EEG-based major depressive disorder recognition by neural oscillation and asymmetry.

Background: Major Depressive Disorder (MDD) is a pervasive mental health issue with significant diagnostic challenges. Electroencephalography (EEG) offers a non-invasive window into the neural dynamics associated with MDD, yet the diagnostic efficacy is contingent upon the appropriate selection of EEG features and brain regions. Methods: In this study, resting-state EEG signals from both eyes-closed and eyes-open conditions were analyzed. We examined band power across various brain regions, assessed the asymmetry of band power between the hemispheres, and integrated these features with clinical characteristics of MDD into a diagnostic regression model. Results: Regression analysis found significant predictors of MDD to be beta2 (16-24 Hz) power in the Prefrontal Cortex (PFC) with eyes open (B = 20.092, p = 0.011), beta3 (24-40 Hz) power in the Medial Occipital Cortex (MOC) (B = -12.050, p < 0.001), and beta2 power in the Right Medial Frontal Cortex (RMFC) with eyes closed (B = 24.227, p < 0.001). Asymmetries in beta1 (12-16 Hz) power with eyes open (B = 28.047, p = 0.018), and in alpha (8-12 Hz, B = 9.004, p = 0.013) and theta (4-8 Hz, B = -13.582, p = 0.008) with eyes closed were also significant predictors. Conclusion: The study confirms the potential of multi-region EEG analysis in improving the diagnostic precision for MDD. By including both neurophysiological and clinical data, we present a more robust approach to understanding and identifying this complex disorder. Limitations: The research is limited by the sample size and the inherent variability in EEG signal interpretation. Future studies with larger cohorts and advanced analytical techniques are warranted to validate and refine these findings.

Association of sleep duration and risk of mental disorder: a systematic review and meta-analysis.

BACKGROUND: The effects of sleep duration on the development of mental illness remain controversial. Therefore, it is necessary to identify the effects of long or short sleep duration on psychological disorders, which could reveal new ways for preventing and treating mental health conditions cheaply. METHODS: Identifying published papers was accomplished by using the following five English databases on March 16, 2022: PubMed, MEDLINE, Embase, Web of Science databases, and Scopus. Cross-sectional and cohort studies were considered if they evaluated the association of sleep duration with all kinds of mental illness in adults. We excluded case reports, editorials, narrative reviews, and studies without detailed information on sleep duration. Summary effect-size estimates were expressed as risk ratios (RRs) or odds ratios (ORs) with 95% confidence intervals and were evaluated using random-effect models. Mantel-Haenszel's random-effects model was used to estimate the inconsistency index (I2) and Tau2 index (measurement of heterogeneity). RESULTS: A total of 52 studies were included in this analysis, consisting of 14 cohort studies and 38 cross-sectional studies. These studies involved a combined sample size of 1,407,891 participants who met the inclusion criteria. Cohort (adjusted RR = 1.42, 95% CI: 1.26-1.60, P < .001, I2 = 37.6%, Tau2 = 0.014) and cross-sectional studies (adjusted OR = 1.67, 95% CI: 1.57-1.77, P < .001, I2 = 79.7%, Tau2 = 0.060) concluded that short sleep duration increased mental disorder risks. The same conclusions were acquired in the subgroup analysis, especially for depression (adjusted RR = 1.43, 95% CI: 1.24-1.65, P < .001, I2 = 80.4%, Tau2 = 0.082), anxiety (adjusted RR = 1.30, 95% CI: 1.04-1.63, P = .002, I2 = 0.0%, Tau2 = 0.000), and PTSD (adjusted RR = 1.35, 95% CI: 1.04-1.76, P = .022, I2 = 24.1%, Tau2 = 0.013) in cohort studies. The results of subgroup analysis indicated that long sleep duration was not a risk factor for depression (adjusted RR = 1.15, 95% CI: 0.98-1.34, P = .088, I2 = 63.4%, Tau2 = 0.045) and anxiety (adjusted RR = 1.37, 95% CI: 0.93-2.03, P = .114, I2 = 0.0%, Tau2 = 0.000). CONCLUSIONS: Short sleep duration, not long sleep duration, is an independent predictor of developing mental disorders, particularly anxiety and depression.

Psychometric Properties of the Chinese Version of the ElectroConvulsive Therapy Cognitive Assessment: An Electroconvulsive Therapy-Specific Cognitive Screening Tool.

OBJECTIVES: Electroconvulsive therapy (ECT) is an effective somatic treatment, but it may be limited by cognitive adverse effects. The existing cognitive screening instruments often lack specificity to ECT-associated cognitive deficits. The ElectroConvulsive Therapy Cognitive Assessment was developed and validated in a clinical setting, but the reliability and validity of the Chinese version of ElectroConvulsive Therapy Cognitive Assessment (ECCA-C) have not been studied in a large clinical sample. METHODS: The ECCA-C and the Montreal Cognitive Assessment (MoCA) were administered to patients with major depressive disorder (MDD) undergoing ECT at 3 time points: pretreatment (baseline), before the fifth treatment, and 1 week posttreatment. The instruments were also administered to a sample of healthy subjects. RESULTS: Sixty-five patients with MDD and 50 age- and sex-matched healthy controls were recruited in this study. Overall, the patient group had statistically significantly lower MoCA and ECCA-C scores than the control group (both P values <0.001). The Cronbach α of the ECCA-C was 0.88 at baseline. Statistically significant decreases over time were observed in ECCA-C: pre-ECT (23.9 ± 4.0) > mid-ECT (21.3 ± 3.4) > post-ECT (18.7 ± 4.8) (all P values <0.001), whereas no statistically significant changes in MoCA scores were found at these 3 time points (F = 1.86, P = 0.165). A cutoff score of 26.5 on the ECCA-C was found to best differentiate between MDD patients and healthy controls. CONCLUSIONS: The ECCA-C showed satisfactory psychometric properties and may be a more sensitive instrument than the MoCA to assess cognitive impairment associated with ECT.

Factors associated with suicidal attempts in female patients with mood disorder.

Aim: This study aims to establish a nomogram model to predict the relevance of SA in Chinese female patients with mood disorder (MD). Method: The study included 396 female participants who were diagnosed with MD Diagnostic Group (F30-F39) according to the 10th Edition of Disease and Related Health Problems (ICD-10). Assessing the differences of demographic information and clinical characteristics between the two groups. LASSO Logistic Regression Analyses was used to identify the risk factors of SA. A nomogram was further used to construct a prediction model. Bootstrap re-sampling was used to internally validate the final model. The Receiver Operating Characteristic (ROC) curve and C-index was also used to evaluate the accuracy of the prediction model. Result: LASSO regression analysis showed that five factors led to the occurrence of suicidality, including BMI (ß = -0.02, SE = 0.02), social dysfunction (ß = 1.72, SE = 0.24), time interval between first onset and first dose (ß = 0.03, SE = 0.01), polarity at onset (ß = -1.13, SE = 0.25), and times of hospitalization (ß = -0.11, SE = 0.06). We assessed the ability of the nomogram model to recognize suicidality, with good results (AUC = 0.76, 95% CI: 0.71-0.80). Indicating that the nomogram had a good consistency (C-index: 0.756, 95% CI: 0.750-0.758). The C-index of bootstrap resampling with 100 replicates for internal validation was 0.740, which further demonstrated the excellent calibration of predicted and observed risks. Conclusion: Five factors, namely BMI, social dysfunction, time interval between first onset and first dose, polarity at onset, and times of hospitalization, were found to be significantly associated with the development of suicidality in patients with MD. By incorporating these factors into a nomogram model, we can accurately predict the risk of suicide in MD patients. It is crucial to closely monitor clinical factors from the beginning and throughout the course of MD in order to prevent suicide attempts.

Development and validation of a nomogram to predict suicidal behavior in female patients with mood disorder.

Introduction: This study aims to explore the risk factors associated with suicidal behavior and establish predictive models in female patients with mood disorders, specifically using a nomogram of the least absolute shrinkage and selection operator (LASSO) regression. Methods: A cross-sectional survey was conducted among 396 female individuals diagnosed with mood disorders (F30-F39) according to the International Classification of Diseases and Related Health Problems 10th Revision (ICD-10). The study utilized the Chi-Squared Test, t-test, and the Wilcoxon Rank-Sum Test to assess differences in demographic information and clinical characteristics between the two groups. Logistic LASSO Regression Analyses were utilized to identify the risk factors associated with suicidal behavior. A nomogram was constructed to develop a prediction model. The accuracy of the prediction model was evaluated using a Receiver Operating Characteristic (ROC) curve. Result: The LASSO regression analysis showed that psychotic symptoms at first-episode (ß = 0.27), social dysfunction (ß = 1.82), and somatic disease (ß = 1.03) increased the risk of suicidal behavior. Conversely, BMI (ß = -0.03), age of onset (ß = -0.02), polarity at onset (ß = -1.21), and number of hospitalizations (ß = -0.18) decreased the risk of suicidal behavior. The area under ROC curve (AUC) of the nomogram predicting SB was 0.778 (95%CI: 0.730-0.827, p < 0.001). Conclusion: The nomogram based on demographic and clinical characteristics can predict suicidal behavior risk in Chinese female patients with mood disorders.

Development and validation of a prediction nomogram for non-suicidal self-injury in female patients with mood disorder.

Background: Non-suicidal self-injury (NSSI) is a highly prevalent behavioral problem among people with mental disorders that can result in numerous adverse outcomes. The present study aimed to systematically analyze the risk factors associated with NSSI to investigate a predictive model for female patients with mood disorders. Methods: A cross-sectional survey among 396 female patients was analyzed. All participants met the mood disorder diagnostic groups (F30-F39) based on the Diseases and Related Health Problems 10th Revision (ICD-10). The Chi-Squared Test, t-test, and the Wilcoxon Rank-Sum Test were used to assess the differences of demographic information and clinical characteristics between the two groups. Logistic LASSO Regression Analyses was then used to identify the risk factors of NSSI. A nomogram was further used to construct a prediction model. Results: After LASSO regression selection, 6 variables remained significant predictors of NSSI. Psychotic symptom at first-episode (ß = 0.59) and social dysfunction (ß = 1.06) increased the risk of NSSI. Meanwhile, stable marital status (ß = -0.48), later age of onset (ß = -0.01), no depression at onset (ß = -1.13), and timely hospitalizations (ß = -0.10) can decrease the risk of NSSI. The C-index of the nomogram was 0.73 in the internal bootstrap validation sets, indicated that the nomogram had a good consistency. Conclusion: Our findings suggest that the demographic information and clinical characteristics of NSSI can be used in a nomogram to predict the risk of NSSI in Chinese female patients with mood disorders.

Cognitive reserve in patients with mood disorders: Validation study of the Chinese version of the cognitive reserve assessment scale in health.

BACKGROUND: Cognitive reserve (CR) is closely associated with cognitive and functional outcome, disease severity, progression and prognosis in psychiatric patients; however, it has not been extensively tested in mood disorders. This study examined the psychometric properties of the Cognitive Reserve Assessment Scale in Health (CRASH) in mood disorder patients. METHODS: Altogether 166 subjects were recruited, 44 with major depressive disorder (MDD), 64 with bipolar disorder (BD), and 58 healthy controls. CR was assessed using the CRASH and the Cognitive Reserve Questionnaire (CRQ). RESULTS: Internal consistency (Cronbach's alpha) was 0.779 for the CRASH. The Receiver Operating Characteristic (ROC) curve analysis revealed an area under the ROC curve (AUC) value of 0.73 (95 % CI: 0.647-0.809). The optimal cut-off score of 51 generated the best combination of sensitivity (0.78) and specificity (0.43) for discriminating between patients with mood disorders and healthy controls. The CRASH score was highly correlated with the CRQ score in both mood disorder patients (rs = 0.586, P < 0.001) and healthy controls (rs = 0.627, P < 0.001), indicating acceptable convergent validity for the CRASH. Within the mood disorder sample, the CRASH score was associated with functional outcomes (FAST: rs = -0.243, P = 0.011). CONCLUSIONS: The CRASH is a useful tool to measure CR in mood disorder with acceptable psychometric properties and could be used in both research and clinical practice.

Transcriptional signatures of the cortical morphometric similarity network gradient in first-episode, treatment-naive major depressive disorder.

Recent studies have shown that major depressive disorder (MDD) is accompanied by alterations in functional and structural network gradients. However, whether changes are present in the cortical morphometric similarity (MS) network gradient, and the relationship between alterations of the gradient and gene expression remains largely unknown. In this study, the MS network was constructed, and its gradient was calculated in 71 patients with first-episode, treatment-naive MDD, and 69 demographically matched healthy controls. Between-group comparisons were performed to investigate abnormalities in the MS network gradient, and partial least squares regression analysis was conducted to explore the association between gene expression profiles and MS network gradient-based alternations in MDD. We found that the gradient was primarily significantly decreased in sensorimotor regions in patients with MDD compared with healthy controls, and increased in visual-related regions. In addition, the altered principal MS network gradient in the left postcentral cortex and right lingual cortex exhibited significant correlations with symptom severity. The abnormal gradient pattern was spatially correlated with the brain-wide expression of genes enriched for neurobiologically relevant pathways, downregulated in the MDD postmortem brain, and preferentially expressed in different cell types and cortical layers. These results demonstrated alterations of the principal MS network gradient in MDD and suggested the molecular mechanisms for structural alternations underlying MDD.

Post COVID-19 mental health symptoms and quality of life among COVID-19 frontline clinicians: a comparative study using propensity score matching approach.

BACKGROUND: The extent and severity of post-COVID-19 mental health symptoms among frontline clinicians are not clear. This study compared mental health symptoms (i.e., depression, anxiety, and insomnia symptoms) and global quality of life (QOL) after the first COVID-19 outbreak between the COVID-19 treating and non-COVID-19 treating frontline clinicians. METHODS: This cross-sectional, comparative, convenient-sampling study was conducted between October 13 and 22, 2020, which was five months after the first COVID-19 outbreak in China was brought under control. The severity of depression, anxiety, insomnia symptoms, and global QOL of the clinicians were assessed using the Patient Health Questionnaire-9 items (PHQ-9), Generalized Anxiety Disorder Scale-7 items (GAD-7), Insomnia Severity Index (ISI), and the World Health Organization Quality of Life Questionnaire-brief version (WHOQOL-BREF), respectively. The propensity score matching (PSM) method was used to identify comparable COVID-19 treating and non-COVID-19 treating frontline clinicians. A generalized linear model (GLM) was used to assess the differences in PHQ-9, GAD-7, ISI, and QOL scores between the COVID-19 treating and non-COVID-19 treating frontline clinicians. RESULTS: In total, 260 COVID-19 treating frontline clinicians and 260 matched non- COVID-19 treating frontline clinicians were included. Non-COVID-19 treating frontline clinicians experienced more frequent workplace violence (WPV) than the COVID-19 treating frontline clinicians (χ2 = 7.6, p = 0.006). COVID-19 treating frontline clinicians reported higher QOL compared to their non-COVID-19 treating frontline counterparts (b = 0.3, p = 0.042), after adjusting for WPV experience. COVID-19 treating and non- COVID-19 treating frontline clinicians reported similar PHQ-9, GAD-7, and ISI total scores (all p values > 0.05). CONCLUSION: This study did not reveal more severe post-COVID-19 mental health symptoms in COVID-19 treating frontline clinicians compared to non-COVID-19 treating frontline clinicians. It is possible that the implementation of timely and appropriate mental health, social and financial supports could have prevented the worsening of mental health symptoms among the COVID-19 treating frontline clinicians after the first COVID-19 outbreak in China.

Network Analysis of Comorbid Anxiety and Insomnia Among Clinicians with Depressive Symptoms During the Late Stage of the COVID-19 Pandemic: A Cross-Sectional Study.

Background: A high proportion of clinicians experienced common anxiety, insomnia and depression during the COVID-19 pandemic. This study examined the item-level association of comorbid anxiety and insomnia symptoms among clinicians who suffered from depressive symptoms during the late stage of the COVID-19 pandemic using network analysis (NA). Methods: Clinicians with depressive symptoms (with a Patients Health Questionnaire (PHQ-9) total score of 5 and above) were included in this study. Anxiety and insomnia symptoms were measured using the Generalized Anxiety Disorder Scale - 7-item (GAD-7) and Insomnia Severity Index (ISI), respectively. Network analysis was conducted to investigate the network structure, central symptoms, bridge symptoms, and network stability of these disturbances. Expected influence (EI) was used to measure the centrality of index. Results: Altogether, 1729 clinicians were included in this study. The mean age was 37.1 [standard deviation (SD)=8.04 years], while the mean PHQ-9 total score was 8.42 (SD=3.33), mean GAD-7 total score was 6.45 (SD=3.13) and mean ISI total score was 8.23 (SD=5.26). Of these clinicians, the prevalence of comorbid anxiety symptoms (GAD-7≥5) was 76.8% (95% CI 74.82-78.80%), while the prevalence of comorbid insomnia symptoms (ISI≥8) was 43.8% (95% CI: 41.50-46.18%). NA revealed that nodes ISI7 ("Interference with daytime functioning") (EI=1.18), ISI4 ("Sleep dissatisfaction") (EI=1.08) and ISI5 ("Noticeability of sleep problem by others") (EI=1.07) were the most central (influential) symptoms in the network model of comorbid anxiety and insomnia symptoms in clinicians. Bridge symptoms included nodes PHQ3 ("Sleep") (bridge EI=0.55) and PHQ4 ("Fatigue") (bridge EI=0.49). Gender did not significantly influence the network structure, but "having the experience of caring for COVID-19 patients" significantly influenced the network structure. Conclusion: Central symptoms and key bridge symptoms identified in this NA should be targeted in the treatment and preventive measures for clinicians suffering from comorbid anxiety, insomnia and depressive symptoms during the late stage of the COVID-19 pandemic.

Network analysis of comorbid depression and anxiety and their associations with quality of life among clinicians in public hospitals during the late stage of the COVID-19 pandemic in China.

BACKGROUND: Mental health problems are common among clinicians working in public hospitals even in the late stage of the COVID-19 pandemic. Network analysis is a novel approach to explore interactions between mental health problems at the symptom level. This study examined the network structure of comorbid depression and anxiety and their associations with quality of life (QOL) among hospital clinicians in China during the late stage of the COVID-19 pandemic. METHODS: A total of 4931 participants were recruited from October 13 to 22, 2020. The nine-item Patient Health Questionnaire (PHQ-9), seven-item Generalized Anxiety Disorder Scale (GAD-7), and the World Health Organization Quality of Life Questionnaire-Brief Version (WHOQOL-BREF) were used to measure depressive and anxiety symptoms, and QOL, respectively. Central and bridge symptoms were identified with centrality and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedure. RESULTS: The prevalence of depression (defined as PHQ-9 total score ≥ 5) was 35.1 % [95 % confidence interval (CI) = 33.73-36.41 %)], the prevalence of anxiety (GAD-7 total score ≥ 5) was 32.5 % (95 % CI = 31.20-33.84 %), while the prevalence of comorbid depression and anxiety was 26.9 % (95 % CI = 25.7-28.2 %). "Impaired motor skills", "Trouble relaxing" and "Uncontrollable worry" were the central symptoms in the whole depression-anxiety network. "Irritability", "Feeling afraid" and "Sad mood" were the most key bridge symptoms linking depression and anxiety. Three symptoms ("Fatigue", "Trouble relaxing" and "Nervousness") were the most strongly and negatively associated with QOL. Neither gender nor the experiences of caring for COVID-19 patients was associated with network global strength, distribution of edge weights or individual edge weights. LIMITATIONS: The causality between variables could not be established. Depressive and anxiety symptoms were assessed by self-report measures, which may result in recall bias and limitations in capturing clinical phenomena. CONCLUSIONS: Both the central (i.e., "Impaired motor skills", "Trouble relaxing" and "Uncontrollable worry") and bridge symptoms (i.e., "Irritability", "Feeling afraid" and "Sad mood") identified in this network analysis should be targeted in specific treatment and preventive measures for comorbid depressive and anxiety symptoms among clinicians in the late stage of the pandemic. Furthermore, "Fatigue", "Trouble relaxing" and "Nervousness" are key symptoms to address to improve clinicians' QOL.

Burnout and well-being of healthcare workers in the post-pandemic period of COVID-19: a perspective from the job demands-resources model.

BACKGROUND: The present study aimed 1) to examine the effects of epidemic-related job stressors, perceived social support and organizational support on the burnout and well-being of Chinese healthcare workers in the period of COVID-19 regular epidemic prevention and control and 2) to investigate the moderating effects of social support and organizational support on the relationship between job stressors and burnout and well-being within the theoretical framework of the Job Demands-Resources (JD-R) model. METHODS: A sample of healthcare workers (N = 3477) from 22 hospitals in Beijing, China participated in the cross-sectional investigation in October 2020 and reported epidemic-related job stressors, perceived social support, organizational support, burnout, anxiety and depression symptoms. RESULTS: 1) Medical doctors, females, people aged from 30 to 50, and those who worked in the second line during the pandemic reported higher scores of psychological symptoms and burnout in the period of regular epidemic prevention and control; 2) Epidemic-related job stressors positively predicted burnout, anxiety, and depression among healthcare workers; 3) Perceived social support and organizational support were negatively related to reported burnout, anxiety and depression symptoms; 4) Social support reduced the adverse effects of epidemic-related job stressors on anxiety and depression but enhanced the association between stressors and burnout; 5) Organizational support mitigated the adverse effects of epidemic-related job stressors on depression. CONCLUSION: The results shed light on preventing burnout and enhancing the psychological well-being of healthcare workers under epidemic prevention and control measures by reducing epidemic-related job stressors and strengthening personal and organizational support systems.

Local dynamic spontaneous brain activity changes in first-episode, treatment-naïve patients with major depressive disorder and their associated gene expression profiles.

BACKGROUND: Major depressive disorder (MDD) is a common debilitating disorder characterized by impaired spontaneous brain activity, yet little is known about its alterations in dynamic properties and the molecular mechanisms associated with these changes. METHODS: Based on the resting-state functional MRI data of 65 first-episode, treatment-naïve patients with MDD and 66 healthy controls, we compared dynamic regional homogeneity (dReHo) of spontaneous brain activity between the two groups, and we investigated gene expression profiles associated with dReHo alterations in MDD by leveraging transcriptional data from the Allen Human Brain Atlas and weighted gene co-expression network analysis. RESULTS: Compared with healthy controls, patients with MDD consistently showed reduced dReHo in both fusiform gyri and in the right temporal pole and hippocampus. The expression profiles of 16 gene modules were correlated with dReHo alterations in MDD. These gene modules were enriched for various biological process terms, including immune, synaptic signalling, ion channels, mitochondrial function and protein metabolism, and were preferentially expressed in different cell types. CONCLUSIONS: Patients with MDD have reduced dReHo in brain areas associated with emotional and cognitive regulation, and these changes may be related to complex polygenetic and polypathway mechanisms.

The MATRICS Consensus Cognitive Battery: Psychometric Properties of the Chinese Version in Young Patients With Major Depression Disorder.

Background: Young patients with major depressive disorder are also associated with cognitive deficits. The development of an accurate and effective battery to measure cognitive impairment in young patients with major depressive disorder (Y-MDD) is necessary for both research and clinical practice. This study was designed to test the psychometric properties of the Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS) Consensus Cognitive Battery (MCCB) in Y-MDD. Method: Fifty Y-MDD patients, 38 euthymic young patients with bipolar disorder (Y-BD), and 51 healthy teenagers were recruited. The MCCB and the Montreal Cognitive Assessment (MoCA) were administered to assess cognitive impairment at baseline. The MCCB was also assessed 2 weeks later in Y-MDD patients. All subjects were between the ages of 13 and 24 years. Result: In the current study, cognitive impairment was greater in Y-BD patients than in Y-MDD patients in some domains. The MCCB has good internal consistency and reliability in Y-MDD patients. The Pearson correlation coefficients for retest reliability were good. Our findings also revealed an acceptable correlation between the MCCB and the MoCA, indicating good concurrent validity of the MCCB. Furthermore, exploratory factor analysis of the MCCB in Y-MDD patients revealed five domains with acceptable internal structures. Conclusion: The MCCB has acceptable psychometric properties and is a sensitive battery of cognitive impairment in Y-MDD patients. In the future, additional studies need to be carried out with larger samples while controlling for the use of psychotropic medications and antidepressants to validate the findings of the present study.

Incidence Trends and Risk Prediction Nomogram for Suicidal Attempts in Patients With Major Depressive Disorder.

Background: Major depressive disorder (MDD) is often associated with suicidal attempt (SA). Therefore, predicting the risk factors of SA would improve clinical interventions, research, and treatment for MDD patients. This study aimed to create a nomogram model which predicted correlates of SA in patients with MDD within the Chinese population. Method: A cross-sectional survey among 474 patients was analyzed. All subjects met the diagnostic criteria of MDD according to the International Statistical Classification of Diseases and Related Health Problems 10th Revision (ICD-10). Multi-factor logistic regression analysis was used to explore demographic information and clinical characteristics associated with SA. A nomogram was further used to predict the risk of SA. Bootstrap re-sampling was used to internally validate the final model. Integrated Discrimination Improvement (IDI) and Akaike Information Criteria (AIC) were used to evaluate the capability of discrimination and calibration, respectively. Decision Curve Analysis (DCA) and the Receiver Operating Characteristic (ROC) curve was also used to evaluate the accuracy of the prediction model. Result: Multivariable logistic regression analysis showed that being married (OR = 0.473, 95% CI: 0.240 and 0.930) and a higher level of education (OR = 0.603, 95% CI: 0.464 and 0.784) decreased the risk of the SA. The higher number of episodes of depression (OR = 1.854, 95% CI: 1.040 and 3.303) increased the risk of SA in the model. The C-index of the nomogram was 0.715, with the internal (bootstrap) validation sets was 0.703. The Hosmer-Lemeshow test yielded a P-value of 0.33, suggesting a good fit of the prediction nomogram in the validation set. Conclusion: Our findings indicate that the demographic information and clinical characteristics of SA can be used in a nomogram to predict the risk of SA in Chinese MDD patients.

Altered brain function and clinical features in patients with first-episode, drug naïve major depressive disorder: A resting-state fMRI study.

Major depressive disorder (MDD) is characterized by heterogeneous clinical performance and neurocognitive impairment. It is important to explore the correlation between global functioning and regional homogeneity (ReHo)/amplitude of low-frequency fluctuation (ALFF) values in MDD patients. 67 first-episode, drug naïve MDD patients and 69 healthy subjects were enrolled in the study. The MATRICS Consensus Cognitive Battery (MCCB) and the Functioning Assessment Short Test (FAST) were used to assess functional impairment in patients. Brain activity was assessed using ReHo and ALFF measurements. The relationship between the clinical features and altered brain function was evaluated using correlation analysis. There were significant differences in the ReHo and ALFF values between MDD patients and healthy subjects. The reduction in ReHo in the left calcarine/lingual gyrus/cuneus was negatively correlated with occupational functioning and the total FAST scores. The reduction in ALFF in the right calcarine/lingual gyrus was positively correlated with the verbal learning aspects of the MCCB. These findings suggest that the altered brain function in the default mode network (DMN) may be related to functional impairments in patients with first-episode, drug naïve major depressive disorder.

Psychometric properties of the MATRICS Consensus Cognitive Battery (MCCB) in Chinese patients with major depressive disorder.

BACKGROUND: Major depression disorder (MDD) is often associated with cognitive impairment. The development of an accurate and effective battery to measure cognitive deficit is necessary for both research and clinical practice in MDD. Our study was designed to test the psychometric properties of the MATRICS Consensus Cognitive Battery (MCCB) in patients with MDD. METHOD: Forty-eight patients with MDD, forty-three euthymic patients with bipolar disorder (BD) and thirty-two remitted patients with schizophrenia (SCH) were recruited from Tianjin Anding Hospital in Tianjin, China. The MCCB, the Montreal Cognitive Assessment (MoCA) and the Hamilton Depression Rating Scale-17 (HDRS-17) were administered to assess cognitive impairment and depressive symptoms at both baseline and one month later. RESULT: Our findings showed that the MCCB had good internal consistency and acceptable reliability in Chinese MDD patients. Our findings revealed a high correlation between the MCCB and the MoCA, indicating good concurrent validity of the MCCB. Furthermore, the MCCB showed high discriminant ability between MDD patients and healthy controls, and a principal component analysis of the MCCB in MDD patients revealed four domains with acceptable internal structure. LIMITATIONS: We did not consider confounding factors, such as the course, severity of symptoms and medication treatments, which might bias the cognitive assessment. In addition, the use of the MoCA as a reference scale for mild cognitive impairment could weaken the concurrent validity of the MCCB in MDD patients. CONCLUSION: Our findings demonstrated that the MCCB may be clinically useful as a cognitive impairment rating battery in Chinese patients with MDD.

The Association Between Child Abuse and Emotional and Behavioral Problems in Chinese School-Aged Boys With Attention Deficit Hyperactivity Disorder.

The aim of our study was to investigate the relationship between child abuse and emotional and behavioral problems in Chinese school-aged boys with attention deficit hyperactivity disorder (ADHD). Forty-eight school-aged boys with ADHD and 77 male healthy controls completed the final assessments that included the Child Behavior Checklist, the Barratt Impulsiveness Scale Version 11, the Screen for Child Anxiety Related Disorders, the Depression Self-Rating Scale for Children, and the Childhood Trauma Questionnaire, Short Form. Our findings showed that child abuse could associate with the behavioral problems in ADHD. Regression analysis further showed that child abuse (especially emotional abuse and physical abuse), adverse living conditions, and school anxiety significantly could be contributors to behavioral problems in boys with ADHD. Our study indicated that child abuse may be associated with the behavioral problems in Chinese school-aged boys with ADHD.