Non-suicidal self-injury (NSSI) is defined as the deliberate, direct, and repetitive damage to one’s own body tissue without suicidal intent, carried out in socially unacceptable ways [1]. This behavior often serves as a way to relieve negative emotions such as tension, anxiety, or self-blame, and may also have interpersonal or self-punitive functions [2].

The global prevalence of NSSI has been rising significantly in recent years. International studies report that the prevalence of NSSI in adolescents ranges from 7.5% to 46.5% [3], with approximately 38.9% among college students and 5.5% to 13.4% among adults [4]. In South Korea, a national mental health screening conducted by the Ministry of Education in 2018 found that 7.9% of middle school students and 6.4% of high school students reported a history of self-injury [5]. However, many adolescents engaging in NSSI refrain from seeking medical help due to fear of stigma or a desire to maintain secrecy.

In the past, NSSI was often seen as either a temporary expression of adolescent rebellion, a symptom of personality disorder, or a precursor to suicide. Even without suicidal intent, NSSI remains dangerous because it is linked to both physical and psychological complications, as well as increased risk of suicidal thoughts and attempts. Recognizing its clinical importance, the Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition proposed NSSI as a condition for further study in 2013 [6]. Nevertheless, research exploring NSSI as an independent clinical entity—rather than a symptom of other psychiatric disorders—remains scarce, especially when considering its high prevalence and societal burden [7].

The causes of NSSI are believed to involve individual, environmental, and neurobiological factors. However, due to the population’s heterogeneity, clinical phenotyping alone is often insufficient to understand its pathophysiology [8]. Consequently, recent research has focused on identifying neurophysiological markers, with quantitative electroencephalography (qEEG) emerging as a promising tool due to its high temporal resolution, noninvasive nature, and sensitivity to dynamic brain function.

Previous studies have suggested that individuals engaging in NSSI may exhibit altered frontal cortical activity, including frontal dysfunction and increased gamma power in frontal and temporal regions [9]. Some studies have also reported correlations between NSSI severity and alpha band asymmetry, as well as associations between right hemispheric hypoactivation and depressive symptoms or auto-aggressive behaviors [10]. In a recent event-related potential study, Liu et al. [11] demonstrated that greater prefrontal P300 activity predicted subsequent reductions in NSSI frequency among adolescents with major depressive disorder, highlighting the potential of frontal neurophysiological markers in forecasting treatment outcomes in this population. Despite these findings, most existing studies have primarily focused on depressive symptoms, and there remains a notable lack of research investigating biological predictors of treatment response in adolescents with NSSI.

Given that electrophysiological biomarkers derived from qEEG may reflect early functional impairments associated with NSSI and capture temporal changes in brain dynamics, they hold potential for both early detection and treatment planning [12]. Therefore, this study aims to explore the utility of qEEG features as neurophysiological predictors of treatment response in female adolescents with repetitive NSSI. The ultimate goal is to establish a foundation for personalized and timely interventions in this high-risk population.

We developed XGBoost-based regression models to predict changes in four clinical outcomes during hospitalization: HoNOS, CGI-S, WHODAS, and GAF. Pre-admission scores for each outcome were included as features to improve model performance. Across all models, predictive accuracy was high, with strong coefficients of determination (R²) and low mean squared error (MSE) values (Table 1).

This study investigated the potential of qEEG features to predict treatment response in female adolescents with repetitive NSSI using a machine learning-based approach. By utilizing multiple clinical outcome measures—namely the Ho-NOS, CGI-S, WHODAS, and GAF—we investigated how EEG-derived electrophysiological signals relate to treatment-related symptom changes during inpatient care.

Previous studies have reported atypical cortical activity among adolescents engaging in NSSI, including increased gamma activity in frontal and temporal regions, and asymmetric alpha power, which have been linked to emotional dysregulation and impulsivity [9,10]. In particular, Liu et al. [11] identified greater prefrontal P300 amplitudes as predictors of subsequent reductions in NSSI among adolescents with major depressive disorder, suggesting a role for attention-related neural mechanisms in treatment responsiveness.

Consistent with previous findings, our results demonstrated that in addition to baseline clinical scores, specific qEEG features—namely Fz-ratio-delta-beta, O1-rel-highbeta, and T7-abs-gamma—significantly contributed to treatment outcome prediction. These features may reflect neurophysiological dysfunctions relevant to NSSI. Fz-ratio-delta-beta, indicative of frontal cross-frequency coupling, has been associated with impaired cognitive control and emotion regulation [23]. O1-relhighbeta suggests heightened occipital arousal, often linked to internalizing symptoms [24], while T7-abs-gamma may reflect disruptions in emotional memory and social-affective processing in the left temporal lobe [25]. Such region- and frequency-specific alterations offer additional explanatory value beyond traditional symptom-based assessment and may help identify individuals less responsive to conventional treatments. Given their non-invasive and scalable nature, EEG biomarkers also hold promise for early intervention and longitudinal monitoring in clinical settings.

Unlike previous qEEG studies, our approach segmented EEG recordings into 30-second epochs, effectively increasing training data samples and enhancing model generalizability—an important advantage for potential clinical application of machine learning in psychiatry. Meanwhile, functional neuroimaging studies have shown that neurobiological markers can predict treatment response. For example, Huang et al. [26] demonstrated that in adolescents with depression and NSSI, altered resting-state functional connectivity within frontolimbic circuits—and its association with elevated pro-inflammatory markers—predicted self-injury severity and treatment trajectories. Our findings extend this line of research into electrophysiology, underscoring that qEEG-derived features may serve as non-invasive biomarkers for personalized intervention strategies in youth with NSSI.

Importantly, we also found that pre-treatment symptom severity—particularly admission scores on the respective clinical scales—was the most influential predictor of outcome across all models. This supports longstanding clinical observations that initial symptom burden may serve as a proxy for both illness severity and the capacity for improvement [27]. Prior studies in adolescent depression and psychotherapy have similarly shown that higher baseline severity often predicts greater absolute symptom reduction [28]. While baseline symptom severity was consistently the strongest predictor, the inclusion of qEEG features provided incremental predictive value across clinical domains. In our SHAP analysis, several neurophysiological features ranked immediately after baseline scores in importance, suggesting that they capture unique aspects of brain function not fully reflected by clinical ratings. Furthermore, supplementary models excluding qEEG features showed slight but consistent reductions in predictive accuracy, highlighting the additive value of electrophysiological data in individualized prediction models.

However, several limitations should be considered when interpreting these findings. First, the sample was restricted to female adolescents recruited from a single clinical center, which may limit the generalizability of the results. In particular, prior studies suggest that sex-specific neurophysiological patterns may influence NSSI-related behaviors. For instance, male adolescents with NSSI have been reported to show higher impulsivity and externalizing symptoms, which may be underpinned by distinct EEG markers such as reduced frontal theta activity or altered sensorimotor rhythms [29]. These findings highlight the importance of future studies including both sexes to clarify potential sex-based neural mechanisms of NSSI and to enhance the generalizability of EEG-based predictive models. Second, the absence of potentially relevant clinical variables—such as the frequency of NSSI episodes, presence of suicidal ideation, and co-occurring psychiatric diagnoses—may have influenced treatment outcomes. NSSI is also characterized by substantial heterogeneity in both clinical presentation and etiological mechanisms, which may not be fully captured by our current analytic framework [30]. Additionally, the retrospective design of the study limits causal inference and restricts the temporal interpretation of neurophysiological changes. The EEG data were based on a single resting-state recording at baseline, precluding the evaluation of dynamic changes in brain activity over the course of treatment. Future prospective studies employing repeated qEEG measurements would help validate the temporal stability of EEG-derived predictors and clarify the neurophysiological trajectories underlying treatment response. Finally, statistical considerations should also be acknowledged. Although our outcome measures were continuous rather than binary classification labels, individual differences in symptom change may have resulted in uneven response distributions. While the LOSO cross-validation strategy helps mitigate overfitting, potential imbalances in response variability could still introduce bias into model training. Larger and more stratified cohorts will be essential for confirming the generalizability and robustness of these predictive models.

In conclusion, this study is among the first to apply machine learning algorithms to predict treatment outcomes using qEEG features in female adolescents with repetitive NSSI. By leveraging both neurophysiological and clinical data, we demonstrated that baseline clinical severity—as well as specific EEG features such as Fz-ratio-delta-beta, O1-rel-highbeta, and T7-abs-gamma—significantly contributed to the prediction of treatment response across multiple clinical domains. These findings underscore the potential clinical utility of EEG-derived functional brain markers in guiding individualized treatment strategies for adolescents at high risk of NSSI.