Hostname: page-component-586b7cd67f-rdxmf Total loading time: 0 Render date: 2024-11-22T00:22:25.989Z Has data issue: false hasContentIssue false
  • English
  • Français

Working memory, executive function and impulsivity in Internet-addictive disorders: a comparison with pathological gambling

Published online by Cambridge University Press:  24 September 2015

Zhenhe Zhou ,
Hongliang Zhou  and
Hongmei Zhu
Zhenhe Zhou*
Affiliation:
Department of Psychiatry, Wuxi Mental Health Center of Nanjing Medical University, Jiangsu Province, P.R. China Wuxi Tongren International Rehabilitation of Hospital, Nanjing Medical University, Jiangsu Province, P.R. China
Hongliang Zhou
Affiliation:
Grade 2013 class 3, Basic Medicine College of Liaoning Medical University, Liaoning Province, P.R. China
Hongmei Zhu
Affiliation:
Department of Psychiatry, Wuxi Mental Health Center of Nanjing Medical University, Jiangsu Province, P.R. China Wuxi Tongren International Rehabilitation of Hospital, Nanjing Medical University, Jiangsu Province, P.R. China
*
Zhenhe Zhou, Department of Psychiatry, Wuxi Mental Health Center of Nanjing Medical University, 156 Qianrong Road, Wuxi 214151, Jiangsu Province, P.R. China. Tel: +86-13358118986; Fax: +86-510-83012201; E-mail: [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

Objective

The purpose of the present study was to test whether individuals with Internet addiction disorder (IAD) presented analogous characteristics of working memory, executive function and impulsivity compared with pathological gambling (PG) patients.

Methods

The subjects included 23 individuals with IAD, 23 PG patients and 23 controls. All of the participants were measured with the digit span task, Wisconsin Card Sorting Test, go/no-go task and Barratt Impulsiveness Scale-11 (BIS-11) under the same experimental conditions.

Results

The results of this study showed that the false alarm rate, total response errors, perseverative errors, failure to maintain set and BIS-11 scores of both the IAD and PG groups were significantly higher than that of the control group. In addition, the forward scores and backwards scores, percentage of conceptual level responses, number of categories completed and hit rate of the IAD and PG groups were significantly lower than that of the control group. Furthermore, the false alarm rate and BIS-11 scores of the IAD group were significantly higher than those of PG patients, and the hit rate was significantly lower than that of the PG patients.

Conclusions

Individuals with IAD and PG patients present deficiencies in working memory, executive dysfunction and impulsivity, and individuals with IAD are more impulsive than PG patients.

Keywords

executive functionimpulsivityInternet addiction disorderpathological gamblingworking memory
Type
Original Articles
Information
Acta Neuropsychiatrica , Volume 28 , Issue 2 , April 2016 , pp. 92 - 100
Check for updates
Copyright
© Scandinavian College of Neuropsychopharmacology 2015 

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

†

These authors are co-first authors.

References

1. KS, Ung. Internet addiction: the emergence of a new clinical disorder. Cyberpsychol Behav 1998;1:237–244.Google Scholar
2. Davis, RA. A cognitive-behavioral model of pathological internet use. Comput Hum Behav 2001;17:187–195.Google Scholar
3. 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:e107306.CrossRefGoogle ScholarPubMed
4. Zhou, ZH, Yuan, GZ, JJ, Yao. Cognitive biases toward internet game-related pictures and executive deficits in individuals with an internet game addiction. PLoS One 2012;7:e48961.Google Scholar
5. Zhou, ZH, Yuan, GZ, JJ, Yao, Li, C, Cheng, ZH. An event‐related potential investigation of deficient inhibitory control in individuals with pathological internet use. Acta Neuropsychiatr 2010;22:228–236.CrossRefGoogle ScholarPubMed
6. Zhou, Z, Li, C, Zhu, H. An error-related negativity potential investigation of response monitoring function in individuals with internet addiction disorder. Front Behav Neurosci 2013;7:1–8.Google Scholar
7. Lin, F, Zhou, Y, Du, Y et al. Abnormal white matter integrity in adolescents with internet addiction disorder: a tract-based spatial statistics study. PLoS One 2012;7:e30253.CrossRefGoogle ScholarPubMed
8. Yuan, K, Qin, W, Wang, G et al. Microstructure abnormalities in adolescents with internet addiction disorder. PLoS One 2011;6:e20708.CrossRefGoogle ScholarPubMed
9. Fuentes, D, Tavares, H, Artes, R, Gorenstein, C. Self-reported and neuropsychological measures of impulsivity in pathological gambling. J Int Neuropsychol Soc 2006;12:907–912.Google Scholar
10. Kraplin, A, Buhringer, G, Oosterlaan, J, Van Den Brink, W, Goschke, T, Goudriaan, AE. Dimensions and disorder specificity of impulsivity in pathological gambling. Addict Behav 2014;39:1646–1651.Google Scholar
11. Goudriaan, AE, Oosterlaan, J, De Beurs, E, Van Den Brink, W. Pathological gambling: a comprehensive review of biobehavioral findings. Neurosci Biobehav Rev 2004;28:123–141.Google Scholar
12. Goudriaan, AE, Oosterlaan, J, De Beurs, E, Van Den Brink, W. Neurocognitive functions in pathological gambling: a comparison with alcohol dependence, Tourette syndrome and normal controls. Addiction 2006;101:534–547.Google Scholar
13. Brand, M, Kalbe, E, Labudda, K, Esther, F, Josef Kesslerb, H, Markowitsch, H. Decision-making impairments in patients with pathological gambling. Psychiatry Res 2005;133:91–99.Google Scholar
14. Stojanov, W, Karayanidis, F, Johnston, P, Bailey, A, Carr, V, Schall, U. Disrupted sensory gating in pathological gambling. Biol Psychiatry 2003;54:474–484.CrossRefGoogle ScholarPubMed
15. Van Holst, RJ, Van Den Brink, W, Veltman, DJ, Goudriaan, AE. Why gamblers fail to win: a review of cognitive and neuroimaging findings in pathological gambling. Neurosci Biobehav Rev 2010;34:87–107.CrossRefGoogle Scholar
16. Boileau, I, Payer, D, Chugani, B et al. In vivo evidence for greater amphetamine-induced dopamine release in pathological gambling: a positron emission tomography study with [(11)C]-(+)-PHNO. Mol Psychiatry 2014;19:1305–1313.CrossRefGoogle Scholar
17. Block, JJ. Issues for DSM-V: internet addiction. Am J Psychiatry 2008;165:306–307.Google Scholar
18. American Psychiatric Association. Diagnostic and statistical manual of mental disorders. DSM-IV, 4th edn. Washington, DC: American Psychiatric Association, 1994.Google Scholar
19. Beard, KW, Wolf, EM. Modification in the proposed diagnostic criteria for internet addiction. Cyberpsychol Behav 2001;4:377–383.Google Scholar
20. Shaw, M, Black, DW. Internet addiction. CNS drugs 2008;22:353–365.Google Scholar
21. Guertler, D, Rumpf, HJ, Bischof, A et al. Assessment of problematic internet use by the compulsive internet use scale and the internet addiction test: a sample of problematic and pathological gamblers. Eur Addict Res 2013;20:75–81.CrossRefGoogle Scholar
22. Tonioni, F, Mazza, M, Autullo, G et al. Is internet addiction a psychopathological condition distinct from pathological gambling? Addict Behav 2014;39:1052–1056.CrossRefGoogle ScholarPubMed
23. Cousijin, J, Vingerhoets, WA, Koenders, L et al. Relationship between working-memory network function and substance use: a 3-year longitudinal fMRI study in heavy cannabis users and controls. Addict Biol 2014;19:282–293.Google Scholar
24. Femandez-Serrano, MJ, Perez-Garcia, M, Verdejo-Garcia, A. What are the specific versus generalized effects of drugs of abuse on neuropsychological performance? Neurosci Biobehav Rev 2011;35:377–406.Google Scholar
25. Mcclernon, FJ, Froeliger, B, Rose, JE. The effects of nicotine and non‐nicotine smoking factors on working memory and associated brain function. Addict Biol 2015. doi: 10.1111/adb.12253. (in press).Google Scholar
26. Ochoa, C, Alvarez-Moya, EM, Penelo, E. Decision-making deficits in pathological gambling: the role of executive functions, explicit knowledge and impulsivity in relation to decisions made under ambiguity and risk. Am J Addict 2013;22:492–499.Google Scholar
27. Zhou, Z, Zhu, H, Li, C, Wang, J. Internet addictive individuals share impulsivity and executive dysfunction with alcohol-dependent patients. Front Behav Neurosci 2014;8:1–8.Google Scholar
28. CH, Ko, GC, Liu, Hsiao, S et al. Brain activities associated with gaming urge of online gaming addiction. J Psychiatr Res 2009;43:739–747.Google Scholar
29. Hamilton, M. Development of a rating scale for primary depressive illness. Br J Soc Clin Psychol 1967;6:278–296.CrossRefGoogle ScholarPubMed
30. Luna, B, Minshew, NJ, Garver, KE et al. Neocortical system abnormalities in autism: an fMRI study of spatial working memory. Neurology 2002;59:834–840.CrossRefGoogle ScholarPubMed
31. Chang, C, Crottaz-Herbette, S, Menon, V. Temporal dynamics of basal ganglia response and connectivity during verbal working memory. Neuroimage 2007;34:1253–1269.CrossRefGoogle ScholarPubMed
32. Mcnab, F, Klingberg, T. Prefrontal cortex and basal ganglia control access to working memory. Nat neurosci 2008;11:103–107.Google Scholar
33. Luerdin, R, Weigand, T, Bogdahn, U, Schmid -Wilcke, T. Working memory performance is correlated with local brain morphology in the medial frontal and anterior cingulate cortex in fibromyalgia patients: structural correlates of pain–cognition interaction. Brain 2008;131:3222–3231.CrossRefGoogle Scholar
34. Alvarez, JA, Emoryv, E. Executive function and the frontal lobes: a meta-analytic review. Neuropsychol Rev 2006;16:17–42.CrossRefGoogle ScholarPubMed
35. Miyake, A, Friedman, NP, Emerson, MJ, Witzki, AH, Howerter, A, Wager, TD. The unity and diversity of executive functions and their contributions to complex ‘frontal lobe’ tasks: a latent variable analysis. Cogn Psychol 2000;41:49–100.Google Scholar
36. Ledgerwood, DM, Orr, ES, Kaploun, KA, Frish, GR, Rupcich, N, Lundahl, LH. Executive function in pathological gamblers and healthy controls. J Gambl Stud 2012;28:89–103.CrossRefGoogle ScholarPubMed
37. Forbush, KT, Shaw, M, Graebe, MA et al. Neuropsychological characteristics and personality traits in pathological gambling. CNS Spectr 2008;13:306–315.CrossRefGoogle ScholarPubMed
38. Nigg, JT. Is ADHD a disinhibitory disorder? Psychol Bull 2001;127:571–598.Google Scholar
39. Madden, GJ, Petry, NM, Badger, GJ, Bickel, WK. Impulsive and self-control choices opioid-dependent patients and non-drug-using control patients: drug and monetary rewards. Exp Clin Psychopharmacol 1997;5:256–262.Google Scholar
40. Henry, C, Mitropoulou, V, New, AS, Koenigsberg, HW, Silveman, J, Siever, LJ. Affective instability and impulsivity in borderline personality and bipolar II disorders: similarities and differences. J Psychiatr Res 2001;35:307–312.CrossRefGoogle ScholarPubMed
41. Lee, HW, Choi, JS, Shin, YC, Lee, JY, Jung, HY, Kwon, JS. Impulsivity in internet addiction: a comparison with pathological gambling. Cyberpsychol Behav Soc Netw 2012;15:373–377.Google Scholar
42. Verdejo-Garcia, A, Alonso-Maroto, L, Fernandez-Calderon, F. Impulsivity and executive functions in polysubstance-using rave attenders. Psychopharmacology 2010;210:377–392.CrossRefGoogle ScholarPubMed
43. Leeman, RF, Potenza, MN. Similarities and differences between pathological gambling and substance use disorders: a focus on impulsivity and compulsivity. Psychopharmacology 2012;219:469–490.Google Scholar
44. Verdejo-Garcia, A, Lopez-Torrecillas, F, Gimenez, CO. Clinical implications and methodological challenges in the study of the neuropsychological correlates of cannabis, stimulant, and opioid abuse. Neuropsychology Rev 2004;14:1–41.Google Scholar
45. Grenard, JL, Ames, SL, Wiser, RW. Working memory capacity moderates the predictive effects of drug-related associations on substance use. Psychol Addict Behav 2008;22:426–432.Google Scholar
46. Cousijin, J, Wisers, RW, Ridderinkhof, KR. Effect of baseline cannabis use and working-memory network function on changes in cannabis use in heavy cannabis users: a prospective fMRI study. Hum Brain Mapp 2013;35:2470–2482.CrossRefGoogle Scholar
47. Widyanto, L, Griffiths, M. ‘Internet addiction’: a critical review. Int J Ment Health Addict 2006;4:31–51.Google Scholar