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Deficit in rewarding mechanisms and prefrontal left/right cortical effect in vulnerability for internet addiction

Published online by Cambridge University Press:  09 March 2016

Michela Balconi  and
Roberta Finocchiaro
Michela Balconi*
Affiliation:
Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy
Roberta Finocchiaro
Affiliation:
Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy Research Unit in Affective and Social Neuroscience, Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy
*
Michela Balconi, Department of Psychology, Catholic University of the Sacred Heart, Milan, Italy. Tel/Fax: +39272342233; E-mail: [email protected]
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Abstract

Objective

The present research explored the cortical correlates of rewarding mechanisms and cortical ‘unbalance’ effect in internet addiction (IA) vulnerability.

Methods

Internet Addiction Inventory (IAT) and personality trait (Behavioural Inhibition System, BIS; Behavioural Activation System, BAS) were applied to 28 subjects. Electroencephalographic (EEG, alpha frequency band) and response times (RTs) were registered during a Go-NoGo task execution in response to different online stimuli: gambling videos, videogames or neutral stimuli. Higher-IAT (more than 50 score, with moderate or severe internet addiction) and lower-IAT (<50 score, with no internet addiction).

Results

Alpha band and RTs were affected by IAT, with significant bias (reduced RTs) for high-IAT in response to gambling videos and videogames; and by BAS, BAS-Reward subscale (BAS-R), since not only higher-IAT, but also BAS and BAS-R values determined an increasing of left prefrontal cortex (PFC) activity (alpha reduction) in response to videogames and gambling stimuli for both Go and NoGo conditions, in addition to decreased RTs for these stimuli categories.

Conclusion

The increased PFC responsiveness and the lateralisation (left PFC hemisphere) effect in NoGo condition was explained on the basis of a ‘rewarding bias’ towards more rewarding cues and a deficit in inhibitory control in higher-IAT and higher-BAS subjects. In contrast lower-IAT and lower-BAS predicted a decreased PFC response and increased RTs for NoGo (inhibitory mechanism). These results may support the significance of personality (BAS) and IAT measures for explaining future internet addiction behaviour based on this observed ‘vulnerability’.

Keywords

alpha bandBASgamblingIATinternet addictionrewarding mechanism
Type
Original Articles
Information
Acta Neuropsychiatrica , Volume 28 , Issue 5 , October 2016 , pp. 272 - 285
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Copyright
© Scandinavian College of Neuropsychopharmacology 2016 

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References

1. Zhou, Y, Lin, FC, Du, YS, Zhao, ZM, Xu, JR, Lei, H. Gray matter abnormalities in internet addiction: a voxel-based morphometry study. Eur J Radiol 2011;79:92–95.CrossRefGoogle ScholarPubMed
2. Han, DH, Lyoo, IK, Renshaw, PF. Differential regional gray matter volumes in patients with online game addiction and professional gamers. J Psychiatr Res 2012;46:507–515.CrossRefGoogle ScholarPubMed
3. Grant, JE, Marc, NP, Weinstein, A, Gorelick, DA. Introduction to behavioral addictions. Am J Drug Alcohol Abuse 2010;36:233–241.CrossRefGoogle ScholarPubMed
4. Park, SM, Lee, JH. How cognitive reappraisal of anger influences risk-taking behavior. Soc Behav Personal 2011;29:411–418.CrossRefGoogle Scholar
5. Yen, JY, Cheng-Fang, Y, Chen, CS, Chang, YH, Yeh, YC, Ko, CH. The bidirectional interactions between addiction, behaviour approach and behaviour inhibition systems among adolescents in a prospective study. Psychiatry Res 2012;200:588–592.CrossRefGoogle ScholarPubMed
6. Caseras, X, Avila, C, Torrubia, R. The measurement of individual differences in behavioural inhibition and behavioural activation systems: a comparison of personality scales. Pers Ind Diff 2003;34:999–1013.CrossRefGoogle Scholar
7. Chen, CY, Huang, MF, Yen, JY et al. Brain correlates of response inhibition in internet gaming disorder. Psychiatr Clin Neurosci 2015;69:201–209.CrossRefGoogle ScholarPubMed
8. Knyazev, GG. Antero-posterior EEG. Spectral power gradient as a correlate of extraversion and behavioral inhibition. Open Neuroimag J 2010;4:114–120.Google ScholarPubMed
9. Balconi, M, Finocchiaro, R. Decisional impairments in cocaine addiction, reward bias, and cortical oscillation ‘unbalance’. Neuropsychiatr Dis Treat 2015;11:777–786.CrossRefGoogle ScholarPubMed
10. Finocchiaro, R, Balconi, M. Reward-system effect and ‘left hemispheric unbalance’: a comparison between drug addiction and high-BAS healthy subjects on gambling behavior. Neuropsychogical Trends 2015;17:37–45.CrossRefGoogle Scholar
11. Baler, RD, Volkow, ND. Drug addiction: the neurobiology of disrupted self-control. Trends Mol Med 2006;12:559–566.CrossRefGoogle ScholarPubMed
12. Bechara, A. Decision making, impulse control and loss of willpower to resist drugs: a neurocognitive perspective. Nat Neurosci 2005;8:1458–1463.CrossRefGoogle ScholarPubMed
13. Dawe, S, Loxton, N. The role of impulsivity in the development of substance use and eating disorders. Neurosci Biobehav Rev 2004;28:343–351.CrossRefGoogle ScholarPubMed
14. Balconi, M, Finocchiaro, R, Canavesio, Y. Reward-system effect (BAS rating), left hemispheric ‘unbalance’(alpha band oscillations) and decisional impairments in drug addiction. Addict Behav 2014;39:1026–1032.CrossRefGoogle ScholarPubMed
15. Balconi, M, Falbo, L, Conte, VA. BIS and BAS correlates with psychophysiological and cortical response systems during aversive and appetitive emotional stimuli processing. Motiv Emotion 2012;36:218–231.CrossRefGoogle Scholar
16. Balconi, M, Brambilla, E, Falbo, L. BIS/BAS, cortical oscillations and coherence in response to emotional cues. Brain Res Bull 2009;80:151–157.CrossRefGoogle ScholarPubMed
17. Balconi, M, Brambilla, E, Falbo, L. Appetitive vs. defensive responses to emotional cues. Autonomic measures and brain oscillation modulation. Brain Res 2009;1296:72–84.CrossRefGoogle ScholarPubMed
18. Balconi, M, Falbo, L, Brambilla, E. BIS/BAS responses to emotional cues: self-report, autonomic measure and alpha band modulation. Pers Individ Dif 2009;47:858–863.CrossRefGoogle Scholar
19. Balconi, M, Mazza, G. Brain oscillations and BIS/BAS (behavioral inhibition/activation system) effects on processing masked emotional cues. ERS/ERD and coherence measures of alpha band. Int J Psychophysiol 2009;74:158–165.CrossRefGoogle ScholarPubMed
20. Balconi, M, Mazza, G. Consciousness and emotion: ERP modulation and attentive vs. pre-attentive elaboration of emotional facial expressions by backward masking. Motiv Emotion 2009;33:113–124.CrossRefGoogle Scholar
21. Balconi, M, Mazza, G. Lateralisation effect in comprehension of emotional facial expression: a comparison between EEG alpha band power and behavioural inhibition (BIS) and activation (BAS) systems. Laterality 2010;15:361–384.CrossRefGoogle ScholarPubMed
22. Carver, CS, White, TL. Behavioral inhibition, behavioral activation, and affective responses to impending reward and punishment: the BIS/BAS Scales. J Pers Soc Psychol 1994;67:319–333.CrossRefGoogle Scholar
23. Fowles, DC. The three arousal model: implications of Gray’s two-factor learningtheory for heart rate, electrodermal activity, and psychopathy. Psychophysiology 1980;17:87–104.CrossRefGoogle Scholar
24. Gray, JA. A critique of Eysenck’s theory of personality. In: Eysenck HJ, editor. A model for personality. Berlin: Springer-Verlag, 1981. pp. 246–276.CrossRefGoogle Scholar
25. Gray, JA, McNaughton, N. The neuropsychology of anxiety: an enquiry into the functions of the septo-hippocampal system, 2nd edn. Oxford: Oxford University Press, 2000.Google Scholar
26. Blum, K, Braverman, ER, Holder, JM et al. Reward deficiency syndrome: a biogenetic model for the diagnosis and treatment of impulsive, addictive, and compulsive behaviors. J Psychoactive Drugs 2000;32:1–112.CrossRefGoogle ScholarPubMed
27. Smillie, LD, Loxton, NJ, Avery, RE. Reinforcement sensitivity theory, research, applications and future. In: Chamorro-Premuzic T, Furnham A, von Stumm S, editors. The Wiley-Blackwell handbook of individual differences. London: Wiley-Blackwell, 2011. pp. 101–131.CrossRefGoogle Scholar
28. Bechara, A, Martin, EM. Impaired decision making related to working memory deficits in individuals with substance addictions. Neuropsychology 2004;18:152–162.CrossRefGoogle ScholarPubMed
29. Adinoff, B. Neurobiologic processes in drug reward and addiction. Harv Rev Psychiatry 2004;12:305–320.CrossRefGoogle ScholarPubMed
30. Limbrick-Oldfield, EH, van Holst, RJ, Clark, L. Fronto-striatal dysregulation in drug addiction and pathological gambling: consistent inconsistencies? Neuroimage Clin 2013;2:385–393.CrossRefGoogle ScholarPubMed
31. Scheres, A, Milham, MP, Knutson, B, Castellanos, FX. Ventral striatal hyporesponsivness during reward anticipation in attention deficit/hyperactivity disorder. Biol Psychiatry 2007;61:720–724.CrossRefGoogle Scholar
32. Yen, JY, Ko, CH, Yen, CF, Chen, CS, Chen, CC. The association between harmful alcohol use and internet addiction among college students: comparison of personality. Psychiatry Clin Neurosci 2009;63:218–224.CrossRefGoogle ScholarPubMed
33. Davidson, RJ. What does the prefrontal cortex ‘do’ in affect: perspectives on frontal EEG asymmetry research. Biol Psychol 2004;67:219–233.CrossRefGoogle ScholarPubMed
34. Harmon-Jones, E. Contributions from research on anger and cognitive dissonance to understanding the motivational functions of asymmetrical frontal brain activity. Biol Psychol 2004;67:51–76.CrossRefGoogle ScholarPubMed
35. Davidson, RJ. Brain asymmetry, the emotions, and mood disorders. Harv Ment Health Lett 1992;9:4–5.Google Scholar
36. Fowles, DC. A motivational theory of psychopathology. In: Spaulding W, editor. Nebraska symposium on motivation: integrated views of motivation and emotion, vol. 41. Lincoln: University of Nebraska Press, 1994. pp. 181–238.Google Scholar
37. Gray, JA. Personality dimensions and emotion systems. In: Ekman P, Davidson RJ, editors. The nature of emotion: fundamental questions. New York, NY: Oxford University Press, 1994. pp. 329–331.Google Scholar
38. Wheeler, RE, Davidson, RJ, Tomarker, AJ. Frontal brain asymmetry and emotional reactivity: a biological substrate of affective style. Psychophysiology 1993;30:82–89.CrossRefGoogle ScholarPubMed
39. Balconi, M, Finocchiaro, R, Canavesio, Y, Messina, R. Reward bias and lateralization in gambling behavior: behavioral activation system and alpha band analysis. Psychiatry Res 2014;219:570–576.CrossRefGoogle ScholarPubMed
40. Knoch, D, Pascual-Leone, A, Meyer, K, Treyer, V, Fehr, E. Diminishing reciprocal fairness by disrupting the right prefrontal cortex. Science 2006;314:829–832.CrossRefGoogle ScholarPubMed
41. Buss, KA, Malmstadt, JR, Dolski, I, Kalin, NH, Goldsmith, HH, Davidson, RJ. Right frontal brain activity,cortisol and withdrawal behavior in 6-month- old infants. Behav Neurosci 2003;117:11–20.CrossRefGoogle Scholar
42. Kalin, NH, Larson, C, Shelton, SE, Davidson, RJ. Asymmetric frontal brain activity, cortisol and behavior associated with fearful temperament in Rhesus monkeys. Behavioral Neuroscience 1998;112:286–292.CrossRefGoogle ScholarPubMed
43. Sobotka, SS, Davidson, RJ, Senulis, JA. Anterior brain electrical asymmetries in response to reward and punishment. Electroencephalogr Clin Neurophysiol 1992;83:236–247.CrossRefGoogle ScholarPubMed
44. Logan, GD, Cowan, WB, Davis, KA. On the ability to inhibit simple and choice reaction time responses: a model and a method. J Exp Psychol Hum Percept Perform 1984;10:276–291.CrossRefGoogle Scholar
45. Kamarajan, C, Rangaswamy, M, Cholrian, DB et al. Theta oscillations during the processing of monetary loss and gain: a perspective on gender and impulsivity. Brain Res 2008;1235:45–62.CrossRefGoogle Scholar
46. Young, KS. Caught in the net. New York, NY: John Wiley & sons, 1998.Google Scholar
47. Beck, AT, Brown, G, Steer, RA. Beck depression inventory II manual. San Antonio, TX: Psychological Corporation, 1996.Google Scholar
48. Petit, G, Kornreich, C, Noël, X, Verbanck, P, Campanella, S. Alcohol-related context modulates performance of social drinkers in a visual Go/No-Go task: a preliminary assessment of event-related potentials. PLoS One 2012;7:e37466 doi:10.1371/journal.pone.0037466.CrossRefGoogle Scholar
49. Leone, L, Pierro, A, Mannetti, L. Validità della versione italiana delle scale BIS/BAS di Carver e White (1994) generalizzabilità della struttura e relazioni con costrutti affini. Giornale Italiano di Psicologia 2002;29:413–434.Google Scholar
50. Laconi, S, Rodgers, RF, Chabrol, H. The measurement of internet addiction: a critical review of existing scales and their psychometric properties. Comput. Human Behav 2014;41:190–202.CrossRefGoogle Scholar
51. Pfurtscheller, G. Event-related synchronization (ERS): an electrophysiological correlate of cortical areas at rest. Electroen Clin Neuro 1992;83:62–69.CrossRefGoogle ScholarPubMed
52. Jasper, HH. The ten-twenty electrode system of International Federation EEG. Electroen Clin Neuro 1958;10:371–375.Google Scholar
53. Pascual-Marqui, RD. Standardized low-resolution brain electromagnetic tomography (sLORETA): technical details. Method Find Exp Clin 2002;24:5–12.Google ScholarPubMed
54. Ludwing, A, Miriani, RM, Langhals, NB, Joseph, MD, David, J. Neuron recordings from microelectrode arrays using a common average reference to improve cortical. J Neurophysiol 2008;101:1679–1689.CrossRefGoogle Scholar
55. Pascual-Marqui, RD, Michel, CM, Lehmann, D. Low resolution electromagnetic tomography: a new method for localizing electrical activity in the brain. Int J Psychophysiol 1994;18:49–65.CrossRefGoogle ScholarPubMed
56. Semlitsch, V, Anderer, P, Schuster, P, Presslich, O. A solution for reliable and valid reduction of ocular artifacts, applied to the P300 ERP. Psychophysiology 1986;23:695–703.CrossRefGoogle Scholar
57. Crean, JP, De Wit, H, Richards, JB. Reward discounting as a measure of impulsive behavior in a psychiatric outpatient population. Exp Clin Psychopharmacol 2008;8:155–162.CrossRefGoogle Scholar
58. Gable, L, Reis, HT, Elliot, AJ. Behavioral activation and inhibition in everyday life. J Pers Soc Psychol 2000;78:1135–1149.CrossRefGoogle ScholarPubMed
59. Harper, J, Malone, SM, Bernat, EM. Theta and delta band activity explain N2 and P3 ERP component activity in a go/no-go task. Clin Neurophysiol 2014;125:124–132.CrossRefGoogle Scholar
60. Yamanaka, K, Yamamoto, Y. Single-trial EEG power and phase dynamics associated with voluntary response inhibition. J Cogn Neurosci 2009;22:714–727.CrossRefGoogle Scholar
61. Barry, RJ. Evoked activity and EEG phase resetting in the genesis of auditory Go/NoGo ERPs. Biol Psychol 2009;80:292–299.CrossRefGoogle ScholarPubMed
62. Kamarajan, C, Porjez, B, Jones, KA et al. The role of brain oscillations as functional correlates of cognitive systems: a study of frontal inhibitory control in alcoholism. Int J Psychophysiol 2004;51:155–180.CrossRefGoogle ScholarPubMed
63. Kamarajan, C, Porjez, B, Jones, K et al. Event-related oscillations in offsprings of alcoholics: neurocognitive disinhibition as a risk for alcoholism. Biol Psychiatry 2006;59:625–634.CrossRefGoogle ScholarPubMed
64. Kirmizi-Alsan, E, Bayraktaroglu, Z, Gurvit, H, Keskin, YH, Emre, M, Demiralp, T. Comparative analysis of event-related potentials during Go/NoGo and CPT: decomposition of electrophysiological markers of response inhibition and sustained attention. Brain Res 2006;1104:114–128.CrossRefGoogle ScholarPubMed
65. Bernat, EM, Nelson, LD, Steele, VR, Gehring, WJ, Patrick, CJ. Externalizing psychopathology and gain–loss feedback in a simulated gambling task: dissociable components of brain response revealed by time–frequency analysis. J Abnorm Psychol 2011;120:352–364.CrossRefGoogle Scholar
66. Gehring, WJ, Willoughby, AR. Are all medial frontal negativities created equal? Toward a richer empirical basis for theories of action monitoring. In: Ullsperger M, Falkenstein M, editors. Errors, conflicts, and the brain. Current opinions on performance monitoring. Leipzig, Germany: Max Planck Institute of Cognitive Neuroscience, 2004. pp. 14–20.Google Scholar
67. Trujillo, LT, Allen, JJ. Theta EEG dynamics of the error-related negativity. Clin Neurophysiol 2007;118:645–668.CrossRefGoogle ScholarPubMed
68. Yordanova, J, Falkenstein, M, Hohnsbein, J, Kolev, V. Parallel systems of error processing in the brain. Neuroimage 2004;22:590–602.CrossRefGoogle ScholarPubMed
69. Cavanagh, JF, Zambrano-Vazquez, L, Allen, JJ. Theta lingua franca: a common mid-frontal substrate for action monitoring processes. Psychophysiology 2011;49:220–238.CrossRefGoogle Scholar
70. Cohen, MX, Elger, CE, Ranganath, C. Reward expectation modulates feedback-related negativity and EEG spectra. Neuroimage 2007;35:968–978.CrossRefGoogle ScholarPubMed
71. Palmero-Soler, E, Dolan, K, Hadamschek, V, Tass, PA. swLORETA: a novel approach to robust source localization and synchronization tomography. Phys Med Biol 2007;52:1783–1800.CrossRefGoogle ScholarPubMed
72. Hester, R, Murphy, K, Garavan, H. Beyond common resources: the cortical basis for resolving task interference. Neuroimage 2004;23:202–212.CrossRefGoogle ScholarPubMed
73. Kamarajan, C, Rangaswamy, M, Manz, N et al. Topography, power, and current source density of θ oscillations during reward processing as markers for alcohol dependence. Hum Brain Mapp 2012;33:1019–1039.CrossRefGoogle ScholarPubMed
74. Başar, E, Başar-Eroglu, C, Karakas, S, Schurmann, M. Are cognitive processes manifested in event-related gamma, alpha, theta and delta oscillations in the EEG? Neurosci Lett 1999;259:165–168.CrossRefGoogle ScholarPubMed
75. Basar, E, Guntekin, B. The key role of alpha activity in ‘creative evolution’. Int J Psychophysiol 2006;61:313–314.Google Scholar
76. Kahana, MJ, Seelig, D, Madsen, JR. Theta returns. Curr Opin Neurobiol 2001;11:739–744.CrossRefGoogle ScholarPubMed
77. Klimesch, W, Schack, B, Sauseng, P. The functional significance of theta and upper alpha oscillations. Exp Psychol 2005;52:99–108.CrossRefGoogle ScholarPubMed
78. Klimesch, W, Hanslmayr, S, Sauseng, P et al. Oscillatory EEG correlates of episodic trace decay. Cereb. Cortex 2006;16:280–290.CrossRefGoogle ScholarPubMed
79. Knyazev, GG. Motivation, emotion, and their inhibitory control mirrored in brain oscillations. Neurosci Biobehav Rev 2007;31:377–395.CrossRefGoogle ScholarPubMed
80. Luu, P, Tucker, DM, Makeig, S. Frontal midline theta and the error-related negativity: neurophysiological mechanisms of action regulation. Clin Neurophysiol 2004;115:1821–1835.CrossRefGoogle ScholarPubMed
81. Camara, E, Rodriguez-Fornells, A, Munte, TF. Functional connectivity of reward processing in the brain. Front Hum Neurosci 2009;2:1–14.Google ScholarPubMed
82. Delgado, MR, Locke, HM, Stenger, VA, Fiez, JA. Dorsal striatum responses to reward and punishment: effects of valence and magnitude manipulations. Cogn Affect Behav Neurosci 2003;3:27–38.CrossRefGoogle ScholarPubMed
83. Delgado, MR, Miller, MM, Inati, S, Phelps, EA. An fMRI study of reward-related probability learning. Neuroimage 2005;24:862–873.CrossRefGoogle ScholarPubMed
84. Knutson, B, Fong, GW, Bennett, SM, Adams, CM, Hommer, D. A region of mesial prefrontal cortex tracks monetarily rewarding outcomes: characterization with rapid event-related fMRI. Neuroimage 2003;18:263–272.CrossRefGoogle ScholarPubMed
85. Marco-Pallares, J, Muller, SV, Munte, TF. Learning by doing: an fMRI study of feedback-related brain activations. Neuroreport 2007;18:1423–1426.CrossRefGoogle ScholarPubMed
86. McClure, SM, York, MK, Montague, PR. The neural substrates of reward processing in humans: the modern role of FMRI. Neuroscientist 2004;10:260–268.CrossRefGoogle ScholarPubMed
87. de Greck, M, Supady, A, Thiemann, R et al. Decreased neural activity in reward circuitry during personal reference in abstinent alcoholics – a fMRI study. Hum Brain Mapp 2009;30:1691–1704.CrossRefGoogle ScholarPubMed
88. Makris, N, Oscar-Berman, M, Jaffin, SK et al. Decreased volume of the brain reward system in alcoholism. Biol Psychiatry 2008;64:192–202.CrossRefGoogle ScholarPubMed
89. Wrase, J, Schlagenhauf, F, Kienast, T et al. Dysfunction of reward processing correlates with alcohol craving in detoxified alcoholics. Neuroimage 2007;35:787–794.CrossRefGoogle ScholarPubMed
90. Diekhof, EK, Falkai, P, Gruber, O. Functional neuroimaging of reward processing and decision-making: a review of aberrant motivational and affective processing in addiction and mood disorders. Brain Res Rev 2008;59:164–184.CrossRefGoogle ScholarPubMed
91. Schoenbaum, G, Roesch, MR, Stalnaker, TA. Orbitofrontal cortex, decision-making and drug addiction. Trends Neurosci 2006;29:116–124.CrossRefGoogle ScholarPubMed
92. Fuster, JM. The prefrontal cortex: anatomy, physiology, and neuropsychology of the frontal lobe, 2nd edn. New York: Raven Press, 1989.Google Scholar
93. Kamarajan, C, Rangaswamy, M, Tang, Y et al. Dysfunctional reward processing in male alcoholics: AnERP study during a gambling task. J Psychiatr Res 2010;44:576–590.CrossRefGoogle ScholarPubMed
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