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April 18, 2026BMC Psychiatry0 citationsOpen Access

Altered functional brain network in adolescents with non-suicidal self-injury: a resting-state EEG study

LZLiqin ZhengNDNa DuSBSiyu Bao

Key Points

  • This study aims to explore the changes in functional brain network connectivity in adolescents with non-suicidal self-injury.
  • Assessed 50 NSSI adolescents with depressive episodes and 25 healthy controls using resting-state EEG signals.

Structured PICO

Does resting-state EEG functional connectivity differ between adolescents with non-suicidal self-injury and healthy controls?

P
Population
75 adolescents (aged 12-25 years), including 50 patients with non-suicidal self-injury (NSSI) and depressive episodes, and 25 healthy controls (HCs), recruited in China.
I
Intervention
Resting-state electroencephalogram (EEG) and clinical scale assessments
C
Comparator
Healthy controls (HCs) with no medical history of neurological or psychiatric disorders
O
Outcome
Brain network functional connectivity (FC) and topological properties measured via Phase Locking Value (PLV), weighted Phase Lag Index (wPLI), and graph theory analysissurrogate

Resting-state EEG reveals distinct functional connectivity alterations in adolescents with non-suicidal self-injury, offering potential neurobiological markers for the condition.

Abstract

Non-suicidal self-injury (NSSI) has a huge impact on the physical and mental health of adolescents, and the changes in the brain network functional connectivity pattern of NSSI are still unclear. Electroencephalogram (EEG) has been widely used in the study of brain function in mental disorders due to its high temporal resolution. Thus, it is of great significance to investigate the brain network functional connectivity (FC) and network characteristics changes in NSSI adolescents. Fifty NSSI adolescents with depressive episodes and 25 healthy controls (HCs) were assessed using eye-open resting state EEG signals and clinical scales. Phase locking value (PLV), weighted Phase Lag Index (wPLI), and graph theory analysis and machine learning analysis were combined to explore the brain functional connectivity and topological properties of adolescents with NSSI. Compared with HCs, adolescents with NSSI revealed significantly decreased connectivity in the alpha band based on the wPLI/PLV-derived network, and significantly increased connectivity and network efficiency in the theta bands in PLV-derived brain network. Reduced characteristic path length and enhanced clustering coefficient/global efficiency/local efficiency were found in the delta/theta band for PLV-derived network, which related to the clinical scale score (i.e. Toronto Alexithymia Scale). The best classification performance was achieved using comprehensive features (combined PLV- and wPLI-derived) and logistic regression classifier. Overall, these findings present novel evidence to understand NSSI neuropathological mechanisms and provide potential targets for NSSI treatment.

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Cite This Study

Zheng et al. (2026) studied this question.

synapsesocial.com/papers/69e3201440886becb653f2ddhttps://doi.org/10.1186/s12888-026-08019-8
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