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Network analysis of the relationship between depression, emotion regulation, and non-suicidal self-injury among adolescents and gender differences

Lingna Song et al · BMC · 2026

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Abstract Background Non-suicidal self-injury (NSSI) poses a serious threat to adolescents’ physical and mental health. This study aims to systematically examine the network structure of the relationship between adolescent depression, emotion regulation, and NSSI, as well as gender differences, from a network analysis perspective. Methods The Adolescent Self-Injury Questionnaire, the Center for Disease Control Depression Scale, and the Emotion Regulation Questionnaire were administered to 690 adolescents from June to October 2024. Regularized partial correlation networks were constructed using Gaussian graph models. Centrality metrics such as node strength and betweenness were calculated. Network comparison tests were conducted to examine differences in network structure, global stability, and edge weights between male and female networks. Results Network analysis revealed that adolescent depressive symptoms, emotion regulation, and NSSI behaviors are closely interrelated. Within the network of depressive symptoms, depressive affect is most closely linked to somatic symptoms (r = 0.58). Depressive affect intensity and anticipated impact exhibited the highest centrality. Positive affect exhibited the highest mediating centrality. The strongest edge connection between the emotional regulation network and the depressive symptoms network was positive affect - cognitive reappraisal (r = 0.34). The strongest edge connection between the NSSI and depression symptom network was interpersonal relationships-NSSI (r = 0.12). In the network model of NSSI and emotion regulation, the edge weights for NSSI–expressive suppression and NSSI–cognitive reappraisal were r = 0.08 and r = −0.07, respectively. Notably, expressive inhibition served as a key node connecting the “depressive affect-NSSI” network. Furthermore, comparing the network structures between males and females revealed no significant differences in overall network structure or total strength between the two groups (p > 0.05). Gender differences in edge weights were observed for NSSI-positive affect, NSSI-depressive affect, NSSI-physical symptoms, and physical symptoms-depressive affect (p < 0.05). Conclusions Adolescent depressive symptoms, emotion regulation, and NSSI behaviors form a network association through specific symptoms, with depressive affect as the core symptom and positive affect as the key hub. Targeted interventions can be implemented for these nodes in the future. Notably, gender differences exist within these networks, suggesting that universal prevention strategies should incorporate gender-specific adaptations to prevent and reduce adolescent depression and NSSI more effectively. Clinical trial number Not applicable.

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APA 7

al, L. S. E. (2026). Network analysis of the relationship between depression, emotion regulation, and non-suicidal self-injury among adolescents and gender differences. https://doi.org/10.1186/s12888-026-07998-y

MLA

al, Lingna Song et. "Network analysis of the relationship between depression, emotion regulation, and non-suicidal self-injury among adolescents and gender differences." 2026. https://doi.org/10.1186/s12888-026-07998-y.

Chicago

al, Lingna Song et. 2026. "Network analysis of the relationship between depression, emotion regulation, and non-suicidal self-injury among adolescents and gender differences.". https://doi.org/10.1186/s12888-026-07998-y.

Harvard

al, L. S. E. 2026, Network analysis of the relationship between depression, emotion regulation, and non-suicidal self-injury among adolescents and gender differences, BMC, available at: https://doi.org/10.1186/s12888-026-07998-y [Accessed 7 Aug. 2026].

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Title
Network analysis of the relationship between depression, emotion regulation, and non-suicidal self-injury among adolescents and gender differences
Author / contributors
Lingna Song et al
Publisher
BMC
Publication year
2026
ISSN
1471-244X
ISSN
1471-244X
Language
English

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