Major depressive disorder: point prevalence, suicidal ideation, and risk factors among Sudanese children and adolescents during Sudan army conflict: a cross-sectional study.

BACKGROUND: Tiredness, poor concentration, disturbed sleep and poor appetite can all be caused by depression, which is a common mental disorder and a leading cause of disability worldwide. This study aimed to assess the prevalence of major depressive disorder, suicidal ideation, and risk factors in Sudanese children and adolescents during the Sudanese army conflict. METHODS: A descriptive cross-sectional community-based study was carried out among Sudanese children between 11 and 17 years old who living in Sudan at the start of the conflict by using a self-administered questionnaire under the guidance of parents, if necessary. The questionnaire was adapted from the Patients Health Questionnaire-9 (PHQ-9) checklist for the assessment of major depression disorder symptoms according to the Diagnostic and Statistical Manual Edition 5th Edition (DSM-5). The questionnaire was translated into Arabic by two expert translators, and its validity and reliability were confirmed. Data analysis was performed using Statistical Package for the Social Sciences version 25 software, and descriptive analysis and any appropriate statistical tests were performed. RESULTS: Among the 963 participants, the mean age was 15.18 ± 2.1 years, 65.5% were female, and 67.7% had major depressive disorder. There was a significant relationship between MDD score, age, sex, current residency status, and traumatic event exposure, with P values less than 0.001 for all variables. CONCLUSION: Major depressive disorder was highly prevalent among Sudanese children and adolescents included in the present study. Additionally, suicidal ideation, which requires immediate intervention, was reported to be very high. The findings will help the government to provide proper mental health interventions for affected people.

Gene expression and brain imaging association study reveals gene signatures in major depressive disorder.

Major depressive disorder is often characterized by changes in the structure and function of the brain, which are influenced by modifications in gene expression profiles. How the depression-related genes work together within the scope of time and space to cause pathological changes remains unclear. By integrating the brain-wide gene expression data and imaging data in major depressive disorder, we identified gene signatures of major depressive disorder and explored their temporal-spatial expression specificity, network properties, function annotations and sex differences systematically. Based on correlation analysis with permutation testing, we found 345 depression-related genes significantly correlated with functional and structural alteration of brain images in major depressive disorder and separated them by directional effects. The genes with negative effect for grey matter density and positive effect for functional indices are enriched in downregulated genes in the post-mortem brain samples of patients with depression and risk genes identified by genome-wide association studies than genes with positive effect for grey matter density and negative effect for functional indices and control genes, confirming their potential association with major depressive disorder. By introducing a parameter of dispersion measure on the gene expression data of developing human brains, we revealed higher spatial specificity and lower temporal specificity of depression-related genes than control genes. Meanwhile, we found depression-related genes tend to be more highly expressed in females than males, which may contribute to the difference in incidence rate between male and female patients. In general, we found the genes with negative effect have lower network degree, more specialized function, higher spatial specificity, lower temporal specificity and more sex differences than genes with positive effect, indicating they may play different roles in the occurrence and development of major depressive disorder. These findings can enhance the understanding of molecular mechanisms underlying major depressive disorder and help develop tailored diagnostic and treatment strategies for patients of depression of different sex.

Butyrate- and Beta-Hydroxybutyrate-Mediated Effects of Interventions with Pro- and Prebiotics, Fasting, and Caloric Restrictions on Depression: A Systematic Review and Meta-Analysis.

To examine the butyrate- and beta-hydroxybutyrate (BHB)-modulated effects of pre- and probiotic interventions, fasting, and caloric restriction interventions, a systematic literature review was carried out with a subsequent meta-analysis. Three pre-and probiotic intervention randomized control trials (RCTs) were included in the meta-analysis. A significant increase in butyrate (standardized mean difference (SMD) [confidence interval (CI)] 0.34; [0.02-0.67]) and an improvement in depression scores (SMD [CI] 0.15, [-0.35-0.70]) through pre- and probiotic interventions were shown in the meta-analysis. The intervention duration of the included studies ranged from three days to four weeks, with the examined population being healthy adults. Butyrate was measured in either plasma or feces, and the depression score was obtained under the Swedish core affect scale, the hospital anxiety and depression scale (HADS), or the depression, anxiety, and stress scale-21 items (DASS-21). In addition to butyrate, the total SCFA concentration also seems to be positively associated with pre- and probiotic administration (SMD [CI] 0.55 [0.15-0.95]). Despite the significant short-chain fatty acid (SCFA) and butyrate concentration changes, no significant correlation between butyrate and depression or between SCFAs and depression could be shown through linear regression models. Nevertheless, the regression coefficient b1 = 1.57 (p = 0.17) for butyrate suggests a strong, positive connection between butyrate and depression. Additionally, three studies were qualitatively analyzed, examining fasting as an intervention and revealing a connection between fasting, BHB, and depression. The association between fasting, BHB, and depression or mood elevation appeared to be related to BHB concentrations, which may be due to the similar biochemical properties of BHB and butyrate. Furthermore, caloric restrictions as alternatives to fasting were proposed as potential long-term interventions.

Longitudinal Association between Stressful Life Events and Suicidal Ideation in Adults with Major Depression Disorder: The Mediating Effects of Insomnia Symptoms.

Stressful life events (SLEs) and suicidal ideation (SI) are prevalent in persons with major depression disorder (MDD). Less is known about the underlying role of insomnia symptoms in the association between SLEs and SI. This three-wave prospective cohort study sought to investigate the longitudinal association among SLEs, insomnia symptoms, and SI in persons with MDD. The study population included 511 persons with MDD (mean [SD] age, 28.7 [6.7] years; 67.1% were females). Generalized estimated equations (GEEs) were utilized to explore prospective association among exposure of SLEs, insomnia symptoms, and SI. Additionally, a structural equation model (SEM) was employed to estimate the longitudinal mediating effect of insomnia symptoms in the relationship between SLEs and SI. Our study demonstrated that cumulative SLEs were determined to be longitudinally associated with SI in persons with MDD. We further observed that the association between SLEs and SI was significantly mediated by insomnia symptoms. Clinicians assessing persons with MDD, especially those with the history of SLE, could carefully evaluate and promptly treat insomnia symptoms as part of personalized assessment of their depressive illness, thereby achieving early prevention and intervention for suicidal behaviors in persons with MDD.

Technical Report: Efficacy and Safety of Low-Intensity Transcranial Magnetic Stimulation in the Remission of Depressive Symptoms in Patients With Treatment-Resistant Depression in Mexico.

Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique that induces action potentials in the stimulated cortical area and has been approved by the Food and Drug Administration (FDA) for the treatment of major depressive disorder (MDD). The prevalence of MDD in Mexico almost tripled after the COVID-19 pandemic. In this study, we evaluated the safety and therapeutic effects of low-intensity TMS (Li-TMS) - characterized by inducing electric currents below the action potential threshold on the cerebral cortex - in 41 subjects diagnosed with treatment-resistant depression (TRD). A Li-TMS device dispensed repetitive magnetic pulses at 30 mT for 60 minutes during 20 sessions (once daily from Monday to Saturday) with the theta burst pattern. Our results suggest that Li-TMS is a safe therapy with antidepressant effects, demonstrated by the decrease in Beck Depression Inventory (BDI) scores and lessening of depressive symptoms.

TanhReLU -based convolutional neural networks for MDD classification.

Major Depression Disorder (MDD), a complex mental health disorder, poses significant challenges in accurate diagnosis. In addressing the issue of gradient vanishing in the classification of MDD using current data-driven electroencephalogram (EEG) data, this study introduces a TanhReLU-based Convolutional Neural Network (CNN). By integrating the TanhReLU activation function, which combines the characteristics of the hyperbolic tangent (Tanh) and rectified linear unit (ReLU) activations, the model aims to improve performance in identifying patterns associated with MDD while alleviating the issue of model overfitting and gradient vanishing. Experimental results demonstrate promising outcomes in the task of MDD classification upon the publicly available EEG data, suggesting potential clinical applications.

[Interaction between depression and insomnia under acupuncture treatment: a secondary analysis of a randomized controlled trial].

OBJECTIVE: To explore the interaction between emotional and insomnia symptom improvement in comorbid depression and insomnia patients treated with optimized acupuncture. METHODS: A secondary analysis was conducted on a multicenter randomized controlled trial of acupuncture treatment for comorbid depression and insomnia. One hundred and forty patients with comorbid depression and insomnia were randomly assigned to an optimized acupuncture group (70 cases, 8 cases dropped out) and a conventional acupuncture group (70 cases, 8 cases dropped out). The patients in the conventional acupuncture group were treated with acupuncture at Yintang (GV 24+), Baihui (GV 20), bilateral Hegu (LI 4) and Taichong (LR 3); the patients in the optimized acupuncture group were treated with additional acupuncture at bilateral Lieque (LU 7), Zhaohai (KI 6), and intradermal needling was applied alternately at bilateral Xinshu (BL 15) and Danshu (BL 19), Shenshu (BL 23) and Anmian (Extra). Both groups received treatments twice a week for six weeks. The Hamilton depression scale-24 (HAMD-24) and Pittsburgh sleep quality index (PSQI) scores were observed before and after treatment for both groups. Mediation analysis was used to assess the mesomeric effect of PSQI and HAMD-24 emotional factors, and latent profile analysis categorized patients into subgroups based on HAMD-24 scores, followed by between-group efficacy comparisons. RESULTS: The indirect effect of the optimized acupuncture on improving depression emotions through PSQI was 3.052 (95%CI [1.407, 4.697]), accounting for 77.5% of the total effect. Conversely, the indirect effect of improving PSQI through the optimized protocol on depression emotions was 0.479 (95%CI [0.024, 0.935]), representing 14.8% of the total effect. Latent profile analysis identified 3 categories based on emotional and insomnia symptoms, and statistically significant differences in efficacy favoring the optimized acupuncture protocol over the conventional acupuncture protocol were observed only in the subgroup with relatively mild depression and insomnia symptoms (PSQI: 95%CI [-10.98, -5.44], P<0.001; HAMD-24: 95%CI [-12.31, -3.73], P =0.001). CONCLUSION: The optimized acupuncture protocol primarily improves insomnia symptoms and further improves depression emotions. Its efficacy advantages are more evident in patients with mild symptoms compared with the conventional acupuncture protocol.

The effects of psilocybin on cognition and emotional processing in healthy adults and adults with depression: a systematic literature review.

INTRODUCTION: Psilocybin, a naturally occurring serotonergic agonist in some mushroom species, has shown promise as a novel, fast-acting pharmacotherapy seeking to overcome the limitations of conventional first-line antidepressants. Studying psilocybin effects on cognition and emotional processing may help to clarify the mechanisms underlying the therapeutic potential of psilocybin and may also support studies with people suffering from depression. Thus, this review aims to provide a comprehensive overview of the current literature regarding the effects of psilocybin on these two key areas in both healthy and depressed populations. METHOD: A systematic search was performed on 29 January 2024, in the PubMed, EBSCOhost, Web of Science and SCOPUS databases. After duplicates removal, study selection was conducted considering pre-specified criteria. Data extraction was then performed. The quality assessment of the studies was carried out using the Cochrane Collaboration tools for randomized (RoB 2.0) and non-randomized (ROBINS-I) controlled trials. RESULTS: Twenty articles were included, with 18 targeting healthy adults and two adults with depression. Results point to impairments within attentional and inhibitory processes, and improvements in the domains of creativity and social cognition in healthy individuals. In the population with depression, only cognitive flexibility and emotional recognition were affected, both being enhanced. The comparison of outcomes from both populations proved limited. CONCLUSIONS: Psilocybin acutely alters several cognitive domains, with a localized rather than global focus, in a dose- and time-dependent manner. However, the significant methodological constraints call for further research, in the context of depression and with standardized protocols, with longitudinal studies also imperative.

Advances in biosensors for major depressive disorder diagnostic biomarkers.

Depression is one of the most common mental disorders and is mainly characterized by low mood or lack of interest and pleasure. It can be accompanied by varying degrees of cognitive and behavioral changes and may lead to suicide risk in severe cases. Due to the subjectivity of diagnostic methods and the complexity of patients' conditions, the diagnosis of major depressive disorder (MDD) has always been a difficult problem in psychiatry. With the discovery of more diagnostic biomarkers associated with MDD in recent years, especially emerging non-coding RNAs (ncRNAs), it is possible to quantify the condition of patients with mental illness based on biomarker levels. Point-of-care biosensors have emerged due to their advantages of convenient sampling, rapid detection, miniaturization, and portability. After summarizing the pathogenesis of MDD, representative biomarkers, including proteins, hormones, and RNAs, are discussed. Furthermore, we analyzed recent advances in biosensors for detecting various types of biomarkers of MDD, highlighting representative electrochemical sensors. Future trends in terms of new biomarkers, new sample processing methods, and new detection modalities are expected to provide a complete reference for psychiatrists and biomedical engineers.

Unlocking new avenues for neuropsychiatric disease therapy: the emerging potential of Peroxisome proliferator-activated receptors as promising therapeutic targets.

RATIONALE: Peroxisome proliferator-activated receptors (PPARs) are transcription factors that regulate various physiological processes such as inflammation, lipid metabolism, and glucose homeostasis. Recent studies suggest that targeting PPARs could be beneficial in treating neuropsychiatric disorders by modulating neuronal function and signaling pathways in the brain. PPAR-α, PPAR-δ, and PPAR-γ have been found to play important roles in cognitive function, neuroinflammation, and neuroprotection. Dysregulation of PPARs has been associated with neuropsychiatric disorders like bipolar disorder, schizophrenia, major depression disorder, and autism spectrum disorder. The limitations and side effects of current treatments have prompted research to target PPARs as a promising novel therapeutic strategy. Preclinical and clinical studies have shown the potential of PPAR agonists and antagonists to improve symptoms associated with these disorders. OBJECTIVE: This review aims to provide an overview of the current understanding of PPARs in neuropsychiatric disorders, their potential as therapeutic targets, and the challenges and future directions for developing PPAR-based therapies. METHODS: An extensive literature review of various search engines like PubMed, Medline, Bentham, Scopus, and EMBASE (Elsevier) databases was carried out with the keywords "PPAR, Neuropsychiatric disorders, Oxidative stress, Inflammation, Bipolar Disorder, Schizophrenia, Major depression disorder, Autism spectrum disorder, molecular pathway". RESULT & CONCLUSION: Although PPARs present a hopeful direction for innovative therapeutic approaches in neuropsychiatric conditions, additional research is required to address obstacles and convert this potential into clinically viable and individualized treatments.

The causal relationship between major depression disorder and thyroid diseases: A Mendelian randomization study and mediation analysis.

BACKGROUND: Studies have been conducted on the relationship between depression and thyroid diseases and function, its causal relationship remains unclear. METHODS: Using summary statistics of genome-wide association studies of European and East Asian ancestry, we conducted 2-sample bidirectional Mendelian randomization to estimate the association between MDD and thyroid function (European: normal range TSH, T4, T3, fT4, TPOAb levels and TPOAb-positives; East Asian: T4) and thyroid diseases (hypothyroidism, hyperthyroidism, and Hashimoto's thyroiditis), and used Mediation analysis to evaluate potential mediators (alcohol intake, antidepressant) of the association and calculate the mediated proportions. RESULTS: It was observed a significant causal association between MDD on hypothyroidism (P = 8.94 × 10-5), hyperthyroidism (P = 8.68 × 10-3), and hashimoto's thyroiditis (P = 3.97 × 10-5) among European ancestry, which was mediated by Alcohol intake (alcohol intake versus 10 years previously for hypothyroidism (P = 0.026), hashimoto's thyroiditis (P = 0.042), and alcohol intake frequency for hypothyroidism (P = 0.015)) and antidepressant (for hypothyroidism (P = 0.008), hashimoto's thyroiditis (P = 0.010)), but not among East Asian ancestry (PMDD-hypothyroidism = 0.016, but ß direction was different; PMDD-hyperthyroidism = 0.438; PMDD-hashimoto's thyroiditis = 0.496). There was no evidence for bidirectional causal association between thyroid function mentioned above and MDD among both ancestry (all P > 0.05). CONCLUSION: We importantly observed a significant causal association between MDD on risk of hypothyroidism, hyperthyroidism, and hashimoto's thyroiditis among European ancestry, and Alcohol intake and antidepressant as mediators for prevention of hypothyroidism, hashimoto's thyroiditis attributable to MDD.

Analysis of functional connectivity changes in attention networks and default mode networks in patients with depression and insomnia.

PURPOSE: Major Depressive Disorder (MDD) and Insomnia Disorder (ID) are prevalent psychiatric conditions often occurring concurrently, leading to substantial impairment in daily functioning. Understanding the neurobiological underpinnings of these disorders and their comorbidity is crucial for developing effective interventions. This study aims to analyze changes in functional connectivity within attention networks and default mode networks in patients with depression and insomnia. METHODS: The functional connectivity alterations in individuals with MDD, ID, comorbid MDD and insomnia (iMDD), and healthy controls (HC) were assessed from a cohort of 174 participants. They underwent rs-fMRI scans, demographic assessments, and scale evaluations for depression and sleep quality. Functional connectivity analysis was conducted using region-of-interest (ROI) and whole-brain methods. RESULTS: The MDD and iMDD groups exhibited higher Hamilton Depression Scale (HAMD) scores compared to HC and ID groups (P < 0.001). Both ID and MDD groups displayed enhanced connectivity between the left and right orbital frontal cortex compared to HC (P < 0.05), while the iMDD group showed reduced connectivity compared to HC and ID groups (P < 0.05). In the left insula, reduced connectivity with the right medial superior frontal gyrus was observed across patient groups compared to HC (P < 0.05), with the iMDD group showing increased connectivity compared to MDD (P < 0.05). Moreover, alterations in functional connectivity between the left thalamus and left temporal pole were found in iMDD compared to HC and MDD (P < 0.05). Correlation analyses revealed associations between abnormal connectivity and symptom severity in MDD and ID groups. CONCLUSIONS: Our findings demonstrate distinct patterns of altered functional connectivity in individuals with MDD, ID, and iMDD compared to healthy controls. These findings contribute to a better understanding of the pathophysiology of depression and insomnia, which could be used as a reference for the diagnosis and treatments of these patients.

Development and validation of a machine learning model for prediction of comorbid major depression disorder among narcolepsy type 1.

BACKGROUND: Major depression disorder (MDD) forms a common psychiatric comorbidity among patients with narcolepsy type 1 (NT1), yet its impact on patients with NT1 is often overlooked by neurologists. Currently, there is a lack of effective methods for accurately predicting MDD in patients with NT1. OBJECTIVE: This study utilized machine learning (ML) algorithms to identify critical variables and developed the prediction model for predicting MDD in patients with NT1. METHODS: The study included 267 NT1 patients from four sleep centers. The diagnosis of comorbid MDD was based on Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5). ML models, including six full models and six compact models, were developed using a training set. The performance of these models was compared in the testing set, and the optimal model was evaluated in the testing set. Various evaluation metrics, such as Area under the receiver operating curve (AUC), precision-recall (PR) curve and calibration curve were employed to assess and compare the performance of the ML models. Model interpretability was demonstrated using SHAP. RESULT: In the testing set, the logistic regression (LG) model demonstrated superior performance compared to other ML models based on evaluation metrics such as AUC, PR curve, and calibration curve. The top eight features used in the LG model, ranked by feature importance, included social impact scale (SIS) score, narcolepsy severity scale (NSS) score, total sleep time, body mass index (BMI), education years, age of onset, sleep efficiency, sleep latency. CONCLUSION: The study yielded a straightforward and practical ML model for the early identification of MDD in patients with NT1. A web-based tool for clinical applications was developed, which deserves further verification in diverse clinical settings.

Factors affecting major depression in Iran: a mixed-method study.

Depression is a major cause of disability and, if left untreated, can increase the risk of suicide. Evidence on the determinants of depression is incomplete, making it challenging to interpret results across studies. This study aims to identify the social, economic, environmental, political, and technological factors influencing the great recession in Iran. The study was conducted in two parts. The first step involved a literature review to identify the factors, using PubMed, Scopus, and Web of Science for the search. The reference lists of all identified articles were reviewed to find relevant studies, and the extracted information was summarized and reported descriptively. The second steps involved compiling and consulting 14 experts from different fields, using a framework analysis method. Twenty-four articles were used as primary sources of information, and a total of 28 factors were found to exist. After removing duplicates and related factors, 19 of these were subsequently declared as factors, resulting in a total of 36 determinants being identified. Most of these factors belong to the social category. The health policies implemented have a significant impact on disease risk factors and ultimately their occurrence. Political decisions and policy-making processes play a crucial role in all areas, particularly in addressing disease risk factors. Severe depression can disrupt all aspects of the healthcare system, underscoring the importance of access to care. Policies concerning physical education, transportation, nutrition, employment, green spaces, recreational facilities, and tobacco are vital in this context. The influence of health policies on disease risk factors and disease occurrence is profound. Severe depression can have far-reaching effects on the healthcare system, emphasizing the critical need for access to care. The formulation of policies to combat depression must be thoroughly evaluated in terms of economic, political, social, technological, and environmental factors. The findings suggest that addressing social inequalities and emphasizing the role of political action, as highlighted by the social determinants of health, should be top priorities in addressing depression. Efforts to prevent depression should incorporate ecological approaches that consider the impact of the socioeconomic environment on depressive symptoms.

Unraveling the molecular basis of cannabidiolic acid methyl Ester's anti-depressive effects in a rat model of treatment-resistant depression.

Major depressive disorder (MDD) stands as a significant cause of disability globally. Cannabidiolic Acid-Methyl Ester (CBDA-ME) (EPM-301, HU-580), a derivative of Cannabidiol, demonstrates immediate antidepressant-like effects, yet it has undergone only minimal evaluation in psychopharmacology. Our goal was to investigate the behavioral and potential molecular mechanisms associated with the chronic oral administration of this compound in the Wistar Kyoto (WKY) genetic model of treatment-resistant depression. Male WKY rats were subjected to behavioral assessments before and after receiving chronic (14-day) oral doses of CBDA-ME (0.5 mg/kg), 15 mg/kg of imipramine or vehicle. At the end of the study, plasma corticosterone levels and mRNA expression of various genes in the medial Prefrontal Cortex and Hippocampus were measured. Behavioral outcomes from CBDA-ME treatment indicated an antidepressant-like effect similar to imipramine, as oral ingestion reduced immobility and increased swimming duration in the Forced Swim Test. Neither treatment influenced locomotion in the Open Field Test nor preference in the Saccharin Preference Test. The behavioral impact in WKY rats coincided with reduced corticosterone serum levels, upregulated mRNA expression of Cannabinoid receptor 1, Fatty Acid Amide Hydrolase, and Corticotropin-Releasing Hormone Receptor 1, alongside downregulation of the Serotonin Transporter in the hippocampus. Additionally, there was an upregulation of CB1 mRNA expression and downregulation of Brain-Derived Neurotrophic Factor in the mPFC. These findings contribute to our limited understanding of the antidepressant effects of CBDA-ME and shed light on its potential psychopharmacological mechanisms. This discovery opens up possibilities for utilizing cannabinoids in the treatment of major depressive disorder and related conditions.

Global prevalence of major depressive disorder in LGBTQ+ samples: A systematic review and meta-analysis of epidemiological studies.

BACKGROUND: LGBTQ+ populations have been reported to have higher rates of depression compared with their heterosexual peers. Such data provided us the impetus to conduct a meta-analysis on the worldwide prevalence of major depressive disorder (MDD) in LGBTQ+ populations and moderating factors that contributed to differences in prevalence estimates between studies. METHODS: A systematic literature search was performed in major international (PubMed, PsycINFO, Web of Science, EMBASE) and Chinese (Chinese Nation Knowledge Infrastructure (CNKI) and WANFANG) databases from dates of inception to 10 December 2021. RESULTS: 48 articles comprising 4,616,903 individuals were included in the meta-analysis. The overall prevalence of MDD was 32.2 % (95%CI: 30.8-33.6 %, I2 = 99.6 %, τ2 = 0.284). MDD prevalence was higher in the LGBTQ+ samples from the United States than other countries, though the difference was not significant in moderator analyses. Moderator analyses indicated point and lifetime prevalence of MDD were significantly higher than estimates based on the past year (Q = 6.270, p = 0.043). Furthermore, studies that relied on convenience sampling had a higher prevalence of MDD than those based on other sampling methods (Q = 8.159, p = 0.017). In meta-regression analyses, mean age (B = 0.03, z = 9.54, p < 0.001) and study quality assessment score (B = 0.24, z = 67.64, p < 0.001) were positively associated with pooled prevalence of MDD while mediation data of year of study (B = -0.08, z = -72.55, p < 0.001) and sample size (B = -1.46, z = -37.83, p < 0.001) were negatively associated with pooled prevalence of MDD in LGBTQ+ samples. CONCLUSIONS: MDD is common among in LGBTQ+ individuals. Considering the negative consequences MDD has on daily life and well-being, appropriate prevention and treatment measures should be provided to vulnerable members of these populations. The findings of this meta-analysis could facilitate identifying at-risk subgroups, developing relevant health policy for LGBTQ+ individuals and allocating health resources from an intersectionality perspective.

Coding of Childhood Psychiatric and Neurodevelopmental Disorders in Electronic Health Records of a Large Integrated Health Care System: Validation Study.

Background: Mental, emotional, and behavioral disorders are chronic pediatric conditions, and their prevalence has been on the rise over recent decades. Affected children have long-term health sequelae and a decline in health-related quality of life. Due to the lack of a validated database for pharmacoepidemiological research on selected mental, emotional, and behavioral disorders, there is uncertainty in their reported prevalence in the literature. objectives: We aimed to evaluate the accuracy of coding related to pediatric mental, emotional, and behavioral disorders in a large integrated health care system's electronic health records (EHRs) and compare the coding quality before and after the implementation of the International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) coding as well as before and after the COVID-19 pandemic. Methods: Medical records of 1200 member children aged 2-17 years with at least 1 clinical visit before the COVID-19 pandemic (January 1, 2012, to December 31, 2014, the ICD-9-CM coding period; and January 1, 2017, to December 31, 2019, the ICD-10-CM coding period) and after the COVID-19 pandemic (January 1, 2021, to December 31, 2022) were selected with stratified random sampling from EHRs for chart review. Two trained research associates reviewed the EHRs for all potential cases of autism spectrum disorder (ASD), attention-deficit hyperactivity disorder (ADHD), major depression disorder (MDD), anxiety disorder (AD), and disruptive behavior disorders (DBD) in children during the study period. Children were considered cases only if there was a mention of any one of the conditions (yes for diagnosis) in the electronic chart during the corresponding time period. The validity of diagnosis codes was evaluated by directly comparing them with the gold standard of chart abstraction using sensitivity, specificity, positive predictive value, negative predictive value, the summary statistics of the F-score, and Youden J statistic. κ statistic for interrater reliability among the 2 abstractors was calculated. Results: The overall agreement between the identification of mental, behavioral, and emotional conditions using diagnosis codes compared to medical record abstraction was strong and similar across the ICD-9-CM and ICD-10-CM coding periods as well as during the prepandemic and pandemic time periods. The performance of AD coding, while strong, was relatively lower compared to the other conditions. The weighted sensitivity, specificity, positive predictive value, and negative predictive value for each of the 5 conditions were as follows: 100%, 100%, 99.2%, and 100%, respectively, for ASD; 100%, 99.9%, 99.2%, and 100%, respectively, for ADHD; 100%, 100%, 100%, and 100%, respectively for DBD; 87.7%, 100%, 100%, and 99.2%, respectively, for AD; and 100%, 100%, 99.2%, and 100%, respectively, for MDD. The F-score and Youden J statistic ranged between 87.7% and 100%. The overall agreement between abstractors was almost perfect (κ=95%). Conclusions: Diagnostic codes are quite reliable for identifying selected childhood mental, behavioral, and emotional conditions. The findings remained similar during the pandemic and after the implementation of the ICD-10-CM coding in the EHR system.

Characterizing Major Depressive Disorder (MDD) using alpha-band activity in resting-state electroencephalogram (EEG) combined with MATRICS Consensus Cognitive Battery (MCCB).

BACKGROUND: The diagnosis of major depressive disorder (MDD) is commonly based on the subjective evaluation by experienced psychiatrists using clinical scales. Hence, it is particularly important to find more objective biomarkers to aid in diagnosis and further treatment. Alpha-band activity (7-13 Hz) is the most prominent component in resting electroencephalogram (EEG), which is also thought to be a potential biomarker. Recent studies have shown the existence of multiple sub-oscillations within the alpha band, with distinct neural underpinnings. However, the specific contribution of these alpha sub-oscillations to the diagnosis and treatment of MDD remains unclear. METHODS: In this study, we recorded the resting-state EEG from MDD and HC populations in both open and closed-eye state conditions. We also assessed cognitive processing using the MATRICS Consensus Cognitive Battery (MCCB). RESULTS: We found that the MDD group showed significantly higher power in the high alpha range (10.5-11.5 Hz) and lower power in the low alpha range (7-8.5 Hz) compared to the HC group. Notably, high alpha power in the MDD group is negatively correlated with working memory performance in MCCB, whereas no such correlation was found in the HC group. Furthermore, using five established classification algorithms, we discovered that combining alpha oscillations with MCCB scores as features yielded the highest classification accuracy compared to using EEG or MCCB scores alone. CONCLUSIONS: Our results demonstrate the potential of sub-oscillations within the alpha frequency band as a potential distinct biomarker. When combined with psychological scales, they may provide guidance relevant for the diagnosis and treatment of MDD.

How much do adverse childhood experiences contribute to adolescent anxiety and depression symptoms? Evidence from the longitudinal study of Australian children.

This study aims to: (i) examine the association between adverse childhood experiences (ACEs) and elevated anxiety and depressive symptoms in adolescents; and (ii) estimate the burden of anxiety and depressive symptoms attributable to ACEs.Data were analyzed from 3089 children followed between Waves 1 (age 4-5 years) and 7 (16-17 years) of the Longitudinal Study of Australian Children. Logistic regression was used to estimate the associations between ACEs and child-reported elevated anxiety and depressive symptoms at age 16-17. Anxiety and depressive symptoms were measured using the Children's Anxiety Scale and Short Mood and Feelings Questionnaire, respectively. The punaf command available in STATA 14 was used to calculate the population attributable fraction (PAF).Before the age of 18 years, 68.8% of the children had experienced two or more ACEs. In the analysis adjusted for confounding factors, including co-occurring ACEs, both history and current exposure to bullying victimisation and parental psychological distress were associated with a statistically significant increased likelihood of elevated anxiety and depressive symptoms at age 16-17. Overall, 47% of anxiety symptoms (95% CI for PAF: 35-56) and 21% of depressive symptoms (95% CI: 12-29) were attributable to a history of bullying victimisation. Similarly, 17% (95% CI: 11-25%) of anxiety and 15% (95% CI: 4-25%) of depressive symptoms at age 16-17 years were attributable to parental psychological distress experienced between the ages of 4-15 years.The findings demonstrate that intervention to reduce ACEs, especially parental psychological distress and bullying victimisation, may reduce the substantial burden of mental disorders in the population.

Exploring Abnormal Brain Functional Connectivity in Healthy Adults, Depressive Disorder, and Generalized Anxiety Disorder through EEG Signals: A Machine Learning Approach for Triple Classification.

Depressive disorder (DD) and generalized anxiety disorder (GAD), two prominent mental health conditions, are commonly diagnosed using subjective methods such as scales and interviews. Previous research indicated that machine learning (ML) can enhance our understanding of their underlying mechanisms. This study seeks to investigate the mechanisms of DD, GAD, and healthy controls (HC) while constructing a diagnostic framework for triple classifications. Specifically, the experiment involved collecting electroencephalogram (EEG) signals from 42 DD patients, 45 GAD patients, and 38 HC adults. The Phase Lag Index (PLI) was employed to quantify brain functional connectivity and analyze differences in functional connectivity among three groups. This study also explored the impact of time window feature computations on classification performance, including the XGBoost, CatBoost, LightGBM, and ensemble models. In order to enhance classification performance, a feature optimization algorithm based on Autogluon-Tabular was proposed. The results indicate that a 12 s time window provides optimal classification performance for the three groups, achieving the highest accuracy of 97.33% with the ensemble model. The analysis further reveals a significant reorganization of the brain, with the most pronounced changes observed in the frontal lobe and beta rhythm. These findings support the hypothesis of abnormal brain functional connectivity in DD and GAD, contributing valuable insights into the neural mechanisms underlying DD and GAD.