Changes of internet behavior of adolescents across the period of COVID-19 pandemic in China.

During the COVID-19 pandemic, internet use and gaming of adolescents had been elevated. On the one hand, internet use and gaming in the period was a good approach to killing quarantined time. However, the increased use of the internet and game of adolescents may also increase the risk of internet addiction. This study aimed to describe the internet behavior changes of adolescents and to understand the impact of clinical features on internet addiction after the adolescents back to school in COVID-19 period. We conducted a cross-sectional cohort study using data collected through online investigation in China. Six hundred and twenty-five adolescents completed the online survey. Results indicated that internet addiction, having gaming use behaviors, Strengths and Difficulties Questionnaire (SDQ) Conduct subscale scores and the SDQ Prosocial subscale scores before the COVID-19 outbreak were significant in predicting the internet addiction after the adolescents back to school. The proportion of internet addiction and gaming behaviors among adolescents in China was changed across the COVID-19 pandemic. Clinical features before the COVID-19 pandemic could predict Internet addiction risk after the adolescents back to school.

Internet behavior patterns of adolescents before, during, and after COVID-19 pandemic.

Background: The outbreak of COVID-19 has affected the mental health of adolescents. To describe the Internet behavior-changing patterns of adolescents and to understand the impact of clinical features on changing patterns during the COVID-19 pandemic. Materials and methods: We conducted a cross-sectional cohort study using data collected through online investigation in China. A total of 625 adolescents completed the online survey from May 15 to June 7, 2020. The adolescents were asked to retrospect to the Internet behaviors and game behaviors of three time periods as follows: before the COVID-19 outbreak in China, during the COVID-19 outbreak in China, and back to school. The clinical variables of the demographic data, family functionality, and emotional and behavioral symptoms were also collected. According to the Internet behaviors and game behaviors patterns across the three time periods, the subjects will be sub-grouped. Results: Four Internet behavior-changing patterns during the COVID-19 was identified: (1) Continuous Normal Group (55.52%); (2) Normal to Internet Addiction Group (5.28%); (3) Internet Addiction to Normal Group (14.56%); and (4) Continuous Internet Addiction Group (24.64%). Years of education, academic score ranking, family functionality, and emotional and behavioral symptoms were different across the four groups. Proportions of game behaviors, scores of Strengths and Difficulties Questionnaire (SDQ), and SDQ subscale during the period before the COVID-19 outbreak were significant in predicting changing patterns. Conclusion: The Internet behavior patterns of adolescents during the COVID-19 period were various. Clinical features before the COVID-19 pandemic may predict changing patterns. The heterogeneity in characteristics between different changing patterns should be considered when intervening in adolescents' problematic Internet behavior.

Disrupted brain network dynamics and cognitive functions in methamphetamine use disorder: insights from EEG microstates.

BACKGROUND: Dysfunction in brain network dynamics has been found to correlate with many psychiatric disorders. However, there is limited research regarding resting electroencephalogram (EEG) brain network and its association with cognitive process for patients with methamphetamine use disorder (MUD). This study aimed at using EEG microstate analysis to determine whether brain network dynamics in patients with MUD differ from those of healthy controls (HC). METHODS: A total of 55 MUD patients and 27 matched healthy controls were included for analysis. The resting brain activity was recorded by 64-channel electroencephalography. EEG microstate parameters and intracerebral current sources of each EEG microstate were compared between the two groups. Generalized linear regression model was used to explore the correlation between significant microstates with drug history and cognitive functions. RESULTS: MUD patients showed lower mean durations of the microstate classes A and B, and a higher global explained variance of the microstate class C. Besides, MUD patients presented with different current density power in microstates A, B, and C relative to the HC. The generalized linear model showed that MA use frequency is negatively correlated with the MMD of class A. Further, the generalized linear model showed that MA use frequency, scores of Two-back task, and the error rate of MA word are correlated with the MMD and GEV of class B, respectively. CONCLUSIONS: Intracranial current source densities of resting EEG microstates are disrupted in MUD patients, hence causing temporal changes in microstate topographies, which are correlated with attention bias and history of drug use.