Advertisement

Brain Imaging and Behavior

, Volume 13, Issue 5, pp 1406–1417 | Cite as

Sex differences in resting-state cerebral activity alterations in internet gaming disorder

  • Yawen Sun
  • Yao Wang
  • Xu Han
  • Wenqing Jiang
  • Weina Ding
  • Mengqiu Cao
  • Yasong Du
  • Fuchun Lin
  • Jianrong Xu
  • Yan ZhouEmail author
Original Research

Abstract

Although evidence has shown that the prevalence rates of Internet gaming disorder (IGD) differ between males and females, few studies have examined whether such sex differences extend to brain function. This study aimed to explore the sex differences in resting-state cerebral activity alterations in IGD. Thirty male participants with IGD (IGDm), 23 female participants with IGD (IGDf), and 30 male and 22 female age-matched healthy controls (HC) underwent resting-state functional MRI. Maps of the amplitude of low-frequency fluctuation (ALFF) and functional connectivity (FC) were constructed. A two-factor ANCOVA model was performed, with sex and diagnosis as the between-subject factors. Then, post hoc pair-wise comparisons were performed using two-sample t-tests within the interaction masks. The Barratt Impulsiveness Scale-11 (BIS-11) was used to assess the behavioral inhibition function. We found that the ALFF values in the orbital part of the left superior frontal gyrus (SFG) were lower in IGDm than in HCm, which were negatively correlated with BIS-11 scores. IGDm also demonstrated lower connectivity between the orbital part of the left SFG and the posterior cingulate cortex (PCC), the right angular gyrus, and the right dorsolateral prefrontal cortex than HCm. Furthermore, IGDm had lower seed connectivity between the orbital part of the left SFG and the PCC than ICDf. Our findings suggest that (1) the altered ALFF values in the orbital part of the left SFG represent a clinically relevant biomarker for the behavioral inhibition function of IGDm; (2) IGD may interact with sex-specific patterns of FC in male and female subjects.

Keywords

Resting-state functional magnetic resonance imaging Internet gaming disorder Sex differences Amplitude of low-frequency fluctuation Functional connectivity 

Notes

Author contributions

YZ, YD FL and JX were responsible for the study concept and design. YW, WJ, WD, MC contributed to the acquisition of data. YS, XH, and YW assisted with data analysis and interpretation of findings. YS drafted the manuscript. All authors critically reviewed content and approved final version for publication.

Funding

This study was funded by the National Natural Science Foundation of China (No. 81571650 and 81571757); Shanghai Municipal Education Commission-Gaofeng Clinical Medicine Grant Support (No. 20172013); Shanghai Science and Technology Committee Medical Guide Project (No. 17411964300); Medical Engineering Cross Research Foundation of Shanghai Jiao Tong University (No. YG2017QN47); Research Seed Fund of Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University (RJZZ17–016); Incubating Program for Clinical Research and Innovation of Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University (PYIII-17-027 and PYIV-17-003) and the Frontier Scientific Significant Breakthrough Project of CAS (QYZDB-SSW-SLH046).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

References

  1. Argyriou, E., Davison, C. B., & Lee, T. T. C. (2017). Response inhibition and internet gaming disorder: A meta-analysis. Addictive Behaviors, 71, 54–60.  https://doi.org/10.1016/j.addbeh.2017.02.026.CrossRefGoogle Scholar
  2. Bayless, D. W., & Daniel, J. M. (2015). Sex differences in myelin-associated protein levels within and density of projections between the orbital frontal cortex and dorsal striatum of adult rats: Implications for inhibitory control. Neuroscience, 300, 286–296.  https://doi.org/10.1016/j.neuroscience.2015.05.029.CrossRefGoogle Scholar
  3. Beard, K. W., & Wolf, E. M. (2001). Modification in the proposed diagnostic criteria for internet addiction. Cyberpsychology & Behavior, 4(3), 377–383.  https://doi.org/10.1089/109493101300210286.CrossRefGoogle Scholar
  4. Becker, J. B., McClellan, M., & Reed, B. G. (2016). Sociocultural context for sex differences in addiction. Addiction Biology, 21(5), 1052–1059.  https://doi.org/10.1111/adb.12383.CrossRefGoogle Scholar
  5. Billieux, J., Chanal, J., Khazaal, Y., Rochat, L., Gay, P., Zullino, D., et al. (2011). Psychological predictors of problematic involvement in massively multiplayer online role-playing games: Illustration in a sample of male cybercafe players. Psychopathology, 44(3), 165–171.  https://doi.org/10.1159/000322525.CrossRefGoogle Scholar
  6. Cahill, L. (2006). Why sex matters for neuroscience. Nature Reviews. Neuroscience, 7(6), 477–484.  https://doi.org/10.1038/nrn1909.CrossRefGoogle Scholar
  7. Chen, X., Wang, Y., Zhou, Y., Sun, Y., Ding, W., Zhuang, Z., et al. (2014). Different resting-state functional connectivity alterations in smokers and nonsmokers with internet gaming addiction. BioMed Research International, 2014, 825787.  https://doi.org/10.1155/2014/825787.Google Scholar
  8. de Ruiter, M. B., Oosterlaan, J., Veltman, D. J., van den Brink, W., & Goudriaan, A. E. (2012). Similar hyporesponsiveness of the dorsomedial prefrontal cortex in problem gamblers and heavy smokers during an inhibitory control task. Drug and Alcohol Dependence, 121(1–2), 81–89.  https://doi.org/10.1016/j.drugalcdep.2011.08.010.CrossRefGoogle Scholar
  9. DeVito, E. E., Meda, S. A., Jiantonio, R., Potenza, M. N., Krystal, J. H., & Pearlson, G. D. (2013). Neural correlates of impulsivity in healthy males and females with family histories of alcoholism. Neuropsychopharmacology, 38(10), 1854–1863.  https://doi.org/10.1038/npp.2013.92.CrossRefGoogle Scholar
  10. Dieter, J., Hoffmann, S., Mier, D., Reinhard, I., Beutel, M., Vollstadt-Klein, S., et al. (2017). The role of emotional inhibitory control in specific internet addiction - an fMRI study. Behavioural Brain Research, 324, 1–14.  https://doi.org/10.1016/j.bbr.2017.01.046.CrossRefGoogle Scholar
  11. Dong, G., Devito, E. E., Du, X., & Cui, Z. (2012). Impaired inhibitory control in 'internet addiction disorder': A functional magnetic resonance imaging study. Psychiatry Research, 203(2–3), 153–158.  https://doi.org/10.1016/j.pscychresns.2012.02.001.CrossRefGoogle Scholar
  12. Dong, G., Li, H., Wang, L., & Potenza, M. N. (2017). The correlation between mood states and functional connectivity within the default mode network can differentiate internet gaming disorder from healthy controls. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 77, 185–193.  https://doi.org/10.1016/j.pnpbp.2017.04.016.CrossRefGoogle Scholar
  13. Dong, G., Wang, Z., Wang, Y., Du, X., & Potenza, M. N. (2018). Gender-related functional connectivity and craving during gaming and immediate abstinence during a mandatory break: Implications for development and progression of internet gaming disorder. Progress in Neuro-Psychopharmacology & Biological Psychiatry.  https://doi.org/10.1016/j.pnpbp.2018.04.009.
  14. Durkee, T., Kaess, M., Carli, V., Parzer, P., Wasserman, C., Floderus, B., et al. (2012). Prevalence of pathological internet use among adolescents in Europe: Demographic and social factors. Addiction, 107(12), 2210–2222.  https://doi.org/10.1111/j.1360-0443.2012.03946.x.CrossRefGoogle Scholar
  15. Ernst, M., Pine, D. S., & Hardin, M. (2006). Triadic model of the neurobiology of motivated behavior in adolescence. Psychological Medicine, 36(3), 299–312.  https://doi.org/10.1017/S0033291705005891. CrossRefGoogle Scholar
  16. Everitt, B. J., Hutcheson, D. M., Ersche, K. D., Pelloux, Y., Dalley, J. W., & Robbins, T. W. (2007). The orbital prefrontal cortex and drug addiction in laboratory animals and humans. Annals of the New York Academy of Sciences, 1121, 576–597.  https://doi.org/10.1196/annals.1401.022.CrossRefGoogle Scholar
  17. Fattore, L., Melis, M., Fadda, P., & Fratta, W. (2014). Sex differences in addictive disorders. Frontiers in Neuroendocrinology, 35(3), 272–284.  https://doi.org/10.1016/j.yfrne.2014.04.003.CrossRefGoogle Scholar
  18. Feng, Q., Chen, X., Sun, J., Zhou, Y., Sun, Y., Ding, W., et al. (2013). Voxel-level comparison of arterial spin-labeled perfusion magnetic resonance imaging in adolescents with internet gaming addiction. Behavioral and Brain Functions, 9(1), 33.  https://doi.org/10.1186/1744-9081-9-33.CrossRefGoogle Scholar
  19. Franklin, T. R., Wetherill, R. R., Jagannathan, K., Johnson, B., Mumma, J., Hager, N., et al. (2014). The effects of chronic cigarette smoking on gray matter volume: Influence of sex. PLoS One, 9(8), e104102.  https://doi.org/10.1371/journal.pone.0104102.CrossRefGoogle Scholar
  20. Giedd, J. N. (2004). Structural magnetic resonance imaging of the adolescent brain. Annals of the New York Academy of Sciences, 1021, 77–85.  https://doi.org/10.1196/annals.1308.009.CrossRefGoogle Scholar
  21. 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.  https://doi.org/10.1038/nrn3119.CrossRefGoogle Scholar
  22. Goldstein, J. M., Seidman, L. J., Horton, N. J., Makris, N., Kennedy, D. N., Caviness Jr., V. S., et al. (2001). Normal sexual dimorphism of the adult human brain assessed by in vivo magnetic resonance imaging. Cerebral Cortex, 11(6), 490–497.CrossRefGoogle Scholar
  23. Goudriaan, A. E., Lapauw, B., Ruige, J., Feyen, E., Kaufman, J. M., Brand, M., et al. (2010). The influence of high-normal testosterone levels on risk-taking in healthy males in a 1-week letrozole administration study. Psychoneuroendocrinology, 35(9), 1416–1421.  https://doi.org/10.1016/j.psyneuen.2010.04.005.CrossRefGoogle Scholar
  24. Hu, Y., Salmeron, B. J., Gu, H., Stein, E. A., & Yang, Y. (2015). Impaired functional connectivity within and between frontostriatal circuits and its association with compulsive drug use and trait impulsivity in cocaine addiction. JAMA Psychiatry, 72(6), 584–592.  https://doi.org/10.1001/jamapsychiatry.2015.1.CrossRefGoogle Scholar
  25. Ide, J. S., Zhornitsky, S., Hu, S., Zhang, S., Krystal, J. H., & Li, C. R. (2017). Sex differences in the interacting roles of impulsivity and positive alcohol expectancy in problem drinking: A structural brain imaging study. Neuroimage Clin, 14, 750–759.  https://doi.org/10.1016/j.nicl.2017.03.015.CrossRefGoogle Scholar
  26. Jenkinson, M., Bannister, P., Brady, M., & Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17(2), 825–841.CrossRefGoogle Scholar
  27. Ko, C. H., Yen, J. Y., Yen, C. F., Chen, C. C., Yen, C. N., & Chen, S. H. (2005). Screening for internet addiction: An empirical study on cut-off points for the Chen internet addiction scale. The Kaohsiung Journal of Medical Sciences, 21(12), 545–551.  https://doi.org/10.1016/s1607-551x(09)70206-2.CrossRefGoogle Scholar
  28. Ko, C. H., Liu, G. C., Hsiao, S., Yen, J. Y., Yang, M. J., Lin, W. C., et al. (2009). Brain activities associated with gaming urge of online gaming addiction. Journal of Psychiatric Research, 43(7), 739–747.  https://doi.org/10.1016/j.jpsychires.2008.09.012.CrossRefGoogle Scholar
  29. Ko, C. H., Hsieh, T. J., Wang, P. W., Lin, W. C., Yen, C. F., Chen, C. S., et al. (2015). Altered gray matter density and disrupted functional connectivity of the amygdala in adults with internet gaming disorder. Progress in Neuro-Psychopharmacology & Biological Psychiatry, 57, 185–192.  https://doi.org/10.1016/j.pnpbp.2014.11.003.CrossRefGoogle Scholar
  30. Kogachi, S., Chang, L., Alicata, D., Cunningham, E., & Ernst, T. (2017). Sex differences in impulsivity and brain morphometry in methamphetamine users. Brain Structure & Function, 222(1), 215–227.  https://doi.org/10.1007/s00429-016-1212-2.CrossRefGoogle Scholar
  31. Kvamme, T. L., Schmidt, C., Strelchuk, D., Chang-Webb, Y. C., Baek, K., & Voon, V. (2016). Sexually dimorphic brain volume interaction in college-aged binge drinkers. Neuroimage Clin, 10, 310–317.  https://doi.org/10.1016/j.nicl.2015.12.004.CrossRefGoogle Scholar
  32. Li, M., Chen, J., Li, N., & Li, X. (2014a). A twin study of problematic internet use: Its heritability and genetic association with effortful control. Twin Research and Human Genetics, 17(4), 279–287.  https://doi.org/10.1017/thg.2014.32.CrossRefGoogle Scholar
  33. Li, Y., Qiao, L., Sun, J., Wei, D., Li, W., Qiu, J., et al. (2014b). Gender-specific neuroanatomical basis of behavioral inhibition/approach systems (BIS/BAS) in a large sample of young adults: A voxel-based morphometric investigation. Behavioural Brain Research, 274, 400–408.  https://doi.org/10.1016/j.bbr.2014.08.041.CrossRefGoogle Scholar
  34. Li, W., Li, Y., Yang, W., Zhang, Q., Wei, D., Li, W., et al. (2015). Brain structures and functional connectivity associated with individual differences in internet tendency in healthy young adults. Neuropsychologia, 70, 134–144.  https://doi.org/10.1016/j.neuropsychologia.2015.02.019.CrossRefGoogle Scholar
  35. Ma, N., Liu, Y., Fu, X. M., Li, N., Wang, C. X., Zhang, H., et al. (2011). Abnormal brain default-mode network functional connectivity in drug addicts. PLoS One, 6(1), e16560.  https://doi.org/10.1371/journal.pone.0016560.CrossRefGoogle Scholar
  36. Mak, L. E., Minuzzi, L., MacQueen, G., Hall, G., Kennedy, S. H., & Milev, R. (2017). The default mode network in healthy individuals: A systematic review and meta-analysis. Brain Connectivity, 7(1), 25–33.  https://doi.org/10.1089/brain.2016.0438.CrossRefGoogle Scholar
  37. Mehta, P. H., & Beer, J. (2010). Neural mechanisms of the testosterone-aggression relation: The role of orbitofrontal cortex. Journal of Cognitive Neuroscience, 22(10), 2357–2368.  https://doi.org/10.1162/jocn.2009.21389.CrossRefGoogle Scholar
  38. Meng, Y., Deng, W., Wang, H., Guo, W., & Li, T. (2015). The prefrontal dysfunction in individuals with internet gaming disorder: A meta-analysis of functional magnetic resonance imaging studies. Addiction Biology, 20(4), 799–808.  https://doi.org/10.1111/adb.12154.CrossRefGoogle Scholar
  39. Munno, D., Cappellin, F., Saroldi, M., Bechon, E., Guglielmucci, F., Passera, R., et al. (2017). Internet addiction disorder: Personality characteristics and risk of pathological overuse in adolescents. Psychiatry Research, 248, 1–5.  https://doi.org/10.1016/j.psychres.2016.11.008.CrossRefGoogle Scholar
  40. Park, S. Y., Kim, S. M., Roh, S., Soh, M. A., Lee, S. H., Kim, H., et al. (2016). The effects of a virtual reality treatment program for online gaming addiction. Computer Methods and Programs in Biomedicine, 129, 99–108.  https://doi.org/10.1016/j.cmpb.2016.01.015.CrossRefGoogle Scholar
  41. 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.CrossRefGoogle Scholar
  42. Raznahan, A., Lee, Y., Stidd, R., Long, R., Greenstein, D., Clasen, L., et al. (2010). Longitudinally mapping the influence of sex and androgen signaling on the dynamics of human cortical maturation in adolescence. Proceedings of the National Academy of Sciences of the United States of America, 107(39), 16988–16993.  https://doi.org/10.1073/pnas.1006025107.CrossRefGoogle Scholar
  43. Rehbein, F., Kleimann, M., & Mossle, T. (2010). Prevalence and risk factors of video game dependency in adolescence: Results of a German nationwide survey. Cyberpsychology, Behavior and Social Networking, 13(3), 269–277.CrossRefGoogle Scholar
  44. Seeley, W. W., Menon, V., Schatzberg, A. F., Keller, J., Glover, G. H., Kenna, H., et al. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. The Journal of Neuroscience, 27(9), 2349–2356.  https://doi.org/10.1523/JNEUROSCI.5587-06.2007.CrossRefGoogle Scholar
  45. Shek, D. T., & Yu, L. (2016). Adolescent internet addiction in Hong Kong: Prevalence, change, and Correlates. Journal of Pediatric and Adolescent Gynecology, 29(1 Suppl), S22–S30.  https://doi.org/10.1016/j.jpag.2015.10.005. CrossRefGoogle Scholar
  46. Shulman, E. P., Harden, K. P., Chein, J. M., & Steinberg, L. (2015). Sex differences in the developmental trajectories of impulse control and sensation-seeking from early adolescence to early adulthood. Journal of Youth and Adolescence, 44(1), 1–17.  https://doi.org/10.1007/s10964-014-0116-9.CrossRefGoogle Scholar
  47. Stoltenberg, S. F., Batien, B. D., & Birgenheir, D. G. (2008). Does gender moderate associations among impulsivity and health-risk behaviors? Addictive Behaviors, 33(2), 252–265.  https://doi.org/10.1016/j.addbeh.2007.09.004.CrossRefGoogle Scholar
  48. Tomasi, D., & Volkow, N. D. (2012). Aging and functional brain networks. Molecular Psychiatry, 17(5), 471–549–458.  https://doi.org/10.1038/mp.2011.81.CrossRefGoogle Scholar
  49. Wang, H., Jin, C., Yuan, K., Shakir, T. M., Mao, C., Niu, X., et al. (2015). The alteration of gray matter volume and cognitive control in adolescents with internet gaming disorder. Frontiers in Behavioral Neuroscience, 9, 64.  https://doi.org/10.3389/fnbeh.2015.00064.Google Scholar
  50. Wang, L., Wu, L., Lin, X., Zhang, Y., Zhou, H., Du, X., et al. (2016). Dysfunctional default mode network and executive control network in people with internet gaming disorder: Independent component analysis under a probability discounting task. European Psychiatry, 34, 36–42.  https://doi.org/10.1016/j.eurpsy.2016.01.2424.CrossRefGoogle Scholar
  51. Weinstein, A., Livny, A., & Weizman, A. (2017). New developments in brain research of internet and gaming disorder. Neuroscience and Biobehavioral Reviews, 75, 314–330.  https://doi.org/10.1016/j.neubiorev.2017.01.040.CrossRefGoogle Scholar
  52. Yan, C. G., Cheung, B., Kelly, C., Colcombe, S., Craddock, R. C., Di Martino, A., et al. (2013). A comprehensive assessment of regional variation in the impact of head micromovements on functional connectomics. Neuroimage, 76, 183–201.  https://doi.org/10.1016/j.neuroimage.2013.03.004.CrossRefGoogle Scholar
  53. Yuan, K., Qin, W., Wang, G., Zeng, F., Zhao, L., Yang, X., et al. (2011). Microstructure abnormalities in adolescents with internet addiction disorder. PLoS One, 6(6), e20708.  https://doi.org/10.1371/journal.pone.0020708.CrossRefGoogle Scholar
  54. Yuan, K., Jin, C., Cheng, P., Yang, X., Dong, T., Bi, Y., et al. (2013). Amplitude of low frequency fluctuation abnormalities in adolescents with online gaming addiction. PLoS One, 8(11), e78708.  https://doi.org/10.1371/journal.pone.0078708.CrossRefGoogle Scholar
  55. Yuan, K., Qin, W., Yu, D., Bi, Y., Xing, L., Jin, C., et al. (2016). Core brain networks interactions and cognitive control in internet gaming disorder individuals in late adolescence/early adulthood. Brain Structure & Function, 221(3), 1427–1442.  https://doi.org/10.1007/s00429-014-0982-7.CrossRefGoogle Scholar
  56. Yuan, K., Yu, D., Cai, C., Feng, D., Li, Y., Bi, Y., et al. (2017). Frontostriatal circuits, resting state functional connectivity and cognitive control in internet gaming disorder. Addiction Biology, 22(3), 813–822.  https://doi.org/10.1111/adb.12348.CrossRefGoogle Scholar
  57. Zang, Y. F., He, Y., Zhu, C. Z., Cao, Q. J., Sui, M. Q., Liang, M., et al. (2007). Altered baseline brain activity in children with ADHD revealed by resting-state functional MRI. Brain Dev, 29(2), 83–91.  https://doi.org/10.1016/j.braindev.2006.07.002.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Yawen Sun
    • 1
  • Yao Wang
    • 1
  • Xu Han
    • 1
  • Wenqing Jiang
    • 2
  • Weina Ding
    • 1
  • Mengqiu Cao
    • 1
  • Yasong Du
    • 2
  • Fuchun Lin
    • 3
  • Jianrong Xu
    • 1
  • Yan Zhou
    • 1
    Email author
  1. 1.Department of Radiology, Ren Ji Hospital, School of MedicineShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China
  2. 2.Department of Child & Adolescent Psychiatry,Shanghai Mental Health CenterShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China
  3. 3.National Center for Magnetic Resonance in Wuhan, State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Wuhan Institute of Physics and MathematicsChinese Academy of SciencesWuhanPeople’s Republic of China

Personalised recommendations