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Objective: This study aimed to investigate prescription patterns in patients with insomnia who still met the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) criteria despite having already been taking hypnotics, and to determine which drug(s) and what combination therapies were preferred. Methods: Sixty-three patients were enrolled in this study. Patients were selected from participants registered at six university hospitals for a prospective study to evaluate the efficacy of melatonin (Circadin).Results: The prescribed hypnotics were clonazepam (n=33), trazodone (n=23), zolpidem (n=22), quetiapine (n=14), mirtazapine (n=12), lorazepam (n=10), alprazolam (n=7), triazolam (n=5), doxepin (n=5), diazepam (n=3), etizolam (n=2), and flunitrazepam (n=1). There were five types of monotherapies (benzodiazepine, zolpidem, trazodone, mirtazapine, and doxepin) and 18 types of combination therapies. The total number of hypnotics used ranged from one to six. The frequency of benzodiazepine use was quite high, at 51/63. Conclusion: This study showed that insomnia can be treated in a wide variety of ways. In particular, 63% of the insomnia treatments in this study used combination therapy. This means that the gap between evidence-based pharmacotherapy and pharmacotherapy used in clinical practice is substantial. This also means that insomnia is still not fully understood and is a heterogeneous condition. In the future, more studies are needed to deepen our understanding of the pathophysiology of insomnia.

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

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.

Objective Suicide is a huge nationwide problem that incurs a lot of socio-economic costs. Suicide also inflicts severe distress on the people left behind. The government of the Republic of Korea has been making many policy efforts to reduce suicide rate. The gatekeeper program, ‘Suicide CARE’, is one of the meaningful modalities for preventing suicide.Methods Multidisciplinary research team collaborated to update the ‘Suicide CARE’ to version 2.0.Results In the ‘Introductory part’, the authors have the time to think about the necessity and significance of the program before conducting full-scale gatekeeper training. In the ‘Careful observation’ part, trainees learn how to understand and recognize the various linguistic, behavioral, and situational signals that a person shows before committing suicide. In the ‘Active listening’ part, trainees learn how to ask suicide with a value-neutral attitude as well listening empathetically. In the ‘Risk evaluation and Expert referral’ part, trainees learn intervening strategies to identify a person’s suicidal intention, plan, and past suicide attempts, and connect the person to appropriate institutes or services.Conclusion Subsequent studies should be conducted to verify the efficacy of the gatekeeper program.

Objective This study investigated the treatment response and cognitive enhancement effects of buspirone augmentation of escitalopram in patients with major depressive disorder (MDD), according to atypical feature subtypes of MDD.Methods An 8 week, randomized, parallel-controlled, open-label study was conducted. The Columbia Atypical Depression Diagnostic Scale was administered to evaluate atypical features. Patients were assigned randomly to the buspirone augmentation or non-buspirone groups. Symptom severity and cognitive function were evaluated using the 17-item Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, Beck Depression Inventory, Beck Anxiety Inventory, digit span test, word fluency test, and Trail Making Tests A and B.Results A total of 89 patients were recruited. There were no significant differences in the measures between the groups; however, among the MDD patients without atypical features, the digit span and word fluency tests were improved by treatment. In the MDD patients without atypical features, the buspirone augmentation group showed a significant improvement on the digit span test compared to the non-buspirone group.Conclusion Buspirone augmentation did not demonstrate significant benefits in MDD patients; however, buspirone augmentation showed greater efficacy for the improvement of cognitive function in MDD patients without atypical features. Our study suggests that atypical features are an important factor for cognitive enhancement in buspirone augmentation treatment in patients with MDD.

Major depressive disorder (MDD) is a leading cause of disability; its symptoms interfere with social, occupational, interpersonal, and academic functioning. However, the diagnosis of MDD is still made by phenomenological approach. The advent of neuroimaging techniques allowed numerous studies to use resting‐state functional magnetic resonance imaging (rs‐fMRI) and estimate functional connectivity for brain‐disease identification. Recently, attempts have been made to investigate effective connectivity (EC) that represents causal relations among regions of interest. In the meantime, to identify meaningful phenotypes for clinical diagnosis, graph‐based approaches such as graph convolutional networks (GCNs) have been leveraged recently to explore complex pairwise similarities in imaging/nonimaging features among subjects. In this study, we validate the use of EC for MDD identification by estimating its measures via a group sparse representation along with a structured equation modeling approach in a whole‐brain data‐driven manner from rs‐fMRI. To distinguish drug‐naïve MDD patients from healthy controls, we utilize spectral GCNs based on a population graph to successfully integrate EC and nonimaging phenotypic information. Furthermore, we devise a novel sensitivity analysis method to investigate the discriminant connections for MDD identification in our trained GCNs. Our experimental results validated the effectiveness of our method in various scenarios, and we identified altered connectivities associated with the diagnosis of MDD., This study validated the use of effective connectivity (EC) for major depressive disorder (MDD) identification by estimating its measures via a group sparse representation along with a structured equation modeling approach in a whole‐brain data‐driven manner from resting‐state functional magnetic resonance imaging. To distinguish drug‐naive MDD patients from healthy controls, we utilize spectral graph convolutional networks (GCNs) based on a population graph to successfully integrate EC and nonimaging phenotypic information. Furthermore, we devise a novel sensitivity analysis method to investigate the discriminant connections for MDD identification in our trained GCNs.

Pro-inflammatory systemic conditions that can cause neuroinflammation and subsequent alterations in brain regions involved in emotional regulation have been suggested as an underlying mechanism for the pathophysiology of major depressive disorder (MDD). A prominent feature of MDD is disruption of circadian rhythms, of which melatonin is considered a key moderator, and alterations in the melatonin system have been implicated in MDD. Melatonin is involved in immune system regulation and has been shown to possess anti-inflammatory properties in inflammatory conditions, through both immunological and non-immunological actions. Melatonin has been suggested as a highly cytoprotective and neuroprotective substance and shown to stimulate all stages of neuroplasticity in animal models. The ability of melatonin to suppress inflammatory responses through immunological and non-immunological actions, thus influencing neuroinflammation and neurotoxicity, along with subsequent alterations in brain regions that are implicated in depression, can be demonstrated by the antidepressant-like effects of melatonin. Further studies that investigate the associations between melatonin, immune markers, and alterations in the brain structure and function in patients with depression could identify potential MDD biomarkers.

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.

The early detection of cognitive impairment is a key issue among the elderly. Although neuroimaging, genetic, and cerebrospinal measurements show promising results, high costs and invasiveness hinder their widespread use. Predicting cognitive impairment using easy-to-collect variables by non-invasive methods for community-dwelling elderly is useful prior to conducting such a comprehensive evaluation. This study aimed to develop a machine learning-based predictive model for future cognitive impairment. A total of 3424 community elderly without cognitive impairment were included from the nationwide dataset. The gradient boosting machine (GBM) was exploited to predict cognitive impairment after 2 years. The GBM performance was good (sensitivity = 0.967; specificity = 0.825; and AUC = 0.921). This study demonstrated that a machine learning-based predictive model might be used to screen future cognitive impairment using variables, which are commonly collected in community health care institutions. With efforts of enhancing the predictive performance, such a machine learning-based approach can further contribute to the improvement of the cognitive function in community elderly.

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.

Major depressive disorder (MDD) shows a high prevalence and is associated with increased disability. While traditional studies aimed to investigate global characteristic neurobiological substrates of MDD, machine learning-based approaches focus on individual people rather than a group. Therefore, machine learning has been increasingly conducted and applied to clinical practice. Several previous neuroimaging studies used machine learning for stratifying MDD patients from healthy controls as well as in differentially diagnosing MDD apart from other psychiatric disorders. Also, machine learning has been used to predict treatment response using magnetic resonance imaging (MRI) results. Despite the recent accomplishments of machine learning-based MRI studies, small sample sizes and the heterogeneity of the depression group limit the generalizability of a machine learning-based predictive model. Future neuroimaging studies should integrate various materials such as genetic, peripheral, and clinical phenotypes for more accurate predictability of diagnosis and treatment response.

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.

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.

Omega-3 fatty acid (n-3 FA) intake is known to have a preventive effect on depressive symptoms in a general population. This study assessed the effects of n-3 FA intake on depressive symptoms and brain function in middle-aged women. Depressive symptoms were screened using the Beck Depression Inventory-II (BDI-II) and Center for Epidemiologic Studies-Depression scale (CES-D) assessment questionnaires, and n-3 FA intakes were assessed using semiquantitative food frequency questionnaire. We found that n-3 FA intakes were negatively associated with depressive symptoms in middle-aged women. Psychiatrists diagnosed the presence of depressive disorders using the 5th edition of the Mental Disorder Diagnosis and Statistics Manual (DSM-5). Resting-state functional magnetic resonance imaging (rs-fMRI) was performed to investigate the association between n-3 FA intake and brain functional connectivity. Functional connectivity of the right middle frontal cortex (default mode network) and the right middle temporal pole (frontoparietal network) was positively associated with depressive symptom scores and negatively associated with n-3 FA intakes. In conclusion, high n-3 FA intake decreases the risk of depressive symptoms and modifies the brain functional connectivity in middle-aged women.

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.

Ketamine was initially used as an anesthetic which could induce cognitive impairment and psychomimetic effects. In initial randomized controlled trials (RCTs) that mostly included a small sample size and were investigator-initiated, ketamine reportedly exerted antidepressant effects 1 to 2 h after a single intravenous infusion in patients with major depressive episodes, particularly treatment-resistant depression (TRD). Interest in ketamine was reported in systematic reviews and meta-analyses, however, many were primarily focused on the rapid onset of ketamine effects without equal attention to its safety and tolerability. Furthermore, several meta-analyses were based on many duplicated RCTs. The initial trends emphasized the clinical utility of ketamine as an antidepressant. The development of esketamine nasal spray by a pharmaceutical company led to an RCT with a large sample size and segmented therapeutic strategy, which provided results applicable to patients with TRD in the real-world clinical environment. However, possible effects of ketamine on cognitive function have not yet been investigated in RCTs. In numerous studies, chronic, recreational use of ketamine reportedly substantially impaired cognitive function in most domains. Although results of several human and animal studies indicated the therapeutic use of ketamine for treatment of depression did not induce cognitive impairment, this issue should be further investigated. Based on the current knowledge about ketamine, future antidepressants are expected to be glutamatergic drugs without ketamine-like adverse events (e.g., psychomimetic symptoms and cognitive impairment), but having only ketamine-like therapeutic properties (e.g., rapid antidepressants effects without time lag).

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.

Objective The purpose of this study was to determine the key components of Korean disaster psychiatric assistant teams (K-DPATs), to set up new mental health service providing system for the disaster victims. Methods We conducted an analytic hierarchy process (AHP) involving disaster mental health experts, using a pairwise comparison questionnaire to compare the relative importance of the key components of the Korean disaster mental health response system. In total, 41 experts completed the first online survey; of these, 36 completed the second survey. Ten experts participated in panel meetings and discussed the results of the survey and AHP process. Results It was agreed that K-DPATs should be independent of the existing mental health system (70.1%), funding for K-DPATs should be provided by the Ministry of Public Safety, and the system should be managed by the Ministry of Health (65.8%). Experts shared the view that K-DPAT leaders would be suitable key decision makers for all types of disaster, with the exception of those involving infectious diseases. Conclusion K-DPAT, a new model for disaster mental health response systems could improve the insufficiency of the current system, address problems such as fragmentation, and fulfill disaster victims' unmet need for early professional intervention.

Objective This study investigated the proposed association between restless legs syndrome (RLS) and the prevalence of hypertension. Methods A meta-analysis was conducted based on searches of the PUBMED, EMBASE, Cochrane Library, and Korean electronic databases. Cohort and cross-sectional studies reporting the incidence of hypertension in individuals with RLS were included. Dichotomous data were pooled to obtain an odds ratio (OR) and 95% confidence interval (CI) for the prevalence of hypertension in individuals with RLS. The main outcome measure of the study was prevalence of hypertension in patients with RLS compared with a control group. Results One cohort study and eight cross-sectional studies were included in the meta-analysis. Individuals with RLS had an increased prevalence of hypertension (all studies: OR=1.13, 95% CI=1.04-1.23; cross-sectional studies: OR=1.12, 95% CI=1.01-1.24). However, in subgroup analyses controlling for cardiovascular risk factors, such as diabetes mellitus and dyslipidemia, the differences in the prevalence of hypertension between RLS and control patients were no longer significant. Conclusion Patients with RLS may have a higher prevalence of hypertension, according to a pooled analysis, but the results remain to be confirmed in well-designed prospective studies.

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.

Mood disorders are a highly prevalent group of mental disorders causing substantial socioeconomic burden. There are various methodological approaches for identifying the underlying mechanisms of the etiology, symptomatology, and therapeutics of mood disorders; however, neuroimaging studies have provided the most direct evidence for mood disorder neural substrates by visualizing the brains of living individuals. The prefrontal cortex, hippocampus, amygdala, thalamus, ventral striatum, and corpus callosum are associated with depression and bipolar disorder. Identifying the distinct and common contributions of these anatomical regions to depression and bipolar disorder have broadened and deepened our understanding of mood disorders. However, the extent to which neuroimaging research findings contribute to clinical practice in the real-world setting is unclear. As traditional or non-machine learning MRI studies have analyzed group-level differences, it is not possible to directly translate findings from research to clinical practice; the knowledge gained pertains to the disorder, but not to individuals. On the other hand, a machine learning approach makes it possible to provide individual-level classifications. For the past two decades, many studies have reported on the classification accuracy of machine learning-based neuroimaging studies from the perspective of diagnosis and treatment response. However, for the application of a machine learning-based brain MRI approach in real world clinical settings, several major issues should be considered. Secondary changes due to illness duration and medication, clinical subtypes and heterogeneity, comorbidities, and cost-effectiveness restrict the generalization of the current machine learning findings. Sophisticated classification of clinical and diagnostic subtypes is needed. Additionally, as the approach is inevitably limited by sample size, multi-site participation and data-sharing are needed in the future.

Background and objective Attitudes toward suicide is one of the important determinants for help-seeking behaviors among suicidal population. We hypothesized that older age groups would have more favorable attitudes toward suicide than would younger groups. Methods We conducted a survey of attitudes toward suicide in a nationally representative sample. Attitudes toward suicide were measured with the Korean version of the Suicide Opinion Questionnaire (SOQ). Multiple linear regression analysis was performed to determine the influence of age on attitudes toward suicide after adjusting for other sociodemographic and clinical variables. Results A total of 1200 people in the general public responded to the survey. Older people expressed less favorable attitudes toward suicide than did younger people. According the multiple linear regression analysis, age was the most influential factor with regard to attitudes toward suicide. Conclusion Contrary to our a priori hypothesis, people in the older age groups had more negative attitudes toward suicide than did those in the younger age groups. The results suggest that negative attitudes toward suicide in the general population may interfere with the help-seeking behavior of people at high risk for suicide. Future studies should directly investigate the relationship between attitudes toward suicide and suicide rates.

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.

OBJECTIVE The provision of care for elderly people with dementia by healthy elderly caregivers is one of the new health-care paradigms in South Korea. The aim of this study was to determine whether this type of care, which includes cognitive stimulation, would improve the cognitive function of dementia patients and the quality of life of the healthy elderly caregiver. METHODS Totals of 132 dementia patients and 197 healthy elderly caregivers participated in this study. We evaluated the cognitive function of the dementia patients at baseline and after providing the program for 6 months using the Korean version of the Consortium to Establish a Registry for Alzheimer's disease, 1st Edition (CERAD-K). We also evaluated the quality of life of the healthy elderly caregivers using the World Health Organization Quality of Life-Short Version (WHOQOL-BREF) at baseline and after 6 months. RESULTS The word-list memory results of CERAD-K for the included dementia patients improved after 6 months (Z=-2.855, p=0.004). The WHOQOL-BREF score among the elderly caregiver also improved significantly (Z=-2.354, p=0.019). CONCLUSION These data suggest that dementia care is associated with improvements in both the cognitive function of dementia patients and the quality of life of the healthy elderly caregivers.

The modern society is a so-called era of big data. Whereas nearly everybody recognizes the "era of big data", no one can exactly define how big the data is a "big data". The reason for the ambiguity of the term big data mainly arises from the widespread of using that term. Along the widespread application of the digital technology in the everyday life, a large amount of data is generated every second in relation with every human behavior (i.e., measuring body movements through sensors, texts sent and received via social networking services). In addition, nonhuman data such as weather and Global Positioning System signals has been cumulated and analyzed in perspectives of big data (Kan et al. in Int J Environ Res Public Health 15(4), 2018 [1]). The big data has also influenced the medical science, which includes the field of psychiatry (Monteith et al. in Int J Bipolar Disord 3(1):21, 2015 [2]). In this chapter, we first introduce the definition of the term "big data". Then, we discuss researches which apply big data to solve problems in the clinical practice of psychiatry.

Background Parkinson’s disease (PD) and drug-induced parkinsonism (DIP) are the major diseases of parkinsonism. To better understand parkinsonism, we aimed to assess the prevalence and incidence of PD and DIP in Korea from 2012 to 2015. Methods We used the Health Insurance Review and Assessment Service database, which covers the entire population in Korea. We used claims during 2011–2015 to assess epidemiology of PD and DIP during 2012–2015. Retrospective cross-sectional study design was employed to assess prevalence, whereas retrospective cohort study design was used to determine incidence. Patients with at least one claim with ICD-10 G20 and who received antiparkinsonian drugs for at least 60 days were classified as having PD. We excluded patients with antiparkinsonian drugs that can be used for indications other than PD. Patients with at least one claim with ICD-10 G211 or G251 during the prescription period of drugs that are frequently related with DIP were classified as having DIP. Incident cases had a disease-free period of 1 year before diagnosis. To evaluate the significance of changes in the prevalence or incidence over time, Poisson regression was used to determine p for trend. Results The prevalence of PD increased from 156.9 per 100,000 persons in 2012 to 181.3 per 100,000 persons in 2015 (p for trendp for trendp for trendp for trend Conclusions Our study suggests that the incidence of PD has gradually decreased whereas, the incidence of DIP increased from 2012 to 2015. Further studies are warranted to examine possible causes of increased DIP incidence in order to develop management strategy for parkinsonism.

Additional file 3: Figure S2. Sensitivity analysis. Number of incident patients with drug-induced parkinsonism by the types and numbers of offending drugs in Korea

The modern society is a so-called era of big data. Whereas nearly everybody recognizes the “era of big data”, no one can exactly define how big the data is a “big data”. The reason for the ambiguity of the term big data mainly arises from the widespread of using that term. Along the widespread application of the digital technology in the everyday life, a large amount of data is generated every second in relation with every human behavior (i.e., measuring body movements through sensors, texts sent and received via social networking services). In addition, nonhuman data such as weather and Global Positioning System signals has been cumulated and analyzed in perspectives of big data (Kan et al. in Int J Environ Res Public Health 15(4), 2018 [1]). The big data has also influenced the medical science, which includes the field of psychiatry (Monteith et al. in Int J Bipolar Disord 3(1):21, 2015 [2]). In this chapter, we first introduce the definition of the term “big data”. Then, we discuss researches which apply big data to solve problems in the clinical practice of psychiatry.

Additional file 1: Table S1. Medications specifically used for Parkinson’s disease. Table S2. Frequencies and prevalence of Parkinson’s disease in Korea from 2012 to 2015. Table S3. Frequencies and prevalence of drug-induced parkinsonism in Korea from 2012 to 2015. Table S4. Frequencies and incidence of Parkinson’s disease in Korea from 2012 to 2015. Table S5. Frequencies and incidence of drug-induced parkinsonism in Korea from 2012 to 2015.

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 In contrast to the previous studies reporting that most suicides occur among people with mental disorders, recent studies have reported various rates of mental disorders in suicide in different geographical regions. We aimed to comprehensively investigate the factors influencing the variation in the prevalence of mental disorders reported among suicide victims. Method The authors searched Embase, Medline, Web of Science, and the Cochrane Library to identify psychological autopsy studies reporting the prevalence of any mental disorders among suicide victims. A meta-regression analysis was conducted to identify the potential effects of geographical regions, the year of publication, measurements of personality disorder, measurements of comorbidity, and the ratio of females on the prevalence of mental disorders in addition to examining the heterogeneity across studies. Results From 4475 potentially relevant studies, 48 studies met eligibility criteria, with 6626 suicide victims. The studies from East Asia had a significantly lower mean prevalence (69.6% [95% CI=56.8 to 80.0]) than those in North America (88.2% [95% CI=79.7–93.5]) and South Asia (90.4% [95% CI=71.8–97.2]). The prevalence of any mental disorder decreased according to the year of publication (coefficients=−0.0715, p Limitations Substantial heterogeneities were identified within all subgroup analyses. Conclusions The prevalence of mental disorders among suicide cases seemed relatively low in the East Asia region, and recently published studies tended to report a lower prevalence of mental disorders. The link between the risk factors and suicide in the absence of a mental disorder should be examined in different geographical and sociocultural contexts.