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Unveiling convergent and divergent intrinsic brain network alternations in depressed adolescents engaged in non-suicidal self-injurious behaviors with and without suicide attempts

  • Linling Li
  • , Zhen Liang
  • , Guohua Li
  • , Hong Xu
  • , Xing Yang
  • , Xia Liu
  • , Xin Zhang
  • , Jianhong Wang
  • , Zhiguo Zhang*
  • , Yongjie Zhou*
  • *Corresponding author for this work
  • Shenzhen University
  • Harbin Institute of Technology Shenzhen

Research output: Contribution to journalArticlepeer-review

Abstract

Aims: Limited understanding exists regarding the neurobiological mechanisms underlying non-suicidal self-injury (NSSI) and suicide attempts (SA) in depressed adolescents. The maturation of brain network is crucial during adolescence, yet the abnormal alternations in depressed adolescents with NSSI or NSSI+SA remain poorly understood. Methods: Resting-state functional magnetic resonance imaging data were collected from 114 depressed adolescents, classified into three groups: clinical control (non-self-harm), NSSI only, and NSSI+SA based on self-harm history. The alternations of resting-state functional connectivity (RSFC) were identified through support vector machine-based classification. Results: Convergent alterations in NSSI and NSSI+SA predominantly centered on the inter-network RSFC between the Limbic network and the three core neurocognitive networks (SalVAttn, Control, and Default networks). Divergent alterations in the NSSI+SA group primarily focused on the Visual, Limbic, and Subcortical networks. Additionally, the severity of depressive symptoms only showed a significant correlation with altered RSFCs between Limbic and DorsAttn or Visual networks, strengthening the fact that increased depression severity alone does not fully explain observed FC alternations in the NSSI+SA group. Conclusion: Convergent alterations suggest a shared neurobiological mechanism along the self-destructiveness continuum. Divergent alterations may indicate biomarkers differentiating risk for SA, informing neurobiologically guided interventions.

Original languageEnglish
Article numbere14684
JournalCNS Neuroscience and Therapeutics
Volume30
Issue number5
DOIs
StatePublished - May 2024
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

Keywords

  • adolescence
  • depression
  • machine learning
  • non-suicidal self-injury
  • resting-state functional connectivity
  • suicidal attempt

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