Clinical and Psychosocial Characteristics of Adolescent Pediatric Patients Hospitalized after Different Types of Suicidal Behaviors-A Preliminary Study.

The objective of this study was to examine the clinical characteristics of adolescents hospitalized after a suicide attempt or instrumental suicide-related behavior. Participants included thirty-six adolescents from the pediatric unit of a Polish hospital who made a nonfatal suicide attempt (SAA) or engaged in instrumental suicide-related behavior (IBA), as well as a general population sample (GPS). Psychosocial features were measured using the Kiddie Schedule for Affective Disorders and Schizophrenia (K-SADS), the Social and Occupational Functioning Assessment Scale (SOFAS), the Suicide Behaviors Questionnaire-Revised (SBQ-R), the Psychache Scale (TPS), the State-Trait Anxiety Inventory (STAI), the Center of Epidemiological Studies Depression Scale for Children (CES-DC), and the Prodromal Questionnaire (PQ-16). The SAA group scored significantly higher than the IBA group and the GPS in modules related to irritability and anhedonia, voice hallucinations and delusions, suicidal acts, thoughts and ideation, and medical lethality. Additionally, the SAA scored higher on the SBQ-R and PQ-16 compared to the IBA group and the GPS. Although anxiety, mental pain, and depressive symptoms could not independently distinguish between the SAA and IBA groups, psychotic symptoms were more frequently present within the SAA group. The above symptoms may be important to consider when screening for suicide risk in the general population.

Machine Learning-Based Identification of Suicidal Risk in Patients With Schizophrenia Using Multi-Level Resting-State fMRI Features.

BACKGROUND: Some studies suggest that as much as 40% of all causes of death in a group of patients with schizophrenia can be attributed to suicides and compared with the general population, patients with schizophrenia have an 8.5-fold greater suicide risk (SR). There is a vital need for accurate and reliable methods to predict the SR among patients with schizophrenia based on biological measures. However, it is unknown whether the suicidal risk in schizophrenia can be related to alterations in spontaneous brain activity, or if the resting-state functional magnetic resonance imaging (rsfMRI) measures can be used alongside machine learning (ML) algorithms in order to identify patients with SR. METHODS: Fifty-nine participants including patients with schizophrenia with and without SR as well as age and gender-matched healthy underwent 13 min resting-state functional magnetic resonance imaging. Both static and dynamic indexes of the amplitude of low-frequency fluctuation (ALFF), the fractional amplitude of low-frequency fluctuations (fALFF), regional homogeneity as well as functional connectivity (FC) were calculated and used as an input for five machine learning algorithms: Gradient boosting (GB), LASSO, Logistic Regression (LR), Random Forest and Support Vector Machine. RESULTS: All groups revealed different intra-network functional connectivity in ventral DMN and anterior SN. The best performance was reached for the LASSO applied to FC with an accuracy of 70% and AUROC of 0.76 (p < 0.05). Significant classification ability was also reached for GB and LR using fALFF and ALFF measures. CONCLUSION: Our findings suggest that SR in schizophrenia can be seen on the level of DMN and SN functional connectivity alterations. ML algorithms were able to significantly differentiate SR patients. Our results could be useful in developing neuromarkers of SR in schizophrenia based on non-invasive rsfMRI.