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 post-admission 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 data. Model performance was evaluated using cross-validation, and feature importance was interpreted using SHapley Additive exPlanations (SHAP) analysis. Results All predictive models demonstrated excellent predictive performance (R2≥0.96, mean squared error [MSE] as low as 0.02). The HoNOS model showed the highest performance (R2=0.99, MSE=0.32), followed by the WHODAS (R2=0.98, MSE=1.32), GAF (R2=0.97, MSE=0.76), and CGI-S (R2=0.96, MSE=0.02) models. Key qEEG predictors included relative low-beta power at Pz, absolute theta power at Fp1, and the delta-to-beta ratio at Cz. Pre-admission clinical severity, particularly CGI-S and HoNOS, also significantly contributed to prediction accuracy. Conclusion Our findings suggest that qEEG features, combined with machine learning, can effectively predict treatment response in adolescents with NSSI, supporting their use as neurophysiological biomarkers for individualized care.