Causal dynamics of sleep, circadian rhythm, and mood symptoms in patients with major depression and bipolar disorder: insights from longitudinal wearable device data.

BACKGROUND: Sleep and circadian rhythm disruptions are common in patients with mood disorders. The intricate relationship between these disruptions and mood has been investigated, but their causal dynamics remain unknown. METHODS: We analysed data from 139 patients (76 female, mean age = 23.5 ± 3.64 years) with mood disorders who participated in a prospective observational study in South Korea. The patients wore wearable devices to monitor sleep and engaged in smartphone-delivered ecological momentary assessment of mood symptoms. Using a mathematical model, we estimated their daily circadian phase based on sleep data. Subsequently, we obtained daily time series for sleep/circadian phase estimates and mood symptoms spanning >40,000 days. We analysed the causal relationship between the time series using transfer entropy, a non-linear causal inference method. FINDINGS: The transfer entropy analysis suggested causality from circadian phase disturbance to mood symptoms in both patients with MDD (n = 45) and BD type I (n = 35), as 66.7% and 85.7% of the patients with a large dataset (>600 days) showed causality, but not in patients with BD type II (n = 59). Surprisingly, no causal relationship was suggested between sleep phase disturbances and mood symptoms. INTERPRETATION: Our findings suggest that in patients with mood disorders, circadian phase disturbances directly precede mood symptoms. This underscores the potential of targeting circadian rhythms in digital medicine, such as sleep or light exposure interventions, to restore circadian phase and thereby manage mood disorders effectively. FUNDING: Institute for Basic Science, the Human Frontiers Science Program Organization, the National Research Foundation of Korea, and the Ministry of Health & Welfare of South Korea.

Identifying predictive factors for mood recurrence in early-onset major mood disorders: A 4-year, multicenter, prospective cohort study.

We investigate the predictive factors of the mood recurrence in patients with early-onset major mood disorders from a prospective observational cohort study from July 2015 to December 2019. A total of 495 patients were classified into three groups according to recurrence during the cohort observation period: recurrence group with (hypo)manic or mixed features (MMR), recurrence group with only depressive features (ODR), and no recurrence group (NR). As a result, the baseline diagnosis of bipolar disorder type 1 (BDI) and bipolar disorder type 2 (BDII), along with a familial history of BD, are strong predictors of the MMR. The discrepancies in wake-up times between weekdays and weekends, along with disrupted circadian rhythms, are identified as a notable predictor of ODR. Our findings confirm that we need to be aware of different predictors for each form of mood recurrences in patients with early-onset mood disorders. In clinical practice, we expect that information obtained from the initial assessment of patients with mood disorders, such as mood disorder type, family history of BD, regularity of wake-up time, and disruption of circadian rhythms, can help predict the risk of recurrence for each patient, allowing for early detection and timely intervention.

Effects of a Rice-Farming Simulation Video Game on Nature Relatedness, Nutritional Status, and Psychological State in Urban-Dwelling Adults During the COVID-19 Pandemic: Randomized Waitlist Controlled Trial.

BACKGROUND: During the COVID-19 pandemic, urban inhabitants faced significant challenges in maintaining connections with nature, adhering to nutritional guidelines, and managing mental well-being. OBJECTIVE: Recognizing the urgent need for innovative approaches, this study was designed to explore the potential benefits of a specific digital intervention, the rice-farming simulation game Sakuna: Of Rice and Ruin, for nature relatedness, nutritional behaviors, and psychological well-being. METHODS: A total of 66 adults without any prior major psychiatric disorders residing in an urban area were recruited for the study. They were randomly assigned to 2 groups through block randomization: the immediate intervention group (IIG; 34/66, 52%) and the waitlist group (32/66, 48%). Participants in the IIG were instructed to play the game for at least 4 days per week for 3 weeks, with each session lasting from 30 minutes to 3 hours. Assessments were performed at baseline, week 1, and week 3. The Nature Relatedness Scale (NR) and Nutrition Quotient Scale were used to evaluate nature relatedness and nutritional state, respectively. Furthermore, psychological state was assessed using the World Health Organization Quality of Life-Brief Version (WHOQOL-BREF), Brief Fear of Negative Evaluation Scale, Social Avoidance and Distress Scale, Toronto Alexithymia Scale, State-Trait Anxiety Inventory, Center for Epidemiologic Studies Depression Scale Revised, and Korean Resilience Quotient. RESULTS: This study's results revealed significant time interactions between the IIG and waitlist group for both the total NR score (P=.001) and the score of the self subdomain of NR (P<.001), indicating an impact of the game on nature relatedness. No group×time interactions were found for the total Nutrition Quotient Scale and subdomain scores, although both groups showed increases from baseline. For psychological state, a significant group×time interaction was observed in the total WHOQOL-BREF score (P=.049), suggesting an impact of the game on quality of life. The psychological (P=.01), social (P=.003), and environmental (P=.04) subdomains of the WHOQOL-BREF showed only a significant time effect. Other psychological scales did not display any significant changes (all P>.05). CONCLUSIONS: Our findings suggest that the rice-farming game intervention might have positive effects on nature relatedness, nature-friendly dietary behaviors, quality of life, anxiety, depression, interpersonal relationships, and resilience among urban adults during the COVID-19 pandemic. The impact of pronature games in confined urban environments provides valuable evidence of how digital technologies can be used to enhance urban residents' affinity for nature and psychological well-being. This understanding can be extended in the future to other digital platforms, such as metaverses. TRIAL REGISTRATION: Clinical Research Information Service (CRIS) KCT0007657; http://tinyurl.com/yck7zxp7.

Ensemble Approach to Combining Episode Prediction Models Using Sequential Circadian Rhythm Sensor Data from Mental Health Patients.

Managing mood disorders poses challenges in counseling and drug treatment, owing to limitations. Counseling is the most effective during hospital visits, and the side effects of drugs can be burdensome. Patient empowerment is crucial for understanding and managing these triggers. The daily monitoring of mental health and the utilization of episode prediction tools can enable self-management and provide doctors with insights into worsening lifestyle patterns. In this study, we test and validate whether the prediction of future depressive episodes in individuals with depression can be achieved by using lifelog sequence data collected from digital device sensors. Diverse models such as random forest, hidden Markov model, and recurrent neural network were used to analyze the time-series data and make predictions about the occurrence of depressive episodes in the near future. The models were then combined into a hybrid model. The prediction accuracy of the hybrid model was 0.78; especially in the prediction of rare episode events, the F1-score performance was approximately 1.88 times higher than that of the dummy model. We explored factors such as data sequence size, train-to-test data ratio, and class-labeling time slots that can affect the model performance to determine the combinations of parameters that optimize the model performance. Our findings are especially valuable because they are experimental results derived from large-scale participant data analyzed over a long period of time.

Potential effectiveness of digital therapeutics specialized in executive functions as adjunctive treatment for clinical symptoms of attention-deficit/hyperactivity disorder: a feasibility study.

Introduction: The role of digital therapeutics (DTx) in the effective management of attention deficit/hyperactivity disorder (ADHD) is beginning to gain clinical attention. Therefore, it is essential to verify their potential efficacy. Method: We aimed to investigate the improvement in the clinical symptoms of ADHD by using DTx AimDT01 (NUROW) (AIMMED Co., Ltd., Seoul, Korea) specialized in executive functions. NUROW, which consists of Go/No-go Task- and N-Back/Updating-based training modules and a personalized adaptive algorithm system that adjusts the difficulty level according to the user's performance, was implemented on 30 Korean children with ADHD aged 6 to 12 years. The children were instructed to use the DTx for 15 min daily for 4 weeks. The Comprehensive attention test (CAT) and Childhood Behavior Checklist (CBCL) were used to assess the children at baseline and endpoint. In contrast, the ADHD-Rating Scale (ARS) and PsyToolkit were used weekly and followed up at 1 month, for any sustained effect. Repeated measures ANOVA was used to identify differences between the participants during visits, while t-tests and Wilcoxon signed-rank tests were used to identify changes before and after the DTx. Results: We included 27 participants with ADHD in this analysis. The ARS inattention (F = 4.080, p = 0.010), hyperactivity (F = 5.998. p < 0.001), and sum (F = 5.902, p < 0.001) significantly improved. After applying NUROW, internalized (t = -3.557, p = 0.001, 95% CI = -3.682--0.985), other (Z = -3.434, p = 0.001, effect size = -0.661), and sum scores (t = -3.081, p = 0.005, 95% CI = -10.126--2.022) were significantly changed in the CBCL. The overall effect was confirmed in the ARS sustained effect analysis even after 1 month of discontinuing the DTx intervention. Discussion: According to caregivers, the findings indicate that DTx holds potential effect as an adjunctive treatment in children with ADHD, especially in subjective clinical symptoms. Future studies will require detailed development and application targeting specific clinical domains using DTx with sufficient sample sizes.Clinical trial registration: KCT0007579.

Association between Plasma Metabolic Profiles of the Antidepressant Escitalopram and Clinical Response in Subjects with Depression.

Liquid chromatography/electrospray ionization-mass spectrometry revealed plasma metabolic profiles for the antidepressant drug escitalopram (ECTP) and associated clinical responses in subjects with major depressive disorder (MDD). Metabolic profiles contribute to variations in responses to drug treatment of depression. To assess clinical responses and treatment outcomes, we quantified the levels of metabolites, including those of the parent drug, in plasma samples collected at different time points (days 0, 7, 14, and 42) during treatment of seven patients with MDD. Results showed that mean plasma levels of key metabolites and ECTP at day 7 were significantly associated with the clinical response at 42 days after treatment. Statistical analyses, including principal component analysis, of key metabolites and ECTP samples at different time points clearly distinguished the clinical responders from non-responder subjects. Although further validation with a larger cohort is necessary, our results indicate that early improvement and plasma levels of key metabolites and ECTP are predictive of therapeutic treatment outcomes and thus can be used to guide the use of ECTP.

Machine Learning-Based Prediction of Attention-Deficit/Hyperactivity Disorder and Sleep Problems With Wearable Data in Children.

Importance: Early detection of attention-deficit/hyperactivity disorder (ADHD) and sleep problems is paramount for children's mental health. Interview-based diagnostic approaches have drawbacks, necessitating the development of an evaluation method that uses digital phenotypes in daily life. Objective: To evaluate the predictive performance of machine learning (ML) models by setting the data obtained from personal digital devices comprising training features (ie, wearable data) and diagnostic results of ADHD and sleep problems by the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version for Diagnostic and Statistical Manual of Mental Disorders, 5th edition (K-SADS) as a prediction class from the Adolescent Brain Cognitive Development (ABCD) study. Design, Setting, and Participants: In this diagnostic study, wearable data and K-SADS data were collected at 21 sites in the US in the ABCD study (release 3.0, November 2, 2020, analyzed October 11, 2021). Screening data from 6571 patients and 21 days of wearable data from 5725 patients collected at the 2-year follow-up were used, and circadian rhythm-based features were generated for each participant. A total of 12 348 wearable data for ADHD and 39 160 for sleep problems were merged for developing ML models. Main Outcomes and Measures: The average performance of the ML models was measured using an area under the receiver operating characteristics curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). In addition, the Shapley Additive Explanations value was used to calculate the importance of features. Results: The final population consisted of 79 children with ADHD problems (mean [SD] age, 144.5 [8.1] months; 55 [69.6%] males) vs 1011 controls and 68 with sleep problems (mean [SD] age, 143.5 [7.5] months; 38 [55.9%] males) vs 3346 controls. The ML models showed reasonable predictive performance for ADHD (AUC, 0.798; sensitivity, 0.756; specificity, 0.716; PPV, 0.159; and NPV, 0.976) and sleep problems (AUC, 0.737; sensitivity, 0.743; specificity, 0.632; PPV, 0.036; and NPV, 0.992). Conclusions and Relevance: In this diagnostic study, an ML method for early detection or screening using digital phenotypes in children's daily lives was developed. The results support facilitating early detection in children; however, additional follow-up studies can improve its performance.

Prediction of impending mood episode recurrence using real-time digital phenotypes in major depression and bipolar disorders in South Korea: a prospective nationwide cohort study.

BACKGROUND: Mood disorders require consistent management of symptoms to prevent recurrences of mood episodes. Circadian rhythm (CR) disruption is a key symptom of mood disorders to be proactively managed to prevent mood episode recurrences. This study aims to predict impending mood episodes recurrences using digital phenotypes related to CR obtained from wearable devices and smartphones. METHODS: The study is a multicenter, nationwide, prospective, observational study with major depressive disorder, bipolar disorder I, and bipolar II disorder. A total of 495 patients were recruited from eight hospitals in South Korea. Patients were followed up for an average of 279.7 days (a total sample of 75 506 days) with wearable devices and smartphones and with clinical interviews conducted every 3 months. Algorithms predicting impending mood episodes were developed with machine learning. Algorithm-predicted mood episodes were then compared to those identified through face-to-face clinical interviews incorporating ecological momentary assessments of daily mood and energy. RESULTS: Two hundred seventy mood episodes recurred in 135 subjects during the follow-up period. The prediction accuracies for impending major depressive episodes, manic episodes, and hypomanic episodes for the next 3 days were 90.1, 92.6, and 93.0%, with the area under the curve values of 0.937, 0.957, and 0.963, respectively. CONCLUSIONS: We predicted the onset of mood episode recurrences exclusively using digital phenotypes. Specifically, phenotypes indicating CR misalignment contributed the most to the prediction of episodes recurrences. Our findings suggest that monitoring of CR using digital devices can be useful in preventing and treating mood disorders.

Prediction of Specific Anxiety Symptoms and Virtual Reality Sickness Using In Situ Autonomic Physiological Signals During Virtual Reality Treatment in Patients With Social Anxiety Disorder: Mixed Methods Study.

BACKGROUND: Social anxiety disorder (SAD) is the fear of social situations where a person anticipates being evaluated negatively. Changes in autonomic response patterns are related to the expression of anxiety symptoms. Virtual reality (VR) sickness can inhibit VR experiences. OBJECTIVE: This study aimed to predict the severity of specific anxiety symptoms and VR sickness in patients with SAD, using machine learning based on in situ autonomic physiological signals (heart rate and galvanic skin response) during VR treatment sessions. METHODS: This study included 32 participants with SAD taking part in 6 VR sessions. During each VR session, the heart rate and galvanic skin response of all participants were measured in real time. We assessed specific anxiety symptoms using the Internalized Shame Scale (ISS) and the Post-Event Rumination Scale (PERS), and VR sickness using the Simulator Sickness Questionnaire (SSQ) during 4 VR sessions (#1, #2, #4, and #6). Logistic regression, random forest, and naïve Bayes classification classified and predicted the severity groups in the ISS, PERS, and SSQ subdomains based on in situ autonomic physiological signal data. RESULTS: The severity of SAD was predicted with 3 machine learning models. According to the F1 score, the highest prediction performance among each domain for severity was determined. The F1 score of the ISS mistake anxiety subdomain was 0.8421 using the logistic regression model, that of the PERS positive subdomain was 0.7619 using the naïve Bayes classifier, and that of total VR sickness was 0.7059 using the random forest model. CONCLUSIONS: This study could predict specific anxiety symptoms and VR sickness during VR intervention by autonomic physiological signals alone in real time. Machine learning models can predict the severe and nonsevere psychological states of individuals based on in situ physiological signal data during VR interventions for real-time interactive services. These models can support the diagnosis of specific anxiety symptoms and VR sickness with minimal participant bias. TRIAL REGISTRATION: Clinical Research Information Service KCT0003854; https://cris.nih.go.kr/cris/search/detailSearch.do/13508.

The relationship between morningness-eveningness and mood symptoms and quality of life in euthymic state of mood disorders: Results from a prospective observational cohort study.

BACKGROUND: The clinical importance of morningness-eveningness, especially in mood disorders, is prevailing. The differential relation of chronotype with diagnoses of early-onset mood disorders, mood symptoms, anxiety, and quality of life was evaluated. METHODS: Early-onset mood disorder patients [n = 419; 146 major depressive disorder (MDD); 123 bipolar I disorder (BDI); 150 bipolar II disorder (BDII)] from the Mood Disorder Cohort Research Consortium were assessed for chronotype using the composite scale for morningness (CSM) and its association with clinical variables obtained during the clinician-verified euthymic state. RESULTS: The mean total CSM of BDI was significantly higher than MDD and BDII (p < 0.001). In all types of mood disorders, higher total CSM was associated with lower Quick inventory of depressive symptomatology (p < 0.005) and higher WHO quality of life (p < 0.005). Such negative correlations between the total CSM and Montgomery-Asberg depression rating were significant in MDD and BDI (p < 0.05) and marginally significant in BDII (p = 0.077). CSM was a significant contributor to quality of life in BDI (p < 0.001) and BDII (p = 0.011), but it was not for MDD. LIMITATIONS: The defined 'euthymic state' that may not fully reflect the remission of episode; limited generalizability due to clinical characteristic of early-onset mood disorder; the disparity between diurnal preference measured by the CSM and chronotype; possible effects of the last mood episode polarity and medication; and, lack of control group. CONCLUSION: Less eveningness was associated with less severe depressive symptoms and better quality of life. This suggests that morningness may reduce residual depressive symptoms and recover function of patients.

Identification of Healthy and Unhealthy Lifestyles by a Wearable Activity Tracker in Type 2 Diabetes: A Machine Learning-Based Analysis.

Lifestyle is a critical aspect of diabetes management. We aimed to define a healthy lifestyle using objectively measured parameters obtained from a wearable activity tracker (Fitbit) in patients with type 2 diabetes. This prospective observational study included 24 patients (mean age, 46.8 years) with type 2 diabetes. Expectation-maximization clustering analysis produced two groups: A (n=9) and B (n=15). Group A had a higher daily step count, lower resting heart rate, longer sleep duration, and lower mean time differences in going to sleep and waking up than group B. A Shapley additive explanation summary analysis indicated that sleep-related factors were key elements for clustering. The mean hemoglobin A1c level was 0.3 percentage points lower at the end of follow-up in group A than in group B. Factors related to regular sleep patterns could be possible determinants of lifestyle clustering in patients with type 2 diabetes.

Postmortem gene expression profiles in the habenulae of suicides: implication of endothelial dysfunction in the neurovascular system.

The habenula (Hb) is an epithalamic structure that links multiple forebrain areas with the mid/hindbrain monoaminergic systems. As an anti-reward center, it has been implicated in the etiology of various neuropsychiatric disorders, particularly those associated with dysregulated reward circuitry. In this regard, Hb has been proposed as a therapeutic target for treatment-resistant depression associated with a higher risk of suicide. Therefore, we aimed to gain insight into the molecular signatures of the Hb in association with suicide in individuals with major depression. Postmortem gene expression analysis identified 251 differentially expressed genes (DEGs) in the Hb tissue of suicides in comparison with Hb tissues from neurotypical individuals. Subsequent bioinformatic analyses using single-cell transcriptome data from the mouse Hb showed that the levels of a subset of endothelial cell-enriched genes encoding cell-cell junctional complex and plasma membrane-associated proteins, as well as the levels of their putative upstream transcriptional regulators, were significantly affected in suicides. Although our findings are based on a limited number of samples, the present study suggests a potential association of endothelial dysfunction in the Hb with depression and suicidal behavior.

Mediation Effect of Insomnia Symptoms on Relation Between Stress and Quality of Life.

OBJECTIVE: Among various causes of insomnia, stress is the most common and representative cause. Insomnia is also known to negatively affect the quality of life (QoL). The objective of this study was to explore the effect of stress on QoL and the mediating role of insomnia symptoms in the relationship between stress and QoL. METHODS: In this study, the mediating effect of insomnia symptoms on the relationship between stress and QoL was analyzed by enrolling 3,714 participants from the Ansung and Ansan cohorts of the Korea Association Resource project from 2001 to 2004. These cohort participants were asked about how much they felt stressed during their everyday life. Insomnia symptoms were evaluated by asking participants whether they had trouble sleeping such as difficulty in falling asleep, disrupted sleep, and early morning awakening due to the lack of a validated questionnaire for this cohort. QoL was evaluated using the World Health Organization QoL Scale Brief Version. RESULTS: In total, stress was positively associated with insomnia symptoms, which in turn predicted QoL. The same result could be derived from subgroup analysis according to sex, and it was confirmed that insomnia symptoms acted as a mediating factor more significantly in female than in male. CONCLUSION: In this study, insomnia symptoms were confirmed to act as a significant mediating factor between stress and QoL, suggesting that insomnia symptoms should be actively identified and controlled to alleviate the negative effect of stress on QoL in clinical practice.

Neurobiological and behavioral mechanisms of circadian rhythm disruption in bipolar disorder: A critical multi-disciplinary literature review and agenda for future research from the ISBD task force on chronobiology.

AIM: Symptoms of bipolar disorder (BD) include changes in mood, activity, energy, sleep, and appetite. Since many of these processes are regulated by circadian function, circadian rhythm disturbance has been examined as a biological feature underlying BD. The International Society for Bipolar Disorders Chronobiology Task Force (CTF) was commissioned to review evidence for neurobiological and behavioral mechanisms pertinent to BD. METHOD: Drawing upon expertise in animal models, biomarkers, physiology, and behavior, CTF analyzed the relevant cross-disciplinary literature to precisely frame the discussion around circadian rhythm disruption in BD, highlight key findings, and for the first time integrate findings across levels of analysis to develop an internally consistent, coherent theoretical framework. RESULTS: Evidence from multiple sources implicates the circadian system in mood regulation, with corresponding associations with BD diagnoses and mood-related traits reported across genetic, cellular, physiological, and behavioral domains. However, circadian disruption does not appear to be specific to BD and is present across a variety of high-risk, prodromal, and syndromic psychiatric disorders. Substantial variability and ambiguity among the definitions, concepts and assumptions underlying the research have limited replication and the emergence of consensus findings. CONCLUSIONS: Future research in circadian rhythms and its role in BD is warranted. Well-powered studies that carefully define associations between BD-related and chronobiologically-related constructs, and integrate across levels of analysis will be most illuminating.

Association Between CLOCK Gene Variants and Restless Legs Syndrome in Koreans.

OBJECTIVE: Previous studies have suggested various causes of restless legs syndrome (RLS), including iron and dopamine concentrations in the brain. Genetic influences have also been reported in many studies. There is also a possibility that circadian clock genes may be involved because symptoms of RLS worsen at night. We investigated whether CLOCK and NPAS2 gene polymorphisms were associated with RLS. METHODS: A total of 227 patients with RLS and 229 non-RLS matched controls were assessed according to the International Restless Legs Syndrome Study Group diagnostic criteria. Genotyping was performed using reverse transcription polymerase chain reaction and high-resolution melting curve analyses. RESULTS: Although the genotype distributions of the CLOCK variants (rs1801260 and rs2412646) were not significantly different between patients with RLS and non-RLS controls, the allele frequencies of CLOCK rs1801260 showed marginally significant differences between the two groups (X2 =2.98, p=0.085). Furthermore, there was a significant difference in the distribution of CLOCK haplotypes (rs1801260-rs2412646) between patients with RLS and non-RLS controls (p=0.013). The distributions of allelic, genotypic, and haplotypic variants of NPAS2 (rs2305160 and rs6725296) were not significantly different between the two groups. CONCLUSION: Our results suggest that CLOCK variants may be associated with decreased susceptibility to RLS.

Validation of the 6-item Evening Chronotype Scale (ECS): a modified version of Composite Scale Morningness.

In a previous study comparing two representative chronotype questionnaires to screen for delayed sleep-phase disorder, six items from the simplified language version of Composite Scale of Morningness (CSM) have been found to be useful and effective for screening evening-type person. In this study, we reverse coded the six items from CSM and named them Evening Chronotype Scale (ECS). The primary aim of this study was to examine the psychometric properties, validity, and test-retest reliability of the ECS when administered on mood disorder patients. The secondary aim was to further examine the relationship between circadian preferences and symptoms of mood disorders. The study sample was of 472 mood disorder patients including major depressive disorder, bipolar disorder I, and bipolar disorder II. The 13-item full version CSM and 6-item ECS were externally validated by self-reported sleep time, wake time, sleep latency, depressive symptoms, hypomanic symptoms, quality of life, and impulsivity. Cronbach's alpha was calculated for the internal consistency of the ECS, and the test-retest reliability analysis was also performed. Our results suggest that the ECS is a reliable and valid instrument to assess circadian preference in mood disorder patients. First, the ECS showed moderate to good internal consistency (Cronbach's alpha = 0.727). Also, it showed external validity comparable to that of the 13-item CSM. Participants who were more evening-oriented according to the ECS slept and woke up later, took longer time to fall asleep, showed more depressive and hypomanic symptoms, and showed lower quality of life and higher impulsivity. As circadian rhythm disruption has been shown to affect the regulation of mood symptoms in patients with mood disorders, assessment of circadian preferences may be crucial in clinical settings. We suggest that ECS appears to be an easy-to-use instrument that is reliable and valid.

Virtual Reality-Based Psychotherapy in Social Anxiety Disorder: fMRI Study Using a Self-Referential Task.

BACKGROUND: Although it has been well demonstrated that the efficacy of virtual reality therapy for social anxiety disorder is comparable to that of traditional cognitive behavioral therapy, little is known about the effect of virtual reality on pathological self-referential processes in individuals with social anxiety disorder. OBJECTIVE: We aimed to determine changes in self-referential processing and their neural mechanisms following virtual reality treatment. METHODS: We recruited participants with and without a primary diagnosis of social anxiety disorder to undergo clinical assessments (Social Phobia Scale and Post-Event Rumination Scale) and functional magnetic resonance imaging (fMRI) scans. Participants with social anxiety disorder received virtual reality-based exposure treatment for 6 sessions starting immediately after baseline testing. After the sixth session, participants with social anxiety disorder completed follow-up scans during which they were asked to judge whether a series of words (positive, negative, neutral) was relevant to them. RESULTS: Of 25 individuals with social anxiety disorder who participated in the study, 21 completed the sessions and follow-up; 22 control individuals also participated. There were no significant differences in age (P=.36), sex (P=.71), or handedness (P=.51) between the groups. Whole-brain analysis revealed that participants in the social anxiety disorder group had increased neural responses during positive self-referential processing in the medial temporal and frontal cortexes compared with those in the control group. Participants in the social anxiety disorder group also showed increased left insular activation and decreased right middle frontal gyrus activation during negative self-referential processing. After undergoing virtual reality-based therapy, overall symptoms of the participants with social anxiety disorder were reduced, and these participants exhibited greater activity in a brain regions responsible for self-referential and autobiographical memory processes while viewing positive words during postintervention fMRI scans. Interestingly, the greater the blood oxygen level dependent changes related to positive self-referential processing, the lower the tendency to ruminate on the negative events and the lower the social anxiety following the virtual reality session. Compared with that at baseline, higher activation was also found within broad somatosensory areas in individuals with social anxiety disorder during negative self-referential processing following virtual reality therapy. CONCLUSIONS: These fMRI findings might reflect the enhanced physiological and cognitive processing in individuals with social anxiety disorder in response to self-referential information. They also provide neural evidence of the effect of virtual reality exposure therapy on social anxiety and self-derogation.

Bipolar II disorder has the highest prevalence of seasonal affective disorder in early-onset mood disorders: Results from a prospective observational cohort study.

BACKGROUND: Many mood disorder patients experience seasonal changes in varying degrees. Studies on seasonality have shown that bipolar disorder has a higher prevalence rate in such patients; however, there is limited research on seasonality in early-onset mood disorder patients. This study estimated the prevalence of seasonality in early-onset mood disorder patients, and examined the association between seasonality and mood disorders. METHODS: Early-onset mood disorder patients (n = 378; 138 major depressive disorder; 101 bipolar I disorder; 139 bipolar II disorder) of the Mood Disorder Cohort Research Consortium and healthy control subjects (n = 235) were assessed for seasonality with Seasonality Pattern Assessment Questionnaire (SPAQ). RESULTS: A higher global seasonality score, an overall seasonal impairment score, and the prevalence of seasonal affective disorder (SAD) and subsyndromal SAD showed that mood disorder subjects had higher seasonality than the healthy subjects. The former subject group had a significantly higher mean overall seasonal impairment score than the healthy subjects (p < .001); in particular, bipolar II disorder subjects had the highest prevalence of SAD, and the diagnosis of bipolar II disorder had significantly higher odds ratios for SAD when compared to major depression and bipolar I disorder (p < .05). CONCLUSIONS: Early-onset mood disorders, especially bipolar II disorder, were associated with high seasonality. A thorough assessment of seasonality in early-onset mood disorders may be warranted for more personalized treatment and proactive prevention of mood episodes.

Highly sensitive and multiplexed one-step RT-qPCR for profiling genes involved in the circadian rhythm using microparticles.

Given the growing interest in molecular diagnosis, highly extensive and selective detection of genetic targets from a very limited amount of samples is in high demand. We demonstrated the highly sensitive and multiplexed one-step RT-qPCR platform for RNA analysis using microparticles as individual reactors. Those particles are equipped with a controlled release system of thermo-responsive materials, and are able to capture RNA targets inside. The particle-based assay can successfully quantify multiple target RNAs from only 200 pg of total RNA. The assay can also quantify target RNAs from a single cell with the aid of a pre-concentration process. We carried out 8-plex one-step RT-qPCR using tens of microparticles, which allowed extensive mRNA profiling. The circadian cycles were shown by the multiplex one-step RT-qPCR in human cell and human hair follicles. Reliable 24-plex one-step RT-qPCR was developed using a single operation in a PCR chip without any loss of performance (i.e., selectivity and sensitivity), even from a single hair. Many other disease-related transcripts can be monitored using this versatile platform. It can also be used non-invasively for samples obtained in clinics.