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  • 1 Korea Institute on Behavioral Addiction, Seoul, Korea; Easy Brain Center, Seoul, , Korea
  • | 2 Department of Psychiatry, Kangbuk Samsung Hospital, Sungkyunkwan University School of Medicine, Seoul, , Korea
  • | 3 Department of Psychiatry, Seoul St Mary’s Hospital, College of Medicine, The Catholic University of Korea, Seoul, , Korea
  • | 4 Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul, , Korea
  • | 5 Department of Psychology, Chonnam National University, Gwangju, , Korea
Open access

Background and aims

Gambling disorder (GD) shares many similarities with substance use disorders (SUDs) in clinical, neurobiological, and neurocognitive features, including decision-making. We evaluated the relationships among, GD, decision-making, and brain-derived neurotrophic factor (BDNF), as measured by serum BDNF levels.

Methods

Twenty-one male patients with GD and 21 healthy sex- and age-matched control subjects were evaluated for associations between serum BDNF levels and the Problem Gambling Severity Index (PGSI), as well as between serum BDNF levels and Iowa Gambling Task (IGT) indices.

Results

The mean serum BDNF levels were significantly increased in patients with GD compared to healthy controls. A significant correlation between serum BDNF levels and PGSI scores was found when controlling for age, depression, and duration of GD. A significant negative correlation was obtained between serum BDNF levels and IGT improvement scores.

Discussion

These findings support the hypothesis that serum BDNF levels constitute a dual biomarker for the neuroendocrine changes and the severity of GD in patients. Serum BDNF level may serve as an indicator of poor decision-making performance and learning processes in GD and help to identify the common physiological underpinnings between GD and SUDs.

Abstract

Background and aims

Gambling disorder (GD) shares many similarities with substance use disorders (SUDs) in clinical, neurobiological, and neurocognitive features, including decision-making. We evaluated the relationships among, GD, decision-making, and brain-derived neurotrophic factor (BDNF), as measured by serum BDNF levels.

Methods

Twenty-one male patients with GD and 21 healthy sex- and age-matched control subjects were evaluated for associations between serum BDNF levels and the Problem Gambling Severity Index (PGSI), as well as between serum BDNF levels and Iowa Gambling Task (IGT) indices.

Results

The mean serum BDNF levels were significantly increased in patients with GD compared to healthy controls. A significant correlation between serum BDNF levels and PGSI scores was found when controlling for age, depression, and duration of GD. A significant negative correlation was obtained between serum BDNF levels and IGT improvement scores.

Discussion

These findings support the hypothesis that serum BDNF levels constitute a dual biomarker for the neuroendocrine changes and the severity of GD in patients. Serum BDNF level may serve as an indicator of poor decision-making performance and learning processes in GD and help to identify the common physiological underpinnings between GD and SUDs.

Introduction

Gambling disorder (GD), a type of behavioral addiction, is characterized by persistent and recurrent maladaptive gambling behavior leading to significant deleterious legal, financial, and psychosocial consequences (Grant, Kim, & Kuskowski, 2004). GD shares many similar clinical and neurobiological features with substance use disorders (SUDs), such as alterations of the mesolimbic dopamine reward pathway (Potenza, 2008), as well as neurocognitive features, including impaired decision-making.

Poor performance on the Iowa Gambling Task (IGT), designed to assess risky decision-making, has been found consistently among SUDs (Noel, Bechara, Dan, Hanak, & Verbanck, 2007). Similarly, patients with GD have demonstrated high risk-taking performance on the task (Lawrence, Luty, Bogdan, Sahakian, & Clark, 2009). Although the biological basis for decision-making is poorly understood, neural systems related to executive function and memory have been implicated (Brand, Recknor, Grabenhorst, & Bechara, 2007).

One protein associated with various cognitive functions such as decision-making and memory is the brain-derived neurotrophic factor (BDNF) (Yamada, Mizuno, & Nabeshima, 2002). BDNF plays an important role in neuronal survival, neurogenesis, and synaptic plasticity. Studies have shown associations between BDNF and changes in behavior and psychopathology in psychiatric disorders such as depression, schizophrenia, and bipolar disorder (Montegia et al., 2007), as well as autism spectrum disorder (Wang et al., 2015). Increases in serum levels of BDNF have been observed in drug addictions (Angelucci et al., 2010), where the involvement of BDNF in the ventral tegmental area-nucleus accumbens (VTA-NAc)-mediated processes has been implicated (Pu, Liu, & Poo, 2006).

In contrast, only a few studies have examined the association between BDNF and GD (Angelucci et al., 2013; Geisel, Banas, Hellweg, & Muller, 2012), and how BDNF levels relate to the severity of GD and the level of impairment in neurocognitive tasks remains unclear. Reduced serum BDNF has been found to relate to poor performance on the IGT (Hori, Yoshimura, Katsuki, Atake, & Nakamura, 2014) and immediate memory (Zhang et al., 2012) in patients with schizophrenia. Associations between low BDNF levels and cognitive impairment have been further confirmed in a large elderly population (Shimada et al., 2014).

In this study, we examined the relationships among GD, BDNF, and decision-making performance on the IGT in a sample of GD patients and compared the serum BDNF levels in GD patients with those in healthy control subjects. We then investigated the association of serum BDNF levels with the severity of GD and IGT indices.

Methods

Participants

Twenty-one male patients who fulfilled the DSM-5 criteria for GD were recruited from the outpatient gambling clinic of the Department of Psychiatry, Gangnam Eulji Hospital, Eulji University, Korea. The diagnoses were determined by a board-certified psychiatrist (SWC) through the examination of past medical records and a semi-structured interview that included questions about the presence of co-occurring disorders. A self-report questionnaire regarding age, weight, height, alcohol-related history, regular medication use, gambling-related history, and clinical variables was also administered. The severity of GD was assessed with the Problem Gambling Severity Index (PGSI), a nine-item self-report assessment measure reported to be useful for both clinical and non-clinical settings (Young & Wohl, 2011). Mood symptoms were assessed using the Beck Depression Inventory (BDI). The exclusion criteria for the patient group were 1) any history of a chronic physical disease, 2) regular use of any medication, and 3) presence of comorbid psychiatric disorders, including alcohol and nicotine dependence. The control group consisted of 21 age- and sex-matched healthy male volunteers who had no current or past psychiatric history or history of medication use.

Measures

Measurement of serum BDNF levels.

A total of 10 ml of blood was drawn from each subject into a serum separator tube. Samples were allowed to clot for 30 minutes before centrifugation for 15 min at approximately 1000 g, after which the serum was removed. All samples were stored at −80 °C. The serum BDNF levels were determined using an ELISA protocol according to the manufacturer’s instructions (DBD00; R & D Systems, Europe).

IGT.

For this computer-administered task, participants were asked to draw from four decks of cards. Each deck consisted of randomly distributed cards with differing amount of gains and penalties, adding up to a pre-set net outcome. Two decks contained cards with low levels of gains (e.g. $50) and penalties (e.g. $40), but their net outcome was favorable (e.g. $100); the other two decks consisted of cards with high gains (e.g. $100) but even higher penalties (e.g. $200), so that their net outcome was unfavorable (e.g. −$250).

All participants were instructed to try to earn as much money as possible by drawing cards one at a time from a deck of their choice. They were informed that some decks were more advantageous than others but were not told the composition of the decks. The entire IGT procedure was completed upon drawing 100 cards.

Three IGT indices were derived with high scores indicating effective strategic thinking: net total score, calculated as the number of draws from advantageous decks minus that from the disadvantageous decks (Barry & Petry, 2008); proportion of advantageous deck selections from the total number of cards; and improvement score, calculated by subtracting the net score of the first block of 20 cards from that of the last block.

Statistical analyses

An analysis of covariance, with age, body-mass index (BMI), and BDI scores entered as covariates, was used to compare serum BDNF levels of the patients and controls. The correlation between serum BDNF levels and the severity of GD based on PGSI scores in the patient group was examined using Pearson partial-correlation analysis, by controlling for age, BDI scores, and the duration of problem gambling. Finally, the association between serum BDNF levels and IGT performance was analyzed using the same method. All data are presented as means ± standard deviations (SD). The significance level was set at p < 0.05. All statistical analyses were conducted using SPSS, version 18.1 (Chicago, Illinois, USA).

Ethics

The Ethical Committee of the Eulji University, Korea, approved this study protocol. In accordance with the Declaration of Helsinki, all subjects were advised about the procedures and signed the written informed consent prior to participation.

Results

The demographic data, gambling-related clinical variables, and IGT indices are listed in Table 1. The mean serum BDNF levels were significantly increased in the patients with GD (29051.44 ± 6237.42 pg/ml) compared to healthy controls (19279.67 ± 4375.58 pg/ml, p < 0.0001) (Figure 1). We also found a significant correlation between serum BDNF levels and PGSI scores (r = 0.56, p < 0.05) after controlling for age, BDI scores, and the duration of problem gambling.

Table 1.

Demographic data, BDI, BDNF, IGT index, and GD related variables

GD (n = 21)Control (n = 21)
VariableM (SD)M (SD)Test statisticsp-value
Age40.52 (12.35)39.29 (3.96)t = 0.4380.664
BMI25.17 (3.42)22.54 (2.43)t = 2.873<0.01
BDI18.48 (11.78)4.10 (3.03)t = 5.420<0.0001
BDNF (pg/ml)29051.44 (6237.42)19279.67 (4375.58)t = 5.877<0.0001
IGT total net score9.14 (21.81)
Advantageous proportion0.55 (0.11)
IGT improvement score2.86 (5.08)
CPGI-PGSI20.10 (4.79)
GD duration (years)8.14 (5.30)
No. of gambling methods*χ2 = 0.0480.827
 One10 (47.6%)
 Multiple (two or more)11 (52.4%)
GD type*χ2 = 2.3330.127
 Action type14 (66.7%)
 Escape type7 (33.3%)
Gambling categorya*χ2 = 2.3330.127
 Strategic7 (33.3%)
 Analytic14 (66.7%)

Note: *Marked variables are categorical variables with N (%), therefore Chi-square test was used. GD: gambling disorder; BMI: body mass index (weight / height2); BDI: Beck Depression Inventory; BDNF: brain-derived neurotrophic factor; IGT total net score: total advantageous deck counts minus total disadvantageous deck counts; Advantageous proportion: advantageous deck counts / total card selection (100 cards); IGT improvement score: block5 IGT net score minus block1 IGT net score; CPGI-PGSI: Canadian Problem Gambling Index-Problem Gambling Severity Index.

Strategic: casino gambling (e.g. Black-Jack); Analytic: sports betting, horse racing, bicycle racing, motor boat racing, stock-trading.

Figure 1.
Figure 1.

The mean serum BDNF levels were significantly increased in the patients with gambling disorder (29051.44 ± 6237.42 pg/ml) compared to healthy controls (19279.67 ± 4375.58 pg/ml, p < 0.0001) by ANCOVA with age, BMI, and the scores of BDI as covariates. The box plots show the median and quartiles, and the whisker caps of the box plots show the mean 5th and 95th percentile values.; * Indicates statistical significance (F = 12.11, p ≤ 0.001)

Citation: Journal of Behavioral Addictions 5, 1; 10.1556/2006.5.2016.010

Serum BDNF levels were also significantly negatively correlated with the IGT improvement scores (r = –0.48, p < 0.05), but not with IGT total net scores (r = –0.163, n.s.) or advantageous proportion (r = –0.19, n.s.).

Discussion

In this study, we found significantly higher serum BDNF levels among patients with GD than in healthy controls, as well as a positive association between the serum BDNF levels and severity of GD. Such findings are in partial agreement with previous studies showing that serum BDNF levels increased in GD (Angelucci et al., 2013; Geisel et al., 2012), although these studies present different results regarding the association between serum BDNF levels and severity of GD. Such discrepancies might be related to external factors that affect serum BDNF levels, including BMI, depression, and other confounding factors (Piccinni et al., 2008). Together with these two previous studies (Angelucci et al., 2013; Geisel et al., 2012), our findings suggest that behavioral addictions might be associated with neural plasticity similar to alterations observed in SUDs. Increased serum BDNF levels might then represent a compensatory mechanism to normalize dopaminergic transmission in VTA and the NAc (Geisel et al., 2012). Another plausible explanation is that increased BDNF plays a role in neuroprotective and stress-preventive processes in patients with GD, especially during stressful situations, as found in those with SUDs (Bhang, Choi, & Ahn, 2010; Geisel et al., 2012).

Although a recent study (Kang et al., 2010) showed that the BDNF Val66Met polymorphism may affect decision-making performance as measured by IGT, to the best of our knowledge, our study is the first to demonstrate a significant association between serum BDNF levels and IGT improvement scores. The IGT improvement score particularly reflects learning processes based on the evaluation of choice-outcomes of rewards and penalties leading to long-term gain or loss. This learning involves discounting immediate rewards while formulating an advantageous strategy based on previous cumulative outcomes. A recent study (Kräplin et al., 2014) found that problem gamblers showed higher overall impulsivity compared to healthy controls and higher ‘choice impulsivity’ compared to a Tourette syndrome group, but similar levels of impulsivity as an alcohol-dependent group. Higher BDNF concentration has also been positively correlated with higher impulsivity in PTSD patients (Martinotti et al., 2015) suggesting that impulsiveness may be associated with greater BDNF expression. Additionally, in murine models, BDNF has been implicated in the actions of serotonergic neurons, particularly in aggression and impulsivity (Lyons et al., 1999). Both BDNF and serotonin regulate the development and plasticity of neural circuits in mood disorders (Martinowich & Lu, 2008). In humans, BDNF Val66Met polymorphism in schizophrenia patients has been associated with aggressive behavior (Spalletta et al., 2010), while serotonin has been found to play a significant role in learning and memory (Meneses & Liy-Salmeron, 2012). Taken together, our results suggest that BDNF may also play a role in learning processes, and that the relationship between BDNF and serotonin needs to be further examined.

Some limitations of this study warrant discussion; our sample size was modest and contained only male GD patients, thus limiting the generalizability of our results. Serum BDNF levels were examined rather than central nervous system BDNF levels. Although BDNF regulation in peripheral blood is still poorly understood, peripheral concentrations are widely used as a mirror of the same brain parameter (Yamada et al., 2002). Because BDNF is known to cross the blood-brain barrier in both directions, a substantial part of peripheral BDNF might originate from neuronal cells of the central nervous system (Karege, Schwald, & Cisse, 2002). At the present, the relationships among BDNF, severity of disorder, and decision-making in GD patients are not clearly delineated, and future studies should consider these limitations in their designs for a better understanding of such relationships. In addition, we did not take into account personality factors in our study design. Previous studies have suggested relationships between pathological gambling and personality characteristics such as novelty-seeking and self-directness (Jiménez-Murcia et al., 2010; Martinotti et al., 2006), but consensus on the relationship between the BDNF levels and these personality traits has yet to be reached due to inconsistent results (Maclaren, Fugelsang, Harrigan, & Dixon, 2011). The results of our study should be interpreted carefully in light of such a limitation.

Conclusions

The findings of this study support the hypothesis that serum BDNF levels may serve as a candidate biomarker for neural plasticity and the severity of GD in these patients. Furthermore, heightened serum BDNF levels in GD may indicate poor decision-making performance, a characteristic feature of SUDs. This study, therefore, is a meaningful addition to the growing body of research supporting the common neurobiological underpinnings of SUDs and GD.

Authors’ contribution

S-WC contributed to obtaining funding, study concept and design, acquisition, analysis and interpretation of data; Y-CS contributed to obtaining funding, and study concept and design and interpretation of the data; JYM contributed to study concept and design, acquisition, analysis and interpretation of data; D-JK and J-SC contributed to study concept and design, and interpretation of data; and SS-HH contributed to analysis and interpretation of data and drafting and revision of the manuscript. All authors had full access to all data in the study and take full responsibility for the integrity of the data and the accuracy of the data analysis.

Conflict of interest

The authors declare no conflict of interest.

Acknowledgements

We are grateful to the patients with GD who participated in this study. We also thank research assistant Minsu Kim for his support of this research.

References

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    • Crossref
    • Search Google Scholar
    • Export Citation
  • Angelucci, F., Ricci, V., Martinotti, G., Palladino, I., Spalletta, G., Caltagirone, C., & Bria, P. (2010). Ecstasy (MDMA)-addicted subjects show increased serum levels of brain-derived neurotrophic factor, independently from a rise of drug-induced psychotic symptoms. Addiction Biology, 15, 365367.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Barry, D., & Petry, N. M. (2008). Predictors of decision-making on the Iowa Gambling Task: Independent effects of lifetime history of substance use disorders and performance on the Trail Making Test. Brain and Cognition, 66, 243252.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Bhang, S. Y., Choi, S. W., & Ahn, J. H. (2010). Changes in plasma brain-derived neurotrophic factor levels in smokers after smoking cessation. Neuroscience Letters, 468, 711.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brand, M., Recknor, E. C., Grabenhorst, F., & Bechara, A. (2007). Decisions under ambiguity and decisions under risk: Correlations with executive functions and comparisons of two different gambling tasks with implicit and explicit rules. Journal of Clinical and Experimental Neuropsychology, 29, 8699.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Geisel, O., Banas, R., Hellweg, R., & Muller, C. A. (2012). Altered serum levels of brain-derived neurotrophic factor in patients with pathological gambling. European Addiction Research, 18, 297301.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Grant, J. E., Kim, S. W., & Kuskowski, M. (2004). Retrospective review of treatment retention in pathological gambling. Comprehensive Psychiatry, 45, 8387.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Hori, H., Yoshimura, R., Katsuki, A., Atake, K., & Nakamura, J. (2014). Relationships between brain-derived neurotrophic factor, clinical symptoms, and decision-making in chronic schizophrenia: Data from the Iowa Gambling Task. Frontiers of Behavioral Neuroscience, 8, 417. doi: 10.3389/fnbeh.2014.00417

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Jiménez-Murcia, S., Alvarez-Moya, E. M., Stinchfield, R., Fernández-Aranda, F., Granero, R., Aymamí, N., Gómez-Peña, M., Jaurrieta, N., Bove, F., & Menchón, J. M. (2010). Age of onset in pathological gambling: Clinical, therapeutic and personality correlates. Journal of Gambling Studies, 26, 235248.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kang, J. I., Namkoong, K., Ha, R. Y., Jhung, K., Kim, Y. T., & Kim, S. J. (2010). Influence of BDNF and COMT polymorphisms on emotional decision making. Neuropharmacology, 58, 11091113.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Karege, F., Schwald, M., & Cisse, M. (2002). Postnatal developmental profile of brain-derived neurotrophic factor in rat brain and platelets. Neuroscience Letters, 328, 261264.

    • Crossref
    • Search Google Scholar
    • Export Citation
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The author instruction is available in PDF.
Please, download the file from HERE

Dr. Zsolt Demetrovics
Institute of Psychology, ELTE Eötvös Loránd University
Address: Izabella u. 46. H-1064 Budapest, Hungary
Phone: +36-1-461-2681
E-mail: jba@ppk.elte.hu

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2020  
Total Cites 4024
WoS
Journal
Impact Factor
6,756
Rank by Psychiatry (SSCI) 12/143 (Q1)
Impact Factor Psychiatry 19/156 (Q1)
Impact Factor 6,052
without
Journal Self Cites
5 Year 8,735
Impact Factor
Journal  1,48
Citation Indicator  
Rank by Journal  Psychiatry 24/250 (Q1)
Citation Indicator   
Citable 86
Items
Total 74
Articles
Total 12
Reviews
Scimago 47
H-index
Scimago 2,265
Journal Rank
Scimago Clinical Psychology Q1
Quartile Score Psychiatry and Mental Health Q1
  Medicine (miscellaneous) Q1
Scopus 3593/367=9,8
Scite Score  
Scopus Clinical Psychology 7/283 (Q1)
Scite Score Rank Psychiatry and Mental Health 22/502 (Q1)
Scopus 2,026
SNIP  
Days from  38
submission  
to 1st decision  
Days from  37
acceptance  
to publication  
Acceptance 31%
Rate  

2019  
Total Cites
WoS
2 184
Impact Factor 5,143
Impact Factor
without
Journal Self Cites
4,346
5 Year
Impact Factor
5,758
Immediacy
Index
0,587
Citable
Items
75
Total
Articles
67
Total
Reviews
8
Cited
Half-Life
3,3
Citing
Half-Life
6,8
Eigenfactor
Score
0,00597
Article Influence
Score
1,447
% Articles
in
Citable Items
89,33
Normalized
Eigenfactor
0,7294
Average
IF
Percentile
87,923
Scimago
H-index
37
Scimago
Journal Rank
1,767
Scopus
Scite Score
2540/376=6,8
Scopus
Scite Score Rank
Cllinical Psychology 16/275 (Q1)
Medicine (miscellenous) 31/219 (Q1)
Psychiatry and Mental Health 47/506 (Q1)
Scopus
SNIP
1,441
Acceptance
Rate
32%

 

Journal of Behavioral Addictions
Publication Model Gold Open Access
Submission Fee none
Article Processing Charge 850 EUR/article
Printed Color Illustrations 40 EUR (or 10 000 HUF) + VAT / piece
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Editorial Board / Advisory Board members: 50%
Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%
Subscription Information Gold Open Access
Purchase per Title  

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
Publication
Programme
2021 Volume 10
Volumes
per Year
1
Issues
per Year
4
Founder Eötvös Loránd Tudományegyetem
Founder's
Address
H-1053 Budapest, Hungary Egyetem tér 1-3.
Publisher Akadémiai Kiadó
Publisher's
Address
H-1117 Budapest, Hungary 1516 Budapest, PO Box 245.
Responsible
Publisher
Chief Executive Officer, Akadémiai Kiadó
ISSN 2062-5871 (Print)
ISSN 2063-5303 (Online)

Senior editors

Editor(s)-in-Chief: Zsolt DEMETROVICS

Assistant Editor(s): Csilla ÁGOSTON

Associate Editors

  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • H. N. Alexander LOGEMANN (ELTE Eötvös Loránd University, Hungary)
  • Anikó MARÁZ (Humboldt University of Berlin, Germany)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)
  • Attila SZABÓ (ELTE Eötvös Loránd University, Hungary)
  • Róbert URBÁN (ELTE Eötvös Loránd University, Hungary)
  • Aviv M. WEINSTEIN (Ariel University, Israel)

Editorial Board

  • Max W. ABBOTT (Auckland University of Technology, New Zealand)
  • Elias N. ABOUJAOUDE (Stanford University School of Medicine, USA)
  • Hojjat ADELI (Ohio State University, USA)
  • Alex BALDACCHINO (University of Dundee, United Kingdom)
  • Alex BLASZCZYNSKI (University of Sidney, Australia)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Beáta BÖTHE (University of Montreal, Canada)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Sam-Wook CHOI (Eulji University, Republic of Korea)
  • Damiaan DENYS (University of Amsterdam, The Netherlands)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Heather HAUSENBLAS (Jacksonville University, USA)
  • Tobias HAYER (University of Bremen, Germany)
  • Susumu HIGUCHI (National Hospital Organization Kurihama Medical and Addiction Center, Japan)
  • David HODGINS (University of Calgary, Canada)
  • Eric HOLLANDER (Albert Einstein College of Medicine, USA)
  • Jaeseung JEONG (Korea Advanced Institute of Science and Technology, Republic of Korea)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Emmanuel KUNTSCHE (La Trobe University, Australia)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Michel LEJOXEUX (Paris University, France)
  • Anikó MARÁZ (Eötvös Loránd University, Hungary)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Frederick GERARD MOELLER (University of Texas, USA)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Nancy PETRY (University of Connecticut, USA)
  • Bettina PIKÓ (University of Szeged, Hungary)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Rory C. REID (University of California Los Angeles, USA)
  • Marcantanio M. SPADA (London South Bank University, United Kingdom)
  • Daniel SPRITZER (Study Group on Technological Addictions, Brazil)
  • Dan J. STEIN (University of Cape Town, South Africa)
  • Sherry H. STEWART (Dalhousie University, Canada)
  • Attila SZABÓ (Eötvös Loránd University, Hungary)
  • Ferenc TÚRY (Semmelweis University, Hungary)
  • Alfred UHL (Austrian Federal Health Institute, Austria)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

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