Review Reports

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This manuscript presents a cross-sectional network analysis exploring the symptom-level relationship between depressive symptoms and prospection bias (PB) in a large sample of university students (N = 1,162). By utilizing the PHQ-9 and NBPS, the authors map the complex interplay between current depressive states and future-oriented cognitive distortions. The analysis identifies key central symptoms and catastrophic negative future thinking, while highlighting current suicidal ideation and future imaginations of self-harm as critical bridge symptoms connecting the two constructs. The study addresses a clinically significant topic, and the application of network analysis provides a valuable, granular perspective on the cognitive mechanisms maintaining depression, offering novel insights that could inform targeted early interventions for at-risk youth.

The authors have presented a methodologically sophisticated manuscript. The application of network psychometrics to bridge depressive symptoms with prospection bias is  innovative. Overall, the statistical analysis plan is robust, transparent, and appropriately selected for the study's objectives. The application of the EBIC graphical LASSO network model, combined with comprehensive stability checks via the bootnet package, demonstrates a strong command of modern network methodology. The inclusion of bridge centrality (networktools), node predictability (mgm), and network comparison tests (NetworkComparisonTest) provides a rigorous framework for addressing the research questions.

However, there are a few minor methodological details that require clarification to ensure full transparency and reproducibility before publication.
1. The authors state in line 130 that they used "Spearman correlations to assess the correlation between the items." Given that the PHQ-9 and the NBPS utilize ordinal Likert-type scales, polychoric correlations are generally the preferred input for estimating Gaussian Graphical Models in psychological networks. If Spearman correlations were strictly used instead of polychoric, the authors should briefly justify this choice in the text, as Spearman can sometimes underestimate the relationship between ordinal variables compared to polychoric estimates.
2. When describing the extended Bayesian Information Criterion (EBIC) for model selection (lines 133-134), the authors should explicitly report the specific tuning parameter used in their analysis.
3. The methodology section does not currently mention how missing data was handled prior to generating the correlation matrix (e.g., listwise deletion, pairwise deletion, or imputation). A brief sentence clarifying this process should be added.

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Dear Authors,
Your study is valuable and raises important issues related to the mental health of people in early adulthood. Overall, the text you presented contains no major flaws and is of good quality. I request two corrections:
1. Please describe in more detail how the study was recruited, whether there were any inclusion/exclusion criteria, and how the form was submitted.
2. Please provide more sociodemographic data in the table (Table 1): place of origin (urban/rural), economic status, previous serious illnesses, and family situation. If this data was not collected, please indicate this as a limitation in the "Discussion" section.

Best regards!

Author Response

Please see the attachment.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Minor revisions are recommended (see attached PDF)

Comments for author File: Comments.pdf

Author Response

Please see the attachment.

Author Response File: Author Response.pdf