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Orsolya Inhóf Institute of Psychology, University of Pécs, Pécs, Hungary

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András N. Zsidó Institute of Psychology, University of Pécs, Pécs, Hungary

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Gábor Perlaki MTA-PTE Clinical Neuroscience MR Research Group, Pécs, Hungary
Pécs Diagnostic Centre, Pécs, Hungary
Department of Neurosurgery, University of Pécs Medical School, Pécs, Hungary

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Gergely Orsi MTA-PTE Clinical Neuroscience MR Research Group, Pécs, Hungary
Pécs Diagnostic Centre, Pécs, Hungary
Department of Neurosurgery, University of Pécs Medical School, Pécs, Hungary

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Beatrix Lábadi Institute of Psychology, University of Pécs, Pécs, Hungary

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Norbert Kovács MTA-PTE Clinical Neuroscience MR Research Group, Pécs, Hungary
Department of Neurology, University of Pécs Medical School, Pécs, Hungary

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Anna Szente Department of Neurology, University of Pécs Medical School, Pécs, Hungary

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Tamás Dóczi MTA-PTE Clinical Neuroscience MR Research Group, Pécs, Hungary
Pécs Diagnostic Centre, Pécs, Hungary
Department of Neurosurgery, University of Pécs Medical School, Pécs, Hungary

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József Janszky MTA-PTE Clinical Neuroscience MR Research Group, Pécs, Hungary
Department of Neurology, University of Pécs Medical School, Pécs, Hungary

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Gergely Darnai Institute of Psychology, University of Pécs, Pécs, Hungary
MTA-PTE Clinical Neuroscience MR Research Group, Pécs, Hungary
Department of Neurology, University of Pécs Medical School, Pécs, Hungary

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Background and aims

Structural differences in higher-order brain areas are common features of behavioral addictions, including Internet addiction (IA) as well. Taking into consideration the limited number of studies and methods used in previous studies on IA, our aim was to investigate the correlates of IA and the morphometry of the frontal lobes.

Methods

To observe these relationships, the high-resolution T1-weighted MR images of 144 healthy, Caucasian, university students were analyzed with volumetry and voxel-based morphometry. The Problematic Internet Use Questionnaire (PIUQ) was used to assess IA.

Results

We found significant correlations between PIUQ subscales and the volume of the right pars opercularis volume and gray matter mass in women.

Discussion and conclusion

The increased gray matter measures of this structure might be explained with the extended effort to control for the impulsive behavior in addiction, and with the increased number of social interactions via the Internet.

Abstract

Background and aims

Structural differences in higher-order brain areas are common features of behavioral addictions, including Internet addiction (IA) as well. Taking into consideration the limited number of studies and methods used in previous studies on IA, our aim was to investigate the correlates of IA and the morphometry of the frontal lobes.

Methods

To observe these relationships, the high-resolution T1-weighted MR images of 144 healthy, Caucasian, university students were analyzed with volumetry and voxel-based morphometry. The Problematic Internet Use Questionnaire (PIUQ) was used to assess IA.

Results

We found significant correlations between PIUQ subscales and the volume of the right pars opercularis volume and gray matter mass in women.

Discussion and conclusion

The increased gray matter measures of this structure might be explained with the extended effort to control for the impulsive behavior in addiction, and with the increased number of social interactions via the Internet.

Introduction

Internet addiction (IA) is considered as a behavioral addiction disorder with high prevalence (Cheng & Li, 2014). It includes not only the increased use of the Internet, but according to the most popular models of IA, it is characterized by preoccupation, negative mood management, withdrawal, craving, loss of control and interest, and other social and occupational problems (Kuss, Griffiths, Karila, & Billieux, 2014; Van Rooij & Prause, 2014).

IA has two different forms: specific and general IA (Montag et al., 2015). In specific, IA forms the problematic use is confined to a defined activity, like online gaming. In general, IA the behavioral addiction manifests in different activity forms. The relationship between them is strong; they have several common features, for example, consequences and underlying etiologies (Koo & Kwon, 2014). However, these types can also be distinguished. The Internet Gaming Disorder is included in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5; Király, Griffiths, & Demetrovics, 2015; Griffiths et al., 2016) and 11th revision of the International Classification of Diseases (Rumpf et al., 2018). In contrast, general IA is still not mentioned in these nosological classification systems. This has clinical importance, and the reason behind this might be the lack of reliable researches related to general IA.

Similarly to other addiction forms, people with IA continue an activity even if it has clear negative consequences. This behavior is the result of low inhibitory control paired with high impulsivity, deficit in self-regulation, goal-directed behavior, decision-making, and executive functions (Billieux & Van der Linden, 2012). These functions are controlled by the frontal lobe brain regions (Crews & Boettiger, 2009); thus, similarly to other addictions, structural differences in IA could be located in these areas. Earlier studies revealed decreased cortical thickness in the lateral orbitrofontal (Hong et al., 2013; Yuan et al., 2013) and precentral gyri (Yuan et al., 2013). Reduced gray matter volume was found in the bilateral dorsolateral prefrontal and orbitofrontal cortices (Yuan et al., 2011), as well as in the right frontal pole (Kühn & Gallinat, 2015). Although the Internet usage pattern is different between the sexes, gender differences were not examined in the above studies. Females are more likely to use the Internet to communicate (Kimbrough, Guadagno, Muscanell, & Dill, 2013), while online gaming is more prevalent among males (Ko, Yen, Chen, Chen, & Yen, 2005).

With the growing prevalence of the IA, there is an increasing need for comprehensive research. The aim of this study was to measure the relationship between IA and the morphometric features of frontal cortical areas, focusing on gender differences. To obtain more reliable results, we combined two different morphometric techniques [voxel-based morphometry (VBM) and volumetry]. We hypothesized a correlation between IA and the size of frontal areas. Previous studies showed significant differences in Internet usage between genders; therefore, we hypothesized that the connection between the two variables will be different as well. Since women primarily use the Internet for communication, our suggestion is that the increased usage correlates positively with the areas responsible for lingual and social interactions.

Methods

Participants

We measured 144 healthy, right-handed, Caucasian adults (64 males; mean age: 22.5 + 2.2, range: 18–30 years), with normal body mass index. All participants used the Internet on a daily basis. None of them reached the clinical cut-off of Beck Depression Inventory (BDI-I; Beck & Beamesderfer, 1974) or State-Trait Anxiety Inventory (STAI; Spielberger, Gorsuch, & Lushene, 1970). The cut-off point was set at 30 of the BDI-I (Beck, Steer, & Carbin, 1988) and at 39 of the STAI (Julian, 2011). None reported alcohol or other substance addiction. Participants were recruited via announcements on university boards, and via online surfaces of the university.

Measures

Questionnaires

We used the Hungarian version of Problematic Internet Use Questionnaire (PIUQ), which has good reliability and validity characteristics (Demetrovics, Szeredi, & Rózsa, 2008). PIUQ consists of three factors: Obsession, Neglect, and Control Disorder.

Obsession subscale contains items regarding obsessive thinking about the Internet and items about withdrawal symptoms (depression and worry) caused by the lack of Internet use. The items of Neglect subscale refer to neglecting everyday activities or social life due to Internet use. The Control Disorder subscale measures the disability to control the Internet use (Demetrovics et al., 2008). The questionnaire contains 18 items, 6 in each subscales. Participants had to rate the items using a 5-point Likert-type scale. According to Koronczai et al. (2011), the suggested cut-off point of PIUQ is 41. Based on this recommended score, 31 subjects for the study population have IA. But in the lack of well-established diagnostic criteria (Kuss & Lopez-Fernandez, 2016; Poli, 2017), we decided to use a multidimensional continuous measure of IA.

During the selection of the participants, we used Edinburgh Handedness Inventory to assess handedness (Oldfield, 1971). According to the Edinburg Handedness Inventory, a handedness index score was computed by dividing the difference between right and left responses by the total number of responses and multiplying the ratio by 100. The scores of our sample ranged from 12.5 to 100 [mean (SD) = 73.41 (23.47)].

MRI examination

For MRI measurements, we used a 3 Tesla MR scanner (MAGNETOM Trio a Tim System, Siemens AG, Erlangen, Germany) with a 12-channel head coil. For the morphometric analyses, isotopic 3D T1-weighted magnetization-prepared rapid gradient echo images were used: TR/TI/TE = 2,530/11.00/3.37 ms, slice thickness = 1 mm, number of sagittal slices = 176, flip angle = 7°, receiver bandwidth = 200 Hz/pixel, FoV = 256 × 256 mm2, matrix size = 256 × 256.

MRI data evaluation

Volumetric analysis.

Freesurfer v6.0 (http://surfer.nmr.mgh.harvard.edu/) was used for cortical reconstruction and volumetric segmentation of the images. This software provides one of the most reliable automated brain segmentation methods for cortical and subcortical structures. It allows to assess the volume and other morphological features of predefined brain structures (Fischl, 2012). Freesurfer’s semi-automatic anatomical processing scripts (autorecon 1, 2, and 3) were executed on all subjects’ data. Visual verifications were performed for all subjects and error corrections were applied whenever it was indicated. Volume information was acquired according to the Desikan–Killiany–Tourville labeling protocol (Klein & Tourville, 2012). The following bilateral cortical regions were under our interest: caudal and rostal middle frontal gyri, lateral and medial orbitofrontal gyri, pars opercularis (POp), pars orbitalis, pars triangularis (PTr), and precentral and superior frontal gyri.

VBM

We used FSL-VBM (http://www.fmrib.ox.ac.uk/fsl) to assess the gray matter volume of the frontal cortex. We carried out an “optimized” VBM protocol with the use of FSL tools (Smith et al., 2004). After brain extraction of the T1-weighted images using BET, we carried out tissue-type segmentation using FMRIB’s Automated Segmentation Tool. Next, we aligned the resulting gray matter partial volume images to the MNI152 standard space using non-linear registration. The resulting images were averaged together with their respective mirror images to create a left–right symmetric study-specific gray matter template. The gray matter partial volume images of all subjects were non-linearly registered to this study-specific template. Modulation of the registered partial volume images followed (to correct for local expansion or contraction owing to the non-linear components of the registration) by multiplying by the Jacobian of the warp field. Then, the modulated segmented images were smoothed with an isotropic Gaussian kernel with a sigma of 3 mm. Finally, to confirm the results of volumetric analysis, we applied voxelwise general linear models on brain structures, which were associated significantly with PIUQ subscales, using permutation-based non-parametric testing (5,000 permutations), correcting for multiple comparisons across space. The results were considered significant for p < .05, corrected for multiple comparisons using threshold-free cluster enhancement, which avoids making an arbitrary choice of the cluster-forming threshold, while preserving the sensitivity benefits of clusterwise correction. The whole frontal cortex defined based on the MNI structural atlas thresholded at 0% was selected as region of interest (ROI).

Statistical analyses

Statistical analysis for volumetry was performed using the SPSS statistical software version 22.0 (IBM Corp. Released 2013; Armonk, NY, USA). The structures of the left and right frontal cortices were investigated separately. The associations between PIUQ scores and volumetry were analyzed with multiple linear regressions. We created separate models for each structure as a dependent variable and PIUQ scores as independent variables. Head size correction was carried out by entering intracranial volume as additional independent variable (Perlaki et al., 2014). The assumptions of the analysis (Chan, 2004) were satisfied. To reduce type-I error, multiple comparisons were corrected using the Benjamini–Hochberg method (false discovery rate = 0.05; Benjamini & Hochberg, 1995). Data of males and females were analyzed separately. Sex differences in PIUQ scores were tested with independent samples t-tests.

Ethics

The research was approved by the local ethical review committee and was carried out in accordance with the Code of Ethics of the World Medical Association (Declaration of Helsinki), and informed consent was obtained from all of the participants involved in the study.

Results

Descriptive data

The mean score for the PIUQ was 31.05 (SD = 9.41, range: 18–53). The subscales mean scores were 9.04 (SD = 3.08, range: 6–17) for Obsession, 11.09 (SD = 3.74, range: 6–24) for Neglect, and 10.91 (SD = 3.96, range: 6–20) for Control Disorder. Significant difference was found between males and females in the Neglect subscale [t(142) = 2.284, p < .05] such that males (mean scores = 11.88, SD = 4.26) scored higher than females (mean scores = 10.46, SD = 3.14).

Volumetry

In males, Control Disorder was positively associated with the volumes of left lateral orbitofrontal and right precentral gyri, whereas Neglect positively correlated with volumes of bilateral orbitofrontal gyri and right precentral gyri. The PIUQ total score was also positively related to the left lateral orbitofrontal gyrus volume. No correlation was found between Obsession and the frontal lobe structures. However, none of these correlations survived correction for multiple comparisons.

In females, Control Disorder was positively associated with the volumes of left precentral gyrus, right POp, and PTr. Obsession was positively correlated with the bilateral POp. Neglect was positively associated with left precentral gyrus, right POp, and PTr volumes, whereas the PIUQ total score was correlated with the right POp and PTr volumes. Only the correlations of the Obsession and Neglect scales with the volume of right POp survived the correction for multiple comparisons. The results of the multiple linear regression analyses are shown in Table 1.

Table 1.

The associations between the subscales and the total score of PIUQ and the volumes of frontal lobe structures in males and females, separately

Control Disorder Obsession Neglect Total score
Male Female Male Female Male Female Male Female
Caudal middle frontal gyrus Left t 1.361 1.517 −0.376 0.371 1.363 1.109 1.006 1.178
β 0.188 0.182 −0.052 0.045 0.187 0.135 0.139 0.143
Right t 0.553 0.924 −1.304 0.222 0.344 0.688 −0.052 0.722
β 0.081 0.110 −0.186 0.027 0.050 0.082 −0.008 0.086
Lateral orbitofrontal gyrus Left t 2.053* 0.030 1.348 0.321 2.230* −0.106 2.251* 0.082
β 0.286 0.004 0.189 0.038 0.307 −0.012 0.310 0.010
Right t 1.436 −0.131 1.301 0.361 2.179* −0.097 1.960 0.027
β 0.213 −0.015 0.191 0.042 0.314 −0.011 0.284 0.003
Medial orbitofrontal gyrus Left t −0.058 0.776 −0.129 0.859 1.531 1.164 0.593 1.033
β −0.009 0.090 −0.019 0.100 0.221 0.135 0.087 0.120
Right t 1.749 0.412 0.351 0.078 1.219 −0.073 1.340 0.183
β 0.262 0.050 0.053 0.009 0.184 −0.009 0.201 0.022
Pars opercularis Left t −0.134 0.982 −1.582 2.227* −1.282 1.101 −1.126 1.547
β −0.019 0.112 −0.213 0.250 −0.175 0.126 −0.154 0.176
Right t −0.430 2.723* 0.669 3.052a,* 0.010 2.978a,* 0.052 3.293*
β −0.067 0.297 0.103 0.331 0.002 0.323 0.008 0.353
Pars orbitalis Left t 0.542 1.168 0.246 1.120 1.555 1.142 0.970 1.287
β 0.073 0.133 0.033 0.128 0.205 0.130 0.129 0.146
Right t −0.602 0.814 0.106 1.432 0.058 0.079 −0.179 0.856
β −0.078 0.093 0.014 0.162 0.008 0.009 −0.023 0.098
Pars triangularis Left t 0.362 0.168 0.224 1.403 0.162 0.441 0.288 0.686
β 0.049 0.019 0.030 0.161 0.022 0.051 0.039 0.079
Right t −0.505 2.524* 0.292 1.541 0.308 2.004* 0.029 2.333*
β −0.068 0.291 0.039 0.183 0.041 0.235 0.004 0.272
Precentral gyrus Left t 1.298 2.369* 1.507 0.125 1.648 2.501* 1.742 1.938
β 0.211 0.281 0.241 0.015 0.264 0.297 0.278 0.233
Right t 2.242* 1.477 0.030 0.405 2.412* 1.010 1.931 1.138
β 0.317 0.180 0.004 0.050 0.336 0.124 0.274 0.140
Rostal middle frontal gyrus Left t 0.268 0.770 −0.897 −0.096 1.589 0.704 0.496 0.554
β 0.040 0.094 −0.132 −0.012 0.232 0.086 0.074 0.068
Right t 1.693 −0.375 1.068 0.104 1.222 0.143 1.563 −0.082
β 0.254 −0.046 0.160 0.013 0.184 0.018 0.234 −0.010
Superior frontal gyrus Left t 1.335 1.816 −0.107 0.625 0.828 1.498 0.853 1.531
β 0.246 0.215 −0.020 0.076 0.153 0.179 0.158 0.183
Right t 0.864 1.544 −1.309 0.508 −0.119 1.383 −0.134 1.332
β 0.145 0.193 −0.127 0.064 −0.020 0.174 −0.023 0.167

Note. PIUQ: Problematic Internet Use Questionnaire.

Significant associations after Benjamini–Hochberg correction.

p < .05.

We measured gender differences in the correlation between PIUQ scales and right POp, where we found significant associations only in females. We found significant interactions between gender and PIUQ scales [Obsession subscale: F(2, 141) = 12.883, p < .01; Neglect subscale: F(2, 141) = 11.783, p < .01].

VBM

The subscales of PIUQ were used separately to predict the amount of gray matter of brain areas in the frontal lobe. Positive correlations were found between the amount of gray matter mass in right POp and Control Disorder, Neglect, and total score in females (Figure 1). PIUQ scores showed no significant association with gray matter mass of the frontal lobe in males.


            Figure 1.
Figure 1.

Voxelwise analysis of the frontal cortex. Red–yellow indicate voxels demonstrating significant positive correlation between the amount of gray matter mass in pars opercularis and PIUQ scores in females. Color bar represents z values. The background image is the MNI152 standard space T1 template. X, Y, and Z values indicate the MNI slice coordinates in millimeter. Images are shown in radiological convention

Citation: Journal of Behavioral Addictions 8, 1; 10.1556/2006.7.2018.135

Discussion

In this study, we measured the structural neural correlates of IA in healthy young male and female habitual Internet users. We found a positive relationship between the PIUQ scales and the volume of the right POp in females using two different brain analysis techniques (VBM and FreeSurfer). Previous studies showed negative correlation between IA and volumes of the frontal cortex; however, these studies used ROIs related to control functions (Kühn & Gallinat, 2015; Yuan et al., 2011) and brain’s reward system (Altbäcker et al., 2016). These papers suggest that the decreased volume of the frontal cortex is associated with the common features of addictions, such as self-control deficit, social problems, impulsivity, and craving (Kuss et al., 2014; Van Rooij & Prause, 2014). Our primary aim was to investigate IA-related gender differences in the frontal cortex. According to previous studies, males are absorbed in online gaming, whereas females use Internet merely for communication (Kimbrough et al., 2013; Weiser, 2000). Thus, compared to males, females do not neglect their social relationships. This was supported by our finding that females reached lower scores on the Neglect subscale of the PIUQ than males. Due to the deficit in control functions, a negative correlation can be described between the IA and the frontal regions. On the contrary, if the social communication is not neglected, a positive correlation can be hypothesized between the volume of social interaction-related structures and the IA. The POp plays an important role in the success of social interactions as well as the control functions (Nishitani, Schürmann, Amunts, & Hari, 2005). The positive relationship between the PIUQ score and the POp could be the results of the increased use of this area during frequent communication.

The absence of the positive association between the IA and IFC in males might be due to the gender differences in Internet using habits. That is, males tend to use the Internet for entertainment purposes, for example, online gaming (Ko et al., 2005); whereas females are more likely to use the Internet for communication, education, and social media activity (Kimbrough et al., 2013; Weiser, 2000). A longitudinal study (Van Deursen, Van Dijk, & Ten Klooster, 2015) showed that sex differences in Internet using habits are consistent over years; however, the number of females in online gaming shows a growing tendency. Provided that females use the Internet to communicate with others, the positive correlation is reasonable between PIUQ scores and the size of IFC. Although the Broca area is on the left hemisphere, the right-sided homologue area, the right POp, can affect the social interactions too, for instance to make sense of emotional expression (Nishitani et al., 2005). The gray matter volume of the right POp showed negative correlation with severe social communication problems; hence, the increased volume may refer to better interpersonal skills (Yamasaki et al., 2010).

Limitations of this study include not measuring the Internet-using habits directly. However, the aforementioned difference might be visible in males scoring higher than females on the Neglect subscale that refers to neglecting social life due to Internet use. Furthermore, although we measured the association between IA and gray matter volume of frontal lobe regions, a longitudinal study design is needed in order to examine a causal relationship. With cross-sectional studies only correlations could be studied, the causality in IA-related brain–behavior relationship cannot be investigated. To decide, if the increased frontal volume results in problematic Internet use or the IA is the reason of specific structural features of brain, longitudinal studies are needed.

Conclusions

In sum, we suggest that the association between IFC and IA could be caused by the effort of controlling the impulsive behavior and the frequency of social interaction. Hence, the positive correlation between PIUQ and the volume of POp was only found in females.

Authors’ contribution

ANZs, GD, OI, BL, NK, TD, and JJ contributed substantially to conception and design. GD, OI, GP, GO, TD, and JJ contributed to acquisition of data. ANZs, GD, OI, GP, GO, BL, NK, and JJ contributed to analysis and interpretation of data. ANZS, DG, OI, and GP drafted the article. GO, BL, NK, TD, AS, and JJ revised the manuscript critically for important intellectual content. All authors gave final approval of the version to be published.

Conflict of interest

The authors declare no conflict of interest.

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    • Crossref
    • Search Google Scholar
    • Export Citation
  • Koronczai, B. , Urbán, R. , Kökönyei, G. , Paksi, B. , Papp, K. , Kun, B. , Arnold, P. , Kállai, J. , & Demetrovics, Z. (2011). Confirmation of the three-factor model of problematic Internet use on off-line adolescent and adult samples. Cyberpsychology, Behavior, and Social Networking, 14(11), 657664. doi:10.1089/cyber.2010.0345

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kühn, S. , & Gallinat, J. (2015). Brains online: Structural and functional correlates of habitual Internet use. Addiction Biology, 20(2), 415422. doi:10.1111/adb.12128

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuss, D. , Griffiths, M. , Karila, L. , & Billieux, J. (2014). Internet addiction: A systematic review of epidemiological research for the last decade. Current Pharmaceutical Design, 20(25), 40264052. doi:10.2174/13816128113199990617

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuss, D. J. , & Lopez-Fernandez, O. (2016). Internet addiction and problematic Internet use: A systematic review of clinical research. World Journal of Psychiatry, 6(1), 143176. doi:10.5498/wjp.v6.i1.143

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Montag, C. , Bey, K. , Sha, P. , Li, M. , Chen, Y.-F. , Liu, W.-Y. , Zhu, Y. K. , Li, C. B. , Markett, S. , Keiper, J. , & Reuter, M. (2015). Is it meaningful to distinguish between generalized and specific Internet addiction? Evidence from a cross-cultural study from Germany, Sweden, Taiwan and China. Asia-Pacific Psychiatry, 7(1), 2026. doi:10.1111/appy.12122

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nishitani, N. , Schürmann, M. , Amunts, K. , & Hari, R. (2005). Broca’s region: From action to language. Physiology, 20(1), 6069. doi:10.1152/physiol.00043.2004

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh Inventory. Neuropsychologia, 9(1), 97113. doi:10.1016/0028-3932(71)90067-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perlaki, G. , Orsi, G. , Plozer, E. , Altbacker, A. , Darnai, G. , Nagy, S. A. , Horvath, R. , Toth, A. , Doczi, T. , Kovacs, N. , Bogner, P. , Schwarcz, A. , & Janszky, J. (2014). Are there any gender differences in the hippocampus volume after head-size correction? A volumetric and voxel-based morphometric study. Neuroscience Letters, 570, 119123. doi:10.1016/j.neulet.2014.04.013

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Poli, R. (2017). Internet addiction update: Diagnostic criteria, assessment and prevalence. Neuropsychiatry, 7(1), 48. doi:10.4172/Neuropsychiatry.1000171

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rumpf, H.-J. , Achab, S. , Billieux, J. , Bowden-Jones, H. , Carragher, N. , Demetrovics, Z. , Higuchi, S. , King, D. L. , Mann, K. , Potenza, M. , Saunders, J. B. , Abbott, M. , Ambekar, A. , Aricak, O. T. , Assanangkornchai, S. , Bahar, N. , Borges, G. , Brand, M. , Chan, E. M. , Chung, T. , Derevensky, J. , Kashef, A. E. , Farrell, M. , Fineberg, N. A. , Gandin, C. , Gentile, D. A. , Griffiths, M. D. , Goudriaan, A. E. , Grall-Bronnec, M. , Hao, W. , Hodgins, D. C. , Ip, P. , Király, O. , Lee, H. K. , Kuss, D. , Lemmens, J. S. , Long, J. , Lopez-Fernandez, O. , Mihara, S. , Petry, N. M. , Pontes, H. M. , Rahimi-Movaghar, A. , Rehbein, F. , Rehm, J. , Scafato, E. , Sharma, M. , Spritzer, D. , Stein, D. J. , Tam, P. , Weinstein, A. , Wittchen, H. U. , Wölfling, K. , Zullino, D. , & Poznyak, V. (2018). Including gaming disorder in the ICD-11: The need to do so from a clinical and public health perspective. Journal of Behavioral Addictions, 7(3), 556561. doi:10.1556/2006.7.2018.59

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, S. M. , Jenkinson, M. , Woolrich, M. W. , Beckmann, C. F. , Behrens, T. E. J. , Johansen-Berg, H. , Bannister, P. R. , De Luca, M. , Drobnjak, I. , Flitney, D. E. , Niazy, R. K. , Saunders, J. , Vickers, J. , Zhang, Y. , De Stefano, N. , Brady, J. M. , & Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23, S208S219. doi:10.1016/j.neuroimage.2004.07.051

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spielberger, C. D. , Gorsuch, R. L. , & Lushene, R. E. (1970). The State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press.

    • Search Google Scholar
    • Export Citation
  • Van Deursen, A. J. A. M. , Van Dijk, J. A. G. M. , & Ten Klooster, P. M. (2015). Increasing inequalities in what we do online: A longitudinal cross sectional analysis of Internet activities among the Dutch population (2010 to 2013) over gender, age, education, and income. Telematics and Informatics, 32(2), 259272. doi:10.1016/j.tele.2014.09.003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van Rooij, A. J. , & Prause, N. (2014). A critical review of “Internet addiction” criteria with suggestions for the future. Journal of Behavioral Addictions, 3(4), 203213. doi:10.1556/JBA.3.2014.4.1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weiser, E. B. (2000). Gender differences in Internet use patterns and Internet application preferences: A two-sample comparison. CyberPsychology & Behavior, 3(2), 167178. doi:10.1089/109493100316012

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yamasaki, S. , Yamasue, H. , Abe, O. , Suga, M. , Yamada, H. , Inoue, H. , Kuwabara, H. , Kawakubo, Y. , Yahata, N. , Aoki, S. , Kano, Y. , Kato, N. , & Kasai, K. (2010). Reduced gray matter volume of pars opercularis is associated with impaired social communication in high-functioning autism spectrum disorders. Biological Psychiatry, 68(12), 11411147. doi:10.1016/j.biopsych.2010.07.012

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, K. , Cheng, P. , Dong, T. , Bi, Y. , Xing, L. , Yu, D. , Zhao, L. , Dong, M. , von Deneen, K. M. , Liu, Y. , Qin, W. , & Tian, J. (2013). Cortical thickness abnormalities in late adolescence with online gaming addiction. PLoS One, 8(1), e53055. doi:10.1371/journal.pone.0053055

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    • Search Google Scholar
    • Export Citation
  • Yuan, K. , Qin, W. , Wang, G. , Zeng, F. , Zhao, L. , Yang, X. , Liu, P. , Liu, J. , Sun, J. , von Deneen, K. M. , Gong, Q. , Liu, Y. , & Tian, J. (2011). Microstructure abnormalities in adolescents with Internet addiction disorder. PLoS One, 6(6), e20708. doi:10.1371/journal.pone.0020708

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  • Koo, H. J. , & Kwon, J.-H. (2014). Risk and protective factors of Internet addiction: A meta-analysis of empirical studies in Korea. Yonsei Medical Journal, 55(6), 1691. doi:10.3349/ymj.2014.55.6.1691

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  • Koronczai, B. , Urbán, R. , Kökönyei, G. , Paksi, B. , Papp, K. , Kun, B. , Arnold, P. , Kállai, J. , & Demetrovics, Z. (2011). Confirmation of the three-factor model of problematic Internet use on off-line adolescent and adult samples. Cyberpsychology, Behavior, and Social Networking, 14(11), 657664. doi:10.1089/cyber.2010.0345

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kühn, S. , & Gallinat, J. (2015). Brains online: Structural and functional correlates of habitual Internet use. Addiction Biology, 20(2), 415422. doi:10.1111/adb.12128

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuss, D. , Griffiths, M. , Karila, L. , & Billieux, J. (2014). Internet addiction: A systematic review of epidemiological research for the last decade. Current Pharmaceutical Design, 20(25), 40264052. doi:10.2174/13816128113199990617

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Kuss, D. J. , & Lopez-Fernandez, O. (2016). Internet addiction and problematic Internet use: A systematic review of clinical research. World Journal of Psychiatry, 6(1), 143176. doi:10.5498/wjp.v6.i1.143

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Montag, C. , Bey, K. , Sha, P. , Li, M. , Chen, Y.-F. , Liu, W.-Y. , Zhu, Y. K. , Li, C. B. , Markett, S. , Keiper, J. , & Reuter, M. (2015). Is it meaningful to distinguish between generalized and specific Internet addiction? Evidence from a cross-cultural study from Germany, Sweden, Taiwan and China. Asia-Pacific Psychiatry, 7(1), 2026. doi:10.1111/appy.12122

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Nishitani, N. , Schürmann, M. , Amunts, K. , & Hari, R. (2005). Broca’s region: From action to language. Physiology, 20(1), 6069. doi:10.1152/physiol.00043.2004

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Oldfield, R. C. (1971). The assessment and analysis of handedness: The Edinburgh Inventory. Neuropsychologia, 9(1), 97113. doi:10.1016/0028-3932(71)90067-4

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Perlaki, G. , Orsi, G. , Plozer, E. , Altbacker, A. , Darnai, G. , Nagy, S. A. , Horvath, R. , Toth, A. , Doczi, T. , Kovacs, N. , Bogner, P. , Schwarcz, A. , & Janszky, J. (2014). Are there any gender differences in the hippocampus volume after head-size correction? A volumetric and voxel-based morphometric study. Neuroscience Letters, 570, 119123. doi:10.1016/j.neulet.2014.04.013

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Poli, R. (2017). Internet addiction update: Diagnostic criteria, assessment and prevalence. Neuropsychiatry, 7(1), 48. doi:10.4172/Neuropsychiatry.1000171

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Rumpf, H.-J. , Achab, S. , Billieux, J. , Bowden-Jones, H. , Carragher, N. , Demetrovics, Z. , Higuchi, S. , King, D. L. , Mann, K. , Potenza, M. , Saunders, J. B. , Abbott, M. , Ambekar, A. , Aricak, O. T. , Assanangkornchai, S. , Bahar, N. , Borges, G. , Brand, M. , Chan, E. M. , Chung, T. , Derevensky, J. , Kashef, A. E. , Farrell, M. , Fineberg, N. A. , Gandin, C. , Gentile, D. A. , Griffiths, M. D. , Goudriaan, A. E. , Grall-Bronnec, M. , Hao, W. , Hodgins, D. C. , Ip, P. , Király, O. , Lee, H. K. , Kuss, D. , Lemmens, J. S. , Long, J. , Lopez-Fernandez, O. , Mihara, S. , Petry, N. M. , Pontes, H. M. , Rahimi-Movaghar, A. , Rehbein, F. , Rehm, J. , Scafato, E. , Sharma, M. , Spritzer, D. , Stein, D. J. , Tam, P. , Weinstein, A. , Wittchen, H. U. , Wölfling, K. , Zullino, D. , & Poznyak, V. (2018). Including gaming disorder in the ICD-11: The need to do so from a clinical and public health perspective. Journal of Behavioral Addictions, 7(3), 556561. doi:10.1556/2006.7.2018.59

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Smith, S. M. , Jenkinson, M. , Woolrich, M. W. , Beckmann, C. F. , Behrens, T. E. J. , Johansen-Berg, H. , Bannister, P. R. , De Luca, M. , Drobnjak, I. , Flitney, D. E. , Niazy, R. K. , Saunders, J. , Vickers, J. , Zhang, Y. , De Stefano, N. , Brady, J. M. , & Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23, S208S219. doi:10.1016/j.neuroimage.2004.07.051

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Spielberger, C. D. , Gorsuch, R. L. , & Lushene, R. E. (1970). The State-Trait Anxiety Inventory. Palo Alto, CA: Consulting Psychologists Press.

    • Search Google Scholar
    • Export Citation
  • Van Deursen, A. J. A. M. , Van Dijk, J. A. G. M. , & Ten Klooster, P. M. (2015). Increasing inequalities in what we do online: A longitudinal cross sectional analysis of Internet activities among the Dutch population (2010 to 2013) over gender, age, education, and income. Telematics and Informatics, 32(2), 259272. doi:10.1016/j.tele.2014.09.003

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Van Rooij, A. J. , & Prause, N. (2014). A critical review of “Internet addiction” criteria with suggestions for the future. Journal of Behavioral Addictions, 3(4), 203213. doi:10.1556/JBA.3.2014.4.1

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Weiser, E. B. (2000). Gender differences in Internet use patterns and Internet application preferences: A two-sample comparison. CyberPsychology & Behavior, 3(2), 167178. doi:10.1089/109493100316012

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yamasaki, S. , Yamasue, H. , Abe, O. , Suga, M. , Yamada, H. , Inoue, H. , Kuwabara, H. , Kawakubo, Y. , Yahata, N. , Aoki, S. , Kano, Y. , Kato, N. , & Kasai, K. (2010). Reduced gray matter volume of pars opercularis is associated with impaired social communication in high-functioning autism spectrum disorders. Biological Psychiatry, 68(12), 11411147. doi:10.1016/j.biopsych.2010.07.012

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, K. , Cheng, P. , Dong, T. , Bi, Y. , Xing, L. , Yu, D. , Zhao, L. , Dong, M. , von Deneen, K. M. , Liu, Y. , Qin, W. , & Tian, J. (2013). Cortical thickness abnormalities in late adolescence with online gaming addiction. PLoS One, 8(1), e53055. doi:10.1371/journal.pone.0053055

    • Crossref
    • Search Google Scholar
    • Export Citation
  • Yuan, K. , Qin, W. , Wang, G. , Zeng, F. , Zhao, L. , Yang, X. , Liu, P. , Liu, J. , Sun, J. , von Deneen, K. M. , Gong, Q. , Liu, Y. , & Tian, J. (2011). Microstructure abnormalities in adolescents with Internet addiction disorder. PLoS One, 6(6), e20708. doi:10.1371/journal.pone.0020708

    • Crossref
    • Search Google Scholar
    • Export Citation
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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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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

  • Sophia ACHAB (Faculty of Medicine, University of Geneva, Switzerland)
  • Alex BALDACCHINO (St Andrews University, United Kingdom)
  • Judit BALÁZS (ELTE Eötvös Loránd University, Hungary)
  • Maria BELLRINGER (Auckland University of Technology, Auckland, New Zealand)
  • Henrietta BOWDEN-JONES (Imperial College, United Kingdom)
  • Damien BREVERS (University of Luxembourg, Luxembourg)
  • Wim VAN DEN BRINK (University of Amsterdam, The Netherlands)
  • Julius BURKAUSKAS (Lithuanian University of Health Sciences, Lithuania)
  • Gerhard BÜHRINGER (Technische Universität Dresden, Germany)
  • Silvia CASALE (University of Florence, Florence, Italy)
  • Luke CLARK (University of British Columbia, Vancouver, B.C., Canada)
  • Jeffrey L. DEREVENSKY (McGill University, Canada)
  • Geert DOM (University of Antwerp, Belgium)
  • Nicki DOWLING (Deakin University, Geelong, Australia)
  • Hamed EKHTIARI (University of Minnesota, United States)
  • Jon ELHAI (University of Toledo, Toledo, Ohio, USA)
  • Ana ESTEVEZ (University of Deusto, Spain)
  • Fernando FERNANDEZ-ARANDA (Bellvitge University Hospital, Barcelona, Spain)
  • Naomi FINEBERG (University of Hertfordshire, United Kingdom)
  • Sally GAINSBURY (The University of Sydney, Camperdown, NSW, Australia)
  • Belle GAVRIEL-FRIED (The Bob Shapell School of Social Work, Tel Aviv University, Israel)
  • Biljana GJONESKA (Macedonian Academy of Sciences and Arts, Republic of North Macedonia)
  • Marie GRALL-BRONNEC (University Hospital of Nantes, France)
  • Jon E. GRANT (University of Minnesota, USA)
  • Mark GRIFFITHS (Nottingham Trent University, United Kingdom)
  • Joshua GRUBBS (University of New Mexico, Albuquerque, NM, USA)
  • Anneke GOUDRIAAN (University of Amsterdam, The Netherlands)
  • 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)
  • Zsolt HORVÁTH (Eötvös Loránd University, Hungary)
  • Susana JIMÉNEZ-MURCIA (Clinical Psychology Unit, Bellvitge University Hospital, Barcelona, Spain)
  • Yasser KHAZAAL (Geneva University Hospital, Switzerland)
  • Orsolya KIRÁLY (Eötvös Loránd University, Hungary)
  • Chih-Hung KO (Faculty of Medicine, College of Medicine, Kaohsiung Medical University, Taiwan)
  • Shane KRAUS (University of Nevada, Las Vegas, NV, USA)
  • Hae Kook LEE (The Catholic University of Korea, Republic of Korea)
  • Bernadette KUN (Eötvös Loránd University, Hungary)
  • Katerina LUKAVSKA (Charles University, Prague, Czech Republic)
  • Giovanni MARTINOTTI (‘Gabriele d’Annunzio’ University of Chieti-Pescara, Italy)
  • Gemma MESTRE-BACH (Universidad Internacional de la Rioja, La Rioja, Spain)
  • Astrid MÜLLER (Hannover Medical School, Germany)
  • Daniel Thor OLASON (University of Iceland, Iceland)
  • Ståle PALLESEN (University of Bergen, Norway)
  • Afarin RAHIMI-MOVAGHAR (Teheran University of Medical Sciences, Iran)
  • József RÁCZ (Hungarian Academy of Sciences, Hungary)
  • Michael SCHAUB (University of Zurich, Switzerland)
  • 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)
  • Hermano TAVARES (Instituto de Psiquiatria do Hospital das Clínicas da Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil)
  • Alexander E. VOISKOUNSKY (Moscow State University, Russia)
  • Aviv M. WEINSTEIN (Ariel University, Israel)
  • Anise WU (University of Macau, Macao, China)

 

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