# Visualized Analysis of Adolescent Non-Suicidal Self-Injury and Comorbidity Networks

## TL;DR

This paper maps the research landscape of adolescent non-suicidal self-injury and its mental health comorbidities using visualization and bibliometric methods.

## Contribution

The study introduces a systematic visualization of comorbidity networks and evolving research trends in adolescent NSSI using advanced bibliometric tools.

## Key findings

- Research on adolescent NSSI has shown explosive growth since 2005.
- Current research hotspots include comorbidity mechanisms, childhood trauma impact, and dynamic assessment.
- Future directions emphasize cross-cultural comparisons and biomarker-informed interventions.

## Abstract

Non-suicidal self-injury (NSSI) has become an increasingly salient mental health concern among adolescents, and it commonly co-occurs with depression, anxiety, borderline personality disorder, substance use, and childhood maltreatment, forming a complex psychological risk structure. Despite a growing body of literature, a systematic understanding of the structural links between NSSI and psychiatric comorbidities remains limited. This study uses bibliometric and visualization methods to map the developmental trajectory and knowledge structure of the field and to identify research hotspots and frontiers. Drawing on the Web of Science Core Collection, we screened 1562 papers published between 2005 and 2024 on adolescent NSSI and comorbid psychological problems. Using CiteSpace 6.3.R1, VOSviewer 1.6.20, and R 4.3.3, we constructed knowledge graphs from keyword co-occurrence, clustering, burst-term detection, and co-citation analyses. The results show an explosive growth of research in recent years. Hotspots center on comorbidity mechanisms of mood disorders, the impact of childhood trauma, and advances in dynamic assessment. Research has evolved from describing behavioral features toward integrative mechanisms, with five current emphases: risk factor modeling, diagnostic standard optimization, cultural sensitivity, stratified intervention strategies, and psychological risks in special populations. With big data and AI applications, the field is moving toward dynamic prediction and precision intervention. Future work should strengthen cross-cultural comparisons, refine comorbidity network theory, and develop biomarker-informed differentiated interventions to advance both theory and clinical practice.