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Xinqi Zhou The Clinical Hospital of the Chengdu Brain Science Institute, Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China

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Renjing Wu The Clinical Hospital of the Chengdu Brain Science Institute, Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China

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Congcong Liu The Clinical Hospital of the Chengdu Brain Science Institute, Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China

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Juan Kou The Clinical Hospital of the Chengdu Brain Science Institute, Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China

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Yuanshu Chen The Clinical Hospital of the Chengdu Brain Science Institute, Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China

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Halley M. Pontes School of Psychological Sciences, University of Tasmania, TAS 7250 Launceston, Australia
The International Cyberpsychology and Addictions Research Laboratory (iCARL), University of Tasmania, Launceston, TAS 7250, Australia

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Dezhong Yao The Clinical Hospital of the Chengdu Brain Science Institute, Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China

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Keith M. Kendrick The Clinical Hospital of the Chengdu Brain Science Institute, Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China

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Benjamin Becker The Clinical Hospital of the Chengdu Brain Science Institute, Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China

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Christian Montag The Clinical Hospital of the Chengdu Brain Science Institute, Key Laboratory for NeuroInformation, University of Electronic Science and Technology of China, Chengdu, China
Department of Molecular Psychology, Institute of Psychology and Education, Ulm University, Ulm, Germany

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Open access

Abstract

Background and aims

Growing concerns about the addictive nature of Internet and computer games led to the preliminary recognition of Internet Gaming Disorder (IGD) as an emerging disorder by the American Psychiatric Association (APA) and the official recognition of Gaming Disorder (GD) as a new diagnosis by the World Health Organization (WHO). While the definition of clear diagnostic criteria for (I)GD represents an important step for diagnosis and treatment of the disorder, potential neurobiological correlates of the criteria remain to be explored.

Methods

The present study employed a dimensional Magnetic Resonance Imaging (MRI) approach to determine associations between (I)GD symptom-load according to the APA and WHO diagnostic frameworks and brain structure in a comparably large sample of n = 82 healthy subjects.

Results

Higher symptom-load on both, the APA and WHO diagnostic frameworks convergently associated with lower volumes of the striatum.

Discussion

The results from this exploratory study provide the first initial evidence for a neurobiological foundation of the proposed diagnostic criteria for (I)GD according to both diagnostic classification systems and suggest that the transition from non-disordered to disordered gaming may be accompanied by progressive neuroplastic changes in the striatum, thus resembling progressive changes in other addictive disorders.

Conclusions

The proposed (I)GD criteria in both diagnostic systems were associated with neurostructural alterations in the striatum, suggesting an association with progressive changes in the motivational systems of the brain.

Abstract

Background and aims

Growing concerns about the addictive nature of Internet and computer games led to the preliminary recognition of Internet Gaming Disorder (IGD) as an emerging disorder by the American Psychiatric Association (APA) and the official recognition of Gaming Disorder (GD) as a new diagnosis by the World Health Organization (WHO). While the definition of clear diagnostic criteria for (I)GD represents an important step for diagnosis and treatment of the disorder, potential neurobiological correlates of the criteria remain to be explored.

Methods

The present study employed a dimensional Magnetic Resonance Imaging (MRI) approach to determine associations between (I)GD symptom-load according to the APA and WHO diagnostic frameworks and brain structure in a comparably large sample of n = 82 healthy subjects.

Results

Higher symptom-load on both, the APA and WHO diagnostic frameworks convergently associated with lower volumes of the striatum.

Discussion

The results from this exploratory study provide the first initial evidence for a neurobiological foundation of the proposed diagnostic criteria for (I)GD according to both diagnostic classification systems and suggest that the transition from non-disordered to disordered gaming may be accompanied by progressive neuroplastic changes in the striatum, thus resembling progressive changes in other addictive disorders.

Conclusions

The proposed (I)GD criteria in both diagnostic systems were associated with neurostructural alterations in the striatum, suggesting an association with progressive changes in the motivational systems of the brain.

Introduction

In 2019 the World Health Organization (WHO) formally recognized Gaming Disorder (GD) as official diagnosis to be included in the upcoming revision of the International Classification of Diseases (ICD, 11th version). Earlier in 2013, the American Psychiatric Association (APA) included Internet Gaming Disorder (IGD) as emerging disorder in the appendix of the DSM-5. Although the specific symptoms for disordered gaming differ across the two diagnostic frameworks, the proposed criteria in both frameworks strongly resemble diagnostic criteria for substance-based addictions, including preoccupation with gaming, loss of control over gaming and continued use despite negative consequences. For the categorical diagnosis of (I)GD both classification systems require a number of symptoms that must be exhibited over 12-months and lead to marked distress and functional impairments.

By proposing clear (I)GD symptom criteria, the classification systems have provided tremendous help for clinicians, researchers, and those in need of treatment for disordered gaming while accounting for growing public concerns about the detrimental effects of excessive gaming on mental health with the Chinese Government even releasing new policies in November 2019 to regulate Internet gaming time in adolescents (https://www.bbc.com/news/world-asia-50315960). It is likely that the inclusion of (I)GD as an addictive disorder may also pathologize and stigmatize normal gaming behavior as the neurobiological mechanisms underlying (I)GD symptoms and their resemblance to changes in other addictive disorders remain highly controversial (Zastrow, 2017). At the clinical and phenomenological levels (I)GD strongly resembles compulsive use and loss of control observed in substance-based addictions. Converging evidence from animal and human studies indicates that the transition from volitional to addictive and compulsive substance use is accompanied by progressive dysregulations in the motivational circuits of the brain. The striatum lies at the heart of these motivational circuits and plays a critical role in both the initial reinforcing effects of drugs as well as the development of compulsive use and progressive loss of control (Koob & Volkow, 2016). Neuroplastic changes in the striatum and associated circuitries engaged in reward processing and habit formation have been demonstrated extensively in animal models of substance use disorders (Everitt & Robbins, 2016). Moreover, concurring evidence from previous animal models and studies in individuals with substance use disorders suggests that different striatal regions control the transition from incentive-driven to compulsive substance use, corresponding to ventral and dorsal parts of the striatum respectively (Everitt & Robbins, 2016; Robbins, Ersche & Everitt, 2008; Vollstadt-Klein et al., 2010; Zhou et al., 2018, 2019). Human neuroimaging studies have also repeatedly reported striatal gray matter alterations in populations with substance use disorders, with the extent of volumetric reductions being associated with escalating substance use and clinical symptom severity (Becker et al., 2015).

Emerging evidence from a growing number of human imaging studies in pathological gambling indicate that striatal circuits play an important role in the initial rewarding effects of gambling as well as the formation of compulsive gambling during later stages of the disorder (Clark, Boileau, & Zack, 2019). These findings suggest that these circuits may also mediate escalating and ultimately compulsive use in behavioral addictions. Furthermore, brain structural alterations in behavioral addictions such as pathological gambling might be less pronounced, probably due to the lack of neurotoxic effects (Clark et al., 2019), even though a growing body of evidence reported altered gray matter volume in individuals with (I)GD, including lower volume in striatal circuits, most consistently affecting the dorsal striatum, specifically the putamen (Qin et al., 2020; Yao et al., 2017). Initial studies reported associations between the extent of gaming or the level of “Internet/social media addiction” - symptoms and gray matter volume in reward processing regions, including the striatum, suggesting that progressive volumetric changes in this region may accompany the onset of (I)GD (Cai et al., 2016; Montag et al., 2017). Moreover, neuroplastic and functional changes of the striatum have already been observed during early stages of the addictive process, suggesting that this region may represent a promising marker to track early stages of the development of addiction (Brand et al., 2019; Everitt & Robbins, 2016; Vollstadt-Klein et al., 2010; Zhou et al., 2019).

To determine whether the proposed symptom-level criteria for (I)GD reflects the hypothesized neurobiological basis of the disorder and if the different symptomatic criteria proposed by the APA and WHO relate to the same neurobiological markers, the present study employed a dimensional neuroimaging approach with the goal to map subclinical symptoms of (I)GD and striatal morphology. This study employed non-parametric voxel-wise regression analyses on high-resolution T1-weighted MRI brain structural images with (I)GD symptom-load assessed by diagnostic-system specific scales (Internet Gaming Disorder Scale – Short-Form, IGDS9-SF, APA framework; Gaming Disorder Test, GDT, WHO framework) (Pontes & Griffiths, 2015; Pontes et al., 2019) as separate predictors in a large sample of healthy young male adults. Symptom load on dimensions such as depression and autism frequently associated with (I)GD were controlled for. Given the substantial associations between IGD and GD scores when assessed with the IGDS9-SF and the GDT (Montag et al., 2019), and the key role of the striatum in substance-based addictions, it was expected that higher symptom-load on both disordered gaming psychometric tests would be accompanied by stronger alterations in striatal morphology (i.e. lower striatal volume).

Method

Participants and procedures

For the present study N = 256 healthy male individuals from the Chengdu Gene Brain Behavior Project were re-contacted via telephone interviews (N = 256, at least 18 years old). All subjects underwent structural MRI assessment prior to the assessment of (I)GD symptoms (1.1 ± 0.764 years in the final sample). Following MRI data quality assessments and exclusion of duplicate datasets, subjects were contacted via telephone and underwent an interview to exclude those with current or previous history of psychiatric disorders and current use of medication or psychotropic substances. Among all eligible participants, N = 119 agreed to participate in the additional electronic data assessment. The severity of (I)GD symptoms was assessed in light of the APA and the WHO diagnostic frameworks. Given the existence of high levels of comorbidity between depressive disorder and autism spectrum disorder in disordered gaming (e.g. Li et al., 2019; Xu et al., 2020), the potential confounding effects for these two psychopathologies were controlled in the present study using the Beck Depression Inventory-II (BDI-II) and the Autism-Spectrum Quotient (ASQ) (Baron-Cohen, Wheelwright, Skinner, Martin, & Clubley, 2001; Beck, Steer, & Brown, 1996). Subjects meeting the clinical cut-off criteria were excluded and symptom severities in both dimensions were additionally included as covariates in the analyses. For the final sample, the following exclusion criteria were applied (1) history or current psychiatric disorders according to DSM-5 (validated by structured clinical interviews at the time of MRI assessment), (2) pathological levels of depressive (BDI-II ≥28) and autistic (ASQ ≥ 30) symptoms, (3) history of, or current medical disorder, including neurological and endocrinological disorders, (4) current or regular use of medication and other psychoactive substances, and (5) left-handedness. Of the eligible participants, a total of n = 82 individuals (age at MRI assessment = 21.8 ± 2.16 years) provided complete self-report and MRI data, and were thus included in the subsequent voxel-based morphometry (VBM) analysis (exclusion of subjects detailed in Fig. 1).

Fig. 1.
Fig. 1.

Flow diagram displaying screening and exclusion of participants.

Citation: Journal of Behavioral Addictions 9, 3; 10.1556/2006.2020.00066

Disordered Gaming symptom load assessment

In the present study Chinese versions of the IGDS9-SF (Yam et al., 2019) and the GDT (Pontes et al., 2019) were administered. The IGDS9-SF assesses IGD according to the APA framework using nine items answered on a five-point Likert scale (from 1 = never to 5 = very often). Moreover, the GDT consists of four items assessing GD according to the WHO framework on a five-point Likert scale (from 1 = never to 5 = very often). Answering with often or very often on the two tests indicates endorsement of the corresponding diagnostic criterion. Consequently, fulfilling five out of nine criteria on the IGDS9-SF and fulfilling all four criteria on the GDT indicates disordered gaming. Internal consistencies were satisfying for both tests (IGDS9-SF α = 0.845 and GDT α = 0.912) in the present sample.

MRI data acquisition

The data were acquired on a 3.0 T GE MR750 system (General Electric Medical Systems, Milwaukee, WI, USA). T1-weighted high-resolution anatomical images were acquired with a spoiled gradient echo pulse sequence, repetition time (TR) = 6 ms, echo time (TE) = 2 ms, flip angle = 9°, field of view (FOV) = 256 × 256 mm, acquisition matrix = 256 × 256, thickness = 1 mm, number of slices = 156.

Brain structural data – preprocessing

VBM analysis allows the voxel-wise estimation of the local gray matter volume (Ashburner & Friston, 2005). Structural MRI data were preprocessed with CAT12 implementing the computational anatomy approach (http://dbm.neuro.uni-jena.de/cat) based on SPM12 (Welcome Department of Cognitive Neurology, London, UK, http://www.fil.ion.ucl.ac.uk/spm/software/spm12) running on MATLAB Version 8.3 (Math Works Inc., Natick, MA). The standard VBM preprocessing protocols of CAT12 (outlined in the CAT12 manual) were employed. Briefly, the T1-weight images were bias-corrected, segmented into gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) and spatially normalized to the standard Montreal Neurological Institute (MNI) space using the East Asian template. GM images were smoothed with a Gaussian kernel of 8 mm full-width at half maximum (FWHM) for subsequent statistical analysis and the total intracranial volume (TIV) was estimated to correct for individual differences in brain size. Default parameters were applied unless indicated otherwise. After preprocessing all images passed visual inspection artefacts and an automated quality protocol. Mean Image Quality Rating (IQR) of the final data was B- (84.37%) summarized by the quality assurance framework of CAT12, which allows the evaluation of essential image parameters such as noise, inhomogeneities, and image resolution, indicating excellent image quality in the present sample.

Statistical analyses

Multiple linear regression models were employed in the Statistical non-Parametric Mapping toolbox (SnPM13, http://www.warwick.ac.uk/snpm) based on 10,000 random permutations, taking GM maps as dependent variables and IGDS9-SF and GDT scores respectively as independent variable (similar approach see Li et al., 2019). Although false positive rates in VBM are not strongly influenced by sample size, a non-parametric estimation has been demonstrated as robust against non-normal distribution of the underlying data and might be more robust independent of sample size (Scarpazza, Tognin, Frisciata, Sartori, & Mechelli, 2015; Silver, Montana, Nichols, & Alzheimer's Disease Neuroimaging, 2011).

For both multiple linear regression models, age, education level, BDI-II, ASQ, and TIV were additionally included as covariates. In line with the regional-specific a priori hypothesis on striatal associations, voxel-wise analyses were restricted to the striatum (Fig. 2C) encompassing both bilateral ventral and dorsal striatal subregions (for a description of the masks see Zhou et al., 2019). Within the striatal mask, a voxel level threshold of p < 0.05 with FWE multiple comparison correction adjusted for the search volume was applied. To further control for the potential effects of time between the brain structural assessments and the assessment of (I)GD symptoms, the time between assessments was controlled for in additional control analyses.

Fig. 2.
Fig. 2.

Lower striatal volume with (I)GD symptom severity. A) Associations between symptom load and brain structure displayed at pFWE <0.05 within the striatum (IGDS9-SF = red, GDT = green, and overlapping = yellow). B) Extracted gray matter from the significant regions and associations with symptom severity. C) The striatal mask, including ventral and dorsal regions as used in the present study.

Citation: Journal of Behavioral Addictions 9, 3; 10.1556/2006.2020.00066

Ethics

The study and its procedures had full approval by the local ethics committee and adhered to the most recent version of the Declaration of Helsinki and all participants were required to provide informed consent.

Results

Demographic characteristic of participants

The present sample reported a mean (±SD) symptom severity of = 18.4 ± 6.53 for the IGDS9-SF and 8.43 ± 3.07 for the GDT. Moreover, the mean trait score for depression (BDI-II) was 6.26 ± 6.98, and for autism (ASQ) was 13.7 ± 3.77. Mean total intracranial volume was 1,563 ± 107, and education level ranged from high school graduate to master's degree student. Categorial analysis of the symptom-level data revealed that in terms of disordered gaming, n = 3 subjects fulfilled the diagnostic criteria for disordered gaming on the APA framework (IGDS9-SF) and n = 1 fulfilled all criteria in the WHO framework (GDT).

(I)GD symptom load and striatal gray matter volumes across the APA and WHO diagnostic frameworks

In line with the regional a priori hypothesis the analysis focused on the bilateral striatum using a small-volume approach. Higher IGDS9-SF symptom severity was significantly associated with lower gray matter volume in the bilateral caudate (pFWE <0.05), whereas higher GDT symptom severity was associated with lower gray matter volume in the right caudate only (pFWE <0.05) (Table 1, Fig. 2A and B). Given the distinct contributions of the ventral and dorsal striatum to addiction (Zhou et al., 2019), significant effects were further mapped and overlapped (Fig. 2A), revealing that the IGDS9-SF and GDT shared 114 voxels in caudate volume. Additionally, for the IGDS9-SF 244 voxels mapped onto the ventral and 88 voxels mapped to the dorsal striatum. For the GDT, 53 voxels mapped onto the ventral and 65 on the dorsal striatum.

Table 1.

Significant negative associations between gray matter volume and the IGDS9-SF and GDT respectively

Regionk (Cluster Size)xyzt Value
IGDS9-SF, negative associations
Caudate R207142634.23
232493.87
Caudate L103−1124−24.11
−62153.87
Caudate L22−1821124.04
GDT, negative associations
Caudate R118152754.32

R = right, L = left.

Associations between (Internet) Gaming Disorder symptom load and striatal gray matter volumes across the APA and WHO diagnostic frameworks were additionally controlled for variations in the time between brain structural data acquisition and questionnaire assessment. To this end the identical statistical analysis pipeline was conducted with additionally including time between the assessments as additional covariate in the multiple regression model.

Findings revealed that higher IGDS9-SF symptom severity was significantly associated with lower gray matter volume in the bilateral caudate (pFWE <0.05, right caudate peak at [12, 26, 2], k = 205, left peak at [−9, 23, 0] and [−18, 21, 12], k = 128 and 15), whereas higher GDT symptom severity was associated with lower gray matter volume in the right caudate (pFWE <0.05, peak at [15, 27, 5], k = 76).

Discussion

In line with the study's hypothesis, the findings from this initial examination of the recently proposed (I)GD criteria revealed that stronger symptom-load across both diagnostic frameworks was associated with lower striatal gray matter volume, primarily in the caudate region. The caudate bridges the ventral and dorsal striatum and is strongly implicated in addiction-relevant domains, with ventral regions being implicated in the initial formation of reward-related salience and associative learning processes (Anderson et al., 2017) whereas dorsal parts mediate habit formation (Everitt & Robbins, 2016). Exaggerated cue-reactivity in the ventral striatum has not only been consistently observed in substance use disorders but also in individuals without substance use disorders (Vollstadt-Klein et al., 2010; Zhou et al., 2019). This finding provides initial and preliminary evidence for a neurobiological correlate of the proposed (I)GD criteria in the classification systems and further suggests that development of (I)GD may be accompanied by progressive neurobiological changes in brain motivational systems that have been suggested to mediate the development of established addictive disorders, including substance use disorders and pathological gambling (Clark et al., 2019; Koob & Volkow, 2016).

Although (I)GD criteria in both frameworks aligned with the respective core substance-based disorder diagnoses, the specific symptom criteria differ slightly. For scientists and practitioners alike, a key issue is related to diagnosing essentially the same condition using the two diagnostic frameworks (APA vs. WHO) and potential neurobiological discrepancies between them. The findings obtained suggest that the APA framework may capture more extensive striatal, particularly ventral striatal regions. The ventral striatum mediates dysregulations in reward-related processes during early, whereas dorsal regions involved in habit formation mediates compulsive use during later stages of the addictive process (Everitt & Robbins, 2016). The discrepancies in the neurobiological associations may reflect a higher sensitivity of the APA framework to detect early stages of disordered gaming, which aligns with the higher prevalence of IGD according to APA criteria in the present sample (see also similar results in a large-scale study Montag et al., 2019).

Previous meta-analytic studies (Qin et al., 2020; Yao et al., 2017) revealed that hyperactivation of the anterior cingulate cortex, dorsolateral prefrontal cortex, inferior frontal gyrus, caudate and precuneus and lower gray matter volume of the putamen, anterior cingulate cortex, orbitofrontal cortex, dorsolateral prefrontal cortex, and supplementary motor characterize (I)GD. A few previous studies that employed whole brain and region of interest analyses have also reported increased caudate volume in (I)GD (Cai et al., 2016; Seok & Sohn, 2018), however the small sample size and differences in the diagnostic criteria and potential confounders limit the comparability between studies. Moreover, these studies employed traditional case-control designs which are often hampered by limitations inherent to the design such as generally increased pathological symptom load, stress or alterations reflecting vulnerability factors that precede the onset of the disorder (Etkin, 2019). To this end, the present study employed the official diagnostic criteria for disordered gaming in combination with a dimensional neuroimaging approach (Li et al., 2019).

The observed association with volumes of the putamen resonate with overarching conceptualizations of the development of substance use disorder and IGD and may reflect the progression towards more clinical stages of (I)GD (Brand et al., 2019; Everitt & Robbins, 2016). On the one hand, the findings in healthy subjects will reflect changes during early stages of Internet gaming, which are still characterized by incentive-driven motives and probably an initial transfer to habitual behavior (specifically for the IGDS9-SF framework more items related to incentive aspects and may thus captured more ventral striatal alterations). On the other hand, the caudate and putamen are both also implicated in motor function (Ena, De Kerchove D'ExaErde, & Schiffmann, 2011), therefore the assumption that the association between symptom load and caudate volume may additionally reflect effects of gaming time cannot be ruled out. In summary, in the context of addictive behavior, the present findings may provide a preliminary approach to explore early stages of habitual control over behavior in IGD, yet long-term studies are needed to examine whether the striatal volume changes determine the transition to IGD.

In this context, it should be mentioned that the IGD criteria according to the DSM-5 seem to grasp disordered gaming earlier than the criteria for GD proposed in the ICD-11 (Montag et al., 2019). As aforementioned, first prevalence estimates of disordered gaming in a German sample were higher when applying the framework of APA compared to WHO. This might be due to the inherent diagnostic differences across the two disordered gaming frameworks as according to the APA five out of fine symptoms are needed for a disordered gaming diagnosis (every constellation is possible), whereas according to the WHO framework all criteria proposed need to be met (Table 2). Some symptoms such as lying about one's own gaming habits and using games to escape negative mood can only be found in the APA framework. An overlap can be seen for disordered gaming symptoms such as preoccupation/increased priority given to gaming, loss of control, engaging in gaming despite negative consequences, and significant impairments due to gaming in everyday life.

Table 2.

Comparison of (I)GD criteria between the APA and WHO framework

APA frameworkWHO framework
DiagnosisFive or more of these symptoms within a yearAll symptoms for at least 12 months
Symptom

The findings of the present study need to be considered in the context of potential limitations. First, the present study applied a dimensional approach in a healthy sample and future studies need to extent to the findings reported to clinical samples with (I)GD according to the current official diagnostic criteria while long-term studies are needed to investigate the association between striatal volume and escalation of Internet gaming and subsequent transition to disordered gaming. Second, since disordered gaming is strongly intervened with the acquisition of new skills, particularly in the motor domain, future studies need to distinguish between neuroplastic changes related to motor learning or the addictive process (e.g. by inclusion of a group of healthy, yet non-disordered, gamers). Third, the study excluded subjects with pathological levels of depression. Together with the inclusion of depression levels as covariate, this allowed controlling at some extent for potential confounding effects. However, this approach may limit the generalizability of the findings.

Despite the potential limitations, the present study is to the best of the authors' knowledge, the first to examine disordered gaming within a comparative framework between the APA and WHO diagnostic framework in the context of MRI. Additionally, the findings reported provide initial evidence for a neurobiological basis of the official disordered gaming diagnostic criteria in accordance with both current diagnostic systems and suggest that the proposed criteria may reflect dysregulations in the motivational systems of the brain.

Funding sources

This work was supported by the National Key Research and Development Program of China (Grant No. 2018YFA0701400), National Natural Science Foundation of China (NSFC, No. 91632117, 31530032); Fundamental Research Funds for Central Universities (ZYGX2015Z002), Science, Innovation and Technology Department of the Sichuan Province (2018JY0001). The position of CM was funded by a Heisenberg grant awarded to him by the German Research Foundation (DFG, MO2363/3-2).

Authors' contribution

XZ, BB, and CM conceptualized, designed, and wrote the initial protocol and draft. XZ conducted and validated the formal analysis. RW, CL, JK, and YC carried out the investigation and MRI acquisition. HMP, DY, KK, BB, and CM reviewed and revised the manuscript. BB and CM obtained funding and administrated the project. All authors contributed to and approved the final version of the manuscript, and take responsibility for the integrity of the data and the accuracy of the data analysis.

Conflict of interest

The authors report no conflict of interest.

References

  • Anderson, B. A., Kuwabara, H., Wong, D. F., Roberts, J., Rahmim, A., Brasic, J. R., et al. (2017). Linking dopaminergic reward signals to the development of attentional bias: A positron emission tomographic study. Neuroimage, 157, 2733. https://doi.org/10.1016/j.neuroimage.2017.05.062.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26(3), 839851. https://doi.org/10.1016/j.neuroimage.2005.02.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The autism-spectrum quotient (AQ): Evidence from asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. Journal of Autism and Developmental Disorders, 31(1), 517. https://doi.org/10.1023/a:1005653411471.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Beck depression inventory-II. San Antonio, 78(2), 490498.

  • Becker, B., Wagner, D., Koester, P., Tittgemeyer, M., Mercer-Chalmers-Bender, K., Hurlemann, R., et al. (2015). Smaller amygdala and medial prefrontal cortex predict escalating stimulant use. Brain, 138(Pt 7), 20742086. https://doi.org/10.1093/brain/awv113.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brand, M., Wegmann, E., Stark, R., Müller, A., Wölfling, K., Robbins, T. W., et al. (2019). The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: Update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors. Neuroscience & Biobehavioral Reviews, 104, 110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, C., Yuan, K., Yin, J., Feng, D., Bi, Y., Li, Y., et al. (2016). Striatum morphometry is associated with cognitive control deficits and symptom severity in internet gaming disorder. Brain Imaging and Behavior, 10(1), 1220.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, L., Boileau, I., & Zack, M. (2019). Neuroimaging of reward mechanisms in gambling disorder: An integrative review. Molecular Psychiatry, 24(5), 674693. https://doi.org/10.1038/s41380-018-0230-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ena, S., De Kerchove D'ExaErde, A., & Schiffmann, S. N. (2011). Unraveling the differential functions and regulation of striatal neuron sub-populations in motor control, reward, and motivational processes. Frontiers in Behavioral Neuroscience, 5, 47.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Etkin, A. (2019). A reckoning and research agenda for neuroimaging in psychiatry. American Journal of Psychiatry, 176(7), 507511.

  • Everitt, B. J., & Robbins, T. W. (2016). Drug addiction: Updating actions to habits to compulsions ten years on. Annual Review of Psychology, 67, 2350. https://doi.org/10.1146/annurev-psych-122414-033457.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koob, G. F., & Volkow, N. D. (2016). Neurobiology of addiction: A neurocircuitry analysis. Lancet Psychiatry, 3(8), 760773. https://doi.org/10.1016/S2215-0366(16)00104-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, J., Xu, L., Zheng, X., Fu, M., Zhou, F., Xu, X., et al. (2019). Common and dissociable contributions of alexithymia and autism to domain-specific interoceptive dysregulations: A dimensional neuroimaging approach. Psychotherapy and Psychosomatics, 88(3), 187189. https://doi.org/10.1159/000495122.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Montag, C., Markowetz, A., Blaszkiewicz, K., Andone, I., Lachmann, B., Sariyska, R., et al. (2017). Facebook usage on smartphones and gray matter volume of the nucleus accumbens. Behavioural Brain Research, 329, 221228.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Montag, C., Schivinski, B., Sariyska, R., Kannen, C., Demetrovics, Z., & Pontes, H. M. (2019). Psychopathological symptoms and gaming motives in disordered gaming-A psychometric comparison between the WHO and APA diagnostic frameworks. Journal of Clinical Medicine, 8(10), 1691. https://doi.org/10.3390/jcm8101691.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pontes, H. M., & Griffiths, M. D. (2015). Measuring DSM-5 internet gaming disorder: Development and validation of a short psychometric scale. Computers in Human Behavior, 45, 137143. https://doi.org/10.1016/j.chb.2014.12.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pontes, H. M., Schivinski, B., Sindermann, C., Li, M., Becker, B., Zhou, M., et al. (2019). Measurement and conceptualization of gaming disorder according to the world health organization framework: The development of the gaming disorder test. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-019-00088-z.

    • Search Google Scholar
    • Export Citation
  • Qin, K., Zhang, F., Chen, T., Li, L., Li, W., Suo, X., et al. (2020). Shared gray matter alterations in individuals with diverse behavioral addictions: A voxel-wise meta-analysis. Journal of Behavioral Addictions, 9(1), 4457.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Robbins, T. W., Ersche, K. D., & Everitt, B. J. (2008). Drug addiction and the memory systems of the brain. Annals of the New York Academy of Sciences, 1141, 121.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scarpazza, C., Tognin, S., Frisciata, S., Sartori, G., & Mechelli, A. (2015). False positive rates in voxel-based morphometry studies of the human brain: Should we be worried? Neuroscience & Biobehavioral Reviews, 52, 4955. https://doi.org/10.1016/j.neubiorev.2015.02.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seok, J. W., & Sohn, J. H. (2018). Altered gray matter volume and resting-state connectivity in individuals with Internet gaming disorder: A voxel-based morphometry and resting-state functional magnetic resonance imaging study. Frontiers in Psychiatry, 9, 77.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Silver, M., Montana, G., Nichols, T. E., & Alzheimer's Disease Neuroimaging, I. (2011). False positives in neuroimaging genetics using voxel-based morphometry data. NeuroImage, 54(2), 9921000. https://doi.org/10.1016/j.neuroimage.2010.08.049.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vollstadt-Klein, S., Wichert, S., Rabinstein, J., Buhler, M., Klein, O., Ende, G., et al. (2010). Initial, habitual and compulsive alcohol use is characterized by a shift of cue processing from ventral to dorsal striatum. Addiction, 105(10), 17411749. https://doi.org/10.1111/j.1360-0443.2010.03022.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, X., Dai, J., Liu, C., Chen, Y., Xin, F., Zhou, F., et al. (2020). Common and disorder-specific neurofunctional markers of dysregulated empathic reactivity in major depression and generalized anxiety disorder. Psychotherapy and Psychosomatics, 89(2), 114116. https://doi.org/10.1159/000504180.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yam, C. W., Pakpour, A. H., Griffiths, M. D., Yau, W. Y., Lo, C. L. M., Ng, J. M., et al. (2019). Psychometric testing of three Chinese online-related addictive behavior instruments among Hong Kong university students. Psychiatric Quarterly, 90(1), 117128. https://doi.org/10.1007/s11126-018-9610-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yao, Y. W., Liu, L., Ma, S. S., Shi, X. H., Zhou, N., Zhang, J. T., et al. (2017). Functional and structural neural alterations in Internet gaming disorder: A systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews, 83, 313324. https://doi.org/10.1016/j.neubiorev.2017.10.029.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zastrow, M. (2017). Correction for Zastrow, News Feature: Is video game addiction really an addiction? Proceedings of the National Academy of Sciences of the U S A, 114(21), E4316. https://doi.org/10.1073/pnas.1707226114.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, F., Zimmermann, K., Xin, F., Scheele, D., Dau, W., Banger, M., et al. (2018). Shifted balance of dorsal versus ventral striatal communication with frontal reward and regulatory regions in cannabis‐dependent males. Human Brain Mapping, 39(12), 50625073.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, X., Zimmermann, K., Xin, F., Zhao, W., Derckx, R. T., Sassmannshausen, A., et al. (2019). Cue reactivity in the ventral striatum characterizes heavy cannabis use, whereas reactivity in the dorsal striatum mediates dependent use. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 4(8), 751762. https://doi.org/10.1016/j.bpsc.2019.04.006.

    • Search Google Scholar
    • Export Citation
  • Anderson, B. A., Kuwabara, H., Wong, D. F., Roberts, J., Rahmim, A., Brasic, J. R., et al. (2017). Linking dopaminergic reward signals to the development of attentional bias: A positron emission tomographic study. Neuroimage, 157, 2733. https://doi.org/10.1016/j.neuroimage.2017.05.062.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ashburner, J., & Friston, K. J. (2005). Unified segmentation. NeuroImage, 26(3), 839851. https://doi.org/10.1016/j.neuroimage.2005.02.018.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Baron-Cohen, S., Wheelwright, S., Skinner, R., Martin, J., & Clubley, E. (2001). The autism-spectrum quotient (AQ): Evidence from asperger syndrome/high-functioning autism, males and females, scientists and mathematicians. Journal of Autism and Developmental Disorders, 31(1), 517. https://doi.org/10.1023/a:1005653411471.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Beck, A. T., Steer, R. A., & Brown, G. K. (1996). Beck depression inventory-II. San Antonio, 78(2), 490498.

  • Becker, B., Wagner, D., Koester, P., Tittgemeyer, M., Mercer-Chalmers-Bender, K., Hurlemann, R., et al. (2015). Smaller amygdala and medial prefrontal cortex predict escalating stimulant use. Brain, 138(Pt 7), 20742086. https://doi.org/10.1093/brain/awv113.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Brand, M., Wegmann, E., Stark, R., Müller, A., Wölfling, K., Robbins, T. W., et al. (2019). The Interaction of Person-Affect-Cognition-Execution (I-PACE) model for addictive behaviors: Update, generalization to addictive behaviors beyond internet-use disorders, and specification of the process character of addictive behaviors. Neuroscience & Biobehavioral Reviews, 104, 110.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Cai, C., Yuan, K., Yin, J., Feng, D., Bi, Y., Li, Y., et al. (2016). Striatum morphometry is associated with cognitive control deficits and symptom severity in internet gaming disorder. Brain Imaging and Behavior, 10(1), 1220.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Clark, L., Boileau, I., & Zack, M. (2019). Neuroimaging of reward mechanisms in gambling disorder: An integrative review. Molecular Psychiatry, 24(5), 674693. https://doi.org/10.1038/s41380-018-0230-2.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Ena, S., De Kerchove D'ExaErde, A., & Schiffmann, S. N. (2011). Unraveling the differential functions and regulation of striatal neuron sub-populations in motor control, reward, and motivational processes. Frontiers in Behavioral Neuroscience, 5, 47.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Etkin, A. (2019). A reckoning and research agenda for neuroimaging in psychiatry. American Journal of Psychiatry, 176(7), 507511.

  • Everitt, B. J., & Robbins, T. W. (2016). Drug addiction: Updating actions to habits to compulsions ten years on. Annual Review of Psychology, 67, 2350. https://doi.org/10.1146/annurev-psych-122414-033457.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koob, G. F., & Volkow, N. D. (2016). Neurobiology of addiction: A neurocircuitry analysis. Lancet Psychiatry, 3(8), 760773. https://doi.org/10.1016/S2215-0366(16)00104-8.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Li, J., Xu, L., Zheng, X., Fu, M., Zhou, F., Xu, X., et al. (2019). Common and dissociable contributions of alexithymia and autism to domain-specific interoceptive dysregulations: A dimensional neuroimaging approach. Psychotherapy and Psychosomatics, 88(3), 187189. https://doi.org/10.1159/000495122.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Montag, C., Markowetz, A., Blaszkiewicz, K., Andone, I., Lachmann, B., Sariyska, R., et al. (2017). Facebook usage on smartphones and gray matter volume of the nucleus accumbens. Behavioural Brain Research, 329, 221228.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Montag, C., Schivinski, B., Sariyska, R., Kannen, C., Demetrovics, Z., & Pontes, H. M. (2019). Psychopathological symptoms and gaming motives in disordered gaming-A psychometric comparison between the WHO and APA diagnostic frameworks. Journal of Clinical Medicine, 8(10), 1691. https://doi.org/10.3390/jcm8101691.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pontes, H. M., & Griffiths, M. D. (2015). Measuring DSM-5 internet gaming disorder: Development and validation of a short psychometric scale. Computers in Human Behavior, 45, 137143. https://doi.org/10.1016/j.chb.2014.12.006.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Pontes, H. M., Schivinski, B., Sindermann, C., Li, M., Becker, B., Zhou, M., et al. (2019). Measurement and conceptualization of gaming disorder according to the world health organization framework: The development of the gaming disorder test. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-019-00088-z.

    • Search Google Scholar
    • Export Citation
  • Qin, K., Zhang, F., Chen, T., Li, L., Li, W., Suo, X., et al. (2020). Shared gray matter alterations in individuals with diverse behavioral addictions: A voxel-wise meta-analysis. Journal of Behavioral Addictions, 9(1), 4457.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Robbins, T. W., Ersche, K. D., & Everitt, B. J. (2008). Drug addiction and the memory systems of the brain. Annals of the New York Academy of Sciences, 1141, 121.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Scarpazza, C., Tognin, S., Frisciata, S., Sartori, G., & Mechelli, A. (2015). False positive rates in voxel-based morphometry studies of the human brain: Should we be worried? Neuroscience & Biobehavioral Reviews, 52, 4955. https://doi.org/10.1016/j.neubiorev.2015.02.008.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Seok, J. W., & Sohn, J. H. (2018). Altered gray matter volume and resting-state connectivity in individuals with Internet gaming disorder: A voxel-based morphometry and resting-state functional magnetic resonance imaging study. Frontiers in Psychiatry, 9, 77.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Silver, M., Montana, G., Nichols, T. E., & Alzheimer's Disease Neuroimaging, I. (2011). False positives in neuroimaging genetics using voxel-based morphometry data. NeuroImage, 54(2), 9921000. https://doi.org/10.1016/j.neuroimage.2010.08.049.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Vollstadt-Klein, S., Wichert, S., Rabinstein, J., Buhler, M., Klein, O., Ende, G., et al. (2010). Initial, habitual and compulsive alcohol use is characterized by a shift of cue processing from ventral to dorsal striatum. Addiction, 105(10), 17411749. https://doi.org/10.1111/j.1360-0443.2010.03022.x.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Xu, X., Dai, J., Liu, C., Chen, Y., Xin, F., Zhou, F., et al. (2020). Common and disorder-specific neurofunctional markers of dysregulated empathic reactivity in major depression and generalized anxiety disorder. Psychotherapy and Psychosomatics, 89(2), 114116. https://doi.org/10.1159/000504180.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yam, C. W., Pakpour, A. H., Griffiths, M. D., Yau, W. Y., Lo, C. L. M., Ng, J. M., et al. (2019). Psychometric testing of three Chinese online-related addictive behavior instruments among Hong Kong university students. Psychiatric Quarterly, 90(1), 117128. https://doi.org/10.1007/s11126-018-9610-7.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yao, Y. W., Liu, L., Ma, S. S., Shi, X. H., Zhou, N., Zhang, J. T., et al. (2017). Functional and structural neural alterations in Internet gaming disorder: A systematic review and meta-analysis. Neuroscience & Biobehavioral Reviews, 83, 313324. https://doi.org/10.1016/j.neubiorev.2017.10.029.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zastrow, M. (2017). Correction for Zastrow, News Feature: Is video game addiction really an addiction? Proceedings of the National Academy of Sciences of the U S A, 114(21), E4316. https://doi.org/10.1073/pnas.1707226114.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, F., Zimmermann, K., Xin, F., Scheele, D., Dau, W., Banger, M., et al. (2018). Shifted balance of dorsal versus ventral striatal communication with frontal reward and regulatory regions in cannabis‐dependent males. Human Brain Mapping, 39(12), 50625073.

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Zhou, X., Zimmermann, K., Xin, F., Zhao, W., Derckx, R. T., Sassmannshausen, A., et al. (2019). Cue reactivity in the ventral striatum characterizes heavy cannabis use, whereas reactivity in the dorsal striatum mediates dependent use. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging, 4(8), 751762. https://doi.org/10.1016/j.bpsc.2019.04.006.

    • 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

Indexing and Abstracting Services:

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  • EBSCO
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  • CABI
  • CABELLS Journalytics

2022  
Web of Science  
Total Cites
WoS
5713
Journal Impact Factor 7.8
Rank by Impact Factor

Psychiatry (SCIE) 18/155
Psychiatry (SSCI) 13/144

Impact Factor
without
Journal Self Cites
7.2
5 Year
Impact Factor
8.9
Journal Citation Indicator 1.42
Rank by Journal Citation Indicator

Psychiatry 35/264

Scimago  
Scimago
H-index
69
Scimago
Journal Rank
1.918
Scimago Quartile Score Clinical Psychology Q1
Medicine (miscellaneous) Q1
Psychiatry and Mental Health Q1
Scopus  
Scopus
Cite Score
11.1
Scopus
Cite Score Rank
Clinical Psychology 10/292 (96th PCTL)
Psychiatry and Mental Health 30/531 (94th PCTL)
Medicine (miscellaneous) 25/309 (92th PCTL)
Scopus
SNIP
1.966

 

 
2021  
Web of Science  
Total Cites
WoS
5223
Journal Impact Factor 7,772
Rank by Impact Factor Psychiatry SCIE 26/155
Psychiatry SSCI 19/142
Impact Factor
without
Journal Self Cites
7,130
5 Year
Impact Factor
9,026
Journal Citation Indicator 1,39
Rank by Journal Citation Indicator

Psychiatry 34/257

Scimago  
Scimago
H-index
56
Scimago
Journal Rank
1,951
Scimago Quartile Score Clinical Psychology (Q1)
Medicine (miscellaneous) (Q1)
Psychiatry and Mental Health (Q1)
Scopus  
Scopus
Cite Score
11,5
Scopus
CIte Score Rank
Clinical Psychology 5/292 (D1)
Psychiatry and Mental Health 20/529 (D1)
Medicine (miscellaneous) 17/276 (D1)
Scopus
SNIP
2,184

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 990 EUR/article for articles submitted after 30 April 2023 (850 EUR for articles submitted prior to this date)
Regional discounts on country of the funding agency World Bank Lower-middle-income economies: 50%
World Bank Low-income economies: 100%
Further Discounts Corresponding authors, affiliated to an EISZ member institution subscribing to the journal package of Akadémiai Kiadó: 100%.
Subscription Information Gold Open Access

Journal of Behavioral Addictions
Language English
Size A4
Year of
Foundation
2011
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

  • Stephanie ANTONS (Universitat Duisburg-Essen, Germany)
  • Joel BILLIEUX (University of Lausanne, Switzerland)
  • Beáta BŐTHE (University of Montreal, Canada)
  • Matthias BRAND (University of Duisburg-Essen, Germany)
  • Ruth J. van HOLST (Amsterdam UMC, The Netherlands)
  • Daniel KING (Flinders University, Australia)
  • Gyöngyi KÖKÖNYEI (ELTE Eötvös Loránd University, Hungary)
  • Ludwig KRAUS (IFT Institute for Therapy Research, Germany)
  • Marc N. POTENZA (Yale University, USA)
  • Hans-Jurgen RUMPF (University of Lübeck, Germany)

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)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Kenneth BLUM (University of Florida, USA)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • 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)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • 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 (Humboldt-Universität zu Berlin, Germany)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Astrid MÜLLER  (Hannover Medical School, Germany)
  • 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)
  • Róbert URBÁN  (ELTE Eötvös Loránd University, Hungary)
  • Johan VANDERLINDEN (University Psychiatric Center K.U.Leuven, Belgium)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Aviv M. WEINSTEIN  (Ariel University, Israel)
  • Kimberly YOUNG (Center for Internet Addiction, USA)

 

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