Network dynamics of depression, anxiety, sleep disturbances, and suicidal symptoms in Chinese adolescents: a longitudinal cross-sectional and cross-lagged panel network analysis

BACKGROUND: Depression in adolescents involves complex interactions among depression, anxiety, sleep disturbances, and suicidal symptoms. Network theory offers insights into dynamic symptom relationships during recovery. METHODS: Of 797 adolescents initially enrolled, 649 with complete baseline data were included in the network analyses; 458 and 277 participants were retained at the 1-month and 3-month follow-ups, respectively. Cross-sectional Gaussian Graphical Models and Cross-Lagged Panel Network (CLPN) analyses examined relationships among nine symptom domains: depression, somatic/subjective anxiety, sleep quantity/quality, daytime insomnia, passive/active sleepiness, and suicidal ideation/tendency. Network centrality and bootstrap validation assessed parameter stability. RESULTS: Cross-sectional networks showed structural invariance across timepoints (p>0.05). Subjective anxiety demonstrated highest centrality at T0-T1, while somatic symptoms dominated at T2. Depression maintained high closeness centrality throughout. Although betweenness centrality also suggested a central role for depression, its lower stability (CS < 0.5) necessitates a more cautious interpretation of this specific metric. CLPN revealed more predictive relationships during T0→T1 (76.5% significant edges) than T1→T2 (24.7%). Active sleepiness strongly predicted subsequent somatic anxiety (B=0.683) and depression (B=0.647). Suicide ideation-tendency showed stable bidirectional connections. Network stability was excellent (CS>0.5) except betweenness centrality. CONCLUSIONS: Central symptoms evolved during recovery, with subjective anxiety initially dominant but somatic symptoms becoming central over time. The early post-treatment period showed heightened symptom network activity, with sleep disturbances identified as robust predictors of subsequent affective deterioration. Findings support dynamic, network-informed interventions targeting evolving symptom centrality and predictive pathways, particularly addressing sleep-related symptoms and suicide risk during critical recovery phases.