干预目标的计算模拟：阐明社区人群焦虑和抑郁症状网络中的应对方式机制
Computational simulation of intervention targets: elucidating coping style mechanisms in anxiety and depressive symptom networks in community populations.
摘要(中文译文)
背景：抑郁障碍和焦虑障碍是全球范围内重要的精神健康负担，对个人、社会及医疗基础设施造成重大影响。在中国，具有全国代表性的流行病学研究报告显示，焦虑障碍的终生患病率为7.6%，抑郁障碍为6.6%。这两种疾病均表现出年龄依赖性异质性，老年人群的易感性显著更高，且共病率升高。方法：该社区人群横断面研究针对不同年龄组构建了Ising网络模型，随后应用NodeIdentifyR算法（NIRA）识别双通路干预靶点——即预期可缓解（治疗）或加重（预防）症状的节点。结果：分析表明，应对方式与焦虑、抑郁之间存在异质性关联，并观察到显著的年龄差异。进一步模拟干预结果提示，年龄特异性的应对特征可作为临床干预的可行靶点。结论：本研究采用精神病理学网络建模，揭示了应对方式与焦虑-抑郁症状之间关联模式及相应干预靶点中与年龄相关的异质性。
Abstract
BACKGROUND: Depressive and anxiety disorders represent globally significant mental health burdens, imposing substantial impacts on individuals, societies, and healthcare infrastructure. In China, nationally representative epidemiological studies report a lifetime prevalence of 7.6% for anxiety disorders and 6.6% for depressive disorders. Both conditions demonstrate age-dependent heterogeneity, with older populations exhibiting significantly higher susceptibility and elevated comorbidity. METHODS: The cross-sectional study of community populations constructed Ising network models for different age groups, and then applied the NodeIdentifyR algorithm (NIRA) to identify dual-pathway intervention targets — nodes projected to alleviate (treatment) or aggravate (prevention) symptoms. RESULTS: The analysis indicated a heterogeneous link between coping styles and anxiety and depression, with notable age differences observed. Further simulation of intervention outcomes suggested that age-specific coping profiles emerged as viable targets for clinical intervention. CONCLUSIONS: Using psychopathological network modeling, this study reveals age-related heterogeneity in the association patterns between coping styles and anxiety-depression symptoms, as well as in the corresponding intervention targets.
如何引用
AMA
Zhenxuan Dong, Dingchao Wu, Xiujun Zhang, Haitao Wang, Hongge Luo. Computational simulation of intervention targets: elucidating coping style mechanisms in anxiety and depressive symptom networks in community populations.. BMC psychology. 2026; doi:10.1186/s40359-026-04442-4.
APA
Zhenxuan Dong, Dingchao Wu, Xiujun Zhang, Haitao Wang, Hongge Luo (2026). Computational simulation of intervention targets: elucidating coping style mechanisms in anxiety and depressive symptom networks in community populations.. BMC psychology. https://doi.org/10.1186/s40359-026-04442-4
GB/T 7714
Zhenxuan Dong, Dingchao Wu, Xiujun Zhang, Haitao Wang, Hongge Luo. Computational simulation of intervention targets: elucidating coping style mechanisms in anxiety and depressive symptom networks in community populations.[J]. BMC psychology, 2026 doi:10.1186/s40359-026-04442-4.
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