童年晚期抑郁症状网络的演化及症状间的纵向关系  

Temporal Change in the Depression Network and Longitudinal Network Associations between Depressive Symptoms during Late Childhood

作　　者：陈嘉慧 任萍 吕沐华[1] 李添 Chen Jiahui;Ren Ping;Lyu Muhua;Li Tian(Collaborative Innovation Center of Assessment for Basic Education Quality,Beijing Normal University,Beijing,100875;State Key Laboratory of Cognitice Neurosecience and Learning,Beijing Normal University,Beijing,100875)

机构地区：[1]北京师范大学中国基础教育质量监测协同创新中心,北京100875 [2]北京师范大学认知神经科学与学习国家重点实验室,北京100875

出　　处：《心理科学》2024年第6期1381-1391,共11页Journal of Psychological Science

基　　金：科技创新2030(2021ZD0200500);中央高校基本科研业务费专项资金(1243300003);认知神经科学与学习国家重点实验室开放课题基金(CNLZD2203)的资助。

摘　　要：为揭示童年晚期抑郁症状网络的演化和症状间的纵向关系,对3042名小学中高年级儿童进行了一年三次的追踪测量。通过三个时间点的偏相关网络分析发现,易激惹和自我憎恨在抑郁症状网络中均呈现较高的中心性。交叉滞后网络分析表明,自我憎恨是影响网络中其他症状的核心症状;而易激惹更容易被其他症状影响;孤独感和负面身体意象对抑郁症状网络的影响存在时间特异性。这些结果揭示了童年晚期学生抑郁症状网络的核心症状及症状之间的纵向发展特征,为儿童青少年抑郁的早期防范、干预和诊疗提供了实证依据。Depression is one of the most prevalent mental health problems among school-aged students.The psychopathology network theory conceptualizes depression as a network system of interconnected symptoms.Yet,information on the central symptoms,the structure of depressive symptoms,and the longitudinal associations between depressive symptoms is still limited among Chinese students in late childhood.Thus,using three waves of data from this group,the present study aimed to explore the structure and change of the depression network,as well as the longitudinal associations among depressive symptoms through the network analysis.A total of 3042 Chinese 4th grade students(50.6%male,Mage=9.36 years old,SD=0.51 years old)were included in this study.Depressive symptoms were assessed using the short version of the Children’s Depression Inventory(CDI-S)at three time points,spaced six months apart(Time 1(T1):November 2021,Time 2(T2):May 2022,and Time 3(T3):November 2022).The data were analyzed in SPSS 24.0 and R 4.2.2.For the regularized partial correlation network,the Graphical Gaussian Model(GGM)estimated the structure of the depression network at three time points.Strength was used in this study to quantify the role of each node.Regarding the cross-lagged panel network,a regression model using a series of nodes logistic regression was used to calculate auto-regressive effects(a node at T1 predicted itself at T2)and cross-lagged effects(a node at T1 predicted another node at T2).Centrality indices,specifically in-expected influence centrality and out-expected influence centrality,were used to differentiate the effects that a node predicting other nodes and being predicted by others.Additionally,network comparison tests(i.e.,a network structure invariance test,a global strength invariance test,and an edge strength invariance test)were performed to assess the differences in network structure and core symptoms across three time points.The regularized partial correlation network analysis showed that self-hatred consistently exhibite

关 键 词：抑郁症状 童年晚期 正则化偏相关分析 交叉滞后网络分析 症状学 

分 类 号：R749.4[医药卫生—神经病学与精神病学]