era of the internet and role-playing fantasy games. American Journal of Psychiatry, 163(3), 381–385. doi: 10.1176/appi.ajp.163.3.381.CrossRefGoogle ScholarPubMed
APA. (2013). Diagnostic and statistical manual of mental disorders (DSM-5®). Arlington, VA: American Psychiatric Pub.Google Scholar
Bandura, A. (1977). Self-efficacy: Toward a unifying theory of behavioral change. Psychological Review, 84(2), 191–215. doi:10.1037/0033-295X.84.2.191.CrossRefGoogle Scholar
Beck, A. T., Epstein, N., Brown, G., & Steer, R. A. (1988a). An inventory for measuring clinical anxiety: Psychometric properties. Journal of Consulting and Clinical Psychology, 56(6), 893–897. doi: 10.1037/0022-006X.56.6.893.CrossRefGoogle Scholar
Beck, A. T., Steer, R. A., & Carbin, M. G. (1988b). Psychometric properties of the Beck Depression Inventory: Twenty-five years of evaluation. Clinical Psychology Review, 8(1), 77–100.CrossRefGoogle Scholar
Berridge, K. C., & Robinson, T. E. (2016). Liking, wanting, and the incentive-sensitization theory of addiction. American Psychologist, 71(8), 670–679. doi: 10.1037/amp0000059.CrossRefGoogle ScholarPubMed
Carcone, D., & Ruocco, A. C. (2017). Six years of research on the national institute of mental health's research domain criteria (RDoC) initiative: A systematic review. Frontiers in Cellular Neuroscience, 11, 46.CrossRefGoogle ScholarPubMed
Cieslik, E. C., Zilles, K., Caspers, S., Roski, C., Kellermann, T. S., Jakobs, O., … Eickhoff, S. B. (2013). Is there ‘one’ DLPFC in cognitive action control? Evidence for heterogeneity from co-activation-based parcellation. Cerebral Cortex, 23(11), 2677–2689. doi: 10.1093/cercor/bhs256.CrossRefGoogle ScholarPubMed
Cole, M. W., Bassett, D. S., Power, J. D., Braver, T. S., & Petersen, S. E. (2014). Intrinsic and task-evoked network architectures of the human brain. Neuron, 83(1), 238–251. doi: https://doi.org/10.1016/j.neuron.2014.05.014.CrossRefGoogle ScholarPubMed
Conner, K. R., Pinquart, M., & Duberstein, P. R. (2008). Meta-analysis of depression and substance use and impairment among intravenous drug users (IDUs). Addiction, 103(4), 524–534.CrossRefGoogle Scholar
Corlett, P. R., Aitken, M. R., Dickinson, A., Shanks, D. R., Honey, G. D., Honey, R. A., … Fletcher, P. C. (2004). Prediction error during retrospective revaluation of causal associations in humans: fMRI evidence in favor of an associative model of learning. Neuron, 44(5), 877–888.Google Scholar
D’Astolfo, L., & Rief, W.. (2017). Learning about Expectation Violation from Prediction Error Paradigms – A Meta-Analysis on Brain Processes Following a Prediction Error. Frontiers in psychology, 8, 1253. doi: 10.3389/fpsyg.2017.01253.CrossRefGoogle ScholarPubMed
Daw, N. D., Gershman, S. J., Seymour, B., Dayan, P., & Dolan, R. J. (2011). Model-based influences on humans' choices and striatal prediction errors. Neuron, 69(6), 1204–1215. doi: https://doi.org/10.1016/j.neuron.2011.02.027.CrossRefGoogle ScholarPubMed
Deserno, L., Beck, A., Huys, Q. J., Lorenz, R. C., Buchert, R., Buchholz, H. G., … Heinze, H. J. (2015). Chronic alcohol intake abolishes the relationship between dopamine synthesis capacity and learning signals in the ventral striatum. European Journal of Neuroscience, 41(4), 477–486.CrossRefGoogle ScholarPubMed
Di, X., Huang, J., & Biswal, B. B. (2017). Task modulated brain connectivity of the amygdala: A meta-analysis of psychophysiological interactions. Brain Structure and Function, 222(1), 619–634.CrossRefGoogle ScholarPubMed
Everitt, B. J., & Robbins, T. W. (2005). Neural systems of reinforcement for drug addiction: From actions to habits to compulsion. Nature Neuroscience, 8(11), 1481–1489.CrossRefGoogle ScholarPubMed
Fauth-Bühler, M., Mann, K., & Potenza, M. N. (2017). Pathological gambling: A review of the neurobiological evidence relevant for its classification as an addictive disorder. Addiction Biology, 22(4), 885–897. doi: 10.1111/adb.12378.CrossRefGoogle ScholarPubMed
Fletcher, P. C., Anderson, J., Shanks, D., Honey, R., Carpenter, T. A., Donovan, T., … Bullmore, E. T. (2001). Responses of human frontal cortex to surprising events are predicted by formal associative learning theory. Nature Neuroscience, 4(10), 1043–1048.CrossRefGoogle ScholarPubMed
García-García, I., Zeighami, Y., & Dagher, A. (2017). Reward prediction errors in drug addiction and Parkinson's disease: From neurophysiology to neuroimaging. Current Neurology and Neuroscience Reports, 17(6), 46. doi: 10.1007/s11910-017-0755-9.CrossRefGoogle ScholarPubMed
Garrison, J., Erdeniz, B., & Done, J. (2013). Prediction error in reinforcement learning: A meta-analysis of neuroimaging studies. Neuroscience & Biobehavioral Reviews, 37(7), 1297–1310.CrossRefGoogle ScholarPubMed
Gentile, D. (2009). Pathological video-game use among youth ages 8 to 18: A national study. Psychological Science, 20(5), 594–602.CrossRefGoogle ScholarPubMed
Goldstein, R. Z., & Volkow, N. D. (2011). Dysfunction of the prefrontal cortex in addiction: Neuroimaging findings and clinical implications. Nature Reviews Neuroscience, 12(11), 652–669.CrossRefGoogle ScholarPubMed
Haber, S. N., & Knutson, B. (2010). The reward circuit: Linking primate anatomy and human imaging. Neuropsychopharmacology, 35(1), 4–26.CrossRefGoogle ScholarPubMed
Han, D. H., Bolo, N., Daniels, M. A., Arenella, L., Lyoo, I. K., & Renshaw, P. F. (2011a). Brain activity and desire for Internet video game play. Comprehensive Psychiatry, 52(1), 88–95. doi: 10.1016/j.comppsych.2010.04.004.CrossRefGoogle Scholar
Han, D. H., Hwang, J. W., & Renshaw, P. F. (2011b). Bupropion sustained release treatment decreases craving for video games and cue-induced brain activity in patients with Internet video game addiction. Psychology of Popular Media Culture, 1(S), 108–117.CrossRefGoogle Scholar
Han, D. H., Kim, Y. S., Lee, Y. S., Min, K. J., & Renshaw, P. F. (2010). Changes in cue-induced, prefrontal cortex activity with video-game play. Cyberpsychology, Behavior, and Social Networking, 13(6), 655–661.CrossRefGoogle ScholarPubMed
Hayashi, T., Ko, J. H., Strafella, A. P., & Dagher, A. (2013). Dorsolateral prefrontal and orbitofrontal cortex interactions during self-control of cigarette craving. Proceedings of the National Academy of Sciences, 110(11), 4422–4427.CrossRefGoogle ScholarPubMed
Keiflin, R., & Janak, P. H. (2015). Dopamine prediction errors in reward learning and addiction: From theory to neural circuitry. Neuron, 88(2), 247–263. doi: https://doi.org/10.1016/j.neuron.2015.08.037.CrossRefGoogle ScholarPubMed
Ko, C. H., Liu, G. C., Hsiao, S., Yen, J. Y., Yang, M. J., Lin, W. C., … Chen, C. S. (2009). Brain activities associated with gaming urge of online gaming addiction. Journal of Psychiatric Research, 43(7), 739–747.CrossRefGoogle ScholarPubMed
Ko, C. H., Liu, G. C., Yen, J. Y., Chen, C. Y., Yen, C. F., & Chen, C. S. (2013). Brain correlates of craving for online gaming under cue exposure in subjects with Internet gaming addiction and in remitted subjects. Addiction Biology, 18(3), 559–569.CrossRefGoogle ScholarPubMed
Kumar, P., Goer, F., Murray, L., Dillon, D. G., Beltzer, M. L., Cohen, A. L., … Pizzagalli, D. A. (2018). Impaired reward prediction error encoding and striatal-midbrain connectivity in depression. Neuropsychopharmacology, 43(7), 1581–1588.CrossRefGoogle ScholarPubMed
Lei, W., Liu, K., Zeng, Z., Liang, X., Huang, C., Gong, K., … Chen, J. (2020). The psychometric properties of the Chinese version internet gaming disorder scale. Addictive Behaviors, 106, 106392. doi: 10.1016/j.addbeh.2020.106392.CrossRefGoogle ScholarPubMed
Li, Q., Wang, Y., Yang, Z., Dai, W., Zheng, Y., Sun, Y., & Liu, X. (2020). Dysfunctional cognitive control and reward processing in adolescents with Internet gaming disorder. Psychophysiology, 57(2), e13469.CrossRefGoogle ScholarPubMed
Linnet, J. (2014). Neurobiological underpinnings of reward anticipation and outcome evaluation in gambling disorder. Frontiers in Behavioral Neuroscience, 8(100), 1–5. doi: 10.3389/fnbeh.2014.00100.CrossRefGoogle ScholarPubMed
Luijten, M., Schellekens, A. F., Kühn, S., Machielse, M. W., & Sescousse, G. (2017). Disruption of reward processing in addiction: An image-based meta-analysis of functional magnetic resonance imaging studies. JAMA Psychiatry, 74(4), 387–398.CrossRefGoogle ScholarPubMed
Lüscher, C., Robbins, T. W., & Everitt, B. J. (2020). The transition to compulsion in addiction. Nature Reviews Neuroscience, 21, 247–263.CrossRefGoogle ScholarPubMed
Ma, S.-S., Worhunsky, P. D., Xu, J.-S., Yip, S. W., Zhou, N., Zhang, J.-T., … Yao, Y.-W. (2019). Alterations in functional networks during cue-reactivity in Internet gaming disorder. Journal of Behavioral Addictions, 8(2), 1–6. doi: 10.1556/2006.8.2019.25.CrossRefGoogle ScholarPubMed
Makris, N., Oscar-Berman, M., Jaffin, S. K., Hodge, S. M., Kennedy, D. N., Caviness, V. S., … Harris, G. J. (2008). Decreased volume of the brain reward system in alcoholism. Biological Psychiatry, 64(3), 192–202. doi: 10.1016/j.biopsych.2008.01.018.CrossRefGoogle ScholarPubMed
Manza, P., Tomasi, D., & Volkow, N. D. (2018). Subcortical local functional hyperconnectivity in Cannabis dependence. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 3(3), 285–293. doi: 10.1016/j.bpsc.2017.11.004.Google ScholarPubMed
McLaren, D. G., Ries, M. L., Xu, G., & Johnson, S. C. (2012). A generalized form of context-dependent psychophysiological interactions (gPPI): A comparison to standard approaches. Neuroimage, 61(4), 1277–1286. doi: 10.1016/j.neuroimage.2012.03.068.CrossRefGoogle ScholarPubMed
Morris, R. W., Vercammen, A., Lenroot, R., Moore, L., Langton, J. M., Short, B., … Weickert, T. W. (2012). Disambiguating ventral striatum fMRI-related BOLD signal during reward prediction in schizophrenia. Molecular Psychiatry, 17(3), 280–289. doi: 10.1038/mp.2011.75.CrossRefGoogle Scholar
Nagai, Y., Kikuchi, E., Lerchner, W., Inoue, K-I, Ji, B., Eldridge, M. A., … Hori, Y. (2016). PET imaging-guided chemogenetic silencing reveals a critical role of primate rostromedial caudate in reward evaluation. Nature Communications, 7, 13605. doi: 10.1038/ncomms13605.CrossRefGoogle ScholarPubMed
Ng, T. H., Alloy, L. B., & Smith, D. V. (2019). Meta-analysis of reward processing in major depressive disorder reveals distinct abnormalities within the reward circuit. Translational Psychiatry, 9(1), 1–10.CrossRefGoogle ScholarPubMed
O'Doherty, J. P. (2004). Reward representations and reward-related learning in the human brain: Insights from neuroimaging. Current Opinion in Neurobiology, 14(6), 769–776. doi: 10.1016/j.conb.2004.10.016.CrossRefGoogle ScholarPubMed
Packard, M. G., & Knowlton, B. J. (2002). Learning and memory functions of the basal ganglia. Annual Review of Neuroscience, 25(1), 563–593. doi: 10.1146/annurev.neuro.25.112701.142937.CrossRefGoogle ScholarPubMed
Park, S. Q., Kahnt, T., Beck, A., Cohen, M. X., Dolan, R. J., Wrase, J., & Heinz, A. (2010). Prefrontal cortex fails to learn from reward prediction errors in alcohol dependence. Journal of Neuroscience, 30(22), 7749–7753. doi: 10.1523/JNEUROSCI.5587-09.2010.CrossRefGoogle ScholarPubMed
Parkes, S. L., Ravassard, P. M., Cerpa, J.-C., Wolff, M., Ferreira, G., & Coutureau, E. (2018). Insular and ventrolateral orbitofrontal cortices differentially contribute to goal-directed behavior in rodents. Cerebral Cortex, 28(7), 2313–2325.CrossRefGoogle ScholarPubMed
Parvaz, M. A., Konova, A. B., Proudfit, G. H., Dunning, J. P., Malaker, P., Moeller, S. J., … Goldstein, R. Z. (2015). Impaired neural response to negative prediction errors in cocaine addiction. Journal of Neuroscience, 35(5), 1872–1879.CrossRefGoogle ScholarPubMed
Patton, J. H., Stanford, M. S., & Barratt, E. S. (1995). Factor structure of the Barratt impulsiveness scale. Journal of Clinical Psychology, 51(6), 768–774.3.0.CO;2-1>CrossRefGoogle ScholarPubMed
Petry, N. M., Rehbein, F., Gentile, D. A., Lemmens, J. S., Rumpf, H. J., Mößle, T., … Borges, G. (2014). An international consensus for assessing internet gaming disorder using the new DSM-5 approach. Addiction, 109(9), 1399–1406. doi: 10.1111/add.12457.CrossRefGoogle ScholarPubMed
Pizzagalli, D. A., Evins, A. E., Schetter, E. C., Frank, M. J., Pajtas, P. E., Santesso, D. L., & Culhane, M. (2008). Single dose of a dopamine agonist impairs reinforcement learning in humans: Behavioral evidence from a laboratory-based measure of reward responsiveness. Psychopharmacology, 196(2), 221–232. doi: 10.1007/s00213-007-0957-y.CrossRefGoogle ScholarPubMed
Potenza, M. (2015). Perspective: Behavioural addictions matter. Nature, 522(7557), S62–S62.CrossRefGoogle ScholarPubMed
Puetz, V. B., Kohn, N., Dahmen, B., Zvyagintsev, M., Schüppen, A., Schultz, R. T., … Konrad, K. (2014). Neural response to social rejection in children with early separation experiences. Journal of the American Academy of Child & Adolescent Psychiatry, 53(12), 1328–1337.CrossRefGoogle ScholarPubMed
Raven, J. (2000). The Raven's progressive matrices: Change and stability over culture and time. Cognitive Psychology, 41(1), 1–48. doi: 10.1006/cogp.1999.0735.CrossRefGoogle ScholarPubMed
Reuter, J., Raedler, T., Rose, M., Hand, I., Gläscher, J., & Büchel, C. (2005). Pathological gambling is linked to reduced activation of the mesolimbic reward system. Nature Neuroscience, 8(2), 147–148.CrossRefGoogle ScholarPubMed
Rolls, E. T., Huang, C.-C., Lin, C.-P., Feng, J., & Joliot, M. (2020). Automated anatomical labelling atlas 3. Neuroimage, 206, 116189. doi: 10.1016/j.neuroimage.2019.116189.CrossRefGoogle ScholarPubMed
Rose, E. J., Ross, T. J., Salmeron, B. J., Lee, M., Shakleya, D. M., Huestis, M., & Stein, E. A. (2012). Chronic exposure to nicotine is associated with reduced reward-related activity in the striatum but not the midbrain. Biological Psychiatry, 71(3), 206–213. doi: 10.1016/j.biopsych.2011.09.013.CrossRefGoogle Scholar
Rose, E. J., Salmeron, B. J., Ross, T. J., Waltz, J., Schweitzer, J. B., McClure, S. M., & Stein, E. A. (2014). Temporal difference error prediction signal dysregulation in cocaine dependence. Neuropsychopharmacology, 39(7), 1732–1742. doi: 10.1038/npp.2014.21.CrossRefGoogle ScholarPubMed
Rosenberg, M. (1986). Conceiving the self. Malabar, FL, USA: RE Krieger.Google Scholar
Rothkirch, M., Tonn, J., Sterzer, P., Köhler, S., & Rothkirch, M. (2017). Neural mechanisms of reinforcement learning in unmedicated patients with major depressive disorder. Brain, 140(4), 1147–1157. doi: 10.1093/brain/awx025.CrossRefGoogle ScholarPubMed
Schultz, W. (1998). Predictive reward signal of dopamine neurons. Journal of Neurophysiology, 80(1), 1–27. doi: 10.1152/jn.1998.80.1.1.CrossRefGoogle ScholarPubMed
Smith, D. V., Gseir, M., Speer, M. E., & Delgado, M. R. (2016). Toward a cumulative science of functional integration: A meta-analysis of psychophysiological interactions. Human Brain Mapping, 37(8), 2904–2917.CrossRefGoogle Scholar
Starcke, K., Antons, S., Trotzke, P., & Brand, M. (2018). Cue-reactivity in behavioral addictions: A meta-analysis and methodological considerations. Journal of Behavioral Addictions, 7(2), 227–238. doi: 10.1556/2006.7.2018.39.CrossRefGoogle ScholarPubMed
Steinberg, E. E., Keiflin, R., Boivin, J. R., Witten, I. B., Deisseroth, K., & Janak, P. H. (2013). A causal link between prediction errors, dopamine neurons and learning. Nature Neuroscience, 16(7), 966–973. doi: 10.1038/nn.3413.CrossRefGoogle Scholar
Tanabe, J., Reynolds, J., Krmpotich, T., Claus, E., Thompson, L. L., Du, Y. P., & Banich, M. T. (2013). Reduced neural tracking of prediction error in substance-dependent individuals. American Journal of Psychiatry, 170(11), 1356–1363. doi: 10.1176/appi.ajp.2013.12091257.CrossRefGoogle ScholarPubMed
Tau, G. Z., Marsh, R., Wang, Z., Torres-Sanchez, T., Graniello, B., Hao, X., … Kangarlu, A. (2014). Neural correlates of reward-based spatial learning in persons with cocaine dependence. Neuropsychopharmacology, 39(3), 545–555.CrossRefGoogle ScholarPubMed
Tobler, P. N., O'Doherty, J. P., Dolan, R. J., & Schultz, W. (2007). Reward value coding distinct from risk attitude-related uncertainty coding in human reward systems. Journal of Neurophysiology, 97(2), 1621–1632. doi: 10.1152/jn.00745.2006.CrossRefGoogle ScholarPubMed
Van Rooij, A. J., Kuss, D. J., Griffiths, M. D., Shorter, G. W., Schoenmakers, T. M., & Van De Mheen, D. (2014). The (co-) occurrence of problematic video gaming, substance use, and psychosocial problems in adolescents. Journal of Behavioral Addictions, 3(3), 157–165. doi: 10.1556/JBA.3.2014.013.CrossRefGoogle ScholarPubMed
Volkow, N. D., Koob, G. F., & McLellan, A. T. (2016). Neurobiologic advances from the brain disease model of addiction. New England Journal of Medicine, 374(4), 363–371. doi: 10.1056/NEJMra1511480.CrossRefGoogle ScholarPubMed
Volkow, N. D., Wise, R. A., & Baler, R. (2017). The dopamine motive system: Implications for drug and food addiction. Nature Reviews Neuroscience, 18(12), 741–752. doi: 10.1038/nrn.2017.130.CrossRefGoogle ScholarPubMed
Wiehler, A., & Peters, J. (2020). Diffusion modeling reveals reinforcement learning impairments in gambling disorder that are linked to attenuated ventromedial prefrontal cortex value representations. bioRxiv.CrossRefGoogle Scholar
Wise, R. A. (2009). Roles for nigrostriatal – not just mesocorticolimbic – dopamine in reward and addiction. Trends in neurosciences, 32(10), 517–524. doi: 10.1016/j.tins.2009.06.004.CrossRefGoogle Scholar
Yanike, M., & Ferrera, V. P. (2014). Interpretive monitoring in the caudate nucleus. eLife, 3, e03727. doi: 10.7554/eLife.03727.001.CrossRefGoogle ScholarPubMed
Young, K. S. (1998). Caught in the net: How to recognize the signs of internet addiction – and a winning strategy for recovery. New York, NY, USA: John Wiley & Sons.Google Scholar
Zhang, Y., Lin, X., Zhou, H., Xu, J., Du, X., & Dong, G. (2016a). Brain activity toward gaming-related cues in Internet gaming disorder during an addiction stroop task. Frontiers in Psychology, 7, 714. doi: 10.3389/fpsyg.2016.00714.CrossRefGoogle Scholar
Zhang, J. T., Yao, Y. W., Li, C. S. R., Zang, Y. F., Shen, Z. J., Liu, L., … Fang, X. Y. (2016b). Altered resting-state functional connectivity of the insula in young adults with Internet gaming disorder. Addiction Biology, 21(3), 743–751. doi: 10.1111/adb.12247.CrossRefGoogle Scholar
Zheng, H., Hu, Y., Wang, Z., Wang, M., Du, X., & Dong, G. (2019). Meta-analyses of the functional neural alterations in subjects with Internet gaming disorder: Similarities and differences across different paradigms. Progress in Neuro-Psychopharmacology and Biological Psychiatry, 94, 109656. doi: 10.1016/j.pnpbp.2019.109656.CrossRefGoogle ScholarPubMed
Supplementary material: File

Lei et al. supplementary material

Lei et al. supplementary material

Download Lei et al. supplementary material(File)
File 1.2 MB