Temporal Change in the Depression Network and Longitudinal Network Associations between Depressive Symptoms during Late Childhood
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(Tl):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 Tl 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 exhibited the highest strength values over time,marking it as a stable central symptom within the depression network.In addition,sadness exhibited the second-highest strength values at T1.In contrast,irritability had showed the second-highest strength values at T2 and T3,highlighting its escalating significance in the network over time.Network comparison tests highlighted that the network structure at T2 and T3 differed from that at T1.The global strength of the depressive symptoms network at T2 and T3 was stronger than the network at T1,suggesting a strengthening connectivity among symptoms over time.Furthermore,cross-lagged panel network analysis also showed that self-hatred was the overall essential influential symptom,which could give rise to other depressive symptoms and,conversely,be exacerbated by other depressive symptoms over time.The study also observed temporal shifts in symptom centrality.Specifically,loneliness displayed the highest out-expected influence centrality on the T1→T2 network,with strong association with T2 self-hatred and T2 friendlessness.Negative body image had the highest out-expected influence centrality on the T2→T3 network,with strong association with T3 self-hatred.Moreover,irritability consistently presented the highest in-expected influence centrality across both T1→T2 and T2→T3 networks,marking it as a prominent outcome within the depression network.The current study enhances the knowledge of children's depression symptomatology through the longitudinal network analysis.By combining regularized partial correlation network analysis and cross-lagged panel network analysis,the findings corroborate that self-hatred and irritability consistently emerge as core symptoms at all time points,while other highly central symptom vary across time points.Consequently,it is imperative to prioritize the prevention and intervention of children's depression by focusing on central symptoms,namely self-hatred and irritability.Meanwhile,time-specific strategies targeting the central symptoms could prove instrumental in preventing the onset and escalation of depression in children.
万方数据
维普
