Objectives: Assessing the risks of youth suicide in educational and clinical settings is crucial.Therefore, this study developed a machine learning model to predict suicide attempts using the Korean Youth Risk Behavior Web-based Survey (KYRBWS). Methods: KYRBWS is conducted annually on Korean middle and high school students to assess their health-related behaviors. The KYRBWS data for 2021, which showed 1206 adolescents reporting suicide attempts out of 54848, was split into the training (n=43878) and test (n=10970) datasets. Thirty-nine features were selected from the KYRBWS questionnaire. The balanced accuracy of the model was employed as a metric to select the best model. Independent validations were conducted with the test dataset of 2021 KYRBWS (n=10970) and the external dataset of 2020 KYRBWS (n=54948). The clinical implication of the prediction by the selected model was measured for sensitivity, specificity, true prediction rate (TPR), and false prediction rate (FPR). Results: Balanced bag of histogram gradient boosting model has shown the best performance (balanced accuracy=0.803). This model shows 76.23% sensitivity, 83.08% specificity, 10.03% TPR, and 99.30% FPR for the test dataset as well as 77.25% sensitivity, 84.62% specificity, 9.31% TPR, and 99.45% FPR for the external dataset, respectively. Conclusion These results suggest that a specific machine learning model can predict suicide attempts among adolescents with high accuracy.

Objective: Suicidal ideation is one of the strongest predictors of suicide, and its relevance to impulsivity in depressed patients has been accumulated. Furthermore, high impulsivity patients show the attenuation of the Nogo amplitude in the GoNogo event-related potential (ERP). The purpose of the current study is to determine the correlation of Nogo ERP to the suicidal ideation depending on the condition of its presence or absence in major depressive disorder (MDD) patients. Methods: A total 162 participants (104 patients with suicidal ideation, 31 patients without suicidal ideation, and 27 healthy controls) were recruited, and performed GoNogo tasks during the electroencephalogram measurement. Depression, anxiety, suicidal ideation and impulsivity were assessed by self-rating scales. The clinical measures, behavioral data and Nogo ERP were compared among groups. Results: The MDD with suicidal ideation (SI) group showed significantly decreased Nogo P3 amplitudes compared to MDD without SI (Fz and Cz electrodes) and control group (all electrodes). The MDD with SI group also had significantly low accuracy of both Go and Nogo trails, compared to the MDD without group. The Nogo P3 amplitudes showed the negative relation to the scores of impulsivity, depression, anxiety and SI. Conclusion Our results concluded that the Nogo P3 ERP amplitude was decreased in MDD patients with SI compared to MDD patients without SI and controls. These findings suggest that the decreased Nogo P3 amplitude is the one of the candidate biomarker for impulsivity in MDD patients to evaluating SI.

The continuous emergence of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) variants has provided insights for updating current coronavirus disease 2019 (COVID-19) vaccines. We examined the neutralizing activity of Abs induced by a BA.4/5-containing bivalent mRNA vaccine against Omicron subvariants BN.1 and XBB.1.5. We recruited 40 individuals who had received a monovalent COVID-19 booster dose after a primary series of COVID-19 vaccinations and will be vaccinated with a BA.4/5-containing bivalent vaccine. Sera were collected before vaccination, one month after, and three months after a bivalent booster.Neutralizing Ab (nAb) titers were measured against ancestral SARS-CoV-2 and Omicron subvariants BA.5, BN.1, and XBB.1.5. BA.4/5-containing bivalent vaccination significantly boosted nAb levels against both ancestral SARS-CoV-2 and Omicron subvariants. Participants with a history of SARS-CoV-2 infection had higher nAb titers against all examined strains than the infection-naïve group. NAb titers against BN.1 and XBB.1.5 were lower than those against the ancestral SARS-CoV-2 and BA.5 strains. These results suggest that COVID-19 vaccinations specifically targeting emerging Omicron subvariants, such as XBB.1.5, may be required to ensure better protection against SARS-CoV-2 infection, especially in high-risk groups.

Objectives: Suicide is the leading cause of death among adolescents in South Korea, and depression and personality profiles have been identified as significant risk factors for self-injurious behavior. This study examined the influence of depressive mood and temperament/ character on self-injury in adolescents. Methods: A total of 116 adolescents (aged 12–18 years) with a primary diagnosis of major depressive disorder (MDD) and their parents were enrolled in this study. The participants were divided into three groups based on adolescent’s self-injury frequency, and their Children’s Depression Inventory (CDI), Youth Self-Report (YSR), and Temperament and Character Inventory (TCI) scores were compared. Finally, mediation analysis was conducted to investigate the relationship between suicidal ideation and self-injury. Results: Of study participants, 75.9% answered that they had suicidal ideation, and 55.2% answered that they had engaged in self-injurious behavior in the last six months. There were significant differences in CDI and suicidal ideation among the groups. After adjusting for age and sex, mediation analysis indicated that depressive mood mediated the relationship between suicidal ideation and self-injury. Conclusion This study emphasizes the importance of evaluating and managing depressive mood severity in adolescents with MDD as these factors partially mediate the transition from suicidal ideation to self-injury.

Objective: The loudness dependence of the auditory evoked potential (LDAEP) is a reliable indicator that is inversely related to central serotonergic activity, and recent studies have suggested an association between LDAEP and suicidal ideation. This study investigated differences in LDAEP between patients with major depressive disorder and high suicidality and those with major depressive disorder and low suicidality compared to healthy controls. Methods: This study included 67 participants: 23 patients with major depressive disorder with high suicidality (9 males, mean age 29.3 ± 15.7 years, total score of SSI-BECK ≥ 15), 22 patients with major depressive disorder with low suicidality (9 males, mean age 42.2 ± 14.4 years, total score of SSI-BECK ≤ 14), and 22 healthy controls (11 males, mean age 31.6 ± 8.7 years). Participants completed the following assessments: Patient Health Questionnaire-9, Beck Depression Inventory-II, Beck Scale for Suicidal ideation, State Anxiety Scale of the State-Trait Anxiety Inventory, Beck Anxiety Inventory, and LDAEP (measured at electrode Cz). Results: There were no sex-related differences among groups (p = 0.821). The high-suicidality group exhibited significantly higher LDAEP compared to the low-suicidality group (0.82 ± 0.79 vs. 0.26 ± 0.36, p = 0.014). No significant differences were found between the control and high-suicidality (p = 0.281) or the control and low-suicidality groups (p = 0.236). Conclusion LDAEP was applied to demonstrate the association between serotonergic activity and suicidal ideation and suicide risk in major depression and may be a candidate of biological marker for preventing suicide in this study.

Objective: The loudness dependence of the auditory evoked potential (LDAEP) is a reliable indicator that is inversely related to central serotonergic activity, and recent studies have suggested an association between LDAEP and suicidal ideation. This study investigated differences in LDAEP between patients with major depressive disorder and high suicidality and those with major depressive disorder and low suicidality compared to healthy controls. Methods: This study included 67 participants: 23 patients with major depressive disorder with high suicidality (9 males, mean age 29.3 ± 15.7 years, total score of SSI-BECK ≥ 15), 22 patients with major depressive disorder with low suicidality (9 males, mean age 42.2 ± 14.4 years, total score of SSI-BECK ≤ 14), and 22 healthy controls (11 males, mean age 31.6 ± 8.7 years). Participants completed the following assessments: Patient Health Questionnaire-9, Beck Depression Inventory-II, Beck Scale for Suicidal ideation, State Anxiety Scale of the State-Trait Anxiety Inventory, Beck Anxiety Inventory, and LDAEP (measured at electrode Cz). Results: There were no sex-related differences among groups (p = 0.821). The high-suicidality group exhibited significantly higher LDAEP compared to the low-suicidality group (0.82 ± 0.79 vs. 0.26 ± 0.36, p = 0.014). No significant differences were found between the control and high-suicidality (p = 0.281) or the control and low-suicidality groups (p = 0.236). Conclusion LDAEP was applied to demonstrate the association between serotonergic activity and suicidal ideation and suicide risk in major depression and may be a candidate of biological marker for preventing suicide in this study.

Objectives: ZZThe aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-basedmedical records. Methods: ZZElectronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes withthree diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independentvalidation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF)and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vectorclassification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find aneffective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results: ZZFive-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis(accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final workingDL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showedslightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions ZZThe current results suggest that the vectorization may have more impact on the performance of classification thanthe machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category,and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machinelearning models.

Objectives: ZZThe aim was to find effective vectorization and classification models to predict a psychiatric diagnosis from text-basedmedical records. Methods: ZZElectronic medical records (n = 494) of present illness were collected retrospectively in inpatient admission notes withthree diagnoses of major depressive disorder, type 1 bipolar disorder, and schizophrenia. Data were split into 400 training data and 94 independentvalidation data. Data were vectorized by two different models such as term frequency-inverse document frequency (TF-IDF)and Doc2vec. Machine learning models for classification including stochastic gradient descent, logistic regression, support vectorclassification, and deep learning (DL) were applied to predict three psychiatric diagnoses. Five-fold cross-validation was used to find aneffective model. Metrics such as accuracy, precision, recall, and F1-score were measured for comparison between the models. Results: ZZFive-fold cross-validation in training data showed DL model with Doc2vec was the most effective model to predict the diagnosis(accuracy = 0.87, F1-score = 0.87). However, these metrics have been reduced in independent test data set with final workingDL models (accuracy = 0.79, F1-score = 0.79), while the model of logistic regression and support vector machine with Doc2vec showedslightly better performance (accuracy = 0.80, F1-score = 0.80) than the DL models with Doc2vec and others with TF-IDF. Conclusions ZZThe current results suggest that the vectorization may have more impact on the performance of classification thanthe machine learning model. However, data set had a number of limitations including small sample size, imbalance among the category,and its generalizability. With this regard, the need for research with multi-sites and large samples is suggested to improve the machinelearning models.

Over the past decade, practice of sharing brain magnetic resonance imaging (MRI) data is increasing given significance of reproducibility and transparency in human neuroscience. Larger multimodal brain MRI databases are needed for more robust research findings considering potential possibilities of large variability in human neuroscience. There are currently more than tens of thousands of shared brain MRI datasets across multiple conditions and hundreds of neuroimaging studies using multimodality through shared brain MRI data repositories. This article critically reviews aims, procedures, and current state of brain MRI data sharing. This review focuses on projects and research findings using structural and functional MRI open databases and is further divided into T1- and diffusion-weighted images for structural MRI as well as resting-state and task-based functional MRI. The challenges and directions are finally discussed. Advances in brain MRI data sharing will lead to more rapid progression in human neuroscience by fostering effective longitudinal, multi-site, multimodal neuroimaging research.
Brain ; Dataset ; Foster Home Care ; Humans ; Information Dissemination ; Magnetic Resonance Imaging ; Neuroimaging ; Neurosciences ; Transcutaneous Electric Nerve Stimulation

OBJECTIVES: According to previous studies, the Chromogranin B (CHGB) gene could be an important candidate gene for schizophrenia which is located on chromosome 20p12.3. Some studies have linked the polymorphism in CHGB gene with the risk of schizophrenia. Meanwhile, smooth pursuit eye movement (SPEM) abnormality has been regarded as one of the most consistent endophenotype of schizophrenia. In this study, we investigated the association between the polymorphisms in CHGB gene and SPEM abnormality in Korean patients with schizophrenia. METHODS: We measured SPEM function in 24 Korean patients with schizophrenia (16 male, 8 female) and they were divided according to SPEM function into two groups, good and poor SPEM function groups. We also investigated genotypes of polymorphisms in CHGB gene in each group. A logistic regression analysis was performed to find the association between SPEM abnormality and the number of polymorphism. RESULTS: The natural logarithm value of signal/noise ratio (Ln S/N ratio) of good SPEM function group was 4.19 ± 0.19 and that of poor SPEM function group was 3.17 ± 0.65. In total, 15 single nucleotide polymorphisms of CHGB were identified and the genotypes were divided into C/C, C/R, and R/R. Statistical analysis revealed that two genetic variants (rs16991480, rs76791154) were associated with SPEM abnormality in schizophrenia (p = 0.004). CONCLUSIONS: Despite the limitations including a small number of samples and lack of functional study, our results suggest that genetic variants of CHGB may be associated with SPEM abnormality and provide useful preliminary information for further study.
Chromogranin B* ; Endophenotypes ; Eye Movements* ; Genetic Variation* ; Genotype ; Humans ; Logistic Models ; Male ; Polymorphism, Single Nucleotide ; Pursuit, Smooth* ; Schizophrenia*