Commentary: Extending longitudinal network approaches - a reflection on Funkhouser et al. (2020)

The Bidirectional Relationship Between Subjective Well-Being and Depression: A Cross-Sectional and Cross-Lagged Network Analysis

Purpose

Network modeling has been suggested as an effective approach to uncover intricate relationships among emotional states and their underlying symptoms. This study aimed to explore the dynamic interactions between subjective well-being (SWB) and depressive symptoms over time, using cross-sectional and cross-lagged network analysis.

Methods

Data were drawn from three waves (2016, 2018, and 2020) of the China Family Panel Studies (CFPS), including 13,409 participants aged 16 and above. SWB was measured through indicators like life satisfaction and future confidence, while depressive symptoms were assessed using the CES-D8 scale. Symptom-level interactions were analyzed via cross-sectional network analysis at each wave, and cross-lagged panel network analysis was employed to examine the temporal dynamics and bidirectional relationships between SWB and depressive symptoms.

Results

The cross-sectional symptom network analysis showed that the number of non-zero edges at T1, T2, and T3 were 50, 44, and 49, respectively, with network densities of 0.90, 0.80, and 0.89. The core symptom "feeling sad" (D7) consistently had a significantly higher strength than other symptoms. The negative correlation between "life satisfaction" (Z2) and depressive symptoms was particularly evident at T3. The cross-lagged symptom network analysis revealed the key roles of "feeling lonely" (D5) and "feeling sad" (D7), as well as "feeling unhappy" (D4) and "not enjoying life" (D6) across different time periods, which may form a negative feedback loop. "Life satisfaction" (Z2) and "confidence in the future" (Z3) exhibited significant protective effects, forming a positive feedback loop that suppresses negative emotions through mutual reinforcement. Stability analysis showed that the network structure was stable, with a centrality stability coefficient of 0.75.

Conclusion

The study reveals a dynamic, bidirectional relationship between SWB and depressive symptoms. These results offer valuable insights for targeted interventions and public health initiatives aimed at improving mental well-being.

Unraveling depressive symptom networks: A three-year longitudinal study among Chinese junior high school adolescents

Adolescence is the peak period for the occurrence of mental health issues, particularly in the stage of junior high school. Depressive symptoms are among the most frequently experienced psychological problems. However, little is known about the symptom-level interaction features of depressive symptoms and the roles of different symptoms across the junior high school stage. To address these gaps, this study conducted a three-year longitudinal study that recruited 1301 Chinese junior high school adolescents (48.2% females; mean age = 12.46 ± 0.62, ranging from 11 to 14 in the first year). The Center for Epidemiological Studies Depression Scale was used to assess depressive symptoms. The regularized partial correlation network and the cross-lagged panel network models were used to explore the symptom-level interaction pattern. In regularized partial correlation networks, "I felt depressed" was a stable central symptom throughout the junior high school stage. Besides, "I felt lonely" and "I felt that people disliked me" were the other central symptoms in grade 7 and grade 8, and "I felt everything I did was an effort" played a central role in grade 9. Within cross-lagged panel networks, "I felt that people disliked me" and "I felt hopeless about the future" had important effects on predicting other depressive symptoms from grade 7 to 8 and from grade 8 to 9. By investigating the longitudinal interaction patterns of depressive symptoms among junior high school adolescents, the current study identifies core symptoms that could be potential prevention or intervention targets and provides a novel insight for understanding depressive symptoms during adolescence in depth.

Developmental Trajectories of Internalizing and Externalizing Symptoms in Youth and Associated Gender Differences: A Directed Network Perspective

Psychopathology in youth is highly prevalent and associated with psychopathology in adulthood. However, the developmental trajectories of psychopathology symptoms, including potential gender differences, are markedly underspecified. The present study employed a directed network approach to investigate longitudinal relationships and gender differences among eight transdiagnostic symptom domains across three years, in a homogenous age sample of youth participants (n = 6,414; mean baseline age = 10.0 years; 78.6% White; Adolescent Brain Cognitive Development study). Anxious/depressed problems and aggressive behaviors were central symptoms and most predictive of increases in other symptom clusters at later timepoints. Rule-breaking behaviors, aggressive behaviors, and withdrawn/depressed problems emerged as bridge symptoms between externalizing and internalizing problems. Results supported cascade models in which externalizing problems predicted future internalizing problems, but internalizing problems also significantly predicted future externalizing problems, which is contrary to cascade models. Network structure, symptom centrality, and patterns of bridge symptoms differed between female and male participants, suggesting gender differences in the developmental trajectories of youth psychopathology. Results provide new insights into symptom trajectories and associated gender differences that may provide promising pathways for understanding disorder (dis)continuity and co-occurrence. The central and bridge symptoms identified here may have important implications for screening and early intervention for youth psychopathology.

The Longitudinal Features of Depressive Symptoms During the COVID-19 Pandemic Among Chinese College Students: A Network Perspective

There is substantial evidence that the Corona Virus Disease 2019 (COVID-19) pandemic increased the risk of depressive symptoms among college students, but the long-term features of depressive symptoms on a symptom level have been poorly described. The current study investigated interaction patterns between depressive symptoms via network analysis. In this longitudinal study, participants included 860 Chinese college students (65.8% female; M_age = 20.6, SD_age = 1.8, range: 17-27) who completed a questionnaire at three-time points three months apart. Results demonstrated that fatigue was the most influential symptom, and the occurrence of fatigue could give rise to other depressive symptoms. In addition to predicting other symptoms, fatigue could be predicted by other symptoms in the measurement. The network structures were similar across time, suggesting that the overall interaction pattern of depressive symptoms was stable over the longitudinal course. These findings suggest that depressive symptoms during the COVID-19 period are associated with the presence of fatigue.

Cross-sectional and longitudinal AUD symptom networks: They tell different stories

Modern theoretical models of Alcohol Use Disorder (AUD) highlight the different functional roles played by various mechanisms associated with different symptoms. Symptom network models (SNMs) offer one approach to modeling AUD symptomatology in a way that could reflect these processes and provide important information on the progression and persistence of disorder. However, much of the research conducted using SNMs relies on cross-sectional data, which has raised questions regarding the extent they reflect dynamic processes. The current study aimed to (a) examine symptom networks of AUD and (b) compare the extent to which cross-sectional network models had similar structures and interpretations as longitudinal network models. 17,360 participants from Wave 1 (2001-2002) and Wave 2 (2003-2004) of the National Epidemiological Survey on Alcohol and Related Conditions (NESARC) were used to model cross-sectional and longitudinal AUD symptom networks. The cross-sectional analyses demonstrate high replicability across waves and central symptoms consistent with other cross-sectional studies on addiction networks. The longitudinal network shared much less similarity than the cross-sectional networks and had a substantially different structure. Given the increasing attention given to the network perspective in psychopathology research, the results of this study raise concerns about interpreting cross-sectional symptom networks as representative of temporal changes occurring within a psychological disorder. We conclude that the psychological symptom network literature should be bolstered with additional research on longitudinal network models.