1Key Laboratory of Cognition and Personality of the Ministry of Education, Faculty of Psychology, Southwest University, Chongqing, China.
2College of Computer and Information Science, College of Software, Southwest University, Chongqing, China.
J Behav Addict. 2024 Mar 7;13(1):177-190. doi: 10.1556/2006.2023.00086. Print 2024 Mar 26.
The high prevalence of internet addiction (IA) has become a worldwide problem that profoundly affects people's mental health and executive function. Empirical studies have suggested trait anxiety (TA) as one of the most robust predictors of addictive behaviors. The present study investigated the neural and socio-psychological mechanisms underlying the association between TA and IA.
Firstly, we tested the correlation between TA and IA. Then we investigated the longitudinal influence of TA on IA using a linear mixed effect (LME) model. Secondly, connectome-based predictive modeling (CPM) was employed to explore neuromarkers of TA, and we tested whether the identified neuromarkers of TA can predict IA. Lastly, stressful life events and default mode network (DMN) were considered as mediating variables to explore the relationship between TA and IA.
A significant positive correlation between TA and IA was found and the high TA group demonstrated higher IA across time. CPM results revealed that the functional connectivity of cognitive control and emotion-regulation circuits and DMN were significantly correlated with TA. Furthermore, a significant association was found between the neuromarkers of TA and IA. Notably, the CPM results were all validated in an independent sample. The results of mediation demonstrated that stressful life events and correlated functional connectivity mediated the association between TA and IA.
Findings of the present study facilitate a deeper understanding of the neural and socio-psychological mechanisms linking TA and IA and provide new directions for developing neural and psychological interventions.
互联网成瘾（IA）的高患病率已成为一个全球性问题，深刻影响着人们的心理健康和执行功能。实证研究表明特质焦虑（TA）是成瘾行为最有力的预测因素之一。本研究旨在探讨 TA 与 IA 之间关联的神经和社会心理机制。
首先，我们检验了 TA 与 IA 之间的相关性。然后，我们使用线性混合效应（LME）模型研究了 TA 对 IA 的纵向影响。其次，我们采用连接组学预测建模（CPM）来探索 TA 的神经标志物，并测试了所确定的 TA 神经标志物是否可以预测 IA。最后，我们将生活压力事件和默认模式网络（DMN）视为中介变量，以探索 TA 与 IA 之间的关系。
发现 TA 与 IA 之间存在显著的正相关关系，且高 TA 组在整个时间内表现出更高的 IA。CPM 结果显示，认知控制和情绪调节回路以及 DMN 的功能连接与 TA 显著相关。此外，还发现 TA 的神经标志物与 IA 之间存在显著关联。值得注意的是，CPM 结果在独立样本中得到了验证。中介效应的结果表明，生活压力事件和相关的功能连接介导了 TA 和 IA 之间的关联。
本研究的结果有助于深入了解 TA 和 IA 之间的神经和社会心理机制，并为开发神经和心理干预措施提供了新的方向。
