Internet and Gaming Addiction: A Systematic Literature Review of Neuroimaging Studies
Abstract
:1. Introduction
1.1. The Rise of Neuroimaging
1.2. Types of Neuroimaging Used to Study Addictive Brain Activity
2. Method
3. Results
3.1. fMRI Studies
Study (Year) | Main Aims | Sample [Design/Method] | Internet addiction diagnosis | Main Results |
---|---|---|---|---|
Dong, Huang & Du [39] | Examined reward and punishment processing in Internet addicts versus healthy controls | 14 male Internet addicts | Internet Addiction Test [61]; Chinese Internet Addiction Test [62,63] | Internet addiction associated with increased activation in orbitofrontal cortex in gain trials, decreased anterior cingulate activation in loss trials compared to normal controls; Enhanced reward sensitivity and decreased loss sensitivity than normal controls |
13 healthy males | ||||
[Reality-simulated fMRI quasi-experimental guessing task for money gain or loss situation using playing cards] | ||||
Dong, Zhou & Zhao [52] | Investigated executive control ability of Internet addicts | 17 male Internet addicts | Internet Addiction Test [64] | Internet addicts had longer reaction time and more response errors in incongruent conditions than controls; reduced medial frontal negativity (MFN) deflection in incongruent conditions than controls |
17 male healthy university students | ||||
[Measured event-related potentials (ERP) via electroencephalogram (EEG) during a quasi-experimental color-word Stroop task] | ||||
Dong, Lu, Zhou & Zhao [53] | Investigated neurological response inhibition in Internet addicts | 12 male Internet addicts | Internet Addiction Test [65] | Internet addicts had (i) lower NoGo-N2 amplitudes (represent response inhibition-conflict monitoring), higher NoGo-P3 amplitudes (inhibitory processes—response evaluation), (ii) longer NoGo-P3 peak latency than controls, and (iii) less efficient information processing and lower impulse control |
12 male healthy control university students | ||||
[Quasi-experimental EEG study: Recordings of event-related brain potentials (ERPs) via EEG during a quasi-experimental go/NoGo task] | ||||
Ge, Ge, Xu, Zhang, Zhao & Kong [66] | Investigated association between P300 component and Internet addiction disorder | 38 Internet addiction patients (21 males) | Internet Addiction Test [64] | Study found similar results for Internet addicts as compared to other substance-related addicts; Cognitive dysfunctions associated with Internet addiction can be improved Internet addicts had longer P300 latencies relative to controls |
48 healthy college student controls (25 males) | ||||
[Quasi-experimental EEG study; P300 ERP measured using standard auditory oddball task using American Nicolet BRAVO instrument] | ||||
Han, Lyoo & Renshaw [40] | Compared regional gray matter volumes in patients with online game addiction (POGA) and professional gamers (PGs) | 20 patients with online game addiction | Young’s Internet Addiction Scale [67] | POGA had higher impulsiveness, perseverative errors, volume in left thalamus gray matter, decreased gray matter volume in inferior temporal gyri, right middle occipital gyrus, left inferior occipital gyrus relative to HC;PGs had increased gray matter volume in left cingulate gyrus, decreased in left middle occipital gyrus and right inferior temporal gyrus relative to HC, and increased in left cingulate gyrus and decreased left thalamus gray relative to POGA |
17 pro-gamers | ||||
18 healthy male controls | ||||
[fMRI study with voxel-wise comparisons of gray matter volume] | ||||
Han, Hwang & Renshaw [41] | Tested effects of bupropion sustained release treatment on brain activity for online video game addicts | 11 male Internet video game addicts | Young’s Internet Addiction Scale [67]; Craving for Internet Video Game Play Scale | During exposure to game cues, IGA had more brain activation in left occipital lobe cuneus, left dorsolateral prefrontal cortex, left parahippocampal gyrus relative to H; After treatment, craving, play time, and cue-induced brain activity decreased in IAG |
8 healthy male controls | ||||
[Quasi-experimental fMRI study at baseline and after six weeks of treatment] | ||||
Han, Kim, Lee, Min & Renshaw [42] | Assessed differences in brain activity between baseline and video game play | 21 university students (14 males) | Young’s Internet Addiction Scale [67]; Craving for Internet Video Game Play Scale | Brain activity in anterior cingulate and orbitofrontal cortex increased in excessive Internet game playing group (EIGP) following exposure to Internet video game cues relative to general players (GP); Increased craving for Internet video games correlated with increased activity in anterior cingulate for all participants |
[Quasi-experimental fMRI study at baseline and after six weeks of videogame play] | ||||
Hoeft, Watson, Kesler, Bettin-ger & Reiss [43] | Investigated gender differences in mesocorti-colimbic system during computer-game play | 22 healthy students (11 males) | Addiction not assessed via self-report | Activation of neural circuitries involved in reward and addiction ( i.e., nucleus accumbens, amygdala, dorso-lateral prefrontal cortex, insular cortex, and orbitofrontal cortex); Males had a larger activation (in right nucleus accumbens, bilateral orbitofrontal cortex, right amygdala) and functional connectivity (left nucleus accumbens and right amygdala) in mesocorticolimbic reward system relative to females |
[Experimental fMRI study performed with 3.0-T Signa scanner (General Electric, Milwaukee, WI, USA) 40 blocks of either 24 s ball game or control condition] | ||||
Hou, Jia, Hu, Fan, Sun, Sun & Zhang [51] | Examined reward circuitry dopamine transporter levels in Internet addicts compared to controls | 5 male Internet addicts | Young’s Internet Addiction Diagnostic Questionnaire [64]; Internet addictive Disorder Diagnostic Criteria [68] | Reduced dopamine transporters indicate addiction: similar neurobiological abnormalities with other behavioural addictions; Striatal dopamine transporter (DAT) levels decreased in Internet addicts (necessary for regulation of striatal dopamine levels) and volume, weight, and uptake ratio of the corpus striatum were reduced; Dopamine levels similar in people with substance addiction |
9 healthy age-matched male controls | ||||
[SPECT study: 99mTc-TRODAT-1 single photon emission computed tomography (SPECT) brain scans using Siemens Diacam/e.cam/icon double detector] | ||||
Kim, Baik, Park, Kim, Choi & Kim [49] | Tested if Internet addiction is associated with reduced levels of dopaminergic receptor availability in the striatum | 5 male Internet addicts | Internet Addiction Test [69]; Internet Addictive Disorder Diagnostic Criteria [68] | Internet addicts had reduced dopamine D2 receptor availability in striatum ( i.e., bilateral dorsal caudate, right putamen);Negative correlation of dopamine receptor availability with Internet addiction severity;Internet addiction found to be related to neurobiological abnormalities in the dopaminergic system as found in substance-related addictions |
7 male controls | ||||
[PET study: Radiolabeled ligand [11C]raclopride and positron emission tomorgraphy via ECAT EXACT scanner used to test dopamine D2 receptor binding potential; fMRI using General Electric Signa version 1.5T MRI scanner; Method for assessing D2 receptor availability: regions of interest (ROI) analysis in ventral striatum, dorsal caudate, dorsal putamen] | ||||
Ko, Liu, Hsiao, Yen, Yang, Lin, Yen & Chen [44] | Identified neural substrates of online gaming addiction by assessing brain areas involved in urge | 10 male online gaming addicts | Chen Internet Addiction Scale (CIAS) [71] | Dissimilar brain activation in gaming addicts: right orbitofrontal cortex, right nucleus accumbens, bilateral anterior cingulate, medial frontal cortex, right dorsolateral prefrontal cortex, right caudate nucleus and this correlated with gaming urge and recalling of gaming experience; Cue induced craving common in substance dependence: similar biological basis of different addictions including online gaming addiction |
[Quasi-experimental fMRI study: Presentation of gaming-related and paired mosaic pictures during fMRI scanning (3T MRscanner); Contrasts in BOLD signals in both conditions analysed; Cue reactivity paradigm] [70] | ||||
Koepp, Gunn, Law-rence, Cunning-ham, Dagher, Jones, Brooks, Bench & Grasby [50] | Provided evidence for striatal dopamine release during a video game play | 8 males | Addiction not assessed via self-report | Reduction of binding of raclopride to dopamine receptors in striatum during video game play relative to baseline; Correlation between performance level and reduced binding potential in all striatal regions; First study to show that dopamine is released during particular behaviours;Ventral and dorsal striata associated with goal-directed behaviour |
[Experimental PET study 953B-Siemens/CTIPET camera; Positron emission tomography (PET) during video game play and under resting condition; Region-of-interest (ROI) analysis;Extracellular dopamine levels measured via differences in [11C]RAC-binding potential to dopamine D2 receptors in ventral and dorsal striata] | ||||
Lin, Zhou, Du, Qin, Zhao, Xu & Lei [48] | Investigated white matter integrity in adolescent Internet addicts | 17 Internet addicts (14 males) | Modified Young’s Internet Addiction Test [72] | Internet addicts had lower FA throughout the brain (orbito-frontal white matter corpus callosum, cingulum, inferior fronto-occipital fasciculus, corona radiation, internal and external capsules);Negative correlations between FA in left genu of corpus callosum and emotional disorders, and FA in left external capsule and Internet addiction; Similarities in brain structures between Internet and substance addicts |
16 healthy controls (14 males) | ||||
[Whole brain voxel-wise analysis of fractional anisotropy (FA) by tract-based spatial statistics (TBSS) and volume of interest analysis were performed using diffusion tensor imaging (DTI) via a 3.0-Tesla Phillips Achieva medical scanner] | ||||
Littel, Luijten, van den Berg, van Rooij, Kee-mink & Franken [56] | Investigated error-processing and response inhibition in excessive gamers | 25 excessive gamers (23 males) | Videogame Addiction Test (VAT) [53] | Similarities with substance dependence and impulse control disorders regarding poor inhibition, high impulsivity in excessive gamers; Excessive gamers: reduced fronto-central ERN amplitudes following incorrect trials in comparison to correct trials leading to poor error-processing |
27 controls (10 males) | ||||
[Electroencephalography (EEG): Go/NoGo paradigm using EEG and ERP recordings] | ||||
Liu, Gao, Osunde, Li, Zhou, Zheng & Li [45] | Applied regional homogeneity method to analyse encephalic functional characteristic of Internet addicts in resting state | 19 college students with Internet addiction (11 males and 8 females) | Modified Diagnostic Questionnaire for Internet Addiction [72] | Internet addicts suffer from functional brain changes leading to abnormalities in regional homogeneity in Internet addicts relative to controls; Internet addicts had increased brain regions in ReHo in resting state (cerebellum, brainstem, right cingulate gyrus, bilateral parahippocampus, right frontal lobe, left superior frontal gyrus, right inferior temporal gyrus, left superior temporal gyrus and middle temporal gyrus) |
19 controls (gender matched) | ||||
[fMRI study: Functional magnetic resonance image using 3.0T Siemens Tesla Trio Tim scanner; Assessed resting state fMRI; Regional homogeneity (ReHo) indicates temporal homogeneity of regional BOLD signal rather than its density] | ||||
Yuan, Qin, Wang, Zeng, Zhao, Yang, Liu, Liu, Sun, von Deneen, Gong, Liu & Tian [46] | Investigated effects of Internet addiction on the microstructural integrity of major neuronal fiber pathways and microstructural changes with duration of Internet addiction | 18 students with Internet addiction (12 males) | Modified Diagnostic Questionnaire for Internet Addiction [72] | Increased FA of left posterior limb of internal capsule (PLIC) and reduced FA in white matter in right parahippocampal gyrus (PHG); Correlation between gray matter volumes in DLPFC, rACC, SMA, and white matter FA changes of PLIC with Internet addiction length; Internet addiction results in changes in brain structure |
18 control subjects (gender matched) | ||||
[fMRI study: Optimised voxel-based morphometry (VBM) technique. Analysed white matter fractional anisotropy (FA) changes by using diffusion tensor imaging (DTI) to associate brain structural changes to Internet addiction length] | ||||
Zhou, Lin, Du, Qin, Zhao, Xu & Lei [47] | Investigated brain gray matter density (GMD) changes in adolescents with Internet addiction using voxel-based morphometry (VBM) analysis on high-resolution T1-weighted structural magnetic resonance images | 18 adolescents with Internet addiction (2 females) | Modified Diagnostic Questionnaire for Internet Addiction [72] | Structural brain changes in adolescents with Internet addiction; Internet addicts had lower GMD in left anterior cingulate cortex (necessary for motor control, cognition, motivation), left posterior cingulate cortex (self-reference), left insula (specifically related to craving and motivation) |
15 healthy controls (2 females) | ||||
[MRI study: Used high-resolution T1-weighted MRIs performed on a 3T MR scanner (3T Achieva Philips), scanned MPRAGE pulse sequences for gray and white matter contrasts; VBM analysis to compare GMD between groups] |
3.2. sMRI Studies
3.3. EEG Studies
3.4. SPECT Studies
3.5. PET Studies
4. Discussion
5. Conclusions
Conflict of Interest
References
- Young, K. Internet addiction over the decade: A personal look back. World Psychiatry 2010, 9, 91. [Google Scholar]
- Tao, R.; Huang, X.Q.; Wang, J.N.; Zhang, H.M.; Zhang, Y.; Li, M.C. Proposed diagnostic criteria for Internet addiction. Addiction 2010, 105, 556–564. [Google Scholar]
- Shaw, M.; Black, D.W. Internet addiction: Definition, assessment, epidemiology and clinical management. CNS Drugs 2008, 22, 353–365. [Google Scholar] [CrossRef]
- Müller, K.W.; Wölfling, K. Computer game and Internet addiction: Aspects of diagnostics, phenomenology, pathogenesis, and therapeutic intervention. Suchttherapie 2011, 12, 57–63. [Google Scholar] [CrossRef]
- Beutel, M.E.; Hoch, C.; Woelfing, K.; Mueller, K.W. Clinical characteristics of computer game and Internet addiction in persons seeking treatment in an outpatient clinic for computer game addiction. Z. Psychosom. Med. Psychother. 2011, 57, 77–90. [Google Scholar]
- Griffiths, M.D. A “components” model of addiction within a biopsychosocial framework. J. Subst. Use 2005, 10, 191–197. [Google Scholar] [CrossRef]
- Kuss, D.J.; Griffiths, M.D. Internet gaming addiction: A systematic review of empirical research. Int. J. Ment. Health Addict. 2012, 10, 278–296. [Google Scholar] [CrossRef]
- American Psychiatric Association DSM-5 Development. Internet Use Disorder. Available online: http://www.dsm5.org/ProposedRevision/Pages/proposedrevision.aspx?rid=573# (accessed on 31 July 2012).
- Adalier, A. The relationship between Internet addiction and psychological symptoms. Int. J. Glob. Educ. 2012, 1, 42–49. [Google Scholar]
- Bernardi, S.; Pallanti, S. Internet addiction: A descriptive clinical study focusing on comorbidities and dissociative symptoms. Compr. Psychiatry 2009, 50, 510–516. [Google Scholar] [CrossRef]
- Xiuqin, H.; Huimin, Z.; Mengchen, L.; Jinan, W.; Ying, Z.; Ran, T. Mental health, personality, and parental rearing styles of adolescents with Internet addiction disorder. Cyberpsychol. Behav. Soc. Netw. 2010, 13, 401–406. [Google Scholar] [CrossRef]
- Johansson, A.; Gotestam, K.G. Internet addiction: Characteristics of a questionnaire and prevalence in Norwegian youth (12-18 years). Scand. J. Psychol. 2004, 45, 223–229. [Google Scholar] [CrossRef]
- Lin, M.-P.; Ko, H.-C.; Wu, J.Y.-W. Prevalence and psychosocial risk factors associated with Internet addiction in a nationally representative sample of college students in Taiwan. Cyberpsychol. Behav. Soc. Netw. 2011, 14, 741–746. [Google Scholar]
- Fu, K.W.; Chan, W.S.C.; Wong, P.W.C.; Yip, P.S.F. Internet addiction: Prevalence, discriminant validity and correlates among adolescents in Hong Kong. Br. J. Psychiatry 2010, 196, 486–492. [Google Scholar] [CrossRef]
- Descartes, R. Treatise of Man; Prometheus Books: New York, NY, USA, 2003. [Google Scholar]
- Repovš, G. Cognitive neuroscience and the “mind-body problem”. Horiz. Psychol. 2004, 13, 9–16. [Google Scholar]
- Volkow, N.D.; Fowler, J.S.; Wang, G.J. The addicted human brain: Insights from imaging studies. J. Clin. Invest. 2003, 111, 1444–1451. [Google Scholar]
- Pavlov, I.P. Conditioned Reflexes: An Investigation of the Physiological Activity of the Cerebral Cortex; Dover: Mineola, NY, USA, 2003. [Google Scholar]
- Skinner, B.F. Science and Human Behavior; Macmillan: New York, NY, USA, 1953. [Google Scholar]
- Everitt, B.J.; Robbins, T.W. Neural systems of reinforcement for drug addiction: From actions to habits to compulsion. Nat. Neurosci. 2005, 8, 1481–1489. [Google Scholar] [CrossRef]
- Kalivas, P.W.; Volkow, N.D. The neural basis of addiction: A pathology of motivation and choice. Am. J. Psychiatry 2005, 162, 1403–1413. [Google Scholar] [CrossRef]
- Goldstein, R.Z.; Volkow, N.D. Drug addiction and its underlying neurobiological basis: Neuroimaging evidence for the involvement of the frontal cortex. Am. J. Psychiatry 2002, 159, 1642–1652. [Google Scholar] [CrossRef]
- Craven, R. Targeting neural correlates of addiction. Nat. Rev. Neurosci. 2006, 7. [Google Scholar]
- Brebner, K.; Wong, T.P.; Liu, L.; Liu, Y.; Campsall, P.; Gray, S.; Phelps, L.; Phillips, A.G.; Wang, Y.T. Nucleus accumbens Long-Term Depression and the expression of behavioral sensitization. Science 2005, 310, 1340–1343. [Google Scholar]
- Wilson, S.J.; Sayette, M.A.; Fiez, J.A. Prefrontal responses to drug cues: A neurocognitive analysis. Nat. Neurosci. 2004, 7, 211–214. [Google Scholar]
- Di Chiara, G. Nucleus accumbens shell and core dopamine: Differential role in behavior and addiction. Behav. Brain Res. 2002, 137, 75–114. [Google Scholar] [CrossRef]
- Koob, G.F.; Le Moal, M. Addiction and the brain antireward system. Ann. Rev. Psychol. 2008, 59, 29–53. [Google Scholar]
- Prochaska, J.O.; DiClemente, C.C.; Norcross, J.C. In search of how people change. Applications to addictive behaviours. Am. Psychol. 1992, 47, 1102–1114. [Google Scholar]
- Potenza, M.N. Should addictive disorders include non-substance-related conditions? Addiction 2006, 101, 142–151. [Google Scholar] [CrossRef]
- Grant, J.E.; Brewer, J.A.; Potenza, M.N. The neurobiology of substance and behavioral addictions. CNS Spectr. 2006, 11, 924–930. [Google Scholar]
- Niedermeyer, E.; da Silva, F.L. Electroencephalography: Basic Principles, Clinical Applications, and Related Fields; Lippincot Williams & Wilkins: Philadelphia, PA, USA, 2004. [Google Scholar]
- Luck, S.J.; Kappenman, E.S. The Oxford Handbook of Event-Related Potential Components; Oxford University Press: New York, NY, USA, 2011. [Google Scholar]
- Bailey, D.L.; Townsend, D.W.; Valk, P.E.; Maisey, M.N. Positron Emission Tomography: Basic Sciences; Springer: Secaucus, NJ, USA, 2005. [Google Scholar]
- Meikle, S.R.; Beekman, F.J.; Rose, S.E. Complementary molecular imaging technologies: High resolution SPECT, PET and MRI. Drug Discov. Today Technol. 2006, 3, 187–194. [Google Scholar] [CrossRef]
- Huettel, S.A.; Song, A.W.; McCarthy, G. Functional Magnetic Resonance Imaging, 2nd ed; Sinauer: Sunderland, MA, USA, 2008. [Google Scholar]
- Symms, M.; Jäger, H.R.; Schmierer, K.; Yousry, T.A. A review of structural magnetic resonance neuroimaging. J. Neurol. Neurosurg. Psychiatry 2004, 75, 1235–1244. [Google Scholar] [CrossRef]
- Ashburner, J.; Friston, K.J. Voxel-based morphometry-The methods. NeuroImage 2000, 11, 805–821. [Google Scholar] [CrossRef]
- Le Bihan, D.; Mangin, J.F.; Poupn, C.; Clark, C.A.; Pappata, S.; Molko, N.; Chabriat, H. Diffusion Tensor Imaging: Concepts and applications. J. Magn. Reson. Imaging 2001, 13, 534–546. [Google Scholar]
- Dong, G.; Huang, J.; Du, X. Enhanced reward sensitivity and decreased loss sensitivity in Internet addicts: An fMRI study during a guessing task. J. Psychiatr. Res. 2011, 45, 1525–1529. [Google Scholar]
- Han, D.H.; Lyoo, I.K.; Renshaw, P.F. Differential regional gray matter volumes in patients with on-line game addiction and professional gamers. J. Psychiatr. Res. 2012, 46, 507–515. [Google Scholar] [CrossRef]
- Han, D.H.; Hwang, J.W.; Renshaw, P.F. Bupropion sustained release treatment decreases craving for video games and cue-induced brain activity in patients with Internet video game addiction. Exp. Clin. Psychopharmacol. 2010, 18, 297–304. [Google Scholar]
- Han, D.H.; Kim, Y.S.; Lee, Y.S.; Min, K.J.; Renshaw, P.F. Changes in cue-induced, prefrontal cortex activity with video-game play. Cyberpsychol. Behav. Soc. Netw. 2010, 13, 655–661. [Google Scholar] [CrossRef]
- Hoeft, F.; Watson, C.L.; Kesler, S.R.; Bettinger, K.E.; Reiss, A.L. Gender differences in the mesocorticolimbic system during computer game-play. J. Psychiatr. Res. 2008, 42, 253–258. [Google Scholar]
- Ko, C.H.; Liu, G.C.; Hsiao, S.M.; Yen, J.Y.; Yang, M.J.; Lin, W.C.; Yen, C.F.; Chen, C.S. Brain activities associated with gaming urge of online gaming addiction. J. Psychiatr. Res. 2009, 43, 739–747. [Google Scholar] [CrossRef]
- Liu, J.; Gao, X.P.; Osunde, I.; Li, X.; Zhou, S.K.; Zheng, H.R.; Li, L.J. Increased regional homogeneity in Internet addiction disorder: A resting state functional magnetic resonance imaging study. Chin. Med. J. 2010, 123, 1904–1908. [Google Scholar]
- Yuan, K.; Qin, W.; Wang, G.; Zeng, F.; Zhao, L.; Yang, X.; Liu, P.; Liu, J.; Sun, J.; von Deneen, K.M.; et al. Microstructure abnormalities in adolescents with Internet Addiction Disorder. PloS One 2011, 6, e20708. [Google Scholar]
- Zhou, Y.; Lin, F.-C.; Du, Y.-S.; Qin, L.-D.; Zhao, Z.-M.; Xu, J.-R.; Lei, H. Gray matter abnormalities in Internet addiction: A voxel-based morphometry study. Eur. J. Radiol. 2011, 79, 92–95. [Google Scholar]
- Lin, F.; Zhou, Y.; Du, Y.; Qin, L.; Zhao, Z.; Xu, J.; Lei, H. Abnormal white matter integrity in adolescents with Internet Addiction Disorder: A tract-based spatial statistics study. PloS One 2012, 7, e30253. [Google Scholar]
- Kim, S.H.; Baik, S.H.; Park, C.S.; Kim, S.J.; Choi, S.W.; Kim, S.E. Reduced striatal dopamine D2 receptors in people with Internet addiction. Neuroreport 2011, 22, 407–411. [Google Scholar] [CrossRef]
- Koepp, M.J.; Gunn, R.N.; Lawrence, A.D.; Cunningham, V.J.; Dagher, A.; Jones, T.; Brooks, D.J.; Bench, C.J.; Grasby, P.M. Evidence for striatal dopamine release during a video game. Nature 1998, 393, 266–268. [Google Scholar]
- Hou, H.; Jia, S.; Hu, S.; Fan, R.; Sun, W.; Sun, T.; Zhang, H. Reduced striatal dopamine transporters in people with Internet addiction disorder. J. Biomed. Biotechnol. 2012, 2012. [Google Scholar]
- Dong, G.; Zhou, H.; Zhao, X. Male Internet addicts show impaired executive control ability: Evidence from a color-word Stroop task. Neurosci. Lett. 2011, 499, 114–118. [Google Scholar] [CrossRef]
- Dong, G.; Lu, Q.; Zhou, H.; Zhao, X. Impulse inhibition in people with Internet addiction disorder: Electrophysiological evidence from a Go/NoGo study. Neurosci. Lett. 2010, 485, 138–142. [Google Scholar] [CrossRef]
- Dong, G.; Zhou, H. Is impulse-control ability impaired in people with Internet addiction disorder: Electrophysiological evidence from ERP studies. Int. J. Psychophysiol. 2010, 77, 334–335. [Google Scholar] [CrossRef]
- Ge, L.; Ge, X.; Xu, Y.; Zhang, K.; Zhao, J.; Kong, X. P300 change and cognitive behavioral therapy in subjects with Internet addiction disorder A 3-month follow-up study. Neural Regen. Res. 2011, 6, 2037–2041. [Google Scholar]
- Littel, M.; Luijten, M.; van den Berg, I.; van Rooij, A.; Keemink, L.; Franken, I. Error-processing and response inhibition in excessive computer game players: An ERP study. Addict. Biol. 2012. [Google Scholar]
- Yu, H.; Zhao, X.; Li, N.; Wang, M.; Zhou, P. Effect of excessive Internet use on the time-frequency characteristic of EEG. Prog. Nat. Sci. 2009, 19, 1383–1387. [Google Scholar] [CrossRef]
- Derogatis, L.R. SCL-90-R Administration, Scoring & Procedure Manual II; Clinical Psychometric Research: Towson, MD, USA, 1994. [Google Scholar]
- Costa, P.T.; McCrae, R.R. Revised NEO Personality Inventory (NEO-PI-R) and the NEO Five-Factor Inventory (NEO-FFI): Professional Manual; Psychological Assessment Resources: Odessa, FL, USA, 1992. [Google Scholar]
- Naqvi, N.H.; Bechara, A. The hidden island of addiction: The insula. Trends Neurosci. 2009, 32, 56–67. [Google Scholar] [CrossRef]
- Young, K.S. Internet Addiction Test (IAT). Available online: http://www.netaddiction.com/index.php?option=com_bfquiz&view=onepage&catid=46&Itemid=106 (accessed on 14 May 2012).
- Tao, R.; Huang, X.; Wang, J.; Liu, C.; Zang, H.; Xiao, L. A proposed criterion for clinical diagnosis of Internet addiction. Med. J. Chin. PLA 2008, 33, 1188–1191. [Google Scholar]
- Wang, W.; Tao, R.; Niu, Y.; Chen, Q.; Jia, J.; Wang, X. Preliminarily proposed diagnostic criteria of pathological Internet use. Chin. Ment. Health J. 2009, 23, 890–894. [Google Scholar]
- Young, K. Internet addiction: The emergence of a new clinical disorder. Cyberpsychol. Behav. 1998, 3, 237–244. [Google Scholar] [CrossRef] [Green Version]
- Young, K.S.; Rogers, R.C. The relationship between depression and Internet addiction. Cyberpsychol. Behav. 1998, 1, 25–28. [Google Scholar] [CrossRef]
- Johnson, S. NPD Group: Total 2010 game software sales flat compared to 2009. Available online: http://www.g4tv.com/thefeed/blog/post/709764/npd-group-total-2010-game-software-sales-flat-compared-to-2009 (accessed on 3 February 2012).
- Young, K. Psychology of computer use: XL. Addictive use of the Internet: A case that breaks the stereotype. Psychol. Rep. 1996, 79, 899–902. [Google Scholar] [CrossRef]
- Goldberg, I. Internet Addictive Disorder (IAD) diagnostic criteria. Available online: http://www.psycom.net/iadcriteria.html (accessed on 23 May 2012).
- Young, K. Caught in the Net; Wiley: New York, NY, USA, 1998. [Google Scholar]
- Bentler, P.M. Comparative fit indexes in structure models. Psychol. Bull. 1990, 107, 238–246. [Google Scholar] [CrossRef]
- Chen, S.H.; Weng, L.C.; Su, Y.J.; Wu, H.M.; Yang, P.F. Development of Chinese Internet Addiction Scale and its psychometric study. Chin. J. Psychol. 2003, 45, 279–294. [Google Scholar]
- Beard, K.W.; Wolf, E.M. Modification in the proposed diagnostic criteria for Internet addiction. Cyberpsychol. Behav. 2001, 4, 377–383. [Google Scholar] [CrossRef]
- Van Rooij, A.J.; Schoenmakers, T.M.; van den Eijnden, R.J.; van de Mheen, D. Videogame Addiction Test (VAT): Validity and psychometric characteristics. Cyberpsychol. Behav. Soc. Netw. 2012. [Google Scholar]
- Ko, C.H.; Yen, J.Y.; Chen, S.H.; Yang, M.J.; Lin, H.C.; Yen, C.F. Proposed diagnostic criteria and the screening and diagnosing tool of Internet addiction in college students. Compr. Psychiatry 2009, 50, 378–384. [Google Scholar]
- Sheehan, D.V.; Lecrubier, Y.; Sheehan, K.H.; Amorim, P.; Janvas, J.; Weiller, E.; Hergueta, T.; Baker, R.; Dunbar, G.C. The Mini-International Neuropsychiatric Interview (M.I.N.I.): The development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. J. Clin. Psychiatry 1998, 59, 22–33. [Google Scholar]
- Tsai, M.C.; Tsai, Y.F.; Chen, C.Y.; Liu, C.Y. Alcohol use disorders identification test (AUDIT): Establishment of cut-off scores in a hospitalized Chinese population. Alcohol. Clin. Exp. Res. 2005, 29, 53–57. [Google Scholar] [CrossRef]
- Heatherton, T.F.; Kozlowski, L.T.; Frecker, R.C.; Fagerström, K.O. The Fagerstrom test for nicotine dependence: A revision of the Fagerstrom tolerance questionnaire. Br. J. Addict. 1991, 86, 1119–1127. [Google Scholar] [CrossRef]
- Beck, A.; Ward, C.; Mendelson, M. An inventory for measuring depression. Arch. Gen. Psychiatry 1961, 4, 561–571. [Google Scholar] [CrossRef]
- Lebcrubier, Y.; Sheehan, D.V.; Weiller, E.; Amorim, P.; Bonora, I.; Sheehan, H.K.; Janavs, J.; Dunbar, G.C. The Mini International Neuropsychiatric Interview (MINI). A short diagnostic structured interview: Reliability and validity according to the CIDI. Eur. Psychiatry 1997, 12, 224–231. [Google Scholar]
- First, M.B.; Gibbon, M.; Spitzer, R.L.; Williams, J.B.W. Structured Clinical Interview for DSM-IV Axis I Disorders: Clinician Version (SCID-CV): Administration Booklet; American Psychiatric Press: Washington, DC, USA, 1996. [Google Scholar]
- Barratt, E.S. Factor analysis of some psychometric measures of impulsiveness and anxiety. Psychol. Rep. 1965, 16, 547–554. [Google Scholar] [CrossRef]
- Lee, H.S. Impulsiveness Scale; Korea Guidance: Seoul, Korea, 1992. [Google Scholar]
- Oldfield, R.C. The assessment and analysis of handedness: The Edinburgh Inventory. Neuropsychologia 1971, 9, 97–113. [Google Scholar] [CrossRef]
- Sheehan, D.V.; Sheehan, K.H.; Shyte, R.D.; Janavs, J.; Bannon, Y.; Rogers, J.E.; Milo, K.M.; Stock, S.L.; Wilkinson, B. Reliability and validity of the Mini International Neurpsychiatric Interview for Children and Adolescents (MINI-KID). J. Clin. Psychiatry 2010, 71, 313–326. [Google Scholar] [CrossRef]
- Huang, X.; Zhang, Z. The compiling of the adolescence time management disposition scale. Acta Psychol. Sin. 2001, 33, 338–343. [Google Scholar]
- Patton, J.H.; Stanford, M.S.; Barratt, E.S. Factor structure of the Barratt Impulsiveness Scale. J. Clin. Psychol. 1995, 51, 768–774. [Google Scholar] [CrossRef]
- Birmaher, B.; Khetarpal, S.; Brent, D.; Cully, M.; Balach, L.; Kaufman, J.; Neer, S.M. The Screen for Child Anxiety-Related Emotional Disorders (SCARED): Scale construction and psychometric characteristics. J. Am. Acad. Child Adolesc. Psychiatry 1997, 36, 545–553. [Google Scholar]
- Epstein, N.B.; Baldwin, L.M.; Bishop, D.S. The McMaster family assessment device. J. Marital Fam. Ther. 1983, 9, 171–180. [Google Scholar] [CrossRef]
- Yang, C.K.; Choe, B.M.; Baity, M.; Lee, J.H.; Cho, J.S. SCL-90-R and 16PF profiles of senior high school students with excessive Internet use. Can. J. Psychiatry 2005, 50, 407–414. [Google Scholar]
- Eysenck, S.B.G.; Pearson, P.R.; Easting, G.; Allsopp, J.F. Age norms for impulsiveness, venturesomeness and empathy in adults. Pers. Individ. Differ. 1985, 6, 613–619. [Google Scholar] [CrossRef]
- Lijffijt, M.; Caci, H.; Kenemans, J.L. Validation of the Dutch translation of the l7 questionnaire. Pers. Individ. Differ. 2005, 38, 1123–1133. [Google Scholar] [CrossRef]
- Lemmens, P.; Tan, E.S.; Knibbe, R.A. Measuring quantity and frequency of drinking in a general population survey: A comparison of five indices. J. Stud. Alcohol 1992, 53, 476–486. [Google Scholar]
- Beck, A.T.; Steer, R. Manual for the Beck Depression Inventory; The Psychological Corporation: San Antonio, TX, USA, 1993. [Google Scholar]
- Yi, Y.S.; Kim, J.S. Validity of short forms of the Korean-Wechsler Adult Intelligence Scale. Korean J. Clin. Psychol. 1995, 14, 111–116. [Google Scholar]
- Goldstein, R.Z.; Alia-Klein, N.; Tomasi, D.; Carrillo, J.H.; Maloney, T.; Woicik, P.A.; Wang, R.; Telang, F.; Volkow, N.D. Anterior cingulate cortex hypoactivations to an emotionally salient task in cocaine addiction. Proc. Natl. Acad. Sci. USA 2009, 106, 9453–9458. [Google Scholar]
- Schoenebaum, G.; Roesch, M.R.; Stalnaker, T.A. Orbitofrontal cortex, decision making and drug addiction. Trends Neurosci. 2006, 29, 116–124. [Google Scholar] [CrossRef]
- Li, C.; Sinha, R. Inhibitory control and emotional stress regulation: Neuroimaging evidence for frontal-limbic dysfunction in psycho-stimulant addiction. Neurosci. Biobehav. Rev. 2008, 32, 581–597. [Google Scholar] [CrossRef]
- Maddock, R.J.; Garrett, A.S.; Buonocore, M.H. Posterior cingulate cortex activation by emotional words: fMRI evidence from a valence decision task. Hum. Brain Mapp. 2003, 18, 30–41. [Google Scholar] [CrossRef]
- Schnitzler, A.; Salenius, S.; Salmelin, R.; Jousmäki, V.; Hari, R. Involvement of primary motor cortex in motor imagery: A neuromagnetic study. Neuroimage 1997, 6, 201–208. [Google Scholar] [CrossRef]
- Schiemanck, S.; Kwakkel, G.; Post, M.W.M.; Kappelle, J.L.; Prevo, A.J.H. Impact of internal capsule lesions on outcome of motor hand function at one year post-stroke. J. Rehabil. Med. 2008, 40, 96–101. [Google Scholar] [CrossRef]
- Rosenberg, B.H.; Landsittel, D.; Averch, T.D. Can video games be used to predict or improve laparoscopic skills? J. Endourol. 2005, 19, 372–376. [Google Scholar] [CrossRef]
- Bora, E.; Yucel, M.; Fornito, A.; Pantelis, C.; Harrison, B.J.; Cocchi, L.; Pell, G.; Lubman, D.I. White matter microstructure in opiate addiction. Addict. Biol. 2012, 17, 141–148. [Google Scholar] [CrossRef]
- Yeh, P.H.; Simpson, K.; Durazzo, T.C.; Gazdzinski, S.; Meyerhoff, D.J. Tract-Based Spatial Statistics (TBSS) of diffusion tensor imaging data in alcohol dependence: Abnormalities of the motivational neurocircuitry. Psychiatry Res. 2009, 173, 22–30. [Google Scholar] [CrossRef]
- Arnone, D.; Abou-Saleh, M.T.; Barrick, T.R. Diffusion tensor imaging of the corpus callosum in addiction. Neuuropsychobiology 2006, 54, 107–113. [Google Scholar] [CrossRef]
- Byun, S.; Ruffini, C.; Mills, J.E.; Douglas, A.C.; Niang, M.; Stepchenkova, S.; Lee, S.K.; Loutfi, J.; Lee, J.K.; Atallah, M.; et al. Internet addiction: Metasynthesis of 1996–2006 quantitative research. Cyberpsychol. Behav. 2009, 12, 203–207. [Google Scholar] [CrossRef]
- Polich, J.; Pollock, V.E.; Bloom, F.E. Meta-analysis of P300 amplitude from males at risk for alcoholism. Psychol. Bull. 1994, 115, 55–73. [Google Scholar] [CrossRef]
- Nichols, J.M.; Martin, F. P300 in heavy social drinkers: The effect of lorazepam. Alcohol 1993, 10, 269–274. [Google Scholar] [CrossRef]
- Sokhadze, E.; Stewart, C.; Hollifield, M.; Tasman, A. Event-Related Potential study of executive dysfunctions in a speeded reaction task in cocaine addiction. J. Neurother. 2008, 12, 185–204. [Google Scholar] [CrossRef]
- Thomas, M.J.; Kalivas, P.W.; Shaham, Y. Neuroplasticity in the mesolimbic dopamine system and cocaine addiction. Br. J. Pharmacol. 2008, 154, 327–342. [Google Scholar]
- Volkow, N.D.; Fowler, J.S.; Wang, G.J.; Swanson, J.M. Dopamine in drug abuse and addiction: Results from imaging studies and treatment implications. Mol. Psychiatry 2004, 9, 557–569. [Google Scholar] [CrossRef]
- Jia, S.W.; Wang, W.; Liu, Y.; Wu, Z.M. Neuroimaging studies of brain corpus striatum changes among heroin-dependent patients treated with herbal medicine, U’finer capsule. Addict. Biol. 2005, 10, 293–297. [Google Scholar] [CrossRef]
- Morrison, C.M.; Gore, H. The relationship between excessive Internet use and depression: A questionnaire-based study of 1319 young people and adults. Psychopathology 2010, 43, 121–126. [Google Scholar] [CrossRef]
- Di Nicola, M.; Tedeschi, D.; Mazza, M.; Martinotti, G.; Harnic, D.; Catalano, V.; Bruschi, A.; Pozzi, G.; Bria, P.; Janiri, L. Behavioral addictions in bipolar disorder patients: Role of impulsivity and personality dimensions. J. Affect. Disord. 2010, 125, 82–88. [Google Scholar] [CrossRef]
- Volkow, N.D.; Fowler, J.S.; Wang, G.J. The addicted human brain viewed in the light of imaging studies: Brain circuits and treatment strategies. Neuropharmacology 2004, 47, 3–13. [Google Scholar] [CrossRef]
- Shaffer, H.J.; LaPlante, D.A.; LaBrie, R.A.; Kidman, R.C.; Donato, A.N.; Stanton, M.V. Toward a syndrome model of addiction: Multiple expressions, common etiology. Harv. Rev. Psychiatry 2004, 12, 367–374. [Google Scholar] [CrossRef]
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Kuss, D.J.; Griffiths, M.D. Internet and Gaming Addiction: A Systematic Literature Review of Neuroimaging Studies. Brain Sci. 2012, 2, 347-374. https://doi.org/10.3390/brainsci2030347
Kuss DJ, Griffiths MD. Internet and Gaming Addiction: A Systematic Literature Review of Neuroimaging Studies. Brain Sciences. 2012; 2(3):347-374. https://doi.org/10.3390/brainsci2030347
Chicago/Turabian StyleKuss, Daria J., and Mark D. Griffiths. 2012. "Internet and Gaming Addiction: A Systematic Literature Review of Neuroimaging Studies" Brain Sciences 2, no. 3: 347-374. https://doi.org/10.3390/brainsci2030347