Altered EEG Microstates Dynamics in Individuals with Subthreshold Depression When Generating Negative Future Events.

Negative bias in prospection may play a crucial role in driving and maintaining depression. Recent research suggests abnormal activation and functional connectivity in regions of the default mode network (DMN) during future event generation in depressed individuals. However, the neural dynamics during prospection in these individuals remain unknown. To capture network dynamics at high temporal resolution, we employed electroencephalogram (EEG) microstate analysis. We examined microstate properties during both positive and negative prospection in 35 individuals with subthreshold depression (SD) and 35 controls. We identified similar sets of four canonical microstates (A-D) across groups and conditions. Source analysis indicated that each microstate map partially overlapped with a subsystem of the DMN (A: verbal; B: visual-spatial; C: self-referential; and D: modulation). Notably, alterations in EEG microstates were primarily observed in negative prospection of individuals with SD. Specifically, when generating negative future events, the coverage, occurrence, and duration of microstate A increased, while the coverage and duration of microstates B and D decreased in the SD group compared to controls. Furthermore, we observed altered transitions, particularly involving microstate C, during negative prospection in the SD group. These altered dynamics suggest dysconnectivity between subsystems of the DMN during negative prospection in individuals with SD. In conclusion, we provide novel insights into the neural mechanisms of negative bias in depression. These alterations could serve as specific markers for depression and potential targets for future interventions.

Cortical gradient perturbation in attention deficit hyperactivity disorder correlates with neurotransmitter-, cell type-specific and chromosome- transcriptomic signatures.

AIMS: This study aimed to illuminate the neuropathological landscape of attention deficit hyperactivity disorder (ADHD) by a multiscale macro-micro-molecular perspective from in vivo neuroimaging data. METHODS: The "ADHD-200 initiative" repository provided multi-site high-quality resting-state functional connectivity (rsfc-) neuroimaging for ADHD children and matched typically developing (TD) cohort. Diffusion mapping embedding model to derive the functional connectome gradient detecting biologically plausible neural pattern was built, and the multivariate partial least square method to uncover the enrichment of neurotransmitomic, cellular and chromosomal gradient-transcriptional signatures of AHBA enrichment and meta-analytic decoding. RESULTS: Compared to TD, ADHD children presented connectopic cortical gradient perturbations in almost all the cognition-involved brain macroscale networks (all pBH <0.001), but not in the brain global topology. As an intermediate phenotypic variant, such gradient perturbation was spatially enriched into distributions of GABAA/BZ and 5-HT2A receptors (all pBH <0.01) and co-varied with genetic transcriptional expressions (e.g. DYDC2, ATOH7, all pBH <0.01), associated with phenotypic variants in episodic memory and emotional regulations. Enrichment models demonstrated such gradient-transcriptional variants indicated the risk of both cell-specific and chromosome- dysfunctions, especially in enriched expression of oligodendrocyte precursors and endothelial cells (all pperm <0.05) as well enrichment into chromosome 18, 19 and X (pperm <0.05). CONCLUSIONS: Our findings bridged brain macroscale neuropathological patterns to microscale/cellular biological architectures for ADHD children, demonstrating the neurobiologically pathological mechanism of ADHD into the genetic and molecular variants in GABA and 5-HT systems as well brain-derived enrichment of specific cellular/chromosomal expressions.

Sampling inequalities affect generalization of neuroimaging-based diagnostic classifiers in psychiatry.

BACKGROUND: The development of machine learning models for aiding in the diagnosis of mental disorder is recognized as a significant breakthrough in the field of psychiatry. However, clinical practice of such models remains a challenge, with poor generalizability being a major limitation. METHODS: Here, we conducted a pre-registered meta-research assessment on neuroimaging-based models in the psychiatric literature, quantitatively examining global and regional sampling issues over recent decades, from a view that has been relatively underexplored. A total of 476 studies (n = 118,137) were included in the current assessment. Based on these findings, we built a comprehensive 5-star rating system to quantitatively evaluate the quality of existing machine learning models for psychiatric diagnoses. RESULTS: A global sampling inequality in these models was revealed quantitatively (sampling Gini coefficient (G) = 0.81, p < .01), varying across different countries (regions) (e.g., China, G = 0.47; the USA, G = 0.58; Germany, G = 0.78; the UK, G = 0.87). Furthermore, the severity of this sampling inequality was significantly predicted by national economic levels (ß = - 2.75, p < .001, R2adj = 0.40; r = - .84, 95% CI: - .41 to - .97), and was plausibly predictable for model performance, with higher sampling inequality for reporting higher classification accuracy. Further analyses showed that lack of independent testing (84.24% of models, 95% CI: 81.0-87.5%), improper cross-validation (51.68% of models, 95% CI: 47.2-56.2%), and poor technical transparency (87.8% of models, 95% CI: 84.9-90.8%)/availability (80.88% of models, 95% CI: 77.3-84.4%) are prevailing in current diagnostic classifiers despite improvements over time. Relating to these observations, model performances were found decreased in studies with independent cross-country sampling validations (all p < .001, BF10 > 15). In light of this, we proposed a purpose-built quantitative assessment checklist, which demonstrated that the overall ratings of these models increased by publication year but were negatively associated with model performance. CONCLUSIONS: Together, improving sampling economic equality and hence the quality of machine learning models may be a crucial facet to plausibly translating neuroimaging-based diagnostic classifiers into clinical practice.

Life events and depression among children and adolescents in southwest China: a two-stage moderated mediation model of social support and cognitive styles.

BACKGROUND: According to data from the National Health Commission in 2018, more than 30 million children and adolescents in China suffered from mental health problems of varying degrees, with depression accounting for the largest proportion. Life events occur at every stage of child and adolescent development. Many studies have found a relationship between life events and depression in children and adolescents, but few studies have further explored the mediating and moderating variables that influence this relationship. Based on theoretical and empirical research on social support and cognitive styles, this study established a two-stage moderated mediating model to test whether social support mediates life events to depression, and cognitive style moderates the two mediating pathways. METHODS: We recruited 3540 participants from primary and secondary schools in Chongqing and collected 2814 valid data. All the participants completed self-report measures of life events, depression, social support and cognitive styles. The moderated mediation model was examined using SPSS PROCESS model 58. RESULTS: (1) There were significant positive correlations between life events and depression. (2) Social support mediates the relationship between life events and depression in children and adolescents. (3) Cognitive style moderates life events to social support and social support to depression. LIMITATIONS: This is a cross-sectional study and the questionnaire is self-reported. CONCLUSIONS: In children and adolescents, life events can influence depression through the mediating role of social support and cognitive styles could moderate its two mediating pathways.

Network analysis of the relationship between negative life events and depressive symptoms in the left-behind children.

BACKGROUND: There are 68.77 million left-behind children in China, who are at a great risk of depression associated with negative life events. Our study aims to investigate the most central symptoms of depression in left-behind children and the relationship between depressive symptoms and negative life events using network analysis. METHOD: A cross-sectional data set (N = 7255) was used, which included children and adolescents aged 7 to 17. Network analysis was used to evaluate: 1) the most central symptoms among the items included in Child Depression Inventory (CDI) of the left-behind children; 2) bridge symptoms between depressive disorder and Adolescent Self-Rating Life Events Check List (ASLEC) of the left-behind children; 3) differences in networks of depressive disorders between left-behind and non-left-behind children, and 4) differences in the network of depression and negative life events between left-behind and non-left-behind children. The stability and centrality indices of the network were also evaluated in the study. RESULTS: The most central symptoms in the CDI among the left-behind children included self-hatred, crying, fatigue, and sadness. The items with the highest bridge strength centrality in the CDI-ASLEC network included academic stress, discrimination, and school performance decrement. Higher bridge strength values indicate a greater risk of contagion to other communities. The connections in the CDI-ASLEC network are denser in the left-behind children than in non-left-behind children. LIMITATIONS: The study which was conducted based on cross-sectional data shows that network analysis can only make undirected estimation, but not causal inferences. CONCLUSIONS: We identified the core symptoms of depression and the bridge symptoms between negative life events and depression in the left-behind children. These findings suggest that more attention should be paid to self-hatred, sadness, and fatigue in the treatment of depression in left-behind children. Intervention for academic stress and discrimination of the left-behind children may help to reduce the contagion of negative life events to depression symptoms.

Impact of the disclosure of diagnosis on posttraumatic stress and growth and quality of life in Chinese patients with hepatocellular carcinoma.

PURPOSE: To evaluate the impact of disclosure/nondisclosure of cancer diagnosis on patients' posttraumatic stress symptoms (PTSS), posttraumatic growth (PTG), and quality of life (QOL). METHODS: Patients with primary hepatocellular carcinoma (HCC) who were admitted for potentially curative treatments in a teaching hospital were recruited. Patients were interviewed at admission regarding their QOL and their attitude towards disclosure of diagnosis. They were interviewed again for QOL, PTSS, and PTG at discharge and at 1 month after discharge. RESULTS: There were 300 patients recruited, 88.3% of whom preferred disclosure of cancer diagnosis. In fact, 162 patients (54.0%) received disclosure of their cancer diagnosis before discharge (disclosed group). However, for the 138 patients whose diagnoses were concealed by their families (uninformed group), 116 patients (84.1%) had learned of their diagnosis of HCC independently within 1 month after discharge. Comparing the scores at 1 month after discharge with scores at discharge showed that the PTSS score significantly declined for patients in the disclosed group and the PTG score significantly decreased for the uninformed patients at 1 month after discharge (p < 0.001 for both comparisons). Additionally, compared with the uninformed group, patients in the disclosed group had lower scores for PTSS (p < 0.001), higher scores for PTG (p < 0.001), better emotional functioning (p < 0.001), and better global QOL (p = 0.006) at 1 month after discharge. CONCLUSIONS: Our findings indicate that concealing the diagnosis of cancer from patients is unlikely to succeed. Additionally, disclosure of diagnosis is beneficial for HCC patients in reducing PTSS and improving PTG and QOL.

Mental health status of Chinese residents during the COVID-19 epidemic.

BACKGROUND: To investigate the mental health status of Chinese residents during the epidemic of COVID-19, as well as to identify the positive and negative factors and regulatory effect of negative cognitive processing bias on mental health. METHODS: A total of 60,199 residents in China were surveyed via an internet-based survey containing a general questionnaire, such as the self-rating depression scale, the state anxiety inventory, and the negative cognitive processing bias questionnaire. An ordered multiple logistic regression analysis model was used to analyze the collected data. RESULTS: The survey revealed mild, moderate, and severe depressive symptoms in 62.65, 11.33, and 6.14% participants, respectively, and mild, moderate, and severe anxiety symptoms in 33.21, 41.27, and 22.99% participants, respectively. Multiple logistic regression analysis showed that factors, such as female gender, being older than 55 years, high school education level, medical staff, marital conflicts, negative attention bias, rumination, and death growth rate, positively affected depression and anxiety symptoms. The good family functionality, democratic working atmosphere, and a myriad of social activities negatively affected the level of depressive and anxiety symptoms. CONCLUSION: Chinese residents exhibited a high prevalence of anxiety and depressive symptoms during the epidemic. Thus, psychological interventions should focus on the vulnerable groups, and cognitive training should focus on reducing the negative cognitive processing bias. This might be an effective way to alleviate the mental stress of the general public during the COVID-19 pandemic.

Distinct effects of over-general autobiographical memory on suicidal ideation among depressed and healthy people.

BACKGROUND: Childhood trauma and over-general autobiographical memory (OGM) are crucial risk factors of suicide. This study aimed to investigate whether suicidal ideation was predicted by one's childhood trauma and OGM and the mechanism of OGM underlying suicidal ideation in depression patients and healthy controls. METHODS: A total of 180 depression patients and 176 matched healthy individuals were recruited in this study. Data were analyzed using descriptive statistics, and Pearson's correlation coefficient was obtained. Path analysis was conducted to test a meditational model. The multigroup comparison was applied to find differences between groups. RESULTS: Significant differences were detected between depression patients and healthy controls with respect to childhood trauma, OGM, suicidal ideation, and suicidal behavior. OGM was positively correlated with both current and worst-point suicidal ideation in the depression group and significantly correlated with worst-point suicidal ideation in the healthy control group. The path model showed that childhood trauma had a direct impact on the current suicidal ideation directly, and an indirect influence through OGM and worst-point suicidal ideation. Multigroup analysis further demonstrated that OGM affected and mediated the current suicidal ideation due to childhood trauma in depression patients, whereas only worst-point suicidal ideation was affected in healthy controls. CONCLUSIONS: The OGM mediates suicidal ideation in depression patients, but only affects the worst-point suicidal ideation in the healthy controls. As it is one of the major risk factors of suicidal ideation in depression, amelioration of OGM might be an useful method to reduce or prevent suicidal ideation in depression patients.

Orbito-frontal cortex mechanism of inhibition of return in current and remitted depression.

Deficient inhibition of return (IOR) for emotional materials is an important cognitive biomarker of depression. However, its neural mechanism and role in depression remission remain largely unknown. Using functional magnetic resonance imaging (fMRI), this study observed the neural foundation of inhibition of return in individuals with current (n = 30) and remitted (n = 27) depression and in healthy controls (n = 33), by using a cue-target task. The results showed that individuals with remitted depression (RMD) possessed a nonavoidant attention model for sad faces, which indicated a cue validity and was correlated with enhanced task- and resting-state activation and function connectivity in orbitofrontal cortex (OFC). The patients with major depressive disorder (MDD), in contrast, displayed an IOR effect for all faces, which indicated a strategy of attention avoidance due to the high cognitive burden in the cue-target task, and was correlated with decreased resting-state activation and function connectivity in OFC. Moreover, the hippocampus, a less-known cortex in IOR, showed a contrary model, that is, lower activation in depression remission and higher task- and resting-state activation in depressive episodes. The results suggest the OFC mechanism of the IOR effect in remitted depression and the hippocampus mechanism of the IOR effect in depressive episodes, which offer potential biomarkers for the clinical treatment of depression.

Depressive symptoms and negative life events: What psycho-social factors protect or harm left-behind children in China?

BACKGROUND: In China, children under 18 years old who are left at rural residences for at least 6 months by either one or both of their parents migrating to work in cities are called "left-behind children (LBC)". Due to restricted family support, they are at a greater risk of developing depressive symptoms than non-left-behind children (NLBC). The objective of this study is to explore how depressive symptoms and stress induced by negative life events such as interpersonal conflicts, punishment and loss, as well as their relationships vary for LBC with different left-behind-related characteristics. METHODS: Using data from a large school-based survey conducted in Chongqing between December 2012 and June 2013, we first identified the differences in depressive symptoms and negative-event-induced stress between LBC and NLBC, and then analyzed the variances among LBC with different left-behind-related characteristics. The data was analyzed with Chi-square test, MANCOVA, ANCOVA, ANOVA, T-test and hierarchical multiple regression analyses. RESULTS: We found that LBC were more stressed when experiencing negative events and had more depressive symptoms than NLBC. Children left behind by both parents were most depressed. Negative-event-induced stress and communication on life difficulties with migrant parents were risk factors for depressive symptoms, whereas adequate communication on academic performance or children's feelings was a protective factor against depressive symptoms. Communication duration and frequency, communication by visiting, communication on academic performance, life difficulties and children's feelings moderated the relationship between stress and depressive symptoms, respectively. Duration of separation, communication duration and frequency, communication on academic performance, learning difficulties and children's feelings moderated the relation between the type of parental migration and depressive symptoms, respectively. CONCLUSIONS: Our findings suggest that children left behind by both parents should be the focus of public attention for their higher susceptibility to stress-related depression. To help LBC stay mentally healthy, governments need to formulate regulations contributing to LBC's family reunion, communities need to involve more residents to attend LBC as "surrogate parents" and teach migrant parents to communicate with LBC properly, and schools need to teach LBC how to deal with stress and communicate with migrant parents.

Impact of disclosure of diagnosis and patient autonomy on quality of life and illness perceptions in Chinese patients with liver cancer.

OBJECTIVE: This prospective cohort study explored the impact of disclosure of diagnosis and patient autonomy on the health-related quality of life (HRQOL) and illness perceptions in Chinese patients with hepatocellular carcinoma (HCC). METHODS: Patients with HCC who were admitted for potentially curative treatments in a teaching hospital were recruited from August 2013 to July 2014. Patients were interviewed at admission regarding their HRQOL and their attitude towards disclosure of diagnosis. They were interviewed again regarding HRQOL and illness perceptions at discharge. RESULTS: There were 218 patients recruited; 57.8% of them were aware of their cancer diagnosis (disclosed group). For 63.8% of the participants, their desire for disclosure or nondisclosure was satisfied (autonomy-satisfied group). When comparing the patients in the disclosed group with the patients who were uninformed, the patients in the disclosed group had higher scores for global HRQOL at discharge (p = 0.013) and higher scores on understanding of their illness regarding illness perceptions (p = 0.022). When comparing the patients in the 'autonomy-satisfied' group with the patients whose desire for disclosure was not satisfied, the patients in the autonomy-satisfied group had better emotional functioning and better global HRQOL at discharge (p < 0.001 and p = 0.001, respectively). Additionally, the patients in the autonomy-satisfied group had higher scores for personal control (p = 0.009) and lower scores for emotional reaction (p = 0.007) regarding illness perceptions, even after controlling for other confounding factors. CONCLUSIONS: Our findings suggest that for patients with HCC who have undergone potentially curative treatment, physicians should satisfy patients' desires for autonomy regarding the disclosure of their diagnosis. Copyright © 2015 John Wiley & Sons, Ltd.

Dataset on the effects of psychological care on depression and suicide ideation in underrepresented children.

Massive increases in the risks of depressive disorders and the ensuing suicide have become the overarching menace for children/adolescents. Despite global consensus to instigate psychological healthcare policy for these children/adolescents, their effects remain largely unclear neither from a small amount of official data nor from small-scale scientific studies. More importantly, in underprivileged children/adolescents in lower-middle-economic-status countries/areas, the data collection may not be as equally accessible as in developed countries/areas, thus resulting in underrepresented observations. To address these challenges, we released a large-scale and multi-center cohort dataset (n = 249,772) showing the effects of primary psychological healthcare on decreasing depression and suicidal ideation in these children/adolescents who were underrepresented in previous studies or current healthcare systems, including unattended children/adolescents, orphans, children/adolescents in especially difficult circumstances, and "left-behind" and "single-parenting" children/adolescents. We provided all individual data recording the depressive symptoms and suicide ideation that had been collected at baseline (Oct 2022) and half-year follow-up (May 2023) from practicing this psychological healthcare system.

Risk factors and prediction model for mental health in Chinese soldiers.

Introduction: This study aimed to explore potential risk factors for mental health concerns, and the prediction model for mental health concerns in Chinese soldiers was constructed through combined eligible risk factors. Methods: This cross-sectional study was performed on soldiers under direct command from Gansu, Sichuan, and Chongqing in China, and the soldiers were selected by cluster convenient sampling from 16 October 2018 to 10 December 2018. The Symptom Checklist-90 (SCL-90) and three questionnaires (Military Mental Health Status Questionnaire, Military Mental Health Ability Questionnaire, and Mental Quality Questionnaire for Army Men) were administered, including demographics, military careers, and 18 factors. Results: Of 1,430 Chinese soldiers, 162 soldiers presented mental disorders, with a prevalence of 11.33%. A total of five risk factors were identified, including serving place (Sichuan vs. Gansu: OR, 1.846, 95% CI: 1.028-3.315, P = 0.038; Chongqing vs. Gansu: OR, 3.129, 95% CI, 1.669-5.869, P = 0.003), psychosis (OR, 1.491, 95% CI, 1.152-1.928, P = 0.002), depression (OR, 1.482, 95% CI, 1.349-1.629, P < 0.001), sleep problems (OR, 1.235, 95% CI, 1.162-1.311, P < 0.001), and frustration (OR, 1.050, 95% CI, 1.015-1.087, P = 0.005). The area under the ROC curve by combining these factors was 0.930 (95% CI: 0.907-0.952) for predicting mental disorders in Chinese soldiers. Conclusion: The findings of this study demonstrate that mental disorders and onset in Chinese soldiers can be predicted on the basis of these three questionnaires, and the predictive value of the combined model was high.

A feasibility study of goal-directed network-based real-time fMRI neurofeedback for anhedonic depression.

Anhedonia is a hallmark symptom of depression that often lacks adequate interventions. The translational gap remains in clinical treatments based on neural substrates of anhedonia. Our pilot study found that depressed individuals depended less on goal-directed (GD) reward learning (RL), with reduced reward prediction error (RPE) BOLD signal. Previous studies have found that anhedonia is related to abnormal activities and/or functional connectivities of the central executive network (CEN) and salience network (SN), both of which belong to the goal-directed system. In addition, it was found that real-time functional magnetic resonance imaging (rt-fMRI) neurofeedback (NF) could improve the balance between CEN and SN in healthy individuals. Therefore, we speculate that rt-fMRI NF of the CEN and SN associated with the GD system may improve depressive and/or anhedonic symptoms. Therefore, this study (1) will examine individuals with anhedonic depression using GD-RL behavioral task, combined with functional magnetic resonance imaging and computational modeling to explore the role of CEN/SN deficits in anhedonic depression; and (2) will utilize network-based rt-fMRI NF to investigate whether it is feasible to regulate the differential signals of brain CEN/SN of GD system through rt-fMRI NF to alleviate depressive and/or anhedonic symptoms. This study highlights the need to elucidate the intervention effects of rt-fMRI NF and the underlying computational network neural mechanisms.

Microbiota alteration of Chinese young male adults with high-status negative cognitive processing bias.

Introduction: Evidence suggests that negative cognitive processing bias (NCPB) is a significant risk factor for depression. The microbiota-gut-brain axis has been proven to be a contributing factor to cognitive health and disease. However, the connection between microbiota and NCPB remains unknown. This study mainly sought to explore the key microbiota involved in NCPB and the possible pathways through which NCPB affects depressive symptoms. Methods: Data in our studies were collected from 735 Chinese young adults through a cross-sectional survey. Fecal samples were collected from 35 young adults with different levels of NCPB (18 individuals were recruited as the high-status NCPB group, and another 17 individuals were matched as the low-status NCPB group) and 60 with different degrees of depressive symptoms (27 individuals were recruited into the depressive symptom group, as D group, and 33 individuals were matched into the control group, as C group) and analyzed by the 16S ribosomal RNA sequencing technique. Results: As a result, the level of NCPB correlated with the degree of depressive symptoms as well as anxiety symptoms and sleep quality (p < 0.01). The ß-diversity of microbiota in young adults was proven to be significantly different between the high-status NCPB and the low-status NCPB groups. There were several significantly increased bacteria taxa, including Dorea, Christensenellaceae, Christe -senellaceae_R_7_group, Ruminococcaceae_NK4A214_group, Eggerthellaceae, Family-XIII, Family_XIII_AD3011_group, Faecalibaculum, and Oscillibacter. They were mainly involved in pathways including short-chain fatty acid (SCFA) metabolism. Among these variable bacteria taxa, Faecalibaculum was found associated with both NCPB and depressive symptoms. Furthermore, five pathways turned out to be significantly altered in both the high-status NCPB group and the depressive symptom group, including butanoate metabolism, glyoxylate and dicarboxylate metabolism, propanoate metabolism, phenylalanine, tyrosine, and tryptophan biosynthesis, valine, leucine, and isoleucine degradation. These pathways were related to SCFA metabolism. Discussion: Fecal microbiota is altered in Chinese young male adults with high status NCPB and may be involved in the biochemical progress that influences depressive symptoms.

The development of the Negative Bias in Prospection Scale: A novel assessment of dysfunctional prospection in depression.

"Prospection" refers to the ability to mentally simulate one's future episodes. Negative bias in prospection, which includes both reduced positivity and enhanced negativity, is considered to be closely related to the development and maintenance of depression. However, there is a relative paucity of measures that adequately assess negative bias in prospection in depressed people. The current study developed and initially validated the Negative Bias in Prospection Scale (NBPS) using a nonclinical sample (n = 959). The validity and reliability of the NBPS were investigated in several ways. Results suggested the 14 NBPS items loaded onto three factors, namely the "Increased negativity," the "Reduced positivity," and the "Overgeneralization." The NBPS demonstrated decent internal consistency and test-retest reliability. Further, the NBPS was strongly associated with severity of depressive symptoms. Moreover, the NBPS was correlated in a theoretically meaningful way with other measures of future-oriented cognitions or negative biases. In conclusion, the NBPS has promising preliminary psychometric properties. It will be an efficient tool for exploring dysfunctional prospection in depression and provides a novel measure for mechanism of change in prospection-based interventions.

Social anxiety and depression symptoms in Chinese left-behind children after the lifting of COVID-19 lockdown: A network analysis.

BACKGROUND: Returning to social life after the lifting of COVID-19 lockdown may increase risk of social anxiety, which is highly co-morbid with depression. However, few studies have reported the association between them. AIMS: To explore the complex relationship between social anxiety and depression symptoms in left-behind children after the lifting of the COVID-19 lockdown. METHODS: A cross-sectional survey was conducted 6 months after the lockdown removal. A total of 3,107 left-behind children completed the survey with a mean age of 13.33 and a response rate of 87.77%. Depression and social anxiety severity were assessed by the DSM-5 Patient Health Questionnaire for Adolescents and the DSM-5 Social Anxiety Disorder Questionnaire, respectively. The symptom-level association between the two disorders was examined using network analysis. RESULTS: After the lifting of COVID-19 lockdown, the prevalence of depression and social anxiety in left-behind children was 19.57% and 12.36%, respectively, with a co-morbidity rate of 8.98%. Network analysis showed that "Social tension" and "Social avoidance" had the greatest expected influence; "Humiliation" and "Motor" were bridge symptom nodes in the network. The directed acyclic graph indicated that "Social fright" was at the upstream of all symptoms. CONCLUSION: Attention should be paid to social anxiety symptoms in left-behind children after the lifting of COVID-19 lockdown. Prevention and intervention measures should be taken promptly to reduce the comorbidity of social anxiety and depression symptoms in the left-behind children after the lifting of lockdown.

Evaluation of Risk of Bias in Neuroimaging-Based Artificial Intelligence Models for Psychiatric Diagnosis: A Systematic Review.

Importance: Neuroimaging-based artificial intelligence (AI) diagnostic models have proliferated in psychiatry. However, their clinical applicability and reporting quality (ie, feasibility) for clinical practice have not been systematically evaluated. Objective: To systematically assess the risk of bias (ROB) and reporting quality of neuroimaging-based AI models for psychiatric diagnosis. Evidence Review: PubMed was searched for peer-reviewed, full-length articles published between January 1, 1990, and March 16, 2022. Studies aimed at developing or validating neuroimaging-based AI models for clinical diagnosis of psychiatric disorders were included. Reference lists were further searched for suitable original studies. Data extraction followed the CHARMS (Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modeling Studies) and PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. A closed-loop cross-sequential design was used for quality control. The PROBAST (Prediction Model Risk of Bias Assessment Tool) and modified CLEAR (Checklist for Evaluation of Image-Based Artificial Intelligence Reports) benchmarks were used to systematically evaluate ROB and reporting quality. Findings: A total of 517 studies presenting 555 AI models were included and evaluated. Of these models, 461 (83.1%; 95% CI, 80.0%-86.2%) were rated as having a high overall ROB based on the PROBAST. The ROB was particular high in the analysis domain, including inadequate sample size (398 of 555 models [71.7%; 95% CI, 68.0%-75.6%]), poor model performance examination (with 100% of models lacking calibration examination), and lack of handling data complexity (550 of 555 models [99.1%; 95% CI, 98.3%-99.9%]). None of the AI models was perceived to be applicable to clinical practices. Overall reporting completeness (ie, number of reported items/number of total items) for the AI models was 61.2% (95% CI, 60.6%-61.8%), and the completeness was poorest for the technical assessment domain with 39.9% (95% CI, 38.8%-41.1%). Conclusions and Relevance: This systematic review found that the clinical applicability and feasibility of neuroimaging-based AI models for psychiatric diagnosis were challenged by a high ROB and poor reporting quality. Particularly in the analysis domain, ROB in AI diagnostic models should be addressed before clinical application.

The Ripple Effect: Unveiling the Bidirectional Relationship Between Negative Life Events and Depressive Symptoms in Medical Cadets.

Background: Previous studies have explored the relationship between negative life events and depression, but little is known about the bidirectional relationship between negative life events and depression, particularly in specific groups of medical cadets. Purpose: This study aimed to explore the relationship between negative life events and depressive symptoms among medical cadets during their four years of college. Methods: An analysis of 4-wave longitudinal data collected from 2015-2018 was conducted using a cross-lagged panel network (CLPN) model to explore the complex causal relationship between negative life events and depressive symptoms in medical cadets (N=433). Results: We found differences in negative life events and depressive symptoms among medical cadets across four network models over four years of university. Nodes A-21, A-20, A-23 and A-24, and depressive symptoms D-6 showed greater lagged effect values. Conclusion: Our findings suggest that there is a lagged and mutually causal interaction between negative life events and depressive symptoms in medical cadets over 4 years of college, but that the predictability of negative life events is more important. However, more attention needs to be paid to the predictive role of depressive symptoms, especially those in early life which are often overlooked. Our study provides new insights into the relationship between negative life events and depressive symptoms in university students and helps to refine strategies for prevention and intervention of depression.

Association of burnout with depression in pharmacists: A network analysis.

Background: Burnout and depression have overlapping symptoms, but the extent of overlap remains unclear, and the complex relationship between burnout and depression in pharmacists is rarely explored. Methods: We investigated burnout and depression in 1,322 frontline pharmacists, and explored the complex relationship between burnout and depression in those pharmacists using network analysis. Results: Network analysis showed that there were 5 communities. A partial overlap was found between burnout and depressive symptoms in pharmacists. The nodes MBI-6 (I have become more callous toward work since I took this job), D18 (My life is meaningless), and D10 (I get tired for no reason) had the highest expected influence value. D1 (I feel down-hearted and blue) and D14 (I have no hope for the future) were bridge symptoms connected with emotional exhaustion and reduced professional efficacy, respectively. Conclusion: A partial overlap exists between burnout and depressive symptoms in pharmacists, mainly in the connection between the emotional exhaustion and reduced professional efficacy and the depressive symptoms. Potential core targets identified in this study may inform future prevention and intervention.