Trait anxiety and corresponding neuromarkers predict internet addiction: A longitudinal study

Area of Science:

• Neuroscience
• Psychology
• Digital Health

Background:

• Internet addiction (IA) is a global mental health concern impacting executive function.
• Trait anxiety (TA) is a key predictor of addictive behaviors.

Purpose of the Study:

• Investigate the neural and socio-psychological mechanisms linking TA and IA.
• Examine the longitudinal relationship between TA and IA.
• Identify neural markers of TA that predict IA.

Main Methods:

• Correlation analysis and linear mixed-effect (LME) models for longitudinal assessment.
• Connectome-based predictive modeling (CPM) to identify brain connectivity markers.
• Mediation analysis incorporating stressful life events and default mode network (DMN) activity.

Main Results:

• A significant positive correlation between TA and IA was observed, with higher TA predicting increased IA over time.
• CPM identified functional connectivity in cognitive control, emotion regulation, and DMN circuits as significant neuromarkers for TA.
• These TA neuromarkers predicted IA, and findings were validated in an independent sample.
• Stressful life events and altered functional connectivity mediated the TA-IA association.

Conclusions:

• The study elucidates the neural and socio-psychological pathways connecting TA and IA.
• Findings suggest potential targets for developing novel interventions for internet addiction.