Prevalence and Correlates of Comorbid Posttraumatic Stress Disorder in Schizophrenia-Spectrum Disorder: A Systematic Review and Meta-Analysis.

OBJECTIVE: Schizophrenia-spectrum disorders and posttraumatic stress disorder (PTSD) share common clinical manifestations, genetic vulnerability, and environmental risk factors. We aimed to conduct a systematic review and meta-analysis of the comorbid prevalence of PTSD among schizophrenia-spectrum disorders. METHODS: We performed a meta-analysis to identify possible contributing factors to the heterogeneity among these studies. We systematically searched electronic databases with no restrictions on language of articles. RESULTS: We extracted 24 samples (18 for current prevalence and 6 for lifetime prevalence) from 22 studies and used a random effects model to estimate the pooled prevalence of PTSD among schizophrenia-spectrum disorders. The current and life prevalence of comorbid PTSD was 10.6% (95% confidence interval [CI]=6.3%-17.3%) and 13.0% (95% CI=5.3%-28.6%), respectively. Studies assessing psychotic experiences/involuntary admission reported the highest prevalence of comorbid PTSD (57.1%, 95% CI=43.6%-59.7%), whereas those assessing various anxiety disorders reported the lowest prevalence (1.1%, 95% CI=1.0%-5.5%). Heterogeneities of the subgroup analysis by similar objectives were largely homogeneous (I2=7.1-34.1). In the qualitative assessment, only two studies (9.1%) were evaluated as having a low risk of bias. CONCLUSION: Our results showed that a careful approach with particular attention to assessing PTSD is essential to reliably estimate the prevalence of PTSD comorbid with schizophrenia-spectrum disorders. The reason for the wide discrepancy in the prevalence of comorbid PTSD among the four groups of studies should be addressed in future research.

Mood and Sleep Status and Mental Disorders During Prolonged Winter-Over Residence in Two Korean Antarctic Stations.

Purpose: 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.

Preventive health behaviors among people with suicide ideation using nationwide cross-sectional data in South Korea.

This study aimed to investigate the association between suicide ideation and health-related behaviors and preventive health service use behaviors. We used data from the 2017 Korea National Health and Nutrition Examination Survey (KNHANES), a nationally representative survey. The final sample included 4486 participants aged 40 years or older. Preventive health behaviors were assessed for smoking, high-risk drinking, physical activities, regular meal intake, influenza vaccination, general health examination, and cancer screening. Logistic regression was used to examine the association between suicide ideation and preventive health behaviors with a series of adjustments for covariates. In general, suicide ideation was associated with unfavorable outcomes of preventive health behaviors, except for flu vaccination. For example, the adjusted prevalence of suicide ideation and non-suicide ideation groups were 54.3% vs. 43.7% for flu vaccination, 23.1% vs. 41.6% for physical activity, and 24.8% vs. 18.6% for high-risk alcohol drinking. After adjustment for covariates, the associations of suicide ideation with behaviors remained significant for physical activity (OR 0.52, 95% CI 0.34-0.81) and high-risk alcohol drinking (OR 2.22, 95% CI 1.34-3.69). Suicide ideation leads to the disruption of self-management of health behaviours, especially for physical activity and high-risk alcohol drinking, independently of depressive feelings.

The Development of a Suicidal Ideation Predictive Model for Community-Dwelling Elderly Aged >55 Years.

PURPOSE: 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.

Development of a Suicide Prediction Model for the Elderly Using Health Screening Data.

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 > 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.

Thalamo-Habenular Connection Differences Between Patients With Major Depressive Disorder and Normal Controls.

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.

Left-right asymmetric and smaller right habenula volume in major depressive disorder on high-resolution 7-T magnetic resonance imaging.

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.

Predicting Depression in Community Dwellers Using a Machine Learning Algorithm.

Depression is one of the leading causes of disability worldwide. Given the socioeconomic burden of depression, appropriate depression screening for community dwellers is necessary. We used data from the 2014 and 2016 Korea National Health and Nutrition Examination Surveys. The 2014 dataset was used as a training set, whereas the 2016 dataset was used as the hold-out test set. The synthetic minority oversampling technique (SMOTE) was used to control for class imbalances between the depression and non-depression groups in the 2014 dataset. The least absolute shrinkage and selection operator (LASSO) was used for feature reduction and classifiers in the final model. Data obtained from 9488 participants were used for the machine learning process. The depression group had poorer socioeconomic, health, functional, and biological measures than the non-depression group. From the initial 37 variables, 13 were selected using LASSO. All performance measures were calculated based on the raw 2016 dataset without the SMOTE. The area under the receiver operating characteristic curve and overall accuracy in the hold-out test set were 0.903 and 0.828, respectively. Perceived stress had the strongest influence on the classifying model for depression. LASSO can be practically applied for depression screening of community dwellers with a few variables. Future studies are needed to develop a more efficient and accurate classification model for depression.

Effects of Virtual Reality-Based Cognitive Training in the Elderly with and without Mild Cognitive Impairment.

OBJECTIVE: This study aimed to introduce a 4-week long fully immersive virtual reality-based cognitive training (VRCT) program that could be applied for both a cognitively normal elderly population and patients with mild cognitive impairment (MCI). In addition, we attempted to investigate the neuropsychological effects of the VRCT program in each group. METHODS: A total of 56 participants, 31 in the MCI group and 25 in the cognitively normal elderly group, underwent eight sessions of VRCT for 4 weeks. In order to evaluate the effects of the VRCT, the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet was administered before and after the program. The program' s safety was assessed using a simulator sickness questionnaire (SSQ), and availability was assessed using the presence questionnaire. RESULTS: After the eighth session of the VRCT program, cognitive improvement was observed in the ability to learn new information, visuospatial constructional ability, and frontal lobe function in both groups. At the baseline evaluation, based on the SSQ, the MCI group complained of disorientation and nausea significantly more than the cognitively normal elderly group did. However, both groups showed a reduction in discomfort as the VRCT program progressed. CONCLUSION: We conclude that our VRCT program helps improve cognition in both the MCI group and cognitively normal elderly group. Therefore, the VRCT is expected to help improve cognitive function in elderly populations with and without MCI.

Cognitive reserve and the effects of virtual reality-based cognitive training on elderly individuals with mild cognitive impairment and normal cognition.

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.

Spectral Power Analysis of Sleep Electroencephalography in Subjects with Different Severities of Obstructive Sleep Apnea and Healthy Controls.

PURPOSE: Previous spectral analysis studies on obstructive sleep apnea (OSA) involved small samples, and the results were inconsistent. We performed a spectral analysis of sleep EEG based on different severities of OSA using the Sleep Heart Health Study data. This study aimed to determine the difference in EEG spectral power during sleep in the non-OSA group and with different severities of OSA in the general population. PATIENTS AND METHODS: The participants (n = 5,804) underwent polysomnography, and they were classified into non-OSA, mild OSA, moderate OSA, and severe OSA groups. The fast Fourier transformation was used to compute the EEG power spectrum for total sleep duration within contiguous 30-second epochs of sleep. The EEG spectral powers of the groups were compared using 4,493 participants after adjusting potential confounding factors that could affect sleep EEG. RESULTS: The power spectra differed significantly among the groups for all frequency bands (p corr < 0.001). We found that the quantitative EEG spectral powers in the beta and sigma bands of total sleep differed (p corr < 0.001) among the participants in the non-OSA group and with different severities of OSA, controlling for covariates. The beta power was higher and the sigma power was lower in the OSA groups than in the non-OSA group. The beta power decreased in the order of severe OSA, moderate OSA, mild OSA, and non-OSA. CONCLUSION: This study suggests that there are differences between the microstructures of PSG-derived sleep EEG of non-OSA participants and those with different severities of OSA.

The Application of a Machine Learning-Based Brain Magnetic Resonance Imaging Approach in Major Depression.

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.

Brain reactivity using fMRI to insomnia stimuli in insomnia patients with discrepancy between subjective and objective sleep.

Subjective-objective discrepancy of sleep (SODS) might be related to the distorted perception of sleep deficit and hypersensitivity to insomnia-related stimuli. We investigated differences in brain activation to insomnia-related stimuli among insomnia patients with SODS (SODS group), insomnia patients without SODS (NOSODS group), and healthy controls (HC). Participants were evaluated for subjective and objective sleep using sleep diary and polysomnography. Functional magnetic resonance imaging was conducted during the presentation of insomnia-related (Ins), general anxiety-inducing (Gen), and neutral (Neu) stimuli. Brain reactivity to the contrast of Ins vs. Neu and Gen vs. Neu was compared among the SODS (n = 13), NOSODS (n = 15), and HC (n = 16) groups. In the SODS group compared to other groups, brain areas including the left fusiform, bilateral precuneus, right superior frontal gyrus, genu of corpus callosum, and bilateral anterior corona radiata showed significantly increased blood oxygen level dependent (BOLD) signal in the contrast of Ins vs. Neu. There was no brain region with significantly increased BOLD signal in the Gen vs. Neu contrast in the group comparisons. Increased brain activity to insomnia-related stimuli in several brain regions of the SODS group is likely due to these individuals being more sensitive to sleep-related threat and negative cognitive distortion toward insomnia.

Prevalence of DSM-5 mixed features: A meta-analysis and systematic review.

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.

Clinical Usefulness of Amisulpride Add-on Therapy in Schizophrenia Patients without Treatment Response to Second-generation Antipsychotics.

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.

Associations between Melatonin, Neuroinflammation, and Brain Alterations in Depression.

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.

Increased use of ketamine for the treatment of depression: Benefits and concerns.

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).

Machine learning-based discrimination of panic disorder from other anxiety disorders.

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.