Background and aims: Smartphone use is becoming commonplace and exerting adequate control over smartphone use has become an important mental health issue. Little is known about the neurobiology underlying problematic smartphone use. We hypothesized that structural abnormalities in the fronto-cingulate brain region could be implicated in problematic smartphone use, similar to that has been reported for Internet gaming disorder and Internet addiction. This study investigated fronto-cingulate gray matter abnormalities in problematic smartphone users, particularly those who spend time on social networking platforms. Methods: The study included 39 problematic smartphone users with excessive use of social networking platforms via smartphone and 49 normal control male and female smartphone users. We conducted voxel-based morphometric analysis with diffeomorphic anatomical registration using an exponentiated Lie algebra algorithm. Region of interest analysis was performed on the fronto-cingulate region to identify whether gray matter volume (GMV) differed between the two groups. Results: Problematic smartphone users had significantly smaller GMV in the right lateral orbitofrontal cortex (OFC) than healthy controls, and there were significant negative correlations between GMV in the right lateral OFC and the Smartphone Addiction Proneness Scale (SAPS) score, including the SAPS tolerance subscale. Conclusions: These results suggest that lateral orbitofrontal gray matter abnormalities are implicated in problematic smartphone use, especially in social networking platform overuse. Small GMV in the lateral OFC was correlated with an increasing tendency to be immersed in smartphone use. Our results suggest that orbitofrontal gray matter abnormalities affect regulatory control over previously reinforced behaviors and may underlie problematic smartphone use.

Internet gaming disorder (IGD) is characterized by addiction to online gaming and reduced executive control, particularly when individuals are exposed to gaming-related cues. Executive control can be measured as vagally mediated heart rate variability (HRV), which corresponds to variability in the time interval between heart beats. In this study, we investigated whether individuals with IGD have altered HRV while playing online games. We hypothesized that while gaming, individuals with IGD would exhibit phasic suppression of vagally mediated HRV, which would reflect executive control dysfunction during game play. To test this, we measured the changes of HRV when young males with IGD were engaged in real-time online gaming. The changes of HRV were associated with the severity of IGD assessed by self-reports and prefrontal gray matter volume (GMV) calculated by voxel-based morphometry. We included 23 IGD subjects and 18 controls in our analyses. Changes in HRV were not statistically different between IGD subjects and controls. Within the IGD group, however, subjects showed significant decreases in high-frequency (HF) HRV during gaming. Furthermore, the degree of decrease correlated with IGD severity and prefrontal GMV. Importantly, this phasic suppression of HF-HRV in response to gaming did not occur in control subjects. In conclusion, young males with IGD showed an altered HRV response while playing an online game, reflecting their difficulties in executive control over gaming. The dynamics between executive control and reward seeking may be out of balance during game play in IGD.

Background and aims: The clinical significance of Internet gaming disorder (IGD) is spreading worldwide, but its underlying neural mechanism still remains unclear. Moreover, the prevalence of IGD seems to be the highest in adolescents whose brains are in development. This study investigated the functional connectivity between large-scale intrinsic networks including default mode network, executive control network, and salience network. We hypothesized that adolescents with IGD would demonstrate different functional connectivity patterns among large-scale intrinsic networks, implying neurodevelopmental alterations, which might be associated with executive dysfunction.Methods: This study included 17 male adolescents with Internet gaming disorder, and 18 age-matched male adolescents as healthy controls. Functional connectivity was examined using seed-to-voxel analysis and seed-to-seed analysis, with the nodes of large-scale intrinsic networks used as region of interests. Group independent component analysis was performed to investigate spatially independent network.Results: We identified aberrant functional connectivity of salience network and default mode network with the left posterior superior temporal sulcus (pSTS) in adolescents with IGD. Furthermore, functional connectivity between salience network and pSTS correlated with proneness to Internet addiction and self-reported cognitive problems. Independent component analysis revealed that pSTS was involved in social brain network.Discussion and conclusions: The results imply that aberrant functional connectivity of social brain network with default mode network and salience network was identified in IGD that may be associated with executive dysfunction. Our results suggest that inordinate social stimuli during excessive online gaming leads to altered connections among large-scale networks during neurodevelopment of adolescents.

Background and aims: Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric comorbidities of Internet addiction (IA); however, the possible mechanisms that contribute to this high comorbidity are still under debate. This study aims to analyze these possible mechanisms by comparing the effect of IA severity and childhood ADHD on inattention, hyperactivity, and impulsivity in young adults with IA. We hypothesized that IA might have associations with ADHD-like cognitive and behavior symptoms aside from childhood ADHD. Methods: Study participants consisted of 61 young male adults. Participants were administered a structured interview. The severity of IA, childhood and current ADHD symptoms, and psychiatry comorbid symptoms were assessed through self-rating scales. The associations between the severity of IA and ADHD symptoms were examined through hierarchical regression analyses. Results: Hierarchical regression analyses showed that the severity of IA significantly predicted most dimensions of ADHD symptoms. By contrast, childhood ADHD predicted only one dimension. Discussion: The high comorbidity of inattention and hyperactivity symptoms in IA should not solely be accounted by an independent ADHD disorder but should consider the possibility of cognitive symptoms related to IA. Functional and structural brain abnormalities associated with excessive and pathologic Internet usage might be related to these ADHD-like symptoms. Conclusion: Inattention and hyperactivity in young adults with IA are more significantly associated with the severity of IA than that of childhood ADHD.

With the impact of the COVID-19 pandemic, the number of patients suffering from depression is rising around the world. It is important to diagnose depression early so that it may be treated as soon as possible. The self-response questionnaire, which has been used to diagnose depression in hospitals, is impractical since it requires active patient engagement. Therefore, it is vital to have a system that predicts depression automatically and recommends treatment. In this paper, we propose a smartphone-based depression prediction system. In addition, we propose depressive features based on multimodal sensor data for predicting depressive mood. The multimodal depressive features were designed based on depression symptoms defined in the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). The proposed system comprises a “Mental Health Protector” application that collects data from smartphones and a big data-based cloud platform that processes large amounts of data. We recruited 106 mental patients and collected smartphone sensor data and self-reported questionnaires from their smartphones using the proposed system. Finally, we evaluated the performance of the proposed system’s prediction of depression. As the test dataset, 27 out of 106 participants were selected randomly. The proposed system showed 76.92% on an f1-score for 16 patients with depression disease, and in particular, 15 patients, 93.75%, were successfully predicted. Unlike previous studies, the proposed method has high adaptability in that it uses only smartphones and has a distinction of evaluating prediction accuracy based on the diagnosis.