Network analysis for the symptom of depression with Children's Depression Inventory in a large sample of school-aged children

A network analysis of the interrelationships between depression, anxiety, insomnia and quality of life among fire service recruits

Background

Research on the mental health and quality of life (hereafter QOL) among fire service recruits after the end of the COVID-19 restrictions is lacking. This study explored the network structure of depression, anxiety and insomnia, and their interconnections with QOL among fire service recruits in the post-COVID-19 era.

Methods

This cross-sectional study used a consecutive sampling of fire service recruits across China. We measured the severity of depression, anxiety and insomnia symptoms, and overall QOL using the nine-item Patient Health Questionnaire (PHQ-9), seven-item Generalized Anxiety Disorder scale (GAD-7), Insomnia Severity Index (ISI) questionnaire, and World Health Organization Quality of Life-brief version (WHOQOL-BREF), respectively. We estimated the most central symptoms using the centrality index of expected influence (EI), and the symptoms connecting depression, anxiety and insomnia symptoms using bridge EI.

Results

In total, 1,560 fire service recruits participated in the study. The prevalence of depression (PHQ-9 ≥ 5) was 15.2% (95% CI: 13.5-17.1%), while the prevalence of anxiety (GAD-7 ≥ 5) was 11.2% (95% CI: 9.6-12.8%). GAD4 ("Trouble relaxing") had the highest EI in the whole network model, followed by ISI5 ("Interference with daytime functioning") and GAD6 ("Irritability"). In contrast, PHQ4 ("Fatigue") had the highest bridge EI values in the network, followed by GAD4 ("Trouble relaxing") and ISI5 ("Interference with daytime functioning"). Additionally, ISI4 "Sleep dissatisfaction" (average edge weight = -1.335), which was the central symptom with the highest intensity value, had the strongest negative correlation with QOL.

Conclusion

Depression and anxiety were important mental health issues to address among fire service recruits in the post-COVID-19 era in China. Targeting central and bridge symptoms identified in network analysis could help address depression and anxiety among fire service recruits in the post-COVID-19 era.

Inter-relationships between suicidality and depressive symptoms among children and adolescents experiencing crisis: A network perspective

OBJECTIVES

Suicidality and depressive symptoms have emerged as major mental health issues reported among children and adolescents experiencing crises. In light of these concerns, this study was conducted to elucidate interrelationships between depressive symptoms and suicidality at an item level within this population during the COVID-19 pandemic, a worldwide public health crisis affecting children and adolescents.

METHOD

A cross-sectional study design was used. Primary and secondary school students completed the Children's Depression Inventory - Short Version (CDI-S) and two standard suicidality questions tapping suicidal ideation and suicide plans, respectively. A network analysis was performed to examine inter-connections between depressive symptoms and suicidality.

RESULTS

A total of 5380 students participated in the study. Prevalence of suicidal ideation and suicide plans were 12.8 % (95 % CI = 11.9 %-13.7 %) and 9.9 % (95 % CI = 9.2 %-10.8 %), respectively; the prevalence of depressive symptoms was 41.2 % (95%CI = 39.8 %-42.5 %). The network analysis identified CDI4 (self-hatred) as the most influential node with the highest centrality, followed by CDI8 (loneliness), CDI5 (crying), and CDI1 (sadness). Additionally, CDI5 (crying), CDI1 (sadness), CDI4 (self-hatred), and CDI10 (feeling unloved) were the most meaningful nodes linking depressive symptoms with suicidality.

CONCLUSIONS

Critical depressive symptoms linked with suicidality among children and adolescents living through the COVID-19 pandemic included self-hatred, loneliness, crying, and sadness. Interventions that target these depressive symptoms may have increased utility in reducing the risk of suicidality within this population.

Cross-Informant Compatibility of Depression Symptoms in Children: A Network Approach

Utilizing multiple informants to assess children's depressive symptoms increases diagnostic accuracy, reliability, and validity of inferences. However, previous studies have found low to moderate agreement among informants. We applied network statistics to gain insight into children and their mothers' differential perceptions of depressive symptoms. The sample included children and mother dyads (n = 185) who applied to psychotherapy services at an outpatient university clinic. Mothers filled out the Child Behavior Checklist, which includes a depression subscale, and children filled out the Children's Depression Inventory. We computed association networks for thirteen depressive symptoms separately for children and mothers using the graphical LASSO. Sadness had the highest strength centrality in the networks of both children and mothers, but the pattern of connectivity and centrality of other symptoms differed. We discussed our findings within the framework of network theory.

A network analysis of psychopathology in young Black children: Implications for predicting outcomes in adolescence

OBJECTIVE

Network analysis may identify specific symptoms involved in the maintenance and development of psychopathology. This approach, however, has not been applied to the study of young Black children, a population facing unique challenges and developmental risks. It is also unclear whether network analysis identifies early symptoms in Black children that are linked to their longer-term difficulties and strengths in adolescence.

METHODS

We conducted a network analysis of emotional and behavioral difficulties in 1238 Black (non-Hispanic) children from the age-3 assessment in the Future of Families and Child Wellbeing Study (47 % female). We also explored whether early childhood symptoms predict subsequent caregiver-reported internalizing and externalizing problems, and youth-reported social competencies and extracurricular and community involvement, at the age-15 assessment.

RESULTS

We identified specific symptoms of externalizing and emotional reactivity as central in the network. Symptoms of emotional reactivity were also involved in comorbidity, bridging different communities of symptoms. Using elastic net models, we identified specific central and bridge symptoms, but also peripheral network symptoms, that contributed uniquely to the prediction of internalizing and externalizing problems in adolescence. Early childhood symptoms were less predictive of positive outcomes in adolescence.

CONCLUSIONS

This study identified central and bridge symptoms in young Black children, an underrepresented population in network analysis research. Some of these central and bridge symptoms, but also peripheral network symptoms, may be useful targets in early interventions to prevent long-term difficulties. Conversely, network approaches to understanding early psychopathology may have less utility for predicting Black children's subsequent strengths in adolescence.

Identification of central symptoms of children depression and development of two short version of Children's Depression Inventory: Based on network analysis and machine learning

BACKGROUND

Using network analysis to study the central symptoms is important for understanding the mechanism of depression symptoms and selecting items for the short version depression screening scale. This study aimed to identify the central symptoms of depression and develop the short and effective depression screening tools for Chinese rural children.

METHODS

Firstly, the 2458 individuals (Mage = 10.74; SDage = 1.64; 51.2 % were female) were recruited from the rural children's mental health database. Children's Depression Inventory (CDI) was used to assess depression symptoms. Then, network analysis was used to identify the central symptoms of depression. The accuracy, stability, and gender invariance of the depression symptoms network were tested. Finally, a short version of CDI with central symptoms (CDI-SC) and a new CDI-10 (CDI-10-N) were developed by network analysis and feature selection techniques to optimize the existing CDI-10. Their performances in screening depression symptoms were validated by the cutoff threshold and machine learning.

RESULTS

The central symptoms of Chinese rural children's depression were sadness, self-hatred, loneliness and self-deprecation. This result was accurate and stable and depression symptoms network has gender invariance. The AUC values of CDI-10-N and CDI-SC are over 0.9. The CDI-10-N has a higher AUC than CDI-10. The optimal cutoff thresholds for CDI-10-N and CDI-SC are 6 and 1. The performance of machine learning on AUC generally outperforms those of cutoff threshold.

CONCLUSIONS

The central symptoms identified in this study should be highlighted in screening depression symptoms, and CDI-10-N and CDI-SC are effective tools for screening depression symptoms in Chinese rural children.

Depressive symptoms and loneliness among early adolescents: a psychometric network analysis approach

BACKGROUND

Previous studies demonstrate a high prevalence of depression and loneliness among adolescents. Although they often co-occur, the relationship between symptoms of depression and loneliness remains poorly understood. This study investigates: (a) the symptoms of depression that are connected to loneliness; (b) the role played by loneliness in the network of depression symptoms; and (c) whether the method used to measure loneliness (single-item direct or multi-item indirect) affects the relationship of loneliness with depressive symptoms.

METHODS

Participants were 496 Polish adolescents (50.8% girls) aged 11 to 13, who completed: (a) the 10-item Major Depressive Disorder subscale of the Revised Child Anxiety and Depression Scale; (b) the 11-item De Jong Gierveld Loneliness Scale (indirect loneliness), and (c) a single direct question evaluating loneliness: 'I'm lonely'. Networks were estimated using a Gaussian Graphical Model.

RESULTS

Loneliness shows a direct relationship with three affective symptoms of depression: sadness, worthlessness, and anhedonia, which mediate relationships with somatic symptoms. In contrast to previous studies, loneliness has the lowest level of centrality among all elements of the network. The method used to assess loneliness did not significantly affect the connections between loneliness and depressive symptoms.

CONCLUSIONS

Loneliness and depression overlap since they are formed by the same cognitive biases and deficits in emotion regulation but differ in the level of generality. In loneliness, they have an interpersonal context, while symptoms of depression can be intrapersonal. This helps us to understand why cognitive interventions, as compared to those which are social, are more effective in reducing loneliness.

Central symptoms and network associations of depressive symptoms among school-aged students: A network analysis

BACKGROUND

Depression is one of the most prevalent mental health problems occurred among school-aged students. Conceptualizing depressive symptoms as a network of interacting symptoms, this study used network analysis to identify central symptoms and network associations of depressive symptoms. The study also investigated how networks of depressive symptoms differ across school aged periods.

METHODS

A total of 2514 Chinese school-aged students in Grades 4 to 11 were recruited and asked to complete the Child Depression Inventory in this study.

RESULTS

The results showed that self-hatred consistently emerged as a central symptom of depressive symptoms across all school stages. Beyond this, each school stage had its unique central symptoms: loneliness was prominent in both elementary school and junior high school, while fatigue was more specific symptom to senior high school. When comparing the network structures across different school stages, there was a significant difference in network structure between elementary school students and junior high school students. The comparison in global strength showed that the network connectivity of depression network is stronger among elementary school students, with showing closer symptom associations.

CONCLUSIONS

By identifying central symptoms and their distinct associations, particularly the pronounced symptom interconnections among elementary school students, this study emphasize the critical importance of early interventions. Recognizing these stage-specific characteristics is essential for the development of effective prevention and intervention programs for depressive symptoms in school-aged students.

Impulsivity as key bridge symptoms in cross-sectional and longitudinal networks of ADHD and ODD

BACKGROUND

Impulsivity is viewed as key to attention-deficit/hyperactivity disorder (ADHD) and disruptive behavior disorders (DBD). Yet, to date, no work has provided an item-level analysis in longitudinal samples across the critical developmental period from childhood into adolescence, despite prior work suggesting items exhibit differential relevance with respect to various types of impairment. The current study conducted a novel longitudinal network analysis of ADHD and oppositional defiant disorder (ODD) symptoms between childhood and adolescence, with the important applied prediction of social skills in adolescence.

METHODS

Participants were 310 children over-recruited for clinical ADHD issues followed longitudinally for six years in total with gold standard diagnostic procedures and parent and teacher ratings of symptoms and social outcomes.

RESULTS

Findings from baseline, Year 3, and Year 6 suggested Difficulty waiting turn, Blurts, and Interrupts/intrudes were key bridge items across cross-sectional and longitudinal parent-reported DBD networks. Furthermore, shortened symptom lists incorporating these symptoms were stronger predictors of teacher-rated social skills 5 years later compared to total DBD scores.

CONCLUSIONS

Such findings are consistent with the trait impulsivity theory of DBD and ADHD and may inform useful screening tools and personalized intervention targets for children at risk for DBD during adolescence.

The development of depressive symptoms in older adults from a network perspective in the English Longitudinal Study of Ageing

An increased understanding of the interrelations between depressive symptoms among older populations could help improve interventions. However, studies often use sum scores to understand depression in older populations, neglecting important symptom dynamics that can be elucidated in evolving depressive symptom networks. We computed Cross-Lagged Panel Network Models (CLPN) of depression symptoms in 11,391 adults from the English Longitudinal Study of Ageing. Adults aged 50 and above (mean age 65) were followed over 16 years throughout this nine-wave representative population study. Using the eight-item Center for Epidemiological Studies Depression Scale, we computed eight CLPNs covering each consecutive wave. Across waves, networks were consistent with respect to the strength of lagged associations (edge weights) and the degree of interrelationships among symptoms (centrality indices). Everything was an effort and could not get going displayed the strongest reciprocal cross-lagged associations across waves. These two symptoms and loneliness were core symptoms as reflected in strong incoming and outgoing connections. Feeling depressed was strongly predicted by other symptoms only (incoming but not strong outgoing connections were observed) and thus was not related to new symptom onset. Restless sleep had outgoing connections only and thus was a precursor to other depression symptoms. Being happy and enjoying life were the least central symptoms. This research underscores the relevance of somatic symptoms in evolving depression networks among older populations. Findings suggest the central symptoms from the present study (everything was an effort, could not get going, loneliness) may be potential key intervention targets to mitigate depression in older adults.

Understanding suicidality in Pacific adolescents in New Zealand using network analysis

INTRODUCTION

Pacific adolescents in New Zealand (NZ) are three to four times more likely than NZ European adolescents to report suicide attempts and have higher rates of suicidal plans. Suicidal thoughts, plans, and attempts, termed suicidality in this study, result from a complex dynamic interplay of factors, which emerging methodologies like network analysis aim to capture.

METHODS

This study used cross-sectional network analysis to model the relationships between suicidality, self-harm, and individual depression symptoms, whilst conditioning on a multi-dimensional set of variables relevant to suicidality. A series of network models were fitted to data from a community sample of New Zealand-born Pacific adolescents (n = 550; 51% male; Mean age (SD) = 17 (0.35)).

RESULTS

Self-harm and the depression symptom measuring pessimism had the strongest associations with suicidality, followed by symptoms related to having a negative self-image about looks and sadness. Nonsymptom risk factors for self-harm and suicidality differed markedly.

CONCLUSIONS

Depression symptoms varied widely in terms of their contribution to suicidality, highlighting the valuable information gained from analysing depression at the symptom-item level. Reducing the sources of pessimism and building self-esteem presented as potential targets for alleviating suicidality amongst Pacific adolescents in New Zealand. Suicide prevention strategies need to include risk factors for self-harm.

Depressive symptom networks in the UK general adolescent population and in those looked after by local authorities

BACKGROUND

Despite the importance of understanding depressive symptom constellations during adolescence and specifically in looked-after children, studies often only apply sum score models to understand depression in these populations, neglecting associations among single symptoms that can be elucidated in network analysis. The few network analyses in adolescents have relied on different measures to assess depressive symptoms, contributing to inconsistent cross-study results.

OBJECTIVE

In three population-based studies using the Short Mood and Feelings Questionnaire, we used network analyses to study depressive symptoms during adolescence and specifically in looked-after children.

METHOD

We computed cross-sectional networks (Gaussian Graphical Model) in three separate datasets: the Mental Health of Children and Young People in Great Britain 1999 survey (n=4235, age 10-15 years), the mental health of young people looked after by local authorities in Great Britain 2002 survey (n=643, age 11-17 years) and the Millennium Cohort Study in the UK 2015 (n=11 176, age 14 years).

FINDINGS

In all three networks, self-hate emerged as a key symptom, which aligns with former network studies. I was no good anymore was also among the most central symptoms. Among looked-after children, I was a bad person constituted a central symptom, while this was among the least central symptom in the other two datasets. The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition symptom I did not enjoy anything was not central.

CONCLUSIONS

Findings indicate that looked-after children's depressive symptoms may be more affected by negative self-evaluation compared with the general population.

CLINICAL IMPLICATIONS

Intervention efforts may benefit from being tailored to negative self-evaluations.

Feeling unloved is the most robust sign of adolescent depression linking to family communication patterns

Using network analysis, this study investigated how family communication patterns (Conversation and Conformity) were related to and predictive of adolescent overall depression severity and specific symptoms. A community sample of adolescents (10-17 years, n = 1327) completed the Children's Depression Inventory and the Revised Family Communication Pattern Instrument. Depressive symptoms were also re-assessed 6 months later. Results showed that Conversation orientation protected against, whereas Conformity orientation increased the risk of adolescent depression. Family communication particularly influenced the child's feeling of being unloved, and feeling unloved was the only symptom prospectively predicted by two communication orientations at baseline. These findings revealed the path linking family factors to adolescent depression and may have implications for future family-based interventions.

Factorial and network structure of the Reynolds Adolescent Depression Scale (RADS-2) in Peruvian adolescents

Depression in young people is considered a public health problem, given that it affects their personal, social, and academic lives; therefore, early detection of depressive symptoms is of importance for a favorable prognosis. This study aimed to estimate the psychometric properties of the second edition of the Reynolds Adolescent Depression Scale (RADS-2) in Peruvian adolescents. The sample was composed of 917 Peruvian adolescents, aged 13 to 18 years (M = 15,241, SD = 1,020), who were selected from two public educational institutions in Metropolitan Lima. Confirmatory factor analysis supported the 25-item model with the four-dimensional structure and its overall and interdimensional reliability. This structure was found to be gender invariant. Finally, network analysis was performed to assess the relationships and centralities of the depressive symptoms of the validated version of the RADS-2. The results show that the RADS-2 measure is a consistent and reliable test that yields valid results in the Peruvian adolescent context.

A network analysis of anxiety and depression symptoms among Chinese nurses in the late stage of the COVID-19 pandemic

Background

Nurses are at high risk for depression and anxiety symptoms after the outbreak of the COVID-19 pandemic. We aimed to assess the network structure of anxiety and depression symptoms among Chinese nurses in the late stage of this pandemic.

Method

A total of 6,183 nurses were recruited across China from Oct 2020 to Apr 2021 through snowball sampling. We used Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder scale-7 (GAD-7) to assess depression and anxiety, respectively. We used the Ising model to estimate the network. The index "expected influence" and "bridge expected influence" were applied to determine the central symptoms and bridge symptoms of the anxiety-depression network. We tested the stability and accuracy of the network via the case-dropping procedure and non-parametric bootstrapping procedure.

Result

The network had excellent stability and accuracy. Central symptoms included "restlessness", "trouble relaxing", "sad mood", and "uncontrollable worry". "Restlessness", "nervous", and "suicidal thoughts" served as bridge symptoms.

Conclusion

Restlessness emerged as the strongest central and bridge symptom in the anxiety-depression network of nurses. Intervention on depression and anxiety symptoms in nurses should prioritize this symptom.

Risk factors for depression in Pacific adolescents in New Zealand: A network analysis

BACKGROUND

Network analysis provides opportunities to gain a greater understanding of the complex interplay of risk factors for depression and heterogeneous symptom presentations. This study used network analysis to discover risk factors associated with both depression severity and depression symptoms amongst Pacific adolescents in New Zealand.

METHODS

Mixed graphical models with regularization were fitted to data from a community sample of New Zealand born, Pacific adolescents, (n = 561; 51% male; Mean age (SD) = 17 (0.35)) and associations between a wide range of potentially explanatory variables and depression severity and depression symptoms investigated. The associations identified were then tested for reliability, using resampling techniques and sensitivity analysis.

RESULTS

In the networks, the explanatory variables associated with both depression severity and depression symptoms were those related to quality of the relationships with mother or friends, school connectedness, and self-assessed weight, but the symptoms they were associated with varied substantially. In the depression severity networks, impulsivity appeared to be a bridging node connecting depression severity with delinquency and negative peer influence.

LIMITATIONS

The data were analysed cross-sectionally, so causal inferences about the directions of relationships could not be inferred and most of the data were self-reported.

CONCLUSIONS

The results illustrate the varied way that adolescent depression can manifest itself in terms of symptoms and suggest specific items on the depression inventory that might be suitable targets for prevention strategies and interventions, based on the risk factor - depression symptom profiles of individuals or groups.

Screening for depressive symptoms in adolescence: A Rasch analysis of the short-form childhood depression inventory-2 (CDI 2:SR[S])

BACKGROUND

Screening for depressive symptoms during adolescence is of high clinical significance. The shorter 12-item version of the Children's Depression Inventory (CDI 2:SR[S]) was specifically developed for this purpose. Evaluations of the CDI 2:SR[S] psychometrics are limited, however. The purpose of this study was to validate the CDI 2: SR[S] for use as a screening measure using Rasch analysis.

METHODS

The CDI 2: SR[S] was administered online to 1513 10-17 year old Western Australian adolescents (635 males, 878 females) from 11 schools. Overall fit, individual item fit, local response dependence, dimensionality, operation of response categories, and differential item functioning (DIF) were examined.

RESULTS

The Rasch analysis demonstrated the CDI 2: SR[S] has good reliability. Thresholds for all items were ordered, showing its three response categories functioned as intended. One item (I have to push myself to do schoolwork) showed misfit. No items were locally dependent. Two items (I am sad) and (I have to push myself to do schoolwork) showed DIF for gender. At the same level of depression, females reported being sad more than males, while males pushed themselves more to do schoolwork than did females. Adolescents (14-17 years) reported significantly higher mean depressive symptom scores than early adolescents (10-13 years).

LIMITATIONS

Sole reliance on adolescent's self-report and limited data about cultural backgrounds are limitations.

CONCLUSIONS

The results support the interval scale measurement properties of the CDI 2: SR[S] and provides educators, clinicians and researchers with a screening measure to assess depressive symptoms in adolescents.

Utilizing network analysis to understand the structure of depression in Chinese adolescents: Replication with three depression scales

Depression involves a heterogenous collection of symptoms. Network perspective views depressive symptoms as an interrelated network. The current study aimed to replicate network analyses on adolescent depression in three samples assessed with three instruments to examine the consistency of network structures and also examine the variance of networks between genders. Three samples of adolescents (total N = 4375, mean age = 15, 49.1% boys) were assessed with PHQ-9, SMFQ and CDI, respectively. Network analyses were carried out on depression symptoms. Network stability, node centrality and network comparisons between genders were examined. Three networks were reliably stable. Sadness and self-hatred were unanimously identified to be central symptoms of adolescent depression in three networks. In addition, fatigue, no good, everything wrong and loneliness also appeared to be central in specific networks. Among three depression networks, PHQ-9 network demonstrated gender difference in network structure. The current study is exploratory in nature. The differences in three networks can be due to various samples or different node inclusions. Further, the study is cross-sectional precluding causal interpretation and the samples are nonclinical. Besides "hallmark" symptom sadness, self-hatred was also identified unanimously in three networks, which demonstrated the significant role self-worth played in adolescent depression. The results also suggested that differences in node inclusion may have influence on the network structure.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12144-022-03201-z.

Comorbidity patterns in depression: A disease network analysis using regional hospital discharge records

BACKGROUND

Depression is a psychiatric disorder with a high comorbidity burden; however, previous comorbidity studies predominately focused on a few common diseases and relied on self-reported data. We aimed to investigate the comorbid status of depression concerning the entire spectrum of chronic diseases using network analysis.

METHOD

Totally, 22,872 depressed inpatients and one-to-one matched controls were enrolled in the retrospective study. Hospital discharge records were aggregated to measure the comorbidities, where those with a prevalence ≥ 1% were selected for further analysis. Based on the co-occurrence frequency, sex- and age-specific comorbidity networks in depressed patients were constructed and the results were compared with the controls. Louvain algorithm was used to detect the highly interlinked communities.

RESULTS

Depressed patients had 4 comorbidities on average, and 84.4% had at least one comorbidity. The comorbidity network in depression cases was more complex than controls (connections of 839 vs. 369). Intricate but distinct communities appeared within the comorbidity network in depressed patients, where the largest community included cerebrovascular diseases, chronic ischaemia heart disease, atherosclerosis and osteoporosis. Sex-specific central diseases existed, and cardiovascular diseases were the major central diseases to both gender. The older the depressed patients, the more severe the central diseases in the comorbidity network.

LIMITATIONS

The causality of the observed interactions could not be determined.

CONCLUSIONS

The application of network analysis on longitudinal healthcare datasets to assess comorbidity patterns can supplement the traditional clinical study approaches. The findings would improve our understanding of depression-related comorbidities and enhance the integrated management of depression.

Reliability and Validity of the Korean Version of Children's Depression Inventory 2 Short Version as a Screening Tool: A Comparison With the Standard 28-Item Version

OBJECTIVE

To investigate the reliability and validity of the Korean version of Children's Depression Inventory 2 Short Version (CDI 2:S) in comparison with its full-length version (CDI 2) as a screening tool for depressive youth.

METHODS

A total of 714 children from the community and 62 psychiatric patients were enrolled in this study. The Korean version of the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version (K-SADS-PL-K) served as the reference standard for computing receiver operating characteristic (ROC) curves. To evaluate the ability of the CDI 2 and CDI 2:S to discriminate major depressive disorders, areas under the curves (AUCs) were compared. To investigate psychometric properties of the CDI 2:S, internal consistency was calculated and confirmatory factor analysis was conducted.

RESULTS

For the CDI 2, the cutoff at 20 yielded the best balance between sensitivity (83%) and specificity (91%). For the CDI 2:S, the cutoff point of 10 resulted in high sensitivity (82%) and high specificity (93%). The short form was proven to be as sensitive and specific as the CDI 2. Further analyses confirmed that the CDI 2:S also had good reliability and validity.

CONCLUSION

The CDI 2:S, a sensitive and brief form of the CDI 2, may serve as a better option in time-constrained psychiatric settings.

Comparison of Hypomanic Symptoms Between Bipolar I and Bipolar II Disorders: A Network Perspective

BACKGROUND

Hypomanic symptoms between bipolar-I disorder (BD-I) and bipolar-II disorder (BD-II) are often indistinguishable in clinical practice. This study compared the network structure of hypomanic symptoms between patients with BD-I and BD-II.

METHODS

The 32-item Hypomania Checklist (HCL-32) was used to assess hypomanic symptoms. Network model was generated in BD-I and BD-II patients. Centrality index of strength was used to quantify the importance of each symptom in the network. The Network Comparison Test (NCT) was used to assess the differences in hypomanic symptoms between BD-I and BD-II patients.

RESULTS

Altogether, 423 patients with BD (BD-I: 191 and BD-II: 232) were included. The most central symptom was HCL17 "I am more flirtatious and/or am more sexually active" (strength _BD-I = 5.21) and HCL12 "I have more ideas, I am more creative" (strength _BD-II = 6.84) in BD-I and BD-II samples, respectively. The results of NCT showed that four nodes (HCL12 "I have more ideas, I am more creative," HCL17 "I am more flirtatious and/or am more sexually active," HCL23 "My thoughts jump from topic to topic," and HCL31 "I drink more alcohol") were significantly different between the BD-I and BD-II samples. Two edges (HCL3 "I am more self-confident"-HCL17 "I am more flirtatious and/or am more sexually active," and HCL10 "I am physically more active (sport, etc.)"-HCL24 "I do things more quickly and/or more easily") were significantly stronger in BD-I compared to BD-II patients.

CONCLUSION

The network structure of hypomanic symptoms is different between BD-I and BD-II patients. Interventions targeting the respective central symptoms and edges should be developed for BD-I and BD-II separately.

Gray Matter Volume in the Developing Frontal Lobe and Its Relationship With Executive Function in Late Childhood and Adolescence: A Community-Based Study

Background: During late childhood and adolescence, the frontal lobe undergoes critical developmental changes, affecting a wide range of executive functions significantly. Conversely, abnormality in the maturation of the frontal lobe during this period may result in a limited ability to effectively use various executive functions. However, at present, it is still unclear how the structural development of the frontal lobe is associated with different aspects of executive functions during this developmental period. To fill the gap in evidence, we aimed to elucidate gray matter volume (GMV) in the frontal lobe and its relationship with multiple aspects of executive functions in late childhood and adolescence. Methods: We recruited our participants aged between 6 and 17 years to assess GMV in the frontal lobe and its relationship with different domains of executive functions in late childhood and adolescence. We used the voxel-based morphometry-DARTEL procedure to measure GMVs in multiple frontal sub-regions and Stroop test and Advanced Test of Attention (ATA) to measure executive functions. We then conducted partial correlation analyses and performed multiple comparisons with different age and sex groups. Results: Overall, 123 participants took part in our study. We found that many regional GMVs in the frontal lobe were negatively correlated with ATA scores in participants in late childhood and positively correlated with ATA scores in participants in adolescence. Only a few correlations of the GMVs with Stroop test scores were significant in both age groups. Although most of our results did not survive false discovery rate (FDR) correction (i.e., FDR <0.2), considering their novelty, we discussed our results based on uncorrected p-values. Our findings indicate that the frontal sub-regions that were involved in attentional networks may significantly improve during late childhood and become stabilized later in adolescence. Moreover, our findings with the Stroop test may also present the possibility of the later maturation of higher-order executive functioning skills. Conclusion: Although our findings were based on uncorrected p-values, the novelty of our findings may provide better insights into elucidating the maturation of the frontal lobe and its relationship with the development of attention networks in late childhood and adolescence.