Problematic internet use is associated with structural alterations in the brain reward system in females

Altbäcker, Anna; Plózer, Enikő; Darnai, Gergely; Perlaki, Gábor; Horváth, Réka; Orsi, G.; Nagy, Szilvia Anett; Bogner, Péter; Schwarcz, Attila; Kovács, Norbert; Komoly, Sámuel; Clemens, Zsófia; Janszky, József

doi:10.1007/s11682-015-9454-9

Cited by 33 publications

(26 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As in our previous study (Altbäcker et al, 2016) and those of others (Park et al, 2017;Yuan et al, 2013), we found differences between IAs and controls. The left STC was found to be thicker for controls.…”

Section: Group Differencessupporting

confidence: 88%

“…Moreover, addictive behavior could further increase impulsivity (de Wit, 2009). Furthermore, a large body of research (Altbäcker et al, 2016;Kühn & Gallinat, 2015;Park et al, 2017;Yuan et al, 2013) have shown that IA has an influence on the morphology of several brain regions. Thus, it seems plausible to claim that the effects of impulsivity, combined with long-term exposure to some specific substance or stimuli, might result in different natures of the relationships between impulsivity and brain structure when compared to HCs.…”

Section: The Distinctive Connection Between Trait Impulsivity and Cormentioning

confidence: 99%

See 1 more Smart Citation

Differentiation between young adult Internet addicts, smokers, and healthy controls by the interaction between impulsivity and temporal lobe thickness

et al. 2019

Self Cite

View full text Add to dashboard Cite

Background and aims: Internet addiction is a non-substance-related addiction disorder with progressively growing prevalence. Internet addiction, like substance-related addictions, has been linked with high impulsivity, low inhibitory control, and poor decision-making abilities. Cortical thickness measurements and trait impulsivity have been shown to have a distinct relationship in addicts compared to healthy controls. Thus, we test whether the cortical correlates of trait impulsivity are different in Internet addicts and healthy controls, using an impulsive control group (smokers). Methods: Thirty Internet addicts (15 females) and 60 age-and gender-matched controls (30 smokers, all young adults aged 19-28 years) were scanned using a 3T MRI scanner and completed the Barratt Impulsiveness Scale. Results: Internet addicts had a thinner left superior temporal cortex than controls. Impulsivity had a significant main effect on the left pars orbitalis and bilateral insula, regardless of group membership. We identified divergent relationships between trait impulsivity and thicknesses of the bilateral middle temporal, right superior temporal, left inferior temporal, and left transverse temporal cortices between Internet addicts and healthy controls. Further analysis with smokers revealed that the left middle temporal and left transverse temporal cortical thickness change might be exclusive to Internet addiction. Discussion: The effects of impulsivity, combined with a long-term exposure to some specific substance or stimuli, might result in different natures of relationships between impulsivity and brain structure when compared to healthy controls. Conclusion: These results may indicate that Internet addiction is similar to substance-related addictions, such that inefficient self-control could result in maladaptive behavior and inability to resist Internet use.

show abstract

Section: Group Differencessupporting

confidence: 88%

Section: The Distinctive Connection Between Trait Impulsivity and Cormentioning

confidence: 99%

Differentiation between young adult Internet addicts, smokers, and healthy controls by the interaction between impulsivity and temporal lobe thickness

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…The gray matter volume of ACC was negative correlated with the incongruent response errors of Stroop task in IGD group [24]. Decreased gray matter volume in similar brain regions were revealed by several other VBM studies of IGD [25][26][27]. Moreover, compared with health normal controls, the individuals with IGD showed increased cortical thickness in the left precentral cortex, precuneus, middle frontal cortex, inferior temporal and middle temporal cortices and decreased cortical thicknesses of the left lateral OFC, insula, lingual gyrus, the right postcentral gyrus, entorhinal cortex and inferior parietal cortex [28].…”

Section: Structural Findings By Mrisupporting

confidence: 59%

The Neuroimaging Findings of Internet Gaming Disorder

Yu¹

2016

Int Arch Addict Res Med

View full text Add to dashboard Cite

“…However, our sample did not include adolescents. When problematic internet behaviors in adolescents were assessed in Korean youth, those were more prevalent in males (Ha and Hwang, 2014), nevertheless, similar structural brain changes have been identified in females with PIU (Altbäcker et al., 2015). In a recent study, about half of the individual differences in compulsive online behaviors were accounted for by genetic factors to an equal degree in both genders.…”

Section: Discussionmentioning

confidence: 69%

Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry

Ioannidis

Chamberlain

Treder

et al. 2016

Journal of Psychiatric Research

View full text Add to dashboard Cite

Problematic internet use is common, functionally impairing, and in need of further study. Its relationship with obsessive-compulsive and impulsive disorders is unclear. Our objective was to evaluate whether problematic internet use can be predicted from recognised forms of impulsive and compulsive traits and symptomatology. We recruited volunteers aged 18 and older using media advertisements at two sites (Chicago USA, and Stellenbosch, South Africa) to complete an extensive online survey. State-of-the-art out-of-sample evaluation of machine learning predictive models was used, which included Logistic Regression, Random Forests and Naïve Bayes. Problematic internet use was identified using the Internet Addiction Test (IAT). 2006 complete cases were analysed, of whom 181 (9.0%) had moderate/severe problematic internet use. Using Logistic Regression and Naïve Bayes we produced a classification prediction with a receiver operating characteristic area under the curve (ROC-AUC) of 0.83 (SD 0.03) whereas using a Random Forests algorithm the prediction ROC-AUC was 0.84 (SD 0.03) [all three models superior to baseline models p < 0.0001]. The models showed robust transfer between the study sites in all validation sets [p < 0.0001]. Prediction of problematic internet use was possible using specific measures of impulsivity and compulsivity in a population of volunteers. Moreover, this study offers proof-of-concept in support of using machine learning in psychiatry to demonstrate replicability of results across geographically and culturally distinct settings.

show abstract

Problematic internet use is associated with structural alterations in the brain reward system in females

Cited by 33 publications

References 47 publications

Differentiation between young adult Internet addicts, smokers, and healthy controls by the interaction between impulsivity and temporal lobe thickness

Differentiation between young adult Internet addicts, smokers, and healthy controls by the interaction between impulsivity and temporal lobe thickness

The Neuroimaging Findings of Internet Gaming Disorder

Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry

Contact Info

Product

Resources

About