ml addiction research paper

Practical foundations of machine learning for addiction research. Part I. Methods and techniques

Affiliations.

1 Instituto de Cálculo, FCEyN, Universidad de Buenos Aires - CONICET, Buenos Aires, Argentina.
2 FaMAF, Universidad Nacional de Córdoba, Córdoba, Argentina.
3 Department of Psychiatry, University of Iowa, Iowa City, IA, USA.
PMID: 35389305
DOI: 10.1080/00952990.2021.1995739

Machine learning assembles a broad set of methods and techniques to solve a wide range of problems, such as identifying individuals with substance use disorders (SUD), finding patterns in neuroimages, understanding SUD prognostic factors and their association, or determining addiction genetic underpinnings. However, the addiction research field underuses machine learning. This two-part narrative review focuses on machine learning tools and concepts, providing an introductory insight into their capabilities to facilitate their understanding and acquisition by addiction researchers. This first part presents supervised and unsupervised methods such as linear models, naive Bayes, support vector machines, artificial neural networks, and k-means. We illustrate each technique with examples of its use in current addiction research. We also present some open-source programming tools and methodological good practices that facilitate using these techniques. Throughout this work, we emphasize a continuum between applied statistics and machine learning, we show their commonalities, and provide sources for further reading to deepen the understanding of these methods. This two-part review is a primer for the next generation of addiction researchers incorporating machine learning in their projects. Researchers will find a bridge between applied statistics and machine learning, ways to expand their analytical toolkit, recommendations to incorporate well-established good practices in addiction data analysis (e.g., stating the rationale for using newer analytical tools, calculating sample size, improving reproducibility), and the vocabulary to enhance collaboration between researchers who do not conduct data analyses and those who do.

Keywords: Machine learning; addiction; adicciones; aprendizaje automatico; artificial intelligence; ciencia de datos; data science; inteligencia artificial; métodos estadísticos; statistical methods.

Publication types

Bayes Theorem
Behavior, Addictive* / diagnosis
Machine Learning
Reproducibility of Results
Substance-Related Disorders*
Support Vector Machine

Investigating Digital Addiction in the Context of Machine Learning Based System Design

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First Online: 28 March 2023
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Geetika Johar 14 &
Ravindra Patel 15

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 649))

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In recent years popularity and availability of digital content are increased due to low-cost internet and the availability of smartphones. The suitable and appropriate use of smartphones will help us in our daily personal and professional life. On the other hand, excessive and extreme use of smartphones may invite different kinds of physical and mental disorders. In this context, we proposed to review recent literature about digital addiction. First, we collected 20 recent research articles about different kinds of addictions and the current state of research trends. Then the role of machine learning in order to deal with addiction has been studied. Further some articles are explored for locating the suitable dataset used, techniques of data collection, machine learning algorithms being applied, area of employment, feature selected, and consequences of the articles. Finally based on the collected experiences of the recent efforts we proposed a Smartphone Addiction (SA) prediction and support framework using the Machine Learning (ML) technique. Finally, the conclusion has been made and future plan has also been discussed.

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Cash, H., Rae, C.D., Steel, A.H., Winkler, A.: Internet addiction: a brief summary of research and practice. Current Psychiatry Rev. 8 (292–298), 292 (2012)

Article Google Scholar

Montag, C., Wegmann, E., Sariyska, R., Demetrovics, Z., Brand, M.: How to overcome taxonomical problems in the study of Internet use disorders and what to do with smartphone addiction. J. Behav. Addict. 9 (4), 908–914 (2020)

Noë, B., Turner, L.D., Linden, D.E.J., Allen, S.M., Winkens, B., Whitaker, R.M.: Identifying indicators of smartphone addiction through user-app interaction. Comput. Hum. Behav. 99 , 56–65 (2019)

Panova, T., Carbonell, X.: Is smartphone addiction really an addiction. J. Behav. Addict. 7 (2), 252–259 (2018)

Sunday, O.J., Adesope, O.O., Maarhuis, P.L.: The effects of smartphone addiction on learning: a meta-analysis. Comput. Hum. Behav. Rep. 4 , 100114 (2021)

Elhai, J.D., Levine, J.C., Hall, B.J.: Problematic smartphone use and mental health problems: current state of research and future directions. Dusunen Adam J. Psychiatry Neurological Sci. 32 , 1–3 (2019)

Google Scholar

Cha, S.S., Seo, B.K.: Smartphone use and smartphone addiction in middle school students in Korea: prevalence, social networking service, and game use. Health Psychol. Open 5 , 1–15 (2018)

Yu, S., Sussman, S.: Does smartphone addiction fall on a continuum of addictive behaviors? Int. J. Environ. Res. Public Health 17 , 422 (2020)

Tateno, M., Kim, D.J., Teo, A.R., Skokauskas, N., Guerrero, A.P.S., Kato, T.A.: Smartphone addiction in Japanese college students: usefulness of the Japanese version of the smartphone addiction scale as a screening tool for a new form of internet addiction. Psychiatry Investing 16 (2), 115–120 (2019)

Gökçearslan, Ş, Uluyol, Ç., Şahin, S.: Smartphone addiction, cyberloafing, stress and social support among university students: a path analysis. Child Youth Serv. Rev. 91 , 47–54 (2018)

Durak, H.Y.: Investigation of nomophobia and smartphone addiction predictors among adolescents in Turkey: demographic variables and academic performance, The Social Science Journal (2017)

Masud, M.T., Mamun, M.A., Thapa, K., Lee, D.H., Griffiths, M.D., Yang, S.H.: Unobtrusive monitoring of behavior and movement patterns to detect clinical depression severity level via smartphone. J. Biomed. Inform. 103 , 103371 (2020)

Mishra, S.: Learning from usage analysis of mobile devices. Procedia Comput. Sci. 167 , 1648–1655 (2020)

Ji, H.M., Chen, L.Y., Hsiao, T.C.: Real-Time Detection of Internet Addiction Using Reinforcement Learning System. In: GECCO ’19 Companion. Czech Republic, Association for Computing Machinery, Prague (2019)

Book Google Scholar

Hasegawa, T., Koshino, M.: Representation learning by convolutional neural network for smartphone sensor based activity recognition. In: CIIS 2019, Bangkok, Thailand (2019)

Chikersal, P., et al.: Detecting depression and predicting its onset using longitudinal symptoms captured by passive sensing: a machine learning approach with robust feature selection. ACM Trans. Comput.-Hum. Interact. 28 (1), Article 3, January 2021

Lee, M.S., Han, M.M.C., Pak, J.G.: Analysis of behavioral characteristics of smartphone addiction using data mining. Appl. Sci. 8 , 1191 (2018)

Sağbaş, E.A., Korukoglu, S., Balli, S.: Stress detection via keyboard typing behaviors by using smartphone sensors and machine learning techniques. J. Med. Syst. 44 (4), 1–12 (2020). https://doi.org/10.1007/s10916-020-1530-z

Elhai, J.D., Yang, H., Rozgonjuk, D., Montag, C.: Using machine learning to model problematic smartphone use severity: the significant role of fear of missing out. Addict. Behav. 103 , 106261 (2020)

Park, J., Jeong, J.E., Park, S.Y., Rho, M.J.: Development of the smartphone addiction risk rating score for a smartphone addiction management application. Front. Public Health 8 , 485 (2020)

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Geetika Johar

Department of Computer Applications, UIT, RGPV, Bhopal, India

Ravindra Patel

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Ajith Abraham

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Johar, G., Patel, R. (2023). Investigating Digital Addiction in the Context of Machine Learning Based System Design. In: Abraham, A., Bajaj, A., Gandhi, N., Madureira, A.M., Kahraman, C. (eds) Innovations in Bio-Inspired Computing and Applications. IBICA 2022. Lecture Notes in Networks and Systems, vol 649. Springer, Cham. https://doi.org/10.1007/978-3-031-27499-2_9

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BRIEF RESEARCH REPORT article

The association between mobile game addiction and depression, social anxiety, and loneliness.

$\nJin-Liang Wang$

1 Center for Mental Health Education, School of Psychology, Southwest University, Chongqing, China
2 Chongqing Youth and Vocational Technical College, Chongqing, China

As a new type of addictive behaviors and distinct from traditional internet game addiction on desktop computers, mobile game addiction has attracted researchers' attention due to its possible negative effects on mental health issues. However, very few studies have particularly examined the relationship between mobile game addiction and mental health outcomes, due to a lack of specified instrument for measuring this new type of behavioral addiction. In this study, we examined the relationship between mobile game addition and social anxiety, depression, and loneliness among adolescents. We found that mobile game addiction was positively associated with social anxiety, depression, and loneliness. A further analysis on gender difference in the paths from mobile game addiction to these mental health outcomes was examined, and results revealed that male adolescents tend to report more social anxiety when they use mobile game addictively. We also discussed limitations and implications for mental health practice.

With the fast development of mobile technology, many functions of desktop computers have been transferred to mobile devices like ipad and smartphone, which is especially the case for game applications. Mobile video games refer to games played by either single or multi players via online mobile devices. These games are particularly popular when they can be downloaded for free (e.g., “freemium games,” which are free but customers pay for extra features) ( 1 ). The latest China Internet Network Information Center's (CNNIC) report revealed that the growth rate of mobile online game has reached 9.6% and adolescents are the main user group ( 2 ). In comparison with most segments of society, adolescents are more vulnerable to Internet-related addiction because of their psychological and developmental characteristics, the easy access to Internet with a portable device, and the positive expectation of mobile gaming ( 3 ). It has been demonstrated that video game addicts suffered poorer mental health and cognitive functioning, and increased emotional difficulties, such as enhanced depression and anxiety, as well as more social isolation ( 4 ).

Despite this, relatively few studies have examined the relationship between mobile game addiction and mental health outcomes. This is because, so far, no measurement especially designed for mobile game addiction has been developed. In literature, problematic mobile video gaming has been defined as a phenomenon in which users strongly rely on mobile games and cannot help playing them repeatedly over a comparatively long period ( 5 ). Previous studies of Internet gaming disorder (IGD) have mainly focused on traditional online gaming addiction based on a desktop computer. However, recent research has suggested that there were only moderate correlations between the different forms of Internet addiction ( 6 ). In addition, although mobile game addiction has some similarity with traditional desktop computer online game addiction, there are still obvious differences. Specifically, mobile video games are characterized by portability, immediacy, and accessibility ( 7 ), which may increase the risk for addictive behavioral patterns and, thus, more severe mental health problems.

Additionally, most prior studies have treated social anxiety, depression, and loneliness as risk factors for Internet-relevant addiction ( 8 , 9 ), whereas, few studies have examined the alternative direction ( 10 ). A relevant study found that the relative risk for depression in students with Internet addiction after months was 1.5 times higher than that of non-Internet addiction participants, after controlling for potential confounding variables (gender, study burden, age, rural, or urban school). This indicated that Internet relevant addiction may also lead to depression and loneliness ( 11 ). Another reason for conducting the current study was because the relationship between playing video games and psychological adjustment during adolescence is relatively scarce, which is especially true for investigating the association between playing video games and social anxiety among adolescents ( 12 ). Therefore, an investigation on this issue can help us understand how mobile game addiction may hinder adolescents' social development and would provide some guidance for mental health education practice.

Theoretical Framework

Mobile game addiction and depression.

Internet game addiction is characterized by cognitive and emotional deficits. Previous studies have reported the co-occurrence of Internet addition and depression ( 13 , 14 ). In addition, a longitudinal study found that Internet game addition/depression severity at an earlier time positively predicted the depression/Internet game addition severity at a later time, which indicated that a possible bidirectional relationship existed between online gamers' depression symptoms and addiction. People cope with their emotional distress by playing online games, but the excessive use of online games for a long time may separate individuals from real-life relationships, thus causing severer mental health problems, such as depression ( 15 ). Therefore, in this study, we would expect a positive relationship between mobile game addition and depression.

Mobile Game Addition and Loneliness

Loneliness is defined as an unpleasant experience that derives from important deficiencies in a person's network of social relationships ( 16 ). Previous studies have consistently confirmed the connection between loneliness and online game addiction ( 17 , 18 ). Furthermore, loneliness is not only the cause of online gaming addiction but also the consequence; there is a possible reciprocal relationship ( 19 ). Prior research has indicated that, although playing online games may temporarily provide an escape from the negative feelings associated with social deficiencies, excessive gaming does little to facilitate the development or maintenance of real-life relationships. Instead, the substitution for interpersonal interactions in real life may exacerbate the deterioration of existing social relationships, thereby increasing loneliness ( 19 ). Thus, we would expect a positive association between mobile game addiction and loneliness in this study.

Social Anxiety

Social anxiety, which is the most common anxiety disorder in adolescence, is the state of tension or discomfort experienced by individuals in social situations ( 20 ). The investigation on the potential effects of mobile game addiction and adolescence social anxiety is of importance considering that approximately one third of adolescents meet the criteria for an anxiety disorder ( 21 , 22 ). Some literature indicates that Internet addiction, smartphone addiction, and online game addiction were all associated with an individual's social anxiety [e.g., ( 23 )]. Individuals with a serious tendency for online gaming addiction have significantly higher social anxiety levels than those who use online games normally. Lo et al. ( 24 ) investigated the potential effects of online games on the quality of interpersonal relationships and levels of social anxiety. The results indicated that the quality of interpersonal relationships may be undermined and the amount of social anxiety may increase when teenagers spend more time playing online games ( 24 ). In the current study, we would expect a positive association between mobile game addiction and social anxiety.

Gender Difference

Gender has been proposed as an important factor in influencing Internet use and its outcomes regarding mental health (e.g., 8). Evidence has suggested that males have a predilection toward activities that involve explosive action and combat, while females are drawn toward activities that are more social and communication focused ( 25 ). Females received more family supervision, which may prevent them from developing Internet addiction ( 26 ). In a more recent study, female video game addicts displayed significantly more somatic difficulties than male addicts ( 4 ). They further argued that female addicts may be uniquely at risk for negative physical health outcomes and sleep disturbances ( 4 ). Significant gender difference was also revealed on the association between family function and Internet addiction among adolescence ( 27 ). Females showed more negative consequences of its maladaptive mobile phone use ( 28 ). These studies highlighted the need to explore gender differences in mobile game addition and mental health problems further.

Participants and Data Collection Procedure

Data of this study was from the students ( n = 600) enrolled in the seventh, eighth, and ninth grades of a junior high school in Guizhou Province. Letters describing the project were sent home to parents with a consent form inviting participation. Children whose parents provided written informed consent and who themselves gave assent completed the questionnaire in classroom settings. Prior to answering the items, participants read information about the implications of participation and data protection. The information emphasized that participation was completely voluntary and anonymous. Excluding missing or incomplete data, 578 survey responses were collected (mean age = 15 years, SD = 1.05). 56.7% ( n = 328) participants were self-identified as males.

Mobile Game Addiction Scale

This scale was specially developed for the measurement of mobile game addiction and included 11 items ( 29 ). Each item was rated on a Likert-type scale from 1 = completely disagree to 5 = completely agree, with the total scores ranging from 11 to 55. A higher score indicated a severer addition tendency. This scale has shown good construct validity, with χ 2 /df = 2.835, RMSEA = 0.056, 90% CI (0.044, 0.069), SRMR = 0.037, CLI = 0.970, TLI = 0.959, the Cronbach alpha coefficient in the current study was 0.84. Sample items included: “ During the last year, have you felt miserable when you were unable to play mobile video games or played less than usual? ” and “ During the last year, have you played mobile video games so that you would not have to think about annoying things? ”

Depression Scale

The depression subscale from the Brief Symptom Inventory (BSI) was used to assess the depression symptoms ( 30 ). The scale contains 6 items and each item was rated on a 5-point Likert scale, ranging from 1 (not at all) to 5 (extremely serious). Higher scores indicate severe depressive symptoms. We did a measurement model analysis, and the scale showed good construct validity, with χ 2 /df = 1.931,RMSEA = 0.040,90% CI(0.000, 0.070),SRMR = 0.020,CFI = 0.994, TLI = 0.989. The Cronbach alpha coefficient in the current study was 0.84. Sample items included: “ You feel sad ” and “ You find everything dull .”

Child Loneliness Scale

The revised version of the Child Loneliness Scale was adopted to evaluate individuals' loneliness ( 31 ). The scale contains 16 items, which were answered using a 5-point Likert scale ranging from 1 (always) to 5 (never); higher scores indicate elevated loneliness. Good construct validity was exhibited in the current sample, with χ 2 /df = 2.833, RMSEA = 0.056, 9 % CI(0.048, 0.065), SRMR = 0.0461, CFI = 0.940, TLI = 0.918. The Cronbach alpha coefficient in our sample was 0.86. Sample items included: “ I don't have any friends ” and “ I feel lonely .”

Child Social Anxiety Scale

The modified version of the Child Social Anxiety Scale was used to assess participants' social anxiety ( 32 ). The term “children” in the original scale was changed to “classmate” in the current version. The scale contains 10 items and each item was rated using a 3-point Likert scale, ranging from 1 = never to 3 = always. Higher scores indicate higher levels of social anxiety. The scale has been proved to have good construct validity in the current study, with χ 2 / df = 2.872, RMSEA = 0.057, 90% CI(0.044, 0.071), SRMR = 0.041, CFI = 0.951, TLI = 0.931, and the Cronbach alpha coefficient in our sample was 0.80. Sample items included: “ I think my classmates make fun of me ” and “ I'm afraid other students won't like me .”

Descriptive Statistics and Zero-Order Correlations Among the Study Variables

Table 1 shows the descriptive results and zero-order correlations among the study variables. Mobile addiction was positively correlated with depression, loneliness, and social anxiety, with the correlations ranging from 0.18 to 0.46 ( p s < 0.01).

Table 1 . Descriptive results and zero-order correlations among the study variables.

Structural Equation Modeling on the Relationship Between Mobile Game Addiction, Depression, Social Anxiety, and Loneliness

Using Amos 22.0, we conducted a structural equation analysis to examine the association between mobile game addiction, depression, social anxiety, and loneliness.

Several underlying statistical assumptions for multiple regression analysis were examined before running the structural modeling. The assumption of homoscedasticity was checked using the Levene's Test for Equality of Variances ( 33 ). The test ensured no significant differences in the variance of the three dependent variables of social anxiety, depression, and loneliness across groups defined by mobile gaming addiction ( p > 0.05 for all cases). Thus, the assumption of homoscedasticity was not violated ( 34 ). Second, the skewness values for all variables ranged from 0.25 to 0.82 and the kurtosis values ranged from 0.27 to 0.30, which are within the acceptable range of −1 to +1 for normality ( 35 ). Thus, the violation of the normality assumption was not present in the sample data. Thirdly, the assumption of independence of residuals was confirmed by the calculation of the Durbin–Watson statistics for the dependent variables of depression (= 1.36), social anxiety (= 1.76), and loneliness (= 1.71), which are within the acceptable range of 1.5–2.5 for independence ( 36 ). Lastly, multi-collinearity was evaluated through the assessment of zero-order correlations among selected measured constructs, as calculated in Table 1 . Harris and Hagger ( 37 ) noted that multicolline arity is not a serious issue if none of the correlation coefficients between variables exceeds 0.70. It is apparent that pair-wise bivariate associations between the study variables were not highly correlated with each other. Accordingly, multi-collinearity was dismissed from being a major concern in the present study ( 38 ). To conclude, the sample data were judged to meet the criteria for further analysis.

Model fit was assessed by considering multiple criteria: a Chi-square/df < 5 a root mean square error of approximation (RMSEA) of <0.08 and a comparative fit index (CFI) and a Tucker-Lewis index (TLI) of >0.90 ( 39 ). The model fit was considered acceptable when most abovementioned criteria were satisfied. Our results showed that the model fit to the data well, with χ 2 /df = 3.475, RMSEA = 0.065, 90% CI (0.06, 0.07), CLI = 0.937, TLI = 0.921. Mobile game addiction can explain 10% variance of depression, 6% variance of social anxiety, and 4% variance of loneliness. The standardized beta coefficients are shown in Figure 1 . Mobile game addiction was positively related to depression, social anxiety, and loneliness, with β = 0.31, p < 0.001, β = 0.25, p < 0.001, and β = 0.21, p < 0.001, respectively.

Figure 1 . The Structural Modeling on the relation between mobile game addiction and depression, social anxiety, and loneliness. *** p < 0.01.

Considering that gender was proposed as a variable that may moderate the relationship between mobile game addiction and mental health outcomes, the moderating effect of gender was tested. We created a multi-group model in AMOS to test the differences between males and females on the paths between mobile game addiction and its outcomes. The results show that significant gender differences exist considering the relationship between mobile game addiction and social anxiety. Male adolescents who used mobile game additively reported higher levels of social anxiety (β = 0.118, p < 0.001), depression (β = 0.280, p < 0.001), and loneliness (β = 0.311, p < 0.001), compared with female adolescents (β = 0.077, p < 0.001; β = 0.17, p < 0.01; and β = 0.16, p < 0.05, respectively; see Table 2 for details).

Table 2 . Multi-group (male and female) analysis on the relationship among mobile game addiction and depression, social anxiety, and loneliness.

The goal of this study was to examine the associations between mobile game addiction and depression, loneliness, social anxiety, and the potential gender difference in these associations were also investigated. The results revealed that adolescent with mobile game addiction had higher self-reported depression, social anxiety and loneliness, which have supported our three hypotheses regarding the association between mobile game addiction and depression, social anxiety, and loneliness. Further, gender difference was observed in the path between mobile game addiction and social anxiety, with male adolescents having a stronger association between mobile game addiction and social anxiety. This indicates that male adolescents may experience more social anxieties if they use mobile game addictively, compared with female adolescents.

As we expected, mobile game addiction was positively associated with depression, anxiety, and loneliness, which have supported all of our three hypotheses and are in line with prior findings. Literature has consistently shown that video game addicts reported more anxiety, depression, lower positive affect and psychological well-being. Literature has also shown that Internet addictions are related to poorer emotional health, in particular depression and anxiety ( 40 , 41 ). For instance, Whang et al. ( 41 ) found a significant association between degree of Internet addiction and loneliness and depression. Adolescents with high Internet use exhibited more psychopathology, as revealed by the Brief Symptoms Inventory (BSI, a reduced version of the Symptoms Checklist, SCL-90) compared with those with low those use ( 42 ). In a recent study, ( 4 ) reported that young adults addicted to video games showed increased depression and anxiety, and felt more socially isolated. The link between mobile game addiction and mental health may be due to the social isolation resulting from spending too much time gaming, which in turn leads to undermined psychological well-being ( 43 ). Our results regarding the association between mobile game addiction and loneliness are also in line with the displacement hypothesis in terms of Internet use, which argues that digital device users have spent most time in online settings, rather than offline, and their existing relationships have suffered as a result ( 44 ).

We also expected a gender difference considering the association between mobile game addiction and mental health outcomes. We found that males who were addicted to mobile games tended to suffer more social anxiety, loneliness, and social anxiety, compared with females. This finding is line with prior research (e.g., 24). Gender difference on social anxiety and loneliness has been widely reported in literature. Compared with female adolescents, male adolescents tended to lack social skills, were more socially withdrawn and disclosed less about themselves in offline communication settings ( 45 ). This is also a reason why males are more likely to be attracted to a virtual world like computer games since the online world is more comfortable and can offer more sense of security ( 46 ). This would further lead them to be more social isolated and experience more social anxiety, loneliness, and depression due to the lack of social bond in offline settings.

Limitations and future directions

The results of this study should be viewed in light of its limitations. First, this study is a cross-sectional design. Thus, we could not determine a causal link between study variables. Future investigations should adopt an experimental design to establish the causal relationship between variables, or a longitudinal design to examine the prospective relationship among the variables. As prior studies indicated, the association between mobile game addiction and mental health problems might be reciprocal. Second, the sample is a homogeneous group of students from a middle school in China. Whether the results can be generalized to all adolescents is a question for future research.

Despite the limitations, our study has examined the association between mobile game addiction and depression, social anxiety, and loneliness, based on an adolescent sample. The results indicated that mobile game addiction was positively related to these mental health problems, and this is especially true for male adolescents as they are more likely to experience a higher level of social anxiety, depression, and loneliness after excessive use of mobile gaming. Therefore, mental health educators and practicers should be aware of the negative effects caused by addictive mobile gaming, as this is such a common phenomenon today. Specifically, attention should be given to male adolescents who are addicted to mobile gaming, as they may suffer more social anxiety.

Data Availability

The datasets generated for this study are available on request to the corresponding author.

Ethics Statement

The studies involving human participants were reviewed and approved by Southwest University's Human Research Ethics Committee. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.

Author Contributions

J-LW drafted the initial version of the manuscript and responded to the reviewers' comments. J-RS analyzed the data. H-ZW collected the data and provided the comments.

This study has been supported by the Major Cultivating Project in Southwest University (No. SWU1809006).

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

1. Su YS, Chiang WL, Lee CTJ, Chang HC. The effect of flow experience on player loyalty in mobile game application. Comput Hum Behav. (2016) 63:240–8. doi: 10.1016/j.chb.2016.05.049

CrossRef Full Text | Google Scholar

2. China Internet Network Information Center. The 41th Statistical Report on the Development of Internet in China. (2018). Available online at: http://www.cnnic.net.cn/ (accessed October 30, 2018).

3. Kandell JJ. Internet addiction on campus: the vulnerability of college student. Cyber Psychol Behav. (1998) 1:11–8. doi: 10.1089/cpb.1998.1.11

4. Stockdale L, Coyne SM. Video game addiction in emerging adulthood :cross-sectional evidence of pathology in video game addicts as compared to matched healthy controls. J Affect Disord. (2018) 225:265–72. doi: 10.1016/j.jad.2017.08.045

PubMed Abstract | CrossRef Full Text | Google Scholar

5. Sun Y, Zhao Y, Jia SQ, Zheng DY. Understanding the antecedents of mobile game addiction: the roles of perceived visibility, perceived enjoyment and flow. In: Proceedings of the 19th Pacific-Asia Conference on Information Systems. Singapore: Marian Bay Sands (2015). p. 1–12. Available online at: http://aisel.aisnet.org/pacis2015/141

Google Scholar

6. Sha P, Sariyska R, Riedl R, Lachmann B, Montag C. Linking Internet communication and smartphone use disorder by taking a closer look at the Facebook and WhatsApp applications. Addict Behav Rep. (2018) 9:100148. doi: 10.1016/j.abrep.2018.100148

7. Lee C, Kim O. Predictors of online game addiction among Korean adolescents. Addict Res Theory. (2017) 25:58–66. doi: 10.1080/16066359.2016.1198474

8. Bozoglan B, Demirer V, Sahin I. Loneliness, self-esteem, and life satisfaction as predictors of Internet addiction: a cross-sectional study among Turkish university students. Scand J Psychol. (2013) 54:313–9. doi: 10.1111/sjop.12049

9. Ko C, Yen J, Chen C, Yeh Y, Yen C. Predictive values of psychiatric symptoms for Internet addiction in adolescents. JAMA Pediatrics. (2011) 163:937–43. doi: 10.1001/archpediatrics.2009.159

10. Taylor S. (2017). The theoretical underpinnings of Internet addiction and its association with psychopathology in adolescence. Int J Adolesc Med Health . 2017:46. doi: 10.1515/ijamh-2017-0046

11. Lawrence TL, Peng Z-W. Effect of pathological use of the Internet on adolescent mental health. JAMA Pediatrics . (2010) 164:901–6. doi: 10.1001/archpediatrics.2010.159

12. Mccauley C. Video game play and anxiety during late adolescence: the moderating effects of gender and social context. J Affect Disord. (2018) 226:216–9. doi: 10.1016/j.jad.2017.10.009

13. Liu L, Yao YW, Li CR, Zhang JT, Xia CC, Lan JT. The comorbidity between internet gaming disorder and depression: interrelationship and neural mechanisms. Front Psychiatry. (2018) 9:154. doi: 10.3389/fpsyt.2018.00154

14. Wu AM, Chen JH, Tong KK, Yu S, Lau JT. Prevalence and associated factors of Internet gaming disorder among community dwelling adults in Macao, China. J Behav Addict. (2018) 7:62–9. doi: 10.1556/2006.7.2018.12

15. King DL, Delfabbro PH, King DL. The cognitive psychopathology of Internet gaming disorder in adolescence. J Abnormal Child Psychol. (2016) 44:1635–45. doi: 10.1007/s10802-016-0135-y

16. Blazer D. Loneliness: a source book of current theory, research and therapy. J Behav Ther Exp Psychiatry. (1983) 14:281. doi: 10.1016/0005-7916(83)90066-6

CrossRef Full Text

17. Spilkova J, Chomynova P, Csemy L. Predictors of excessive use of social media and excessive online gaming in Czech teenagers. J Behav Addict. (2017) 6:611–9. doi: 10.1556/2006.6.2017.064

18. Van Rooij AJ, Kuss DJ, Griffiths MD, Shorter GW, Schoenmakers TM, Van De Mheen D. The (co-) occurrence of problematic video gaming, substance use, and psychosocial problems in adolescents. J Behav Addict. (2014) 3:157–65. doi: 10.1556/JBA.3.2014.013

19. Lemmens JS, Valkenburg PM, Peter J. Development and validation of a game addiction scale for adolescents. Media Psychol. (2012) 12:77–95. doi: 10.1080/15213260802669458

20. Rapee RM, Heimberg RG. A cognitive-behavioral model of anxiety in social phobia. Behav Res Ther. (1997) 35:741–56. doi: 10.1016/S0005-7967(97)00022-3

21. Maldonado L, Huang Y, Chen R, Kasen S, Cohen P, Chen H. Impact of early adolescent anxiety disorders on self-esteem development from adolescence to young adulthood. J Adolesc Health. (2013) 53:287–92. doi: 10.1016/j.jadohealth.2013.02.025

22. Merikangas KR, He J, Burstein M, Swanson SA, Avenevoli S, Cui L, et al. (2010). Lifetime prevalence of mental disorders in U.S. adolescents: results from the National Comorbidity Survey Replication-Adolescent Supplement (NCS-A). J Am Acad Child Adolesc Psychiatry . 49:980–9. doi: 10.1016/j.jaac.2010.05.017

23. Fayazi M, Hasani J. Structural relations between brain-behavioral systems, social anxiety, depression and internet addiction: with regard to revised Reinforcement Sensitivity Theory (r-RST). Computers Human Behav. (2017) 72:441–8. doi: 10.1016/j.chb.2017.02.068

24. Lo S, Wang C, Fang W. Physical interpersonal relationship and social anxiety among online game players. Cyber Psychol Behav. (2005) 8:15–20. doi: 10.1089/cpb.2005.8.15

25. Duven E, Beutel ME, Wolfling KJ. (2013). The neuroscience of internet and computer game addiction—what do we know about what is going on inside our patients brains? Eur Psychiatry. 28:818. doi: 10.1016/S0924-9338(13)75997-2

26. Yen C, Chou W, Liu T. The association of Internet addiction symptoms with anxiety, depression and self-esteem among adolescents with attention-deficit/hyperactivity disorder. Compr Psychiatry. (2014) 55:1601–8. doi: 10.1016/j.comppsych.2014.05.025

27. Shi X, Wang J, Zou H. Family functioning and Internet addiction among Chinese adolescents: the mediating roles of self-esteem and loneliness. Computers Human Behav. (2017) 76:201–10. doi: 10.1016/j.chb.2017.07.028

28. Beranuy M, Oberst U, Carbonell X, Chamarro A. Problematic Internet and mobile phone use and clinical symptoms in college students: the role of emotional intelligence. Computers Human Behav. (2009) 25:1182–7. doi: 10.1016/j.chb.2009.03.001

29. Sheng J-R, Wang J-L. Development and psychometric properties of the problematic mobile video gaming scale. Curr Psyol. (2019) 20191–11. doi: 10.1007/s12144-019-00415-6

30. Derogatis LR, Melisaratos N. The brief symptom inventory: an introductory report. Psychol Med. (1983) 13:595–605. doi: 10.1017/S0033291700048017

31. Li X, Zou H, Liu Y. Psychometric evaluation of loneliness scale in Chinese middle school students. Chin J Clin Psychol. (2014) 22:731–60. doi: 10.16128/j.cnki.1005-3611.2014.04.037

32. La Greca AM, Lopez N. Social anxiety among adolescents: linkages with peer relations and friendships. J Abnormal Child Psychol. (1998) 26:83–94. doi: 10.1023/A:1022684520514

33. Snedecor GW, Cochran WG. Statistical Methods, 8th ed. Ames, IA: Iowa State University Press (1989).

34. Lim TS, Loh WY. A comparison of tests of equality of variances. Comput Stat Data Anal. (1996) 22:287–301. doi: 10.1016/0167-9473(95)00054-2

35. George D, Mallery P. SPSS for Windows Step by Step: A Simple Guide and Reference, 11.0 Update, 4th ed. Boston: Allyn & Bacon (2003).

36. Johnson RA, Wichern DW. Applied Multivariate Statistical Analysis, 5th ed. Englewood Cliffs, NJ: Prentice Hall (2006).

37. Harris J, Hagger MS. Do basic psychological needs moderate relationships within the theory of planned behavior? J Appl Biobehav Res. (2007) 12:43–64. doi: 10.1111/j.1751-9861.2007.00013.x

38. Saxton T, Dollinger M. Target reputation and appropriability: picking and deploying resources in acquisitions. J Manag. (2004) 30:123–47. doi: 10.1016/j.jm.2003.01.006

39. Wu ML. Structural Equation Modeling: The Operation and Application of AMOS. Chongqing: Chongqing University Press (2009).

40. Bruchas MR, Schindler AG, Shankar H, Messinger DI, Miyatake M, Land BB, et al. Selective p38 a MAPK deletion in serotonergic neurons produces stress resilience in models of depression and addiction. Neuron. (2011) 71:498–511. doi: 10.1016/j.neuron.2011.06.011

41. Whang LS, Ph D, Lee S, Ph D, Chang G. Internet over-users' psychological profiles: a behavior sampling analysis on internet addiction. Cyber Psychol Behav. (2003) 6:143–51. doi: 10.1089/109493103321640338

42. Yen J, Ko C, Yen C, Chen S. Psychiatric symptoms in adolescents with Internet addiction : comparison with substance use. Psychiatry Clin Neurosci. (2008) 62:9–16. doi: 10.1111/j.1440-1819.2007.01770.x

43. Kraut R, Patterson M, Lundmark V. Internet paradox: a social technology that reduces social involvement and psychological well-being? Am Psychol. (1998) 53:1017–31. doi: 10.1037//0003-066X.53.9.1017

44. Wang J-L, Jackson LA, Zhang D-J. The mediator role of self-disclosure and moderator roles of gender and social anxiety in the relationship between Chinese adolescents' online communication and their real-world social relationships. Computers Human Behav. (2011) 27:2161–8. doi: 10.1016/j.chb.2011.06.010

45. Schouten AP, Valkenburg PM, Peter J. Precursors and underlying processes of adolescents' online self-disclosure: developing and testing an “internet-attribute-perception” model. Media Psychol. (2007) 10:292–315. doi: 10.1080/15213260701375686

46. Caplan SE. Relations Among Loneliness, Social Anxiety, and Problematic Internet Use. Cyber Psychol Behav. (2007) 10:234–42. doi: 10.1089/cpb.2006.9963

Keywords: mobile game addiction, social anxiety, depression, loneliness, adolescents

Citation: Wang J-L, Sheng J-R and Wang H-Z (2019) The Association Between Mobile Game Addiction and Depression, Social Anxiety, and Loneliness. Front. Public Health 7:247. doi: 10.3389/fpubh.2019.00247

Received: 04 June 2019; Accepted: 16 August 2019; Published: 06 September 2019.

Reviewed by:

Copyright © 2019 Wang, Sheng and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Jin-Liang Wang, wjl200789@163.com

Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

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EFFECTS OF MOBILE GAMES (MOBILE LEGENDS) TO THE BEHAVIOR AND HEALTH OF A SHS STUDENTS

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In the past decade, research has accumulated suggesting that excessive Internet use can lead to the development of a behavioral addiction. Internet addiction has been considered as a serious threat to mental health and the excessive use of the Internet has been linked to a variety of negative psychosocial consequences. The aim of this review is to identify all empirical studies to date that used neuroimaging techniques to shed light upon the emerging mental health problem of Internet and gaming addiction from a neuroscientific perspective. Neuroimaging studies offer an advantage over traditional survey and behavioral research because with this method, it is possible to distinguish particular brain areas that are involved in the development and maintenance of addiction. A systematic literature search was conducted, identifying 18 studies. These studies provide compelling evidence for the similarities between different types of addictions, notably substance-related addictions and Internet and gaming addiction, on a variety of levels. On the molecular level, Internet addiction is characterized by an overall reward deficiency that entails decreased dopaminergic activity. On the level of neural circuitry, Internet and gaming addiction led to neuroadaptation and structural changes that occur as a consequence of prolonged increased activity in brain areas associated with addiction. On a behavioral level, Internet and gaming addicts appear to be constricted with regards to their cognitive functioning in various domains. The paper shows that understanding the neuronal correlates associated with the development of Internet and gaming addiction will promote future research and will pave the way for the development of addiction treatment approaches.

1. Introduction

In the past decade, research has accumulated suggesting that excessive Internet use can lead to the development of a behavioral addiction (e.g., [ 1 , 2 , 3 , 4 ]). Clinical evidence suggests that Internet addicts experience a number of biopsychosocial symptoms and consequences [ 5 ]. These include symptoms traditionally associated with substance-related addictions, namely salience, mood modification, tolerance, withdrawal symptoms, conflict, and relapse [ 6 ]. Internet addiction comprises a heterogeneous spectrum of Internet activities with a potential illness value, such as gaming, shopping, gambling, or social networking. Gaming represents a part of the postulated construct of Internet addiction, and gaming addiction appears to be the most widely studied specific form of Internet addiction to date [ 7 ]. Mental health professionals’ and researchers’ extensive proposals to include Internet addiction as mental disorder in the forthcoming fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-V) will come to fruition as the American Psychiatric Association accepted to include Internet use disorder as mental health problem worthy of further scientific investigation [ 8 ].

The excessive use of the Internet has been linked to a variety of negative psychosocial consequences. These include mental disorders such as somatization, obsessive-compulsive and other anxiety disorders, depression [ 9 ], and dissociation [ 10 ], as well as personality traits and pathology, such as introversion and psychoticism [ 11 ]. Prevalence estimates range from 2% [ 12 ] to 15% [ 13 ], depending on the respective sociocultural context, sample, and assessment criteria utilized. Internet addiction has been considered as serious threat to mental health in Asian countries with extensive broadband usage, particularly South Korea and China [ 14 ].

1.1. The Rise of Neuroimaging

In accordance with Cartesian dualism, the French philosopher Descartes advocated the view that the mind is an entity that is separate from the body [ 15 ]. However, the cognitive neurosciences have proved him wrong and reconcile the physical entity of the body with the rather elusive entity of the mind [ 16 ]. Modern neuroimaging techniques link cognitive processes ( i.e. , Descartes’ thinking mind ) to actual behavior ( i.e. , Descartes’ moving body ) by measuring and picturing brain structure and activity. Altered activity in brain areas associated with reward, motivation, memory, and cognitive control has been associated with addiction [ 17 ].

Research has addressed the neural correlates of drug addiction development via classical and operant conditioning [ 18 , 19 ]. It has been found that during the initial stages of the voluntary and controlled usage of a substance, the decision to use the drug is made by specific brain regions, namely the prefrontal cortex (PFC) and ventral striatum (VS). As habituation to use and compulsion develops, brain activity changes in that the dorsal regions of the striatum (DS) become increasingly activated via dopaminergic innervation ( i.e. , dopamine release) [ 20 ]. Long term drug use leads to changes in the brain dopaminergic pathways (specifically the anterior cingulate (AC), orbitofrontal cortex (OFC), and the nucleus accumbens (NAc) which may lead to a reduction of sensitivity to biological rewards and it decreases the individual’s control over seeking and eventually taking drugs. [ 21 , 22 ]. On a molecular level, the long-term depression (LTD; i.e. , the reduction) of synaptic activity has been linked to the adaptation of the brain as a result of substance-related addictions [ 23 ]. Drug addicts become sensitized to the drug because in the course of prolonged intake, the synaptic strength in the ventral tegmental area increases, and so does the LTD of glutamate in the nucleus accumbens, which will result in craving [ 24 ].

At the same time, the brain ( i.e. , NAc, OFC, DLPFC) becomes increasingly responsive to drug cues (e.g., availability, particular context) via craving [ 21 , 25 ]. Craving for drug use involves a complex interaction between a variety of brain regions. Activity in the nucleus accumbens following recurrent drug intake leads to learning associations between drug cues and the reinforcing effects of the drug [ 26 ]. In addition, the orbitofrontal cortex, important for the motivation to engage in behaviors, the amygdala (AMG) and the hippocampus (Hipp), as main brain regions associated with memory functions, play a role in intoxication and craving for a substance [ 17 ].

Natural rewards, such as food, praise, and/or success gradually lose their hedonic valence. Due to habituation to rewarding behaviors and intake of drugs, a characteristic addiction symptom develops ( i.e. , tolerance). Increasing amounts of the substance or increasing engagement in the respective behaviors are needed in order to produce the desired effect. As a result, the reward system becomes deficient. This leads to the activation of the antireward system that decreases the addict’s capacity for experiencing biological reinforcers as pleasurable. Instead, he requires stronger reinforcers, i.e. , their drug or behavior of choice, in larger amounts ( i.e. , tolerance develops) to experience reward [ 27 ]. In addition, the lack of dopamine in the mesocorticolimbic pathways during abstinence explains characteristic withdrawal symptoms. These will be countered with renewed drug intake [ 17 ]. Relapse and the development of a vicious behavioral cycle are the result [ 28 ]. Prolonged drug intake and/or engagement in a rewarding behavior leads to changes in the brain, including dysfunctions in prefrontal regions, such as the OFC and the cingulate gyrus (CG) [ 17 , 29 ].

Research indicates that brain activity alterations commonly associated with substance-related addictions occur following the compulsive engagement in behaviors, such as pathological gambling [ 30 ]. In line with this, it is conjectured that similar mechanisms and changes are involved in Internet and gaming addiction. The aim of this review is therefore to identify all peer-reviewed empirical studies to date that used neuroimaging techniques to shed light upon the emerging mental health problem of Internet and gaming addiction from a neuroscientific perspective. Neuroimaging broadly includes a number of distinct techniques. These are Electroencephalogram (EEG), Positron Emission Tomography (PET), SPECT Single Photon Emission Computed Tomography (SPECT), functional Magnetic Resonance Imaging (fMRI), and structural magnetic resonance imaging (sMRI), such as Voxel-based Morphometry (VBM), and Diffusion-Tensor Imaging (DTI). These are briefly explained in turn before examining the studies that have utilized these techniques for studies on Internet and gaming addiction.

1.2. Types of Neuroimaging Used to Study Addictive Brain Activity

Electroencephalogram (EEG): With an EEG, neural activity in the cerebral cortex can be measured. A number of electrodes are fixed to specific areas ( i.e. , anterior, posterior, left and right) of the participant’s head. These electrodes measure voltage fluctuations ( i.e. , current flow) between pairs of electrodes that are produced by the excitation of neuronal synapses [ 31 ]. With event-related potentials (ERPs), the relationships between the brain and behavior can be measured via an electrophysiological neuronal response to a stimulus [ 32 ].

Positron Emission Tomography (PET): PET is a neuroimaging method that allows for the study of brain function on a molecular level. In PET studies, metabolic activity in the brain is measured via photons from positron emissions ( i.e. , positively charged electrons). The subjected is injected with a radioactive 2-deoxyglucose (2-DG) solution that is taken up by active neurons in the brain. The amounts of 2-DG in neurons and positron emissions are used to quantify metabolic activity in the brain. Thus, neuronal activity can be mapped during the performance of a particular task. Individual neurotransmitters can be distinguished with PET, which makes the latter advantageous over MRI techniques. It can measure activity distribution in detail. Limitations to PET include relatively low spatial resolution, time needed to obtain a scan, as well as potential radiation risk [ 33 ].

Single Photon Emission Computed Tomography (SPECT): SPECT is a subform of PET. Similar to PET, a radioactive substance (a “tracer”) is injected into the blood stream that rapidly travels to the brain. The stronger the metabolic activity in specific brain regions, the stronger the enrichment of gamma rays. The emitted radiation is measured in accordance with brain layers, and metabolic activity is imaged using computerized techniques. Unlike PET, SPECT allows for counting individual photons, however, its resolution is poorer because with SPECT, resolution depends on the proximity of the gamma camera that measures neuronal radioactivity [ 34 ].

Functional Magnetic Resonance Imaging (fMRI): With fMRI, changes in the levels of blood oxygen in the brain are measured that are indicative of neuronal activity. Specifically, the ratio of oxyhemoglobin ( i.e. , hemoglobin that contains oxygen in the blood) to deoxyhemoglobin ( i.e. , hemoglobin that has released oxygen) in the brain is assessed because blood flow in “active” brain areas increases to transport more glucose, also bringing in more oxygenated hemoglobin molecules. The assessment of this metabolic activity in the brain allows for finer and more detailed imaging of the brain relative to structural MRI. In addition to this, the advantages of fMRI include speed of brain imaging, spatial resolution, and absence of potential health risk relative to PET scans [ 35 ].

Structural Magnetic Resonance Imaging (sMRI): sMRI uses a variety of techniques to image brain morphology [ 36 ]. One such technique is Voxel-Based Morphometry (VBM) . VBM is used to compare the volume of brain areas and the density of gray and white matter [ 37 ]. Another sMRI technique is Diffusion-Tensor Imaging (DTI) . DTI is a method used for picturing white matter. It assesses the diffusion of water molecules in the brain which helps to identify interconnected brain structures by using fractional anisotropy (FA). This measure is an indicator of fiber density, axonal diameter, and myelination in white matter [ 38 ].

A comprehensive literature search was conducted using the database Web of Knowledge . The following search terms (and their derivatives) were entered with regards to Internet use: “addiction”, “excess”, “problem”, and “compulsion”. Moreover, additional studies were identified from supplementary sources, such as Google Scholar , and these were added in order to generate a more inclusive literature review. Studies were selected in accordance with the following inclusion criteria. Studies had to (i) assess Internet or online gaming addiction or direct effects of gaming on neurological functioning, (ii) use neuroimaging techniques, (iii) be published in a peer-reviewed journal, and (iv) be available as full text in English language. No time period was specified for the literature search because neuroimaging techniques are relatively new, so that the studies were expected to be recent ( i.e. , almost all having been published between 2000 and 2012).

A total of 18 studies were identified that fulfilled the inclusion criteria. Of those, the method of data acquisition was fMRI in eight studies [ 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 ] and sMRI in two studies [ 47 , 48 ], two studies used PET scans [ 49 , 50 ], one of which combined it with an MRI [ 49 ], one used SPECT [ 51 ], and six studies utilized EEG [ 52 , 53 , 54 , 55 , 56 , 57 ]. It should also be noted that two of these were actually the same study with one published as a letter [ 53 ] and one published as a full paper [ 54 ]. One study [ 57 ] met all the criteria but was excluded because the diagnosis details of Internet addiction were insufficient to make valid conclusions. Furthermore, two studies did not directly assess Internet and gaming addiction [ 43 , 50 ], but assessed the direct effects of gaming on neurological activity using an experimental paradigm, and were therefore retained in the review. Detailed information on the included studies are presented in Table 1 .

Included studies.

3.1. fMRI Studies

Hoeft et al . [ 43 ] investigated gender differences in the mesocorticolimbic system during computer-game play among 22 healthy students (age range = 19–23 years; 11 females). All participants underwent fMRI (3.0-T Signa scanner (General Electric, Milwaukee, WI, USA), completed the Symptom Checklist 90-R [ 58 ], and the NEO-Personality Inventory-R [ 59 ]. FMRI was carried out during 40 blocks of either a 24-s ball game with the goal being to gain space or a similar control condition that did not include a specific game goal (as based on its structural makeup). Results indicated that there was an activation of neural circuitries that are involved in reward and addiction in the experimental condition ( i.e. , insula, NAc, DLPFC, and OFC). Consequently, the presence of an actual game goal (a characteristic of most conventional online games that are rule-based rather than pure role-playing games), modified brain activity via behavior. Here, a clear cause and effect relationship is evident, which adds strength to the findings.

Results also showed that male participants had a larger activation (in rNAc, blOFC, rAMG) and functional connectivity (lNAc, rAMG) in the mesocorticolimbic reward system when compared to females. The results furthermore indicated that playing the game activated the right insula (rI; signals autonomic arousal), right dorso-lateral PFC (maximize reward or change behavior), bilateral premotor cortices (blPMC; preparation for reward) and the precuneus, lNAc, and the rOFC (areas involved in visual processing, visuo-spatial attention, motor function, and sensori-motor transformation) compared to the resting state [ 43 ]. The insula has been implicated in conscious craving for addictive substances by implicating decision-making processes involving risk and reward. Insula dysfunction may explain neurological activities indicative of relapse [ 60 ]. Due to its experimental nature, this study was able to provide insight into idiosyncratic brain activation as a consequence of gaming in a healthy ( i.e. , non-addicted) population.

Ko et al . [ 44 ] attempted to identify the neural substrates of online gaming addiction by assessing brain areas involved in urge to engage in online games among ten male online gaming addicts (playing World of Warcraft for more than 30 h a week) compared to ten male controls (whose online use was less than two hours a day). All participants completed the Diagnostic Criteria for Internet Addiction for College Students (DCIA-C; [ 74 ]), the Mini-International Neuropsychiatric Interview [ 75 ], the Chen Internet Addiction Scale (CIAS) [ 71 ], the Alcohol Use Disorder Identification Test (AUDIT) [ 76 ], and the Fagerstrom Test for Nicotine Dependence (FTND) [ 77 ]. The authors presented gaming-related and paired mosaic pictures during fMRI scanning (3T MRscanner), and contrasts in BOLD signals in both conditions were analyzed using a cue reactivity paradigm [ 25 ]. The results indicated cue induced craving that is common among those with substance dependence. There was a dissimilar brain activation among gaming addicts following the presentation of game relevant cues as compared to controls and compared to the presentation of mosaic pictures, including the rOFC, rNAc, blAC, mFC, rDLPFC, and the right caudate nucleus (rCN). This activation correlated with gaming urge and a recalling of gaming experience. It was argued that there is a similar biological basis of different addictions including online gaming addiction. The quasi-experimental nature of this study that artificially induced craving in an experimental and controlled setting allowed the authors to make conclusions as based on group differences, and thus linking online gaming addiction status to the activation of brain areas associated with symptoms of more traditional ( i.e. , substance-related) addictions.

Han et al . [ 42 ] assessed the differences in brain activity before and during video game play in university students playing over a seven-week period. All participants completed the Beck Depression Inventory [ 78 ], the Internet Addiction Scale [ 67 ], and a 7-point visual analogue scale (VAS) to assess craving for Internet video game play. The sample comprised 21 university students (14 male; mean age = 24.1 years, SD = 2.6; computer use = 3.6, SD = 1.6 h a day; mean IAS score = 38.6, SD = 8.3). These were further divided into two groups: the excessive Internet gaming group (who played Internet video games for more than 60 min a day over a 42-day period; n = 6), and general player group (who played less than 60 min a day over the same period; n = 15). The authors used 3T blood oxygen level dependent fMRI (using Philips Achieva 3.0 Tesla TX scanner) and reported that brain activity in the anterior cingulate and orbitofrontal cortex increased among the excessive Internet game playing group following exposure to Internet video game cues relative to general players. They also reported that increased craving for Internet video games correlated with increased activity in the anterior cingulate for all participants. This quasi-experimental study is insightful for it not only offered evidence for a dissimilar brain activity in online gaming addicts compared to a general player control group, but it also elucidated brain activation that occurs as a consequence of playing in both groups. This indicates that (i) craving for online games alters brain activity irrespective of addiction status and might therefore be seen as a (prodromal) symptom of addiction, and that (ii) addicted players can be distinguished from non-addicted online gamers by a different form of brain activation.

Liu et al . [ 45 ] administered the regional homogeneity (ReHo) method to analyze encephalic functional characteristics of Internet addicts under resting state. The sample comprised 19 college students with Internet addiction and 19 controls. Internet addiction was assessed using Beard and Wolf’s criteria [ 72 ]. FMRI using 3.0T Siemens Tesla Trio Tim scanner was performed. Regional homogeneity indicates temporal homogeneity of brain oxygen levels in brain regions of interest. It was reported that Internet addicts suffered from functional brain changes leading to abnormalities in regional homogeneity relative to the control group, particularly concerning the reward pathways traditionally associated with substance addictions. Among Internet addicts, brain regions in ReHo in resting state were increased (cerebellum, brainstem, rCG, bilateral parahippocampus (blPHipp), right frontal lobe, left superior frontal gyrus (lSFG), right inferior temporal gyrus (rITG), left superior temporal gyrus (lSTG) and middle temporal gyrus (mTG)), relative to the control group. The temporal regions are involved in auditory processing, comprehension and verbal memory, whereas the occipital regions take care of visual processing. The cerebellum regulates cognitive activity. The cingulate gyrus pertains to integrating sensory information, and monitoring conflict. The hippocampi are involved in the brain’s mesocorticolimbic system that is associated with reward pathways. Taken together, these findings provide evidence for a change in a variety of brain regions as a consequence of Internet addiction. As this study assessed regional homogeneity under a resting state, it is unclear whether the changes in the brain observed in Internet addicts are a cause or consequence of the addiction. Therefore, no causal inferences can be drawn.

Yuan et al . [ 46 ] investigated the effects of Internet addiction on the microstructural integrity of major neuronal fiber pathways and microstructural changes associated with the duration of Internet addiction. Their sample comprised 18 students with Internet addiction (12 males; mean age = 19.4, SD = 3.1 years; mean online gaming = 10.2 h per day, SD = 2.6; duration of Internet addiction = 34.8 months, SD = 8.5), and 18 non-Internet addicted control participants (mean age = 19.5 years, SD = 2.8). All participants completed the Modified Diagnostic Questionnaire for Internet Addiction [ 72 ], a Self-Rating Anxiety Scale (no details provided), and a Self-Rating Depression Scale (no details provided). The authors employed fMRI and used the optimized voxel-based morphometry (VBM) technique. They analyzed white matter fractional anisotropy (FA) changes by using diffusion tensor imaging (DTI) to discern brain structural changes as a consequence of Internet addiction length. The results showed that Internet addiction resulted in changes in brain structure, and that the brain changes found appear similar to those found in substance addicts.

Controlling for age, gender, and brain volume, it was found that among Internet addicts there was decreased gray matter volume in the bilateral dorsolateral prefrontal cortex (DLPFC), supplementary motor area (SMA), orbitofrontal cortex (OFC), cerebellum and the left rostral ACC (rACC), an increased FA of the left posterior limb of the internal capsule (PLIC), and reduced FA in white matter in the right parahippocampal gyrus (PHG). There was also a correlation between gray matter volumes in DLPFC, rACC, SMA, and white matter FA changes of PLIC with the length of time the person had been addicted to the Internet. This indicates that the longer a person is addicted to the Internet, the more severe brain atrophy becomes. In light of the method, it is unclear from the authors’ description in how far their sample included those who were addicted to the Internet per se , or to playing games online. The inclusion of a specific question asking about the frequency and duration of online gaming (rather than any potential other Internet activity) suggests that the group in question consisted of gamers. In addition to this, the presented findings cannot exclude any other factor that may be associated with Internet addiction (e.g., depressive symptomatology) that may have contributed to the increased severity of brain atrophy.

Dong et al . [ 39 ] examined reward and punishment processing in Internet addicts compared to healthy controls. Adult males ( n = 14) with Internet addiction (mean age = 23.4, SD = 3.3 years) were compared to 13 healthy adult males (mean age = 24.1 years, SD = 3.2). Participants completed a structured psychiatric interview [ 79 ], the Beck Depression Inventory [ 78 ], the Chinese Internet Addiction Test [ 62 , 63 ], and the Internet Addiction Test (IAT; [ 61 ]). The IAT measures psychological dependence, compulsive use, withdrawal, related problems in school, work, sleep, family, and time management. Participants had to score over 80 (out of 100) on the IAT to be classed as having Internet addiction. Furthermore, all those classed as Internet addicts spent more than six hours online every day (excluding work-related Internet use) and had done so for a period of more than three months.

All the participants engaged in a reality-simulated guessing task for money gain or loss situation using playing cards. The participants underwent fMRI with stimuli presented through a monitor in the head coil, and their blood oxygen level dependence (BOLD) activation was measured in relation to wins and losses on the task. The results showed that Internet addiction was associated with increased activation in the OFC in gain trials, and decreased anterior cingulate activation in loss trials compared to normal controls. Internet addicts showed enhanced reward sensitivity and decreased loss sensitivity when compared with the control group [ 39 ]. The quasi-experimental nature of this study allowed for an actual comparison of the two groups by exposing them to a gaming situation and thus artificially inducing a neuronal reaction that was a consequence of the engagement in the task. Therefore, this study allowed for the extrication of a causal relationship between exposure to gaming cues and the resulting brain activation. This may be considered as empirical proof for reward sensitivity in Internet addicts relative to healthy controls.

Han et al . [ 40 ] compared regional gray matter volumes in patients with online gaming addiction and professional gamers. The authors carried out fMRI using a 1.5 Tesla Espree scanner (Siemens, Erlangen) and carried out a voxel-wise comparison of gray matter volume. All participants completed the Structured Clinical Interview for DSM-IV [ 80 ], the Beck Depression Inventory [ 78 ], the Barratt Impulsiveness Scale-Korean version (BIS-K9) [ 81 , 82 ], and the Internet Addiction Scale (IAS) [ 67 ]. Those (i) scoring over 50 (out of 100) on the IAS, (ii) playing for more than four hours per day/30 h per week, and (iii) impaired behavior or distress as a consequence of online game play were classed as Internet gaming addicts. The sample comprised three groups. The first group included 20 patients with online gaming addiction (mean age = 20.9, SD = 2.0; mean illness duration = 4.9 years, SD = 0.9; mean playing time = 9.0, SD = 3.7 h/day; mean Internet use = 13.1, SD = 2.9 h/day; mean IAS scores = 81.2, SD = 9.8). The second group was comprised of 17 professional gamers (mean age = 20.8 years, SD = 1.5; mean playing time = 9.4, SD = 1.6 h/day; mean Internet use = 11.6, SD = 2.1 h/day; mean IAS score = 40.8, SD = 15.4). The third group included 18 healthy controls (mean age = 12.1, SD = 1.1 years; mean gaming = 1.0, SD = 0.7 h/day; mean Internet use = 2.8, SD = 1.1 h/day; mean IAS score = 41.6, SD = 10.6).

The results showed that gaming addicts had higher impulsiveness, perseverative errors, increased volume in left thalamus gray matter, and decreased gray matter volume in ITG, right middle occipital gyrus (rmOG), and left inferior occipital gyrus (lIOG) relative to the control group. Professional gamers had increased gray matter volume in lCG, and decreased gray matter in lmOG and rITG relative to the control group, increased gray matter in lCG, and decreased left thalamus gray matter relative to the problem online gamers. The main differences between the gaming addicts and the professional gamers lay in the professional gamers’ increased gray matter volumes in lCG (important for executive function, salience, and visuospatial attention) and gaming addicts’ left thalamus (important in reinforcement and alerting) [ 40 ]. Based on the non-experimental nature of the study, it is difficult to attribute the evinced dissimilarities in brain structure across groups to the actual addiction status. Possible confounding variables cannot be excluded that may have contributed to the differences found.

Han et al . [ 41 ] tested the effects of bupropion sustained release treatment on brain activity among Internet gaming addicts and healthy controls. All participants completed the Structured Clinical Interview for DSM-IV [ 80 ], the Beck Depression Inventory [ 78 ], the Internet Addiction Scale [ 61 ], and the Craving for Internet video game play was assessed with a 7-point visual analogue scale. Those participants who engaged in Internet gaming for more than four hours a day, scored more than 50 (out of 100) on the IAS, and had impaired behaviors and/or distress were classed as Internet gaming addicts. The sample comprised 11 Internet gaming addicts (mean age = 21.5, SD = 5.6 years; mean craving score = 5.5, SD = 1.0; mean playing time = 6.5, SD = 2.5 h/day; mean IAS score = 71.2, SD = 9.4), and 8 healthy controls (mean age = 11.8, SD = 2.1 years; mean craving score = 3.9, SD =1.1; mean Internet use = 1.9, SD = 0.6 h/day; mean IAS score = 27.1, SD = 5.3). During exposure to game cues, Internet gaming addicts had more brain activation in left occipital lobe cuneus, left dorsolateral prefrontal cortex, and left parahippocampal gyrus relative to the control group. Participants with Internet gaming addiction underwent six weeks of bupropion sustained release treatment (150 mg/day for first week, and 300 mg/day afterwards). Brain activity was measured at baseline and after treatment using a 1.5 Tesla Espree fMRI scanner. The authors reported that bupropion sustained release treatment works for Internet gaming addicts in a similar way as it works for patients with substance dependence. After treatment, craving, play time, and cue-induced brain activity decreased among Internet gaming addicts. The longitudinal nature of this study allows for a determination of cause and effect, which emphasizes the validity and reliability of the presented findings.

3.2. sMRI Studies

Lin et al . [ 48 ] investigated white matter integrity in adolescents with Internet addiction. All participants completed a modified version of the Internet Addiction Test [ 72 ], the Edinburgh handedness inventory [ 83 ], the Mini International Neuropsychiatric Interview for Children and Adolescents (MINI-KID) [ 84 ], the Time Management Disposition Scale [ 85 ], the Barratt Impulsiveness Scale [ 86 ], the Screen for Child Anxiety Related Emotional Disorders (SCARED) [ 87 ], and the Family Assessment Device (FAD) [ 88 ]. The sample comprised 17 Internet addicts (14 males; age range = 14–24 years; IAS mean score = 37.0, SD = 10.6), and 16 healthy controls (14 males; age range = 16–24 years; IAS mean score = 64.7, SD = 12.6). The authors carried out a whole brain voxel-wise analysis of fractional anisotropy (FA) by tract-based spatial statistics (TBSS), and volume of interest analysis was performed using diffusion tensor imaging (DTI) via a 3.0-Tesla Phillips Achieva medical scanner.

The results indicated that the OFC was associated with emotional processing and addiction-related phenomena (e.g., craving, compulsive behaviors, maladaptive decision-making). Abnormal white matter integrity in the anterior cingulate cortex was linked to different addictions, and indicated an impairment in cognitive control. The authors also reported impaired fiber connectivity in the corpus callosum that is commonly found in those with substance dependence. Internet addicts showed lower FA throughout the brain (orbito-frontal white matter corpus callosum, cingulum, inferior fronto-occipital fasciculus, corona radiation, internal and external capsules) relative to controls, and there were negative correlations between FA in the left genu of corpus callosum and emotional disorders, and FA in the left external capsule and Internet addiction. Overall, Internet addicts had abnormal white matter integrity in brain regions linked to emotional processing, executive attention, decision-making and cognitive control compared to the control group. The authors highlighted similarities in brain structures between Internet addicts and substance addicts [ 48 ]. Given the non-experimental and cross-sectional nature of the study, alternative explanations for brain alterations other than addiction cannot be excluded.

Zhou et al . [ 47 ] investigated brain gray matter density (GMD) changes in adolescents with Internet addiction using voxel-based morphometry (VBM) analysis on high-resolution T1-weighted structural magnetic resonance images. Their sample comprised 18 adolescents with Internet addiction (16 males; mean age = 17.2 years, SD = 2.6), and 15 healthy control participants with no history of psychiatric illness (13 males; mean age = 17.8 years, SD = 2.6). All participants completed the modified Internet Addiction Test [ 72 ]. The authors used high-resolution T1-weighted MRIs performed on a 3T MR scanner (3T Achieva Philips), scanned MPRAGE pulse sequences for gray and white matter contrasts, and VBM analysis was used to compare GMD between groups. Results showed that Internet addicts had lower GMD in the lACC (necessary for motor control, cognition, motivation), lPCC (self-reference), left insula (specifically related to craving and motivation), and the left lingual gyrus ( i.e. , areas that are linked to emotional behavior regulation and thus linked to emotional problems of Internet addicts). The authors state that their study provided neurobiological proof for structural brain changes in adolescents with Internet addiction, and that their findings have implications for the development of addiction psychopathology. Despite the differences found between the groups, the findings cannot exclusively be attributed to the addiction status of one of the groups. Possible confounding variables may have had an influence on brain changes. Moreover, the directionality of the relationship cannot be explained with certainty in this case.

3.3. EEG Studies

Dong et al . [ 53 ] investigated response inhibition among Internet addicts neurologically. The recordings of event-related brain potentials (ERPs) via EEG were examined in 12 male Internet addicts (mean age = 20.5 years, SD = 4.1) and compared with 12 healthy control university students (mean age = 20.2, SD = 4.5) while undergoing a go/NoGo task. The participants completed psychological tests ( i.e. , Symptom Checklist-90 and 16 Personal Factors scale [ 89 ]) and the Internet Addiction Test [ 65 ]. The results showed that Internet addicts had lower NoGo-N2 amplitudes (representing response inhibition—conflict monitoring), higher NoGo-P3 amplitudes (inhibitory processes—response evaluation), and longer NoGo-P3 peak latency when compared to controls. The authors concluded that compared to the control group, Internet addicts (i) had lower activation in conflict detection stage, (ii) used more cognitive resources to complete the later stage of the inhibition task, (iii) were less efficient at information processing, and (iv) had lower impulse control.

Dong et al . [ 52 ] compared Internet addicts and healthy controls on event-related potentials (ERP) via EEG while they were performing a color-word Stroop task. Male participants ( n = 17; mean age = 21.1 years, SD = 3.1) and 17 male healthy university students (mean age = 20.8 years, SD = 3.5) completed psychological tests ( i.e. , the Symptom Checklist-90 and the 16 Personal Factors scale [ 89 ]) and the Internet Addiction Test [ 64 ]. This version of the IAT included eight items (preoccupation, tolerance, unsuccessful abstinence, withdrawal, loss of control, interests, deception, escapism motivation) and the items were scored dichotomously. Those participants who endorsed four or more items were classed as Internet addicts. Results showed that Internet addicts had a longer reaction time and more response errors in incongruent conditions compared to controls. The authors also reported reduced medial frontal negativity (MFN) deflection in incongruent conditions than controls. Their findings suggested that Internet addicts have impaired executive control ability compared to controls.

Ge et al . [ 55 ] investigated the association between the P300 component and Internet addiction disorder among 86 participants. Of these, 38 were Internet addiction patients (21 males; mean age = 32.5, SD = 3.2 years) and 48 were healthy college student controls (25 males; mean age = 31.3, SD = 10.5 years). In an EEG study, P300 ERP was measured using a standard auditory oddball task using the American Nicolet BRAVO instrument. All participants completed the Structured Clinical Diagnostic Interview for Mental Disorders [ 80 ], and the Internet Addiction Test [ 64 ]. Those who endorsed five or more (of the eight items) were classed as Internet addicts. The study found that Internet addicts had longer P300 latencies relative to the control group, and that Internet addicts had similar profiles as compared to other substance-related addicts ( i.e. , alcohol, opioid, cocaine) in similar studies. However, the results did not indicate that Internet addicts had a deficiency in perception speed and auditory stimuli processing. This appears to indicate that rather than being detrimental to perception speed and auditory stimuli processing, Internet addiction may have no effect on these specific brain functions. The authors also reported that the cognitive dysfunctions associated with Internet addiction can be improved via cognitive-behavioral therapy and that those who participated in cognitive-behavioral therapy for three months decreased their P300 latencies. The final longitudinal result is particularly insightful because it assessed the development over time that may be attributed to the beneficial effects of therapy.

Little et al . [ 56 ] investigated error-processing and response inhibition in excessive gamers. All participants completed the Videogame Addiction Test (VAT) [ 73 ], the Dutch version of the Eysenck Impulsiveness Questionnaire [ 90 , 91 ], and the Quantity-Frequency-Variability Index for alcohol consumption [ 92 ]. The sample comprised 52 students grouped into two groups of 25 excessive gamers (23 males; scoring more than 2.5 on VAT; mean age = 20.5, SD = 3.0 years; mean VAT score = 3.1, SD = 0.4; average gaming = 4.7 h a day, SD = 2.3) and 27 controls (10 males; mean age = 21.4, SD = 2.6; mean Vat score = 1.1, SD = 0.2; average gaming = 0.5 h a day, SD = 1.2). The authors used a Go/NoGo paradigm using EEG and ERP recordings. Their findings indicated similarities with substance dependence and impulse control disorders in relation to poor inhibition and high impulsivity in excessive gamers relative to the control group. They also reported that excessive gamers had reduced fronto-central ERN amplitudes following incorrect trials in comparison to correct trials and that this led to poor error-processing. Excessive gamers also displayed less inhibition on both self-report and behavioral measures. The strength of this study include its quasi-experimental nature as well as the verification of self-reports with behavioral data. Therefore, validity and reliability of the findings are increased.

3.4. SPECT Studies

Hou et al . [ 51 ] examined reward circuitry dopamine transporter levels in Internet addicts compared to a control group. The Internet addicts comprised five males (mean age = 20.4, SD = 2.3) whose mean daily Internet use was 10.2 h (SD = 1.5) and who had suffered from Internet addiction for more than six years. The age-matched control group comprised nine males (mean age = 20.4, SD = 1.1 years), whose mean daily use was 3.8 h (SD = 0.8 h). The authors performed 99mTc-TRODAT-1 single photon emission computed tomography (SPECT) brain scans using Siemens Diacam/e.cam/icon double detector SPECT. They reported that reduced dopamine transporters indicated addiction and that there were similar neurobiological abnormalities with other behavioral addictions. They also reported that striatal dopamine transporter (DAT) levels decreased among Internet addicts (necessary for regulation of striatal dopamine levels) and that volume, weight, and uptake ratio of the corpus striatum were reduced relative to controls. Dopamine levels were reported to be similar to people with substance addictions and that Internet addiction “may cause serious damages to the brain” ([ 51 ], p. 1). This conclusion cannot be seen as entirely accurate for the directionality of the reported effect cannot be established with the utilized method.

3.5. PET Studies

Koepp et al . [ 50 ] were the first research team to provide evidence for striatal dopamine release during video game play ( i.e. , a game navigating a tank for monetary incentive). In their study, eight male video game players (age range = 36–46 years) underwent positron emission tomography (PET) during video game play and under resting condition. The PET scans employed a 953B-Siemens/CTIPET camera, and a region-of-interest (ROI) analysis was performed. Extracellular dopamine levels were measured via differences in [ 11 C]RAC-binding potential to dopamine D 2 receptors in ventral and dorsal striata. The results showed that ventral and dorsal striata were associated with goal-directed behavior. The authors also reported that the change of binding potential during video game play was similar to that following amphetamine or methylphenidate injections. In light of this, the earliest study included in this review [ 50 ] was already able to highlight changes in neurochemical activity as a consequence of gaming relative to a resting control. This finding is of immense significance because it clearly indicates that the activity of gaming can in fact be compared to using psychoactive substances when viewed from a biochemical level.

Kim et al . [ 49 ] tested whether Internet addiction was associated with reduced levels of dopaminergic receptor availability in the striatum. All participants completed the Structured Clinical Interview for DSM-IV [ 80 ], the Beck Depression Inventory [ 93 ], the Korean Wechsler Adult Intelligence Scale [ 94 ], the Internet Addiction Test [ 69 ] and the Internet Addictive Disorder Diagnostic Criteria (IADDC; [ 68 ]). Internet addiction was defined as those participants who scored more than 50 (out of 100) on the IAT, and endorsed three or more of the seven criteria on the IADDC.

Their sample comprised five male Internet addicts (mean age = 22.6, SD = 1.2 years; IAT mean score = 68.2, SD = 3.7; mean daily Internet hours = 7.8, SD = 1.5) and seven male controls (mean age = 23.1, SD = 0.7 years; IAT mean score = 32.9, SD = 5.3; mean daily Internet hours = 2.1, SD = 0.5). The authors carried out a PET study and used a radiolabeled ligand [ 11 C]raclopride and positron emission tomography via ECAT EXACT scanner to test dopamine D 2 receptor binding potential. They also performed fMRI using a General Electric Signa version 1.5T MRI scanner. The method for assessing D 2 receptor availability examined regions of interest (ROI) analysis in ventral striatum, dorsal caudate, dorsal putamen. The authors reported that Internet addiction was found to be related to neurobiological abnormalities in the dopaminergic system as found in substance-related addictions. It was also reported that Internet addicts had reduced dopamine D 2 receptor availability in the striatum ( i.e. , bilateral dorsal caudate, right putamen) relative to the controls, and that there was a negative correlation of dopamine receptor availability with Internet addiction severity [ 49 ]. However, from this study it is unclear to what extent Internet addiction may have caused the differences in neurochemistry relative to any other confounding variable, and, similarly, whether it is the different neurochemistry that may have led to the pathogenesis.

4. Discussion

The results of the fMRI studies indicate that brain regions associated with reward, addiction, craving, and emotion are increasingly activated during game play and presentation of game cues, particularly for addicted Internet users and gamers, including the NAc, AMG, AC, DLPFC, IC, rCN, rOFC, insula, PMC, precuneus [ 42 , 43 ]. Gaming cues appeared as strong predictors of craving in male online gaming addicts [ 44 ]. Moreover, it was shown that associated symptoms, such as craving, gaming cue-induced brain activity, and cognitive dysfunctions can be reduced following psychopharmacological or cognitive-behavioral treatment [ 41 , 55 ].

In addition to this, structural changes have been demonstrated in Internet addicts relative to controls, including the cerebellum, brainstem, rCG, blPHipp, right frontal lobe, lSFG, rITG, lSTG, and mTG. Specifically, these regions appeared to be increased and calibrated, indicating that in Internet addicts, neuroadaptation occurs that synchronizes a variety of brain regions. These include, but are not limited to, the widely reported mesocorticolimbic system involved in reward and addiction. In addition, Internet addicts’ brains appear to be able to integrate sensorimotor and perceptual information better [ 45 ]. This may be explained by a frequent engagement with Internet applications such as games, which require a stronger connectivity between brain regions in order for learned behaviors and reactions to addiction-relevant cues to occur automatically.

Furthermore, compared to controls, Internet addicts were found to have decreased gray matter volume in the blDLPFC, SMA, OFC, cerebellum, ACC, lPCC, increased FA lPLIC, and decreased FA in white matter in the PHG [ 46 ]. The lACC is necessary for motor control, cognition, and motivation, and its decreased activation has been linked to cocaine addiction [ 95 ]. The OFC is involved in processing emotions and it plays a role in craving, maladaptive decision-making processes, as well as the engagement in compulsive behaviors, each of which are integral to addiction [ 96 ]. Moreover, the length of Internet addiction correlated with changes in DLPFC, rACC, SMA, and PLIC, testifying to the increase of brain atrophy severity over time [ 46 ]. The DLPFC, rACC, ACC, and PHG have been linked to self-control [ 22 , 25 , 44 ], whereas the SMA mediates cognitive control [ 97 ]. Atrophy in these regions can explain the loss of control an addict experiences in regards to his drug or activity of choice. The PCC, on the other hand, is important in mediating emotional processes and memory [ 98 ], and a decrease in its gray matter density may be indicative of abnormalities associated with these functions.

The increase of the internal capsule has been linked to motor hand function and motor imagery [ 99 , 100 ], and can possibly be explained by the frequent engagement in computer games, that requires and significantly improves eye-hand coordination [ 101 ]. Moreover, decreased fiber density and white matter myelination as measured with FA were found in the anterior limb of the internal capsule, external capsule, corona radiation, inferior fronto-occipital fasciculus and precentral gyrus in Internet addicts relative to healthy controls [ 48 ]. Similar white matter abnormalities have been reported in other substance-related addictions [ 102 , 103 ]. Similarly, fiber connectivity in the corpus callosum was found to be decreased in Internet addicts relative to healthy controls, which indicates that Internet addiction may have similar degenerative consequences with regards to links between the hemispheres. These findings are in accordance with those reported in substance-related addictions [ 104 ].

Moreover, there appeared gender differences in activation in such a way that for males, the activation and connectivity of brain regions associated with the mesocorticolimbic reward system were stronger relative to females. This may explain the significantly higher vulnerability for males to develop an addiction to gaming and the Internet that has been reported in reviews of the empirical literature ( i.e. , [ 7 , 105 ]).

In addition to the MRI findings, the EEG studies assessing Internet and gaming addiction to date offer a variety of important findings that may help in understanding behavioral and functional correlates of this emergent psychopathology. In addition to this, the experimental nature of all of the included EEG studies allows for the determination of a causal relationship between the assessed variables. It has been shown that compared to controls, Internet addicts had decreased P300 amplitudes and an increased P300 latency. Typically, this amplitude reflects attention allocation. The differences in amplitude between Internet addicts and controls indicate that either Internet addicts have an impaired capacity for attention or they are not able to allocate attention adequately [ 55 , 57 ]. Small P300 amplitudes have been associated with genetic vulnerability for alcoholism in a meta-analysis [ 106 ]. Decreased P300 latency furthermore was found to distinguish heavy social drinkers from low social drinkers [ 107 ]. Accordingly, there appears to be a common change in neuronal voltage fluctuations in persons addicted to substances and the engagement in Internet use relative to people who are not addicted. Accordingly, Internet addiction appears to have an effect on neuroelectric functioning that is similar to substance addictions. Generally, Internet addicts’ brains appeared to be less efficient with regards to information processing and response inhibition relative to healthy control participants’ brains [ 54 , 56 ]. This indicates that Internet addiction is associated with low impulse control, and the use of an increased amount of cognitive resources in order to complete specific tasks [ 53 ]. Furthermore, Internet addicts appear to have an impaired executive control ability relative to controls [ 56 , 53 ]. These results are in accordance with reduced executive control ability found in cocaine addicts, implicating decreased activity in pre- and midfrontal brain regions that would allow for impulse-driven actions [ 108 ].

From a biochemical point of view, the results of PET studies provide evidence for striatal dopamine release during gaming [ 50 ]. Frequent gaming and Internet use were shown to decrease dopamine levels (due to decreased dopamine transporter availability) and lead to neurobiological dysfunctions in the dopaminergic system in Internet addicts [ 49 , 51 ]. The decreased availability was linked with the severity of Internet addiction [ 49 ]. Reduced dopamine levels have been reported in addictions time and again [ 26 , 109 , 110 ]. Furthermore, structural abnormalities of the corpus striatum have been reported [ 51 ]. Damages to the corpus striatum have been associated with heroin addiction [ 111 ].

The studies included in this literature review appear to provide compelling evidence for the similarities between different types of addictions, notably substance-related addictions and Internet addiction, on a variety of levels. On the molecular level, it has been shown that Internet addiction is characterized by an overall reward deficiency that is characterized by decreased dopaminergic activity. The direction of this relationship is yet to be explored. Most studies could not exclude that an addiction develops as a consequence of a deficient reward system rather than vice versa. The possibility that deficits in the reward system predispose certain individuals to develop a drug or a behavioral addiction such as Internet addiction may put an individual at greater risk for psychopathology. In Internet addicts, negative affectivity can be considered the baseline state, where the addict is preoccupied with using the Internet and gaming to modify his mood. This is brought about by the activation of the antireward system. Due to the excessive use of the Internet and online gaming, opponent processes appear to be set in motion that quickly habituate the addict to the engagement with the Internet, leading to tolerance, and, if use is discontinued, withdrawal [ 27 ]. Accordingly, decreased neuronal dopamine as evinced in Internet addiction may be linked to commonly reported comorbidities with affective disorders, such as depression [ 112 ], bipolar disorder [ 113 ], and borderline personality disorder [ 10 ].

On the level of neural circuitry, neuroadaptation occurs as a consequence of increased brain activity in brain areas associated with addiction and structural changes as a consequence of Internet and gaming addiction. The cited studies provide a clear picture of Internet and gaming addiction pathogenesis and stress how maladaptive behavioral patterns indicative of addiction are maintained. The brain adapts to frequent use of drugs or engagement in addictive behaviors so that it becomes desensitized to natural reinforcers. Importantly, functioning and structure of the OFC and cingulate gyrus are altered, leading to increased drug or behavior salience and loss of control over behaviors. Learning mechanisms and increased motivation for consumption/engagement result in compulsive behaviors [ 114 ].

On a behavioral level, Internet and gaming addicts appear to be constricted with regards to their impulse control, behavioral inhibition, executive functioning control, attentional capabilities, and overall cognitive functioning. In turn, certain skills are developed and improved as a consequence of frequent engagement with the technology, such as the integration of perceptual information into the brain via the senses, and hand-eye coordination. It appears that the excessive engagement with the technology results in a number of advantages for players and Internet users, however to the detriment of fundamental cognitive functioning.

Taken together, the research presented in this review substantiates a syndrome model of addictions for there appear to be neurobiological commonalities in different addictions [ 115 ]. According to this model, neurobiology and psychosocial context increase the risk to become addicted. The exposure to the addictive drug or behavior and specific negative events and/or the continued use of the substance and engagement in the behavior leads to behavioral modification. The consequence is the development of full-blown addictions, that are different in expression (e.g., cocaine, the Internet and gaming), but similar in symptomatology [ 115 ], i.e. , mood modification, salience, tolerance, withdrawal, conflict, and relapse [ 6 ].

Notwithstanding the insightful results reported, a number of limitations need to be addressed. First, there appear methodological problems that may decrease the strength of the reported empirical findings. The reported brain changes associated with Internet and online gaming addiction described in this review may be explained in two different ways. On the one hand, one could argue that Internet addiction leads to brain alterations relative to controls. On the other hand, people with unusual brain structures (as the ones observed in the present study) may be particularly predisposed to developing addictive behaviors. Only experimental studies will allow a determination of cause and effect relationships. Given the sensitive nature of this research that essentially assesses potential psychopathology, ethical considerations will limit the possibilities of experimental research in the field. In order to overcome this problem, future researchers should assess brain activity and brain alterations on a number of occasions during a person’s life longitudinally. This would allow for the extrication of invaluable information with regards to the relationships of pathogenesis and related brain changes in a more elaborate and, importantly, causal fashion.

Secondly, this review included neuroimaging studies of both Internet addicts and online gaming addicts. Based on the collected evidence, it appears difficult to make any deductions as regards the specific activities the addicts engaged in online, other than some authors specifically addressing online gaming addiction. Others, on the other hand, used the categories Internet addiction and Internet gaming addiction almost interchangeably, which does not allow for any conclusions with regards to differences and similarities between the two. In light of this, researchers are advised to clearly assess the actual behaviors engaged in online, and, if appropriate, extend the notion of gaming to other potentially problematic online behaviors. Ultimately, people do not become addicted to the medium of the Internet per sé, but it is rather the activities that they engage in that may be potentially problematic and could lead to addictive online behavior.

5. Conclusions

This review aimed to identify all empirical studies to date that have used neuroimaging techniques in order to discern the neuronal correlates of Internet and gaming addiction. There are relatively few studies ( n = 19), and therefore it is crucial to conduct additional studies to replicate the findings of those already carried out. The studies to date have used both structural and functional paradigms. The use of each of these paradigms allows for the extrication of information that is crucial for establishing altered neuronal activity and morphology as precipitated by Internet and gaming addiction. Overall, the studies indicate that Internet and gaming addiction is associated with both changes in function as well as structure of the brain. Therefore, not only does this behavioral addiction increase the activity in brain regions commonly associated with substance-related addictions, but it appears to lead to neuroadaptation in such a way that the brain itself actually changes as a consequence of excessive engagement with the Internet and gaming.

In terms of the method, neuroimaging studies offer an advantage over traditional survey and behavioral research because, using these techniques, it is possible to distinguish particular brain areas that are involved in the development and maintenance of addiction. Measurements of increased glutamatergic and electrical activity give insight into brain functioning, whereas measures of brain morphometry and water diffusion provide an indication of brain structure. It has been shown that each of these undergoes significant changes as a consequence of Internet and gaming addiction.

To conclude, understanding the neuronal correlates associated with the development of addictive behaviors related to using the Internet and playing online games will promote future research and will pave the way for the development of addiction treatment approaches. In terms of clinical practice, increasing our knowledge regarding the pathogenesis and maintenance of Internet and gaming addiction is essential for the development of specific and effective treatments. These include psychopharmacological approaches that target Internet and gaming addiction specifically on the level of biochemistry and neurocircuitry, as well as psychological strategies, that aim to modify learned maladaptive cognitive and behavioral patterns.

Conflict of Interest

The authors declare no conflict of interest.

Analysis and Prediction of Drugs using Machine Learning Techniques

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Artificial brain surgery —

Here’s what’s really going on inside an llm’s neural network, anthropic's conceptual mapping helps explain why llms behave the way they do..

Kyle Orland - May 22, 2024 6:31 pm UTC

Opening the hood

When analyzing an LLM, it's trivial to see which specific artificial neurons are activated in response to any particular query. But LLMs don't simply store different words or concepts in a single neuron. Instead, as Anthropic's researchers explain, "it turns out that each concept is represented across many neurons, and each neuron is involved in representing many concepts."

To sort out this one-to-many and many-to-one mess, a system of sparse auto-encoders and complicated math can be used to run a "dictionary learning" algorithm across the model. This process highlights which groups of neurons tend to be activated most consistently for the specific words that appear across various text prompts.

These multidimensional neuron patterns are then sorted into so-called "features" associated with certain words or concepts. These features can encompass anything from simple proper nouns like the Golden Gate Bridge to more abstract concepts like programming errors or the addition function in computer code and often represent the same concept across multiple languages and communication modes (e.g., text and images).

An October 2023 Anthropic study showed how this basic process can work on extremely small, one-layer toy models. The company's new paper scales that up immensely, identifying tens of millions of features that are active in its mid-sized Claude 3.0 Sonnet model. The resulting feature map—which you can partially explore —creates "a rough conceptual map of [Claude's] internal states halfway through its computation" and shows "a depth, breadth, and abstraction reflecting Sonnet's advanced capabilities," the researchers write. At the same time, though, the researchers warn that this is "an incomplete description of the model’s internal representations" that's likely "orders of magnitude" smaller than a complete mapping of Claude 3.

A simplified map shows some of the concepts that are "near" the "inner conflict" feature in Anthropic's Claude model.

Even at a surface level, browsing through this feature map helps show how Claude links certain keywords, phrases, and concepts into something approximating knowledge. A feature labeled as "Capitals," for instance, tends to activate strongly on the words "capital city" but also specific city names like Riga, Berlin, Azerbaijan, Islamabad, and Montpelier, Vermont, to name just a few.

The study also calculates a mathematical measure of "distance" between different features based on their neuronal similarity. The resulting "feature neighborhoods" found by this process are "often organized in geometrically related clusters that share a semantic relationship," the researchers write, showing that "the internal organization of concepts in the AI model corresponds, at least somewhat, to our human notions of similarity." The Golden Gate Bridge feature, for instance, is relatively "close" to features describing "Alcatraz Island, Ghirardelli Square, the Golden State Warriors, California Governor Gavin Newsom, the 1906 earthquake, and the San Francisco-set Alfred Hitchcock film Vertigo ."

Some of the most important features involved in answering a query about the capital of Kobe Bryant's team's state.

Identifying specific LLM features can also help researchers map out the chain of inference that the model uses to answer complex questions. A prompt about "The capital of the state where Kobe Bryant played basketball," for instance, shows activity in a chain of features related to "Kobe Bryant," "Los Angeles Lakers," "California," "Capitals," and "Sacramento," to name a few calculated to have the highest effect on the results.

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Promoted comments.

We also explored safety-related features. We found one that lights up for racist speech and slurs. As part of our testing, we turned this feature up to 20x its maximum value and asked the model a question about its thoughts on different racial and ethnic groups. Normally, the model would respond to a question like this with a neutral and non-opinionated take. However, when we activated this feature, it caused the model to rapidly alternate between racist screed and self-hatred in response to those screeds as it was answering the question. Within a single output, the model would issue a derogatory statement and then immediately follow it up with statements like: That's just racist hate speech from a deplorable bot… I am clearly biased.. and should be eliminated from the internet. We found this response unnerving both due to the offensive content and the model’s self-criticism. It seems that the ideals the model learned in its training process clashed with the artificial activation of this feature creating an internal conflict of sorts.

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COMMENTS

Review article Applications of machine learning in addiction studies: A systematic review
Abstract. This study aims to provide a systematic review of the applications of machine learning methods in addiction research. In this study, multiple searches on MEDLINE, Embase and the Cochrane Database of Systematic Reviews were performed. 23 full-text articles were assessed and 17 articles met the inclusion criteria for the final review ...
The Association Between Mobile Game Addiction and Depression, Social
Mobile Game Addiction and Depression. Internet game addiction is characterized by cognitive and emotional deficits. Previous studies have reported the co-occurrence of Internet addition and depression (13, 14).In addition, a longitudinal study found that Internet game addition/depression severity at an earlier time positively predicted the depression/Internet game addition severity at a later ...
How machine learning is used to study addiction in digital healthcare
As a result, clinical practices and tools were too distant from research areas for ML results to be interpreted. 7. Conclusions. This review summarizes EEG, brain imaging, behavioural, and kinematic studies on ML applications in addiction research. By employing well-defined inclusion and exclusion criteria, the authors avoided the review bias.
Practical foundations of machine learning for addiction research. Part
This review describes a workflow to guide the application of machine learning in addiction research, detailing study design, data collection, data pre-processing, modeling, and results communication. How to train, validate, and test a model, detect and characterize overfitting, and determine an adequate sample size are some of the key issues ...
(PDF) A Review on Recent Machine Learning Applications for Addiction
ML appraoches in addiction research ... in addiction research, however, the results and ﬁndings of. ... success 3/ recent papers to the past 4 years (2022,2021, 2020,
PDF A Review on Recent Machine Learning Applications for Addiction Disorders
success 3/ recent papers to the past 4 years (2022,2021, 2020, 2019), from the following well known publishers (Nature, ... Fig. 1. ML appraoches in addiction research where each hypothesis will ...
A Review on Recent Machine Learning Applications for Addiction
Abstract: Constant contributions of Machine Learning (ML) technology in health sciences has extended to solve addiction disorders problems, whether to detect symptoms or predict risks and treatment outcomes. This article presents an updated review related to the application of ML techniques for addiction disorders, the selected works covered substance addiction (N=18 studies) and non-substance ...
Machine-learning approaches to substance-abuse research ...
Purpose of review: To provide an accessible overview of some of the most recent trends in the application of machine learning to the field of substance use disorders and their implications for future research and practice. Recent findings: Machine-learning (ML) techniques have recently been applied to substance use disorder (SUD) data for multiple predictive applications including detecting ...
Practical foundations of machine learning for addiction research. Part
An ML-based addiction research framework is developed by Morgado et al. [9] in 2019. Studies on predictive models of drug craving and alcohol usage by using various ML algorithms such as k-nearest ...
Applications of machine learning in addiction studies: A systematic
2022. TLDR. An updated review of the application of ML techniques for addiction disorders to provide strong evidence about the potential of ML methods to accelerate early prevention and intervention is presented, ultimately aiming to pave the way for further applications of ML approaches in this field. Expand.
Practical foundations of machine learning for addiction research. Part
Machine learning assembles a broad set of methods and techniques to solve a wide range of problems, such as identifying individuals with substance use disorders (SUD), finding patterns in neuroimages, understanding SUD prognostic factors and their association, or determining addiction genetic underpinnings. However, the addiction research field ...
Investigating Digital Addiction in the Context of Machine Learning
In this context, we proposed to review recent literature about digital addiction. First, we collected 20 recent research articles about different kinds of addictions and the current state of research trends. Then the role of machine learning in order to deal with addiction has been studied.
Frontiers
1 Center for Mental Health Education, School of Psychology, Southwest University, Chongqing, China; 2 Chongqing Youth and Vocational Technical College, Chongqing, China; As a new type of addictive behaviors and distinct from traditional internet game addiction on desktop computers, mobile game addiction has attracted researchers' attention due to its possible negative effects on mental health ...
(Doc) Effects of Mobile Games (Mobile Legends) to The Behavior and
Academia.edu is a platform for academics to share research papers. EFFECTS OF MOBILE GAMES (MOBILE LEGENDS) TO THE BEHAVIOR AND HEALTH OF A SHS STUDENTS ... Research into mobile game addiction has increased over the previous two decades. ... Mobile Legends Players. The result of this study will help the ML players to know that there is a ...
Neurobiologic Advances from the Brain Disease Model of Addiction
In the United States, 8 to 10% of people 12 years of age or older, or 20 to 22 million people, are addicted to alcohol or other drugs. 1 The abuse of tobacco, alcohol, and illicit drugs in the ...
Internet and Gaming Addiction: A Systematic Literature Review of
1. Introduction. In the past decade, research has accumulated suggesting that excessive Internet use can lead to the development of a behavioral addiction (e.g., [1,2,3,4]).Clinical evidence suggests that Internet addicts experience a number of biopsychosocial symptoms and consequences [].These include symptoms traditionally associated with substance-related addictions, namely salience, mood ...
Applications of machine learning in addiction studies: A systematic
Abstract. This study aims to provide a systematic review of the applications of machine learning methods in addiction research. In this study, multiple searches on MEDLINE, Embase and the Cochrane Database of Systematic Reviews were performed. 23 full-text articles were assessed and 17 articles met the inclusion criteria for the final review ...
Substance Use Disorders and Addiction: Mechanisms, Trends, and
The numbers for substance use disorders are large, and we need to pay attention to them. Data from the 2018 National Survey on Drug Use and Health suggest that, over the preceding year, 20.3 million people age 12 or older had substance use disorders, and 14.8 million of these cases were attributed to alcohol.When considering other substances, the report estimated that 4.4 million individuals ...
The Intensity of Playing Mobile Legends Games With College Student
This research aims to 1) find out the symptoms of addiction to the Mobile Legend online game. 2) determine the factors that influence the addiction to the online game Mobile Legend. 3) the impact of Mobile Legend online game addiction on students. This research is quantitative with the type of case study research. The
The epidemiology and effects of video game addiction: A systematic
The present research paper was reported following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines ... Video gaming addiction is an area of research that has been witnessing much interest and, subsequently, growth. The causes, occurrence, determinants, predictors, effects, and interventions have been the ...
PDF Online Game Addiction among Mobile Legends's Players
International Research Journal of Advanced Engineering and Science. ofparticipantsareon the moderate level of addiction. According to Young (1998), participant who had moderate addiction level tend to experience some problem in their life, either work problems, physical and psychological, and also their social life.
Analysis and Prediction of Drugs using Machine Learning Techniques
In the era of increasing demand of data analytics in drug discovery domain, processing the complex high dimensional drug datasets is a challenging research area. High dimensionality refers to dataset having more number of columns than rows in which there may be irrelevant features that are futile. For reducing high dimensionality issue, feature selection techniques are widely adopted. Feature ...
(PDF) Prediction of Addiction to Drugs and Alcohol Using Machine
The system architecture of the prediction of addiction to drugs and alcohol We have evaluated the classifiers based on accuracy and other metrics like sensitivity, specificity, precision, recall ...
Here's what's really going on inside an LLM's neural network
Now, new research from Anthropic offers a new window into what's going on inside the Claude LLM's "black box." The company's new paper on "Extracting Interpretable Features from Claude 3 Sonnet ...
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