透视图(图形)
灵敏度(控制系统)
心理学
网络结构
干预(咨询)
网络分析
人工智能
网络模型
认知心理学
计算机科学
机器学习
社会网络分析
心理健康
发展心理学
复杂网络
结构方程建模
作者
Xiaoyan Bi,Shutao Ma,Hongbo Cui,Liang Zhang,Yankun Ma
摘要
BACKGROUND: Reward sensitivity plays a crucial role in shaping emotional and behavioural responses, yet its internal structure and dynamic interplay with depression remain underexplored. This study systematically examined the network structure of reward sensitivity and its associations with depression using a network analysis framework. METHODS: = 14.32, 52.82% female), we constructed multiple cross-sectional and bridge networks, as well as a cross-lagged panel network model (CLPN). RESULTS: (1) Cross-sectional network analysis reveals that positive feedback serves as the core node in the depression group, while hobbies dominate in the healthy group; (2) bridging network analysis identifies hobbies and positive affect as key bridging nodes linking reward sensitivity and depression; (3) CLPN analysis demonstrates that greater engagement in hobbies at baseline significantly predicts lower levels of depression 6 months later. CONCLUSIONS: This study provided a novel network perspective on the structural and temporal characteristics of reward sensitivity in depression. The findings underscored the importance of targeting specific reward types in intervention strategies and personalised mental health approaches.
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