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Jae Myeong Kang,1,* Seong-Jin Cho,1,* Seo-Eun Cho,1 Taemo Bang,2 Byung Do Chae,3 Eojin Yi,4 Seung Min Bae,1 Kyoung-Sae Na,1 Jaehun Jung,5 Seung-Gul Kang1 1Department of Psychiatry, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea; 2Artificial Intelligence and Big-Data Convergence Center, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea; 3Unit of Frontier Exploration, Korea Polar Research Institute, Incheon, Republic of Korea; 4Department of Plastic and Reconstructive Surgery, Yonsei University College of Medicine, Seoul, Republic of Korea; 5Department of Preventive Medicine, Gachon University College of Medicine, Incheon, Republic of Korea*These authors contributed equally to this workCorrespondence: Seung-Gul Kang, Department of Psychiatry, Gil Medical Center, Gachon University, College of Medicine, 21, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea, Tel +82-32-468-9932, Fax +82-32-468-9962, Email kangsg@gachon.ac.krPurpose: Antarctica is a region with extreme climate, characterized by extreme cold and photoperiod. No research has been conducted on the mental health of Korean Antarctic dispatchers. The aim of this study was to investigate the status of mental illness and changes in mood and sleep among Korean crew members staying for a long-term period in the Antarctic station.Methods: From 2017 to 2020, crew members who were dispatched from South Korea to two Antarctic stations for a one-year period participated in this study. The crew were evaluated for mood and sleep status and mental illness through psychological tests and interviews by board-certified psychiatrists once before departure and twice during their stay in Antarctica. The incidence of mental illness was confirmed and changes in sleep and depression were analyzed.Results: A total of 88 participants were included in the final analysis, and 7 of them (8.0%) were diagnosed with mental disorders such as insomnia in early winter. The total Beck Depression Inventory (BDI) score increased significantly in the early winter period, and the total Insomnia Severity Index (ISI) and Pittsburgh Sleep Quality Inventory (PSQI) scores increased in both early and late winter. The difference in changes in mood and sleep symptoms before, during, and after dispatch between the two stations was not significant.Conclusion: This is the first study to investigate the mental illness and mood and sleep status of Korean crews dispatched to Antarctica. In early winter, there were significant increases in mental illness and depressive symptoms, and a worsening of sleep status.Keywords: Antarctic, depression, insomnia, mental illness, sleep, winter-over

Continuous positive airway pressure (CPAP) is the treatment of choice for obstructive sleep apnea (OSA). However, compliance with CPAP varies from study to study, and studies on predictors of compliance with CPAP are insufficient in Korea. The aim of this study was to identify factors predicting compliance with CPAP therapy in patients with OSA. We retrospectively reviewed medical records, polysomnography (PSG) records, and self-report questionnaires of patients with OSA. Criteria for compliance was the use of CPAP devices for ≥ 4 h per night for ≥ 70% of the consecutive 30 nights (i.e., 21 days) during the first 3 months of treatment initiation. The patients were classified into two groups: compliant and non-compliant. Logistic regression analyses were performed to identify clinical factors and PSG parameters associated with compliance with CPAP. Among 188 participants, 80 were classified into the compliant group and 108 were classified into the non-compliant group. The ratio of stage N1 (P = 0.011) and health insurance coverage (P = 0.007) were significantly associated with compliance with CPAP, with an explanatory power of 18.6% (R2 = 0.186, P < 0.001). This study indicated that the stage N1 ratio and health insurance coverage were significant predictors of CPAP compliance. It is necessary to confirm whether the relationship between a high stage N1 ratio and compliance is reproduced in a larger sample and in individuals from other countries.

Purpose: This study examined the current status of counseling services provided by the Korea Counseling Center for Fertility and Depression, analyzing the characteristics of peripartum women and baby-rearing mothers and establishing guidelines for providing psychological support, and suggesting measures for improving the system.Methods: Data on 3,660 peripartum women & their spouses and baby-rearing mothers counseled through the service over the last 4 years were collected and a demographic analysis was conducted. By analyzing the clinical information of 216 peripartum women and 219 baby-rearing mothers who have registered with the Center and received routine counseling services, factors affecting depression were identified. Finally, a paired sample t-test was conducted to verify the effect of counseling services.Results: An overall 20.4% of pregnant women & their spouses were screened for high risk for depression, of whom 27.3% received registered counseling services; further, 26.2% of baby-rearing parents were at high-risk group for depression, of whom 25% received registered counseling services. Results of a logistic regression analysis suggested that, for peripartum women, level of education and conflicts with partner and family were the crucial factors predicting moderate or severe depression. For baby-rearing mothers, obstetric history of spontaneous abortion was the crucial predicting factor.Conclusion: For the early detection and prevention of peripartum depression, screening tests that start from early pregnancy should be routinely administered. Further, continuous management—covering the periods before and after childbirth—should be provided by establishing organic ties between domestic projects.

OBJECTIVE: This study aimed to investigate the existence of a difference in quality of life (QOL) between individuals with and without significant subjective-objective discrepancy (SOD) in total sleep time (TST). METHODS: From the Sleep Heart Health Study (SHHS) 2, 2540 individuals who had completed polysomnography, a morning sleep survey, and the 36-item Short-Form Health Survey (SF-36) were included in the analyses. The participants were classified as normoestimators (estimation of TST < ± 60 min), underestimators (underestimation of TST ≥ 60 min), or overestimators (overestimation of TST ≥ 60 min). The standardized SF-36 QOL scores were compared among the three groups. An adjusted partial correlation analysis was conducted between SOD and QOL. RESULTS: Of the 2540 participants, 1617 (63.7%), 433 (17.0%), and 490 (19.3%) were assigned to the normoestimator, underestimator, and overestimator groups, respectively. The bodily pain and social functioning components of the SF-36 score were significantly lower in the underestimators than in the normoestimators, whereas the physical functioning component was significantly lower in the overestimators than in the normoestimators. The absolute value of SOD in the TST showed a significant negative correlation with the physical and mental components of the SF-36. CONCLUSIONS: QOL was significantly better in the normoestimator than in the other groups and linearly correlated with the absolute value of SOD. This study suggests that a high prevalence of positive and negative sleep misperception in a community population can be a potential factor associated with poor QOL and potential comorbidities.

Background Insomnia is a common disorder that affects a vast number of patients; the hyperarousal theory of insomnia postulates that patients with insomnia are physiologically activated not only at nighttime but also during the daytime. We aimed to investigate the differences in the resting-state functional connectivity (RSFC) of the thalamus with cortical areas between patients with insomnia disorder and healthy controls. Methods All participants completed clinical questionnaires and underwent portable polysomnography and resting-state fMRI. Results Patients in the insomnia group (n = 50) showed increased RSFC between the thalamus and right medial superior frontal area, bilateral middle temporal areas, left rectus and right parahippocampal areas compared with controls (n = 42) after controlling for age, sex and education level. Among the pairs that showed increased connectivity, several functional connections were negatively correlated with sleep efficiency, measured by polysomnography.Limitations: We used a small sample size. Conclusion We consider these results on increased thalamocortical hyperactivity in brain areas related to sensory functions as providing evidence for the hyperarousal theory of insomnia.

The association between air pollutants and psychiatric disorders has been investigated in many countries. However, results for the association between air pollutants and emergency room (ER) visits for psychiatric disorders are inconsistent. Further, systematic large-scale studies relating to the same are lacking, especially in South Korea.We aimed to investigate the acute and short-term cumulative effect of air pollutants on ER visits for psychiatric disorders in South Korea.The data on nitrogen dioxide (NODuring the study period, a total of 79,092 ER visits for psychiatric disorders were identified and tested for association with NOAir pollutants increased ER visits for psychiatric disorders with the highest RR of depression due to NOWe investigated the effect of air pollution on emergency room visits caused by major psychiatric disorders in prominent cities in South Korea. Using the Distributed Lag Nonlinear Model, an advanced analysis method, we calculated the acute effect and short-term cumulative effect. Air pollutants increased ER visits for psychiatric disorders with the highest relative risk of depression due to NO

Objective Patients with breathing-related sleep disorder (BRSD) often complain of psychiatric symptoms such as depression in addition to snoring, excessive sleepiness, and disturbed sleep. However, the relationship between psychiatric symptoms and severity of sleep apnea in BRSD is controversial. We conducted this study to investigate the relationship between psychiatric symptoms and sleep electroencephalography (EEG) findings in BRSD patients using spectral analysis. Methods All participants underwent polysomnography and evaluation using Symptom Checklist-90-Revised (SCL-90-R) scale. We analyzed the absolute spectral power density values of standard EEG frequency bands in the participants (n = 169) with BRSD during the non-rapid eye movement (NREM) sleep period. We performed correlation analysis between the domain scores of SCL-90-R scale and the absolute values of the EEG frequency bands. Results Significant positive correlation was observed between the absolute spectral power density values in the slow oscillation band and the degree of paranoid ideation (r = 0.226, p = 0.028) and depression (r = 0.216, p = 0.044) in SCL-90-R. The multiple linear regression model showed that higher paranoid ideation domain score (B = 0.007, p = 0.020), younger age (B = -0.011, p < 0.001), and female sex (B = 0.213, p = 0.004) were associated with higher slow oscillation power during NREM sleep. Conclusion The results of the present study suggested a relationship between sleep EEG and psychiatric symptoms in patients with BRSD. This relationship needs to be validated with further studies.

The habenula is one of the most important brain regions for investigating the etiology of psychiatric diseases such as major depressive disorder (MDD). However, the habenula is challenging to delineate with the naked human eye in brain imaging due to its low contrast and tiny size, and the manual segmentation results vary greatly depending on the observer. Therefore, there is a great need for automatic quantitative analytic methods of the habenula for psychiatric research purposes. Here we propose an automated segmentation and volume estimation method for the habenula in 7 Tesla magnetic resonance imaging based on a deep learning-based semantic segmentation network. The proposed method, using the data of 69 participants (33 patients with MDD and 36 normal controls), achieved an average precision, recall, and dice similarity coefficient of 0.869, 0.865, and 0.852, respectively, in the automated segmentation task. Moreover, the intra-class correlation coefficient reached 0.870 in the volume estimation task. This study demonstrates that this deep learning-based method can provide accurate and quantitative analytic results of the habenula. By providing rapid and quantitative information on the habenula, we expect our proposed method will aid future psychiatric disease studies.

In South Korea, the suicide rate is more than double the OECD average, and precise identification of the cause is required for suicide prevention. Psychological autopsy is used to reveal factors related to suicidal behavior; however, such studies are lacking in Korea. This study investigated the factors related to suicide using psychological autopsies in Incheon, a major city in Korea. In total, 46 cases were investigated using the Korea-Psychological Autopsy Checklist (K-PAC), and data on mental health conditions and psychosocial factors of suicide decedents and their families were analyzed. It was estimated that 87% of individuals of suicides had a mental health condition before death, but only 15.2% continued treatment or counseling. Most individuals who died of suicide showed warning signs before death, but only 19.6% of survivors of suicide loss noticed them. Mental health concerns before and after the death of the individual were also identified in more than half of their families. To prevent suicide, intensive and continuous treatment for psychiatric conditions and prompt recognition of active response to suicide warning signs are required. Care for the mental health of family members is also important.

Objective The response to antipsychotics in patients with schizophrenia is still unsatisfactory. Therefore, augmentation with other antipsychotics is common in clinical situations. The purpose of this study was to evaluate the improvement of psychiatric symptoms and side effects after amisulpride add-on therapy. Methods Forty patients with schizophrenia or schizoaffective disorder without treatment response to second-generation antipsychotics were included in this study. Psychotic symptoms were evaluated using the Positive and Negative Syndrome Scale (PANSS) and the Korean version of Calgary Depression Scale for Schizophrenia (CDSS) at baseline, 4 weeks, and 8 weeks after the addition of amisulpride. Results Among the 29 subjects who completed the 8-week study, 34.5% were responders according to PANSS total score. At week 8, the mean positive (p < 0.001), negative (p < 0.001), general (p < 0.001), and total (p < 0.001) PANSS scores and CDSS scores (p = 0.002) showed significant improvement compared to baseline. There was no increase in extrapyramidal side effects according to Simpson Angus Scale (p = 0.379) and Barnes Akathisia Rating Scale (p = 0.070) and no weight gain (p = 0.308) after the add-on treatment. Conclusion The addition of amisulpride for schizophrenia patients without therapeutic response to second-generation antipsychotics is considered an effective and safe treatment. This study's results suggested that augmentation of second-generation antipsychotics with amisulpride could be a useful option for patients with schizophrenia unresponsive to second-generation antipsychotics. Further studies investigating the efficacy of amisulpride add-on therapy using placebo control are necessary to confirm these results.

Backgrounds Panic disorder is a highly prevalent psychiatric disorder that substantially impairs quality of life and psychosocial function. Panic disorder arises from neurobiological substrates and developmental factors that distinguish it from other anxiety disorders. Differential diagnosis between panic disorder and other anxiety disorders has only been conducted in terms of a phenomenological spectrum. Methods Through a machine learning-based approach with heart rate variability (HRV) as input, we aimed to build algorithms that can differentiate panic disorder from other anxiety disorders. Five algorithms were used: random forest (RF), gradient boosting machine (GBM), support vector machine (SVM), artificial neural network (ANN), and regularized logistic regression (LR). 10-fold cross-validation with five repeats was used to build the final models. Results A total of 60 patients with panic disorder and 61 patients with other anxiety disorders (aged between 20 and 65 years) were recruited. The L1-regularized LR showed the best accuracy (0.784), followed by ANN (0.730), SVM (0.730), GBM (0.676), and finally RF (0.649). LR also had good performance in other measures, such as F1-score (0.790), specificity (0.737), sensitivity (0.833), and Matthews correlation coefficient (0.572). Limitations Cross-sectional design and limited sample size is limitations. Conclusion This study demonstrated that HRV can be used to differentiate panic disorder from other anxiety disorders. Future studies with larger sample sizes and longitudinal design are required to replicate the diagnostic utility of HRV in a machine learning approach.

Background Suicide is a serious social and public health problem. Social stigma and prejudice reduce the accessibility of mental health care services for high-risk groups, resulting in them not receiving interventions and committing suicide. A suicide prediction model is necessary to identify high-risk groups in the general population. Methods We used national medical check-up data from 2009 to 2015 in Korea. The latest medical check-up data for each subject was set as an index point. Analysis was undertaken for an overall follow-up period (index point to the final tracking period) as well as for a one-year follow-up period. The training set was cross-validated fivefold. The predictive model was trained using a random forest algorithm, and its performance was measured using a separate test set not included in the training. Results The analysis covered 372,813 individuals, with an average (SD) overall follow-up duration of 1.52 (1.52) years. When we predicted suicide during the overall follow-up period, the area under the receiver operating characteristic curve (AUC) was 0.849, sensitivity was 0.817, and specificity was 0.754. The performance of the predicted suicide risk model for one year from the index point was AUC 0.818, sensitivity 0.788, and specificity 0.657. Conclusions This is probably the first suicide predictive model using machine learning based on medical check-up data from the general population. It could be used to screen high-risk suicidal groups from the population through routine medical check-ups. Future studies may test preventive interventions such as exercise and alcohol in these high-risk groups.

Purpose: This study aimed to review the history and examine the current service system of the Korea Counseling Center for Fertility and Depression (KCCFD) by analyzing the characteristics of its users, and to suggest measures to improve the system.Methods: Data on 883 infertile patients counseled through the KCCFD’s counseling service over the last 2 years were collected for a demographic analysis. The clinical information of 396 female subjects who received regular counseling after registration were analyzed to investigate factors influencing depression. Finally, a matching sample t-test was conducted to verify the effects of the counseling service.Results: The screening test showed that 50.7% of the infertile patients in our study were registered at our centers and received registered counseling. A total of 27.6% of the subjects was identified as being at high-risk for depression, and 71.3% of those at high-risk received registered counseling. The logistic regression analysis showed that being a homemaker, having a history of psychiatric disorder, and having undergone in vitro fertilization three or more times were significant factors that predict moderate or more severe depression. The female infertility patients who underwent counseling showed a significant decrease in depression, anxiety, general stress, and infertility stress.Conclusion: Screening tests need to be performed prior to assisted reproductive technology if any of the 3 risk factors is present. The rate of registered counseling, regular follow-up using psychological tests, and cross-linkages with affiliated agencies should serve as the metrics for the quality control of the counseling service. Early detection of individuals at high-risk for depression calls for an activated liaison among affiliated agencies and expanded regional centers.

Kyoung-Sae Na,1 Zong Woo Geem,2 Seo-Eun Cho3 1Department of Psychiatry, Gachon University College of Medicine, Incheon, 21565, Republic of Korea; 2College of IT Convergence, Gachon University, Seongnam, 13120, Republic of Korea; 3Department of Psychiatry, Gil Medical Center, Incheon, 21565, Republic of KoreaCorrespondence: Seo-Eun ChoDepartment of Psychiatry, Gil Medical Center, Namdong-daero 774 beon-gil, Namdong-gu, Incheon, 21565, Republic of Korea, Tel +82-32-460-3879, Fax +82-32-466-3267, Email arztin01@gilhospital.comPurpose: Suicide is an important health and social concern worldwide. Both suicidal ideation and suicide rates are higher in the elderly population than in other age groups; thus, more careful attention and targeted interventions are required. Therefore, we have developed a model to predict suicidal ideation in the community-dwelling elderly aged of > 55 years.Patients and Methods: A random forest algorithm was applied to those who participated in the Korea Welfare Panel. We used a total of 26 variables as potential predictors. To resolve the imbalance in the dataset resulting from the low frequency of suicidal ideation, training was performed by applying the synthetic minority oversampling technique. The performance index was calculated by applying the predictive model to the test set, which was not included in the training process.Results: A total of 6410 elderly Korean aged of > 55 (mean, 71.48; standard deviation, 9.56) years were included in the analysis, of which 2.7% had suicidal ideation. The results for predicting suicidal ideation using the 26 chosen variables showed an AUC of 0.879, accuracy of 0.871, sensitivity of 0.750, and specificity of 0.874. The most significant variable in the predictive model was the severity of depression, followed by life satisfaction and self-esteem factors. Basic demographic variables such as age and gender demonstrated a relatively small effect.Conclusion: Machine learning can be used to create algorithms for predicting suicidal ideation in community-dwelling elderly. However, there are limitations to predicting future suicidal ideation. A predictive model that includes both biological and cognitive indicators should be created in the future.Keywords: suicide, mental health, machine learning, artificial intelligence

The habenula (Hb) has been hypothesized to play an essential role in major depressive disorder (MDD) as it is considered to be an important node between fronto-limbic areas and midbrain monoaminergic structures based on animal studies. In this study, we aimed to investigate the differences in volume and T1 value of the Hb between patients with MDD and healthy control (HC) subjects. Analysis for the Hb volumes was performed using high-resolution 7-T magnetic resonance (MR) image data from 33 MDD patients and 36 healthy subjects. Two researchers blinded to the clinical data manually delineated the habenular nuclei and Hb volume, and T1 values were calculated based on overlapping voxels. We compared the Hb volume and T1 value between the MDD and HC groups and compared the volume and T1 values between the left and right Hbs in each group. Compared to HC subjects, MDD patients had a smaller right Hb volume; however, there was no significant volume difference in the left Hb between groups. In the MDD group, the right Hb was smaller in volume and lower in T1 value than the left Hb. The present findings suggest a smaller right Hb volume and left-right asymmetry of Hb volume in MDD. Future high-resolution 7-T MR imaging studies with larger sample sizes will be needed to derive a more definitive conclusion.

Background: The thalamus and habenula are thought to be key brain regions in the etiology of major depressive disorder (MDD); however, few studies have investigated the structural connection between them. We compared the number of white matter tracts between the thalamus and habenula between patient with MDD and normal controls (NCs).Methods: The habenula and thalamus region of interest masks were extracted from brain magnetic resonance imaging data and individual tractography analysis was performed. First, we compared the number of fiber connections from the habenula to the thalamus between the MDD (n = 34) and NC (n = 37) groups and also compared hemispherical differences to investigate possible asymmetries.Results: There was a significant difference in the number of tracts in the right habenula-left mediodorsal thalamus pair between the two groups. For hemispherical fiber connections, the waytotal ratio of the right ipsilateral tract between the thalamus and habenula was significantly higher than that of the left ipsilateral tract in both groups.Conclusion: The number of right habenula-left mediodorsal thalamus tracts was higher in patients with MDD than in NCs. These results indicate that MDD is related to the disintegration of the left thalamus-right habenula tract function with an increased number of tracts as a compensational mechanism.

This study aimed to investigate the applicability of machine learning to predict obstructive sleep apnea (OSA) among individuals with suspected OSA in South Korea. A total of 92 clinical variables for OSA were collected from 279 South Koreans (OSA, n = 213, no OSA, n = 66), from which seven major clinical indices were selected. The data were randomly divided into training data (OSA, n = 149, no OSA, n = 46) and test data (OSA, n = 64, no OSA, n = 20). Using the seven clinical indices, the OSA prediction models were trained using four types of machine learning models—logistic regression, support vector machine (SVM), random forest, and XGBoost (XGB)—and each model was validated using the test data. In the validation, the SVM showed the best OSA prediction result with a sensitivity, specificity, and area under curve (AUC) of 80.33%, 86.96%, and 0.87, respectively, while the XGB showed the lowest OSA prediction performance with a sensitivity, specificity, and AUC of 78.69%, 73.91%, and 0.80, respectively. The machine learning algorithms showed high OSA prediction performance using data from South Koreans with suspected OSA. Hence, machine learning will be helpful in clinical applications for OSA prediction in the Korean population.

Background Cognitive reserve (CR) is a concept proposed to account for discrepancies between the extent of brain pathology and clinical manifestations of that pathology. This study aimed to explore the associations between CR and the effects of cognitive training using fully immersive virtual reality (VR). Methods A total of 44 older adults (22 cognitively normal, 22 with mild cognitive impairment) underwent eight cognitive training sessions using VR for a period of 4 weeks. CR was assessed using the Cognitive Reserve Index questionnaire (CRIq). To evaluate baseline cognitive function and the effects of VR training, the Consortium to Establish a Registry for Alzheimer's Disease (CERAD) neuropsychological battery was administered to all participants before and after the training. Results Greater improvement in the total CERAD score was seen for cognitively normal participants with higher versus lower scores on the Education subdomain of the CRIq. Among patients with mild cognitive impairment, none of the CRIq subdomain scores (Education, Working Activity, Leisure Time) were related to a change in CERAD total scores. The CRIq total score did not predict the improvement of global cognition in either group. Conclusions This study revealed different impacts of CR on cognitive training according to the participants' cognitive status. It also suggests that employing three proxies of CR rather than using a composite score would provide a more accurate understanding of one's CR.

Objective: Suicide attempts of the older adults are known to be more serious than that of the younger adults. Despite its major social impact in South Korea, the behavioral mechanism of serious suicide attempt (SSA) in old people remains to be elucidated. Thus, we investigated the risk factors for SSA in older and younger suicide attempters in the emergency department.Methods: Demographic data, clinical information, and the level of seriousness of suicide with Risk Rescue Rating Scale were compared between older (age ≥65) and younger (age Results: Among 370 patients, 37 were older adults (10%; aged 74.41 ± 6.78), more likely to have another medical disease (p < 0.001), and a higher suicide completion rate (16.2 vs. 5.4%, p = 0.023). In the younger group, old age (B = 0.090, p < 0.001), male sex (B = −0.038, p = 0.019), and impression of schizophrenia (B = 0.074, p = 0.027) were associated with a higher risk-rescue ratio and interpersonal stress condition was associated with a lower risk-rescue ratio (B = −0.045, p = 0.006). In the older group, however, no variables were included significant in the regression model for the Risk Rescue Rating Scale.Conclusions: Demographic and clinical factors such as old age, male sex, interpersonal stress, and impression of schizophrenia were associated with lethality in the younger suicide attempters. However, no factors were associated with SSA in the older adult group. Different mechanisms may underly the lethality in old age suicide.

This study aimed to compare the mean pulse rate (PR) and mean blood pressure (BP) between patients with obstructive sleep apnea (OSA) and those with simple snoring (SS) during a 24-hour period, and to investigate the correlation between apnea-hypopnea index (AHI), PR, and BP in sleep-related breathing disorder (SRBD) patients with and without hypertension, diabetes mellitus (DM), and cardiovascular diseases (CVDs).Ninety SRBD patients underwent full-night polysomnography, and ambulatory BP and PR were monitored for 24 hours. Participants were classified into OSA (AHI ≥ 5) and control (SS) (AHI5) groups, and BP and PR were compared. Participants were also divided into groups with and without hypertension, CVDs, or DM to analyze the correlation between AHI, BP, and PR in each group.Mean PRs during the daytime period and during the whole 24-hour period in the OSA group were significantly higher than those in the SS group after controlling for potential confounders. No significant difference was observed in mean BP between the groups. Partial correlation analysis after controlling for confounders showed significant correlation between AHI and PR during daytime and the 24-hour period in participants without hypertension, DM, or CVDs, but not in participants with these conditions.The significant differences and correlations only in PR (not in BP) found in this study suggest that PR could be an early marker for SRBD in individuals without comorbidities, and that an increased sympathetic tone could be responsible for future occurrence of CVD.

Early detection of oppositional defiant behavior is warranted for timely intervention in children at risk. This study aimed to build a predictive model of persistent oppositional defiant behavior based on a machine learning algorithm.With nationwide cohort data collected from 2012 to 2017, a tree-based ensemble model, random forest, was exploited to build a predictive model for persistent oppositional defiant behavior. The persistent oppositional defiant behavior was defined by the presence of oppositional defiant behavior for all the five years. The area under the receiver operating characteristic curve (AUC), overall accuracy, sensitivity, specificity, and Matthew's correlation coefficients (MCC) were measured.Data of 1,323 children were used for building the machine learning-based predictive model. The baseline mean ± standard deviation month-age of the participants was 51.0 ± 1.2 months. The proportion of persistent oppositional defiant behavior was 0.98% (13/1323). In the hold-out test set, the overall accuracy, AUC, sensitivity, specificity, and MCC were 0.955, 0.982, 1.000, 0.954, and 0.417, respectively.Our study demonstrated that the machine learning-based approach is useful for predicting persistent oppositional defiant behavior in preschool-aged children.

Background The definition of mixed features by the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) remains controversal; however, there has been no systematic review of the prevalence of DSM-5 mixed features. We conducted a meta-analysis and systematic review to examine the prevalence of DSM-5-defined mixed features in major depressive episodes (MDE) and manic/hypomanic episodes. Methods We systematically searched all literature types (i.e., observational, cross-sectional, cohort, retrospective chart review, and post-hoc analysis) in electronic databases including MEDLINE, Embase, CINAHL, PsycINFO, and Web of Science from 2013 to 2020. Results A total of 17 studies with 20 samples were selected. The pooled prevalences of the mixed features in MDE and manic/hypomanic episodes were 11.6% (95% confidence interval [CI] = 7.9-16.7%) and 26.8 (95% CI = 17.0-39.5%), respectively. The prevalence of mixed features during major depressive disorder in East Asian countries was the lowest, which ranged from 0-2.2%. The subgroup analysis did not identify any influential factors for substantial heterogeneity. Most of the individual studies demonstrated moderate to high risk of bias. Conclusions Despite the increasing attention and controversy surrounding DSM-5-defined mixed features, few studies have systematically estimated the prevalence. Future studies with appropriate design and sample sizes should measure the prevalence of mixed features during MDE and manic/hypomanic episodes.

Depression is a mental illness that causes significant disturbances in daily life. Depression is commonly associated with low mood, severe health problems, and substantial socioeconomic burden; hence, it is necessary to be able to detect depression earlier. We utilized the medical check-up cohort database of the National Health Insurance Sharing Service in Korea. We split the total dataset into training (70%) and test (30%) sets. Subsequently, five-fold cross validation was performed in the training set. The holdout test set was only used in the last step to evaluate the performance of the predictive model. Random forest algorithm was used for the predictive model. The analysis included 433,190 individuals who had a national medical check-up from 2009-2015, which included 10,824 (2.56%) patients in the depression group. The area under the receiver-operating curve was 0.849. Other performance metrics included a sensitivity of 0.737, specificity of 0.824, positive predictive value of 0.097, negative predictive value of 0.992, and accuracy of 0.780. Our predictive model could contribute to proactively reducing depression prevalence by administering interventions to prevent depression in patients receiving medical check-up. Future studies are needed to prospectively validate the predictability of this model.

Because suicide is irreversible, prevention is paramount. For the optimal strategy to reduce lethal means, we sought to investigate age- and sex-associated variations in suicide methods. Data on annual causes of death from 1991 to 2015 in the Republic of Korea were used. Major sociodemographic correlates of the five suicide methods were analyzed by multiple multinominal logistic regression analysis. Among a total of 239,565 suicides from 1991 to 2015, hanging was most common. Gas poisoning sharply increased from 2007 to 2015. The gap between hanging and the second most common method of suicide has increased from 659 in 2004 to 4,433 in 2015. Charcoal burning was most commonly used by males younger than 45 years of age, whereas pesticide was commonly used by both sexes ages 55 years and older. Our results suggest that age- and sex-specific suicide prevention strategies are needed, particularly for gas and pesticide poisoning.

Patients with simple snoring (SS) often complain of poor sleep quality despite a normal apnoea-hypopnoea index (AHI). We aimed to identify the difference in power spectral density of electroencephalography (EEG) between patients with SS and those with obstructive sleep apnoea (OSA). We compared the absolute power spectral density values of standard EEG frequency bands between the SS (n = 42) and OSA (n = 129) groups during the non-rapid eye movement (NREM) sleep period, after controlling for age and sex. We also analysed partial correlation between AHI and the absolute values of the EEG frequency bands. The absolute power spectral density values in the beta and delta bands were higher in the OSA group than in the SS group. AHI also positively correlated with beta power in the OSA group as well as in the combined group (OSA + SS). In conclusion, higher delta and beta power during NREM sleep were found in the OSA group than in the SS group, and beta power was correlated with AHI. These findings are microstructural characteristics of sleep-related breathing disorders.

Many studies have reported that suicides tend to occur on Mondays. However, owing to a lack of controls, conclusive findings on the potential effects of a day of the week on suicides have been lacking. We analyzed public data for causes of death from 1997 to 2015 in the Republic of Korea. Accidental death was used as a control group. The probability of suicide on each day of the week according to age group was calculated. A total of 377,204 deaths (188,601 suicides and 188,603 accidental deaths) were used. The frequency of suicide was highest on Monday and decreased throughout the week until Saturday. Accidental death was highest on Saturday and showed no variations according to weekday. For people in their teens and 20s, the probabilities of suicide on Monday were 9% and 10% higher, respectively, than those on Sunday. As age increased, the differences in suicide probability according to the day of the week were attenuated. The so-called Blue Monday effect is real, particularly for people in their teens and 20s. Suicide prevention strategies that aim to attenuate the burden and stress of Mondays should be planned.

Background Many studies have reported that selective serotonin reuptake inhibitors (SSRI) are associated with an increased risk of bleeding. Mirtazapine and bupropion, which commonly lack serotonin reuptake inhibition, have been recommended as alternatives for patients who are at risk for bleeding. However, the evidence for these recommendations is insufficient. Methods We conducted a systematic search, systematic review, and meta-analysis to investigate an evidence-based approach for the bleeding risks of mirtazapine and bupropion. From 1946 to May 2017, a total of 3981 studies were searched from PubMed, Embase, and the Cochrane Library. Among the studies, two independent reviewers selected studies per predefined eligibility criteria. Results A total of five meta-analyses were conducted. Patients taking mirtazapine were at a greater risk of gastrointestinal bleeding (OR = 1.17, 95% CI = 1.01–1.38) than those who did not take antidepressants. No differences were observed in the bleeding risk between mirtazapine and SSRI or between bupropion and SSRI. Limitations The number of studies included in the meta-analysis was small. Conclusion Our results suggest that it is premature to recommend mirtazapine and bupropion for patients who have a bleeding risk. More studies with larger sample sizes and longitudinal follow-ups are warranted.

Suicide poses a serious problem globally, especially among the elderly population. To tackle the issue, this study aimed to develop a model for predicting suicide by using machine learning based on the elderly population. To obtain a large sample, the study used the big data health screening cohort provided by the National Health Insurance Sharing Service. By applying a machine learning technique, a predictive model that comprehensively utilized various factors was developed to select the elderly aged &gt, 65 years at risk of suicide. A total of 48,047 subjects were included in the analysis. Individuals who died by suicide were older, and the number of men was significantly greater. The suicide group had a more prominent history of depression, with the use of medicaments significantly higher. Specifically, the prescription of benzodiazepines alone was associated with a high suicide risk. Furthermore, body mass index, waist circumference, total cholesterol, and low-density lipoprotein level were lower in the suicide group. We developed a model for predicting suicide by using machine learning based on the elderly population. This suicide prediction model can satisfy the performance to some extent by employing only the medical service usage behavior without subjective reports.

Introduction Patients with insomnia frequently experience sleep/insomnia-related anxiety; this anxiety has been associated with hyperarousal. We investigated the underlying brain function changes in patients with insomnia during emotional task performance that induced sleep/insomnia-related anxiety. Methods Functional magnetic resonance imaging (fMRI) was performed during emotional task performance in healthy individuals and patients with insomnia who met the diagnostic criteria of insomnia disorder based on the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, and had chronic insomnia for more than 6 months. The participants underwent fMRI scanning during three types of emotional task performance—insomnia-anxiety task, reading sentences that cause insomnia-related anxiety; general-anxiety task, reading sentences that cause anxiety for everyone; and neutral task, reading neutral sentences that do not cause emotional anxiety. The images obtained from fMRI and blood oxygen level-dependent (BOLD) signal changes were compared between patients with insomnia and healthy controls. Interim analysis was performed with the data of 13 patients with insomnia and 9 controls. Results The brain activity in the left anterior cingulate was higher during insomnia-anxiety task performance than that during general-anxiety task performance in the insomnia group (voxel-wise uncorrected p < 0.05; cluster size, 100). In the insomnia group, the brain activity during insomnia-anxiety task performance was not lower in any brain area than that during general-anxiety task performance. Conclusion We show that patients with chronic insomnia experience sleep anxiety related with hyperarousal in the left anterior cingulate area. Additional subject recruitment and re-analysis are needed to confirm the findings of this interim analysis. Support This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B03032431).

Background: Mental disorders play an important role both in clinical courses and recidivism among juvenile offenders. In view of the public health, prevalence of the mental disorders among the population is most fundamental element for planning strategy and establishing budget. Methods: A systematic search was conducted in the Embase, PubMed, PsycInfo, Web of Science, and Google scholar for studies reporting the prevalence rate of mental disorders in May 2018. Systematic review and meta-analysis was conducted to identify prevalence of mental disorders. Findings: A total 51,209 (32,616 boys and 8,393 girls) juvenile offenders from 90 studies in the 27 countries were included. The mean prevalence rate of any mental disorder was 0.791 (95% CI 0.786-0.796). The mean number of mental disorders per person was 2.1 in boys and 3.0 in girls. Boys (0.669 (95% CI 0.663-0.674)) had a higher prevalence rate than girls (0.530 (95% CI 0.515-0.546)). Depressive disorder (0.343 (95% CI 0.325-0.361)) vs 0.131 (95% CI 0.126-0.137)), bipolar disorder (0.374 (95% CI 0.349-0.399)) vs 0.163 (95% CI 0.157-0.170)), and posttraumatic stress disorder (0.243 (95% CI 0.225-0.261)) vs 0.083 (95% CI 0.781-0.882)), were more common in girls than in boys. The prevalence of psychotic disorder 0.060 (95% CI 0.056-0.064) was 30-fold higher than that in the general population. Interpretation: Mental disorders are substantially high among juvenile offenders. Active evaluation and intervention for the mental health are warranted particularly in offending girls. Funding: None. Declaration of Interest: We declare no competing interests.

Background: Magnesium (Mg2+), an endogenous N-methyl-D-aspartate receptor antagonist, has received increased attention recently because of its role in the pathophysiology of and treatment response in depression. However, whether Mg2+ level is decreased in depression is not firmly established. We aimed to conduct a systematic review and meta-analysis to help making consensus for the association between Mg2+ levels and depression.Methods: A systematic search was conducted in the electronic database resources PubMed and Embase. After a careful selection of relevant studies, a meta-analysis using the random effects model was conducted in each measuring source, such as serum, plasma, and cerebrospinal fluid (CSF).Results: A total of 18 studies were included in this study. Among 11 studies that measured Mg2+ in the serum, Mg2+ level was lower in patients with depression than in controls (weighted mean difference = −.088, 95% confidence interval = −.164 to −.012). In the sensitivity analysis by removin...