Predicting Treatment Response in Female Adolescents With Non-Suicidal Self-Injury Using Neurophysiological Biomarkers and Machine Learning.

ABSTRACT:

Objective

This study investigated whether quantitative electroencephalography (qEEG) features, combined with clinical data, could predict treatment outcomes in female adolescents with non-suicidal self-injury (NSSI).

Methods

We analyzed clinical and EEG data from 104 female adolescent inpatients with repetitive NSSI. Resting-state EEG was recorded, and various brain activity patterns across frequency bands were extracted. Clinical outcomes were assessed using pre- and postadmission scores on the Health of the Nation Outcome Scales (HoNOS), Clinical Global Impression-Severity (CGI-S), World Health Organization Disability Assessment Schedule (WHODAS), and Global Assessment of Functioning (GAF). Machine learning models were trained to predict outcomes using EEG and medication d

SUBMITTER: Kim SY 

PROVIDER: S-EPMC12835630 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature