Background: An increasing number of people are becoming addicted to the internet as a result of overuse. The Internet Addiction Test (IAT) is a popular tool for evaluating internet use behaviors. The interaction between different symptoms and the relationship between IAT and clinical diagnostic criteria are not well understood. Objective: This study aimed to explore the core symptoms of internet addiction (IA) and the correlation between different symptoms of the IA symptom network. Network analysis was also conducted to explore the association between the IAT scale and the Diagnostic and Statistical Manual of Mental Disorders–5th edition (DSM-5) criteria for IA. Methods: We recruited 4480 internet users (aged 14-24 years), and they completed the IAT. The final analysis included 63.50% (2845/4480) of the participants after screening the submitted questionnaires. Participants were classified into IA group and non-IA (NIA) group. By using partial correlation with Lasso regularization networks, we identified the core symptoms of IA in each group and compared the group differences in network properties (strength, closeness, and betweenness). Then, we analyzed the symptom networks of the DSM-5 diagnostic criteria and IAT scale for IA. Results: A total of 12.47% (355/2845) of the patients were in the IA group and 87.52% (2490/2845) of the patients were in the NIA group, and both groups were evaluated for the following nodes: IAT_06 (school work suffers; strength=0.511), IAT_08 (job performance suffers; strength=0.531), IAT_15 (fantasize about being on the web; strength=0.474), IAT_17 (fail to stop being on the web; strength=0.526), and IAT_12 (fear about boredom if offline; strength=0.502). The IA groups had a stronger edge between IAT_09 (defensive or secretive about being on the web) and IAT_18 (hidden web time) than the NIA groups. The items in DSM-5 had a strong association with IAT_12 (weight=−0.066), IAT_15 (weight=−0.081), IAT_17 (weight=−0.106), IAT_09 (weight=−0.198), and IAT_18 (weight=−0.052). Conclusions: The internet use symptom network of the IA group is significantly different from that of the NIA group. Nodes IAT_06 (school work affected) and IAT_08 (work performance affected) are the resulting symptoms affected by other symptoms, whereas nodes IAT_12 (fear about boredom if offline), IAT_17 (inability to stop being on the web), and IAT_15 (fantasize about being on the web) are key symptoms that activate other symptoms of IA and are strongly linked to the inability to control the intention to play games in the DSM-5.

Aims: The purpose of this study was to investigate the association between spontaneous regional activity and brain functional connectivity, which maybe can distinguish insomnia while being responsive to repetitive transcranial magnetic stimulation (rTMS) treatment effects in insomnia patients.Methods: Using resting‐state functional magnetic resonance imaging data from 38 chronic insomnia patients and 36 healthy volunteers, we compared the amplitude of low‐frequency fluctuations (ALFF) between the two groups. Of all the patients with insomnia, 20 received rTMS for 4 weeks, while 18 patients received a 4‐week pseudo‐stimulation intervention. Seed‐based resting‐state functional connectivity (RSFC) analysis was conducted from regions with significantly different ALFF values, and the association between RSFC value and Pittsburgh Sleep Quality Index score was determined.Results: Our results revealed that insomnia patients presented a significantly higher ALFF value in the posterior cingulate cortex (PCC), whereas a significantly lower ALFF value was observed in the superior parietal lobule (SPL). Moreover, significantly reduced RSFC was detected from both PCC to prefrontal cortex connections, as well as from left SPL to frontal pole connections. In addition, RSFC from frontal pole to left SPL negatively predicted sleep quality (PSQI) and treatment response in patients' group.Conclusion: Our findings suggest that disrupted frontoparietal network connectivity may be a biomarker for insomnia in middle‐aged adults, reinforcing the potential of rTMS targeting the frontal lobes. Monitoring pretreatment RSFC could offer greater insight into how rTMS treatments are responded to by insomniacs.

Background and aims: Sex differences in internet gaming disorder (IGD) remain unknown. Investigating sex-specific neural features that underlie the core risk factor (i.e., risk-taking) of IGD would help in understanding sex-specific vulnerabilities to IGD and advance sex-specific treatments and prevention for IGD. Methods: 111 participants (28 IGD males, 27 IGD females, 26 recreational game user (RGU) males, 30 RGU females) completed a probability discounting task during fMRI scanning. Results: First, among RGUs, males showed a higher risk-taking tendency and greater neural activation associated with risk/value evaluation for reward (the ventromedial prefrontal cortex (vmPFC), anterior cingulate cortex (ACC), left putamen) and smaller activation associated with cognitive control (the inferior frontal gyrus) than females during the contrast of risky-safe choices. Moreover, males showed a greater modulatory effect of risky choices on the connection from the vmPFC/ACC to the left putamen than females. Second, IGD males showed decreased activation in the vmPFC/ACC and left putamen compared to RGU males, whereas this decrease did not exist in IGD females. Discussion: Males show a higher risk-taking tendency than females. Altered neural substrates associated with risky decision-making exist in IGD males but not in IGD females. Conclusions: The present findings fill the gap in information on the behavioral and neural substrates underlying IGD among females and demonstrate that a high risk-taking tendency is a risk factor and core symptom only in IGD males but not in IGD females. It is necessary to design and adopt distinct treatments and prevention strategies for IGD in males and females.