Background: Transgender individuals experience a high level of distress over the lifespan and are at a higher risk of experiencing mental health conditions.Objective: The present study mainly aimed to investigate the complex relationship between early maladaptive schemas (EMSs), adverse childhood experiences and mental health conditions in adulthood among transgender individuals.Method: 220 participants completed an anonymous online study assessing sociodemographic and medical status, experience of childhood adversity (CTQ), EMSs (YSQ-S3), transgender specific quality of life (ETLI), current depression (PHQ-8), and generalised anxiety severity (GAD-7).Results: Transgender individuals tended to have a high prevalence for experiencing childhood adversity as well as for depressive and generalised anxiety symptoms and low quality of life in adulthood, while assigned female at birth (AFAB) transgender tend to be more vulnerable than assigned male at birth (AMAB) transgender. In general, transgender individuals showed an elevated pronounced level of EMSs, while AFAB transgender tended to focus more on the negative aspects in life, to strive more to pursue perfection and tended more to be highly critical towards others and especially themselves, as well as tended to believe that people should be harshly punished for making mistakes than AMAB transgender. According to the network analysis, schema domain and their EMSs were related to all assessed psychopathological outcomes, especially the schema domains Disconnection and Rejection and Impaired Autonomy and Performance seemed to be related to experience of childhood adversity and quality of life in adulthood.Conclusions: Transgender individuals seem to experience a high level of childhood adversity, which possibly leads to the development of an elevated level of EMSs. EMSs and their schema domains are associated with elevated depressive and generalised symptoms, as well as lower quality of life.

Background: Dissociative symptoms are suggested to compete with the effectiveness of psychotherapeutic treatment and frequently co-occur with early life trauma and self-harm patterns, including self-injury, suicidal ideation, and suicide attempts, which are characteristic for borderline personality disorder (BPD).Objective: We explore the connections between dissociation and other BPD features like self-harm and childhood traumatization and examine the impact of trait dissociation on both immediate and follow-up psychotherapy outcomes.Method: In this naturalistic prospective evaluation study, we investigated psychopathology including general psychological distress (SCL-90), depression (BDI-II), and borderline-specific pathology (BSL) in 131 patients with BPD pre and post of a certified 8-week inpatient Dialectical Behaviour Therapy (DBT), as well as 3- and 6-month post inpatient treatment. Prior to therapy, we evaluated trait dissociation (Dissociative Experience Scale), early life trauma (Childhood Trauma Questionnaire), and self-harm patterns (clinical interview). We performed a network analysis to explore the interplay between dissociation, self-harm, and childhood traumatization. To analyse the influence of dissociation on treatment outcomes, we employed linear mixed models.Results: Psychopathology (SCL-90, BDI-II and BSL) exhibited significant reductions at post-treatment and follow-up assessments when compared to baseline measurements. Higher levels of trait dissociation were consistently related to higher psychopathology but did not suggest poorer symptom reduction during DBT. However, trait dissociation predicted reduced 6-month follow-up benefits from DBT, regardless of the baseline symptom burden. The network analysis revealed a close association between self-injury and derealization/depersonalization, while the frequency of suicide attempts was more closely connected to childhood emotional abuse, which was identified as a central node of the network.Conclusions: Dissociation was not related to poorer outcomes regarding inpatient DBT. However, trait dissociation predicted reduced follow-up benefits, highlighting the need to address dissociation during psychotherapeutic interventions. Based on the estimated network structure, treating dissociative symptoms could potentially mitigate self-injury while especially childhood emotional abuse was linked with suicide attempts.

The high suicide rate among emerging adults is a pressing public health issue. Identifying key suicide risk factors and understanding the mechanisms through which they affect individuals is crucial for intervention.

To uncover the complex factors influencing suicide risk among emerging adults and to elucidate how these risk factors interact and contribute to the overall risk of suicide.

An online survey assessed mental health and suicide risk factors among 29,111 college students. Higher-risk students (n = 4820) were further evaluated using the Adolescent Suicidal Tendency Scale. This two-phase approach identified initial risk factors and subsequent suicide risk, which were analyzed through logistic regression, Glasso, and Bayesian network methods.

Logistic regression results indicated that adverse life events and social support can predict suicide risk, with the model achieving an Area Under the Curve (AUC) of 0.783. Glasso network analysis revealed a highly interconnected symptom network among all factors, where the highest centrality nodes, such as depression (1.465) and neuroticism personality traits (1.139), played central roles in the evolving dynamics of suicide risk. The Bayesian network analysis emphasized the mediating role of social support in the relationship between other risk factors and suicidal ideation.

The lack of repeated measurements and the exclusion of pandemic-related variables may limit a comprehensive understanding of the risk factors.

Intervening in the mental health issues of individuals with suicidal tendencies and strengthening social support are crucial for reducing suicide risk, and this deserves the attention of mental health professionals.

There is heterogeneity in existing depression scales, which may lead to measurement bias. This study aimed to investigate the clustering of depressive symptoms in adolescents, including central symptoms as well as similarities and differences among four commonly used depression scales. Additionally, the study examined gender differences in network structures to explore the suitability of these scales.

This study performed an exploratory psychometric network analysis of depression in 620 Chinese adolescents aged 15-17 years using R, which included four depression scales. The mean age of the adolescents was 15.95 ± 0.66 years. Among them, 279 (45.0 %) were boys, 316 (51.0 %) were girls, and 25 (4.0 %) did not report gender.

The network analysis revealed seven distinct symptom clusters among adolescents with depressive symptoms, including "Negative Emotions and Pessimism", "Loss of Positive Emotions", "Interpersonal Issues", "Retardation and Attention Issues", "Fear and Decision-making Difficulties", "Irritability or Stress" and "Low Self-esteem and Self-efficacy". These clusters varied in item distribution across the different scales. Moreover, network comparisons indicated notable gender differences in the structure of depressive symptom networks. Girls exhibited stronger associations between depressive symptoms, particularly concentrating core symptoms in clusters related to "Interpersonal Issues" and "Low Self-esteem and Self-efficacy".

Heterogeneity among depression scales may bias detection and identification in adolescents. Selecting appropriate scales based on participant characteristics and symptom clusters is essential. Furthermore, future screening and interventions should account for gender differences.

Civil unrest related to political change can have detrimental effects on the mental health of the population. This study investigated probable Adjustment Disorder (AjD) among individuals with different attitudes in the context of a highly controversial judicial reform in Israel. The relationships between AjD symptoms across three groups were explored: those who support the proposed changes, others who oppose them, and those who adopt a neutral stance.

This study was conducted with a nationally representative sample of Israeli adults (n = 1999). A Symptoms Network analysis of AjD was performed using the International Adjustment Disorder Questionnaire, examining groups with different levels of social unrest engagement.

Participants actively opposing the judicial reform reported higher levels of probable AjD compared to neutral participants and those in favor of the reform. Similarly, neutral participants reported higher levels of probable AjD than those in favor of the reform. The network structure of AjD was similar across the three groups. Item five ("difficult to relax/feel calm since the social unrest") was the node with the highest strength centrality in all networks.

This study utilized cross-sectional methodology and self-report measures.

Mental health needs should be actively addressed by policymakers and mental health professionals during social unrest, regardless of political stance. The high rates of probable AjD and the similarity in the structure of AjD across the groups suggest common underlying mechanisms and a significant adjustment crisis in a large portion of the population following social unrest.

Sickle cell disease (SCD) is the most prevalent inherited haemoglobinopathy characterised by chronic pain with acute painful episodes due to vaso-occlusion. The effective management of pain by adults with SCD influences their health outcomes. Opioids remain essential for most pain syndromes, but non-pharmacological interventions are preferred for daily pain due to the risk of addiction. However, their effectiveness is variable. Understanding the underlying processes associated with pain is crucial for developing more effective non-pharmacological strategies. This study aimed to enhance comprehension of the pain mechanisms in SCD to identify potential areas of action for effective non-pharmacological interventions.

An evaluation was conducted on the severity and interference of pain, pain-related cognitions and emotions. We used network analysis to simultaneously examine the intricate relationships between these variables.

A pain intensity exceeding 4 at a steady state distinguishes a subgroup at elevated risk of negative pain-related emotions and cognitions. The network analysis revealed intricate interconnections, with three distinct subgroups of variables mimicking the Neuromatrix model (cognitive-evaluative, motivational-affective and sensory-discriminative subgroups). The derived directed acyclic graph suggests potential mechanisms between these three subgroups, with catastrophising having a pivotal role.

This study extends previous research by providing a comprehensive network analysis of pain-related variables in SCD, offering novel insights into the complex interplay between pain experience, cognitions and emotions. These findings have important clinical implications, as they suggest that targeting dysfunctional pain cognitions and/or negative emotions may be beneficial for improving pain management and quality of life in SCD.

This study was the first to use network analyses to understand simultaneously multiple relationships between variables referring to pain, and pain-related negative emotions and cognitions in adults with SCD. Findings, providing support to the Neuromatrix model, offer novel insight to better understand pain and the associated negative emotions and cognition in SCD. The derived directed acyclic graph explored potential underlying psychological processes associated with pain that could be specifically targeted by future effective psychological interventions.

Brooding is a critical symptom and prognostic factor of major depressive disorder (MDD), which involves passively dwelling on self-referential dysphoria and related abstractions. The neurobiology of brooding remains under characterized. We aimed to elucidate neural dynamics underlying brooding, and explore their responses to neurofeedback intervention in MDD.

We investigated functional MRI (fMRI) dynamic functional network connectivity (dFNC) in 36 MDD subjects and 26 healthy controls (HCs) during rest and brooding. Rest was measured before and after fMRI neurofeedback (MDD-active/sham: n = 18/18, HC-active/sham: n = 13/13). Baseline brooding severity was recorded using Ruminative Response Scale - Brooding subscale (RRS-B).

Four recurrent dFNC states were identified. Measures of time spent were not significantly different between MDD and HC for any of these states during brooding or rest. RRS-B scores in MDD showed significant negative correlation with measures of time spent in dFNC state 3 during brooding (r = -0.4, p = 0.002, FDR-significant). This state comprises strong connections spanning several brain systems involved in sensory, attentional and cognitive processing. Time spent in this anti-brooding dFNC state significantly increased following neurofeedback only in the MDD active group (z = -2.09, FWE-p = 0.034).

The sample size was small and imbalanced between groups. Brooding condition was not examined post-neurofeedback.

We identified a densely connected anti-brooding dFNC brain state in MDD. MDD subjects spent significantly longer time in this state after active neurofeedback intervention, highlighting neurofeedback's potential for modulating dysfunctional brain dynamics to treat MDD.

There is an intricate relationship between sleep disturbances and perceived stress in firefighters. Network analysis offers novel perspectives for examining the interactions between diseases. Hence, this study aimed to elucidate the relationship between sleep disturbances and perceived stress in firefighters through network analysis approaches.

A total of 786 Chinese firefighters were included in this study. Two methodologies, the regularized partial correlation network (RPCN) and the directed acyclic graph (DAG), were employed to perform network analysis.

Within the RPCN, P2 "Unable to control important things" and P7 "Unable to control irritations in life" were identified as central symptoms that simultaneously maintained sleep disturbances and perceived stress among firefighters. S7 "Daytime dysfunction" and P3 "Felt nervous and stressed" were classified as bridge symptoms, connecting sleep disturbances with perceived stress and perpetuating their comorbid nature. From a probabilistic priority perspective, the DAG demonstrated that the bidirectional spiral between sleep disturbances and perceived stress might be attributed to the fact that the upstream symptom S7 "Daytime dysfunction" influenced the perceived self-efficacy dimension of perceived stress, which in turn influenced other sleep disturbance symptoms.

Our findings not only provided innovative insights into further understanding the relationship between sleep disturbances and perceived stress among firefighters, but also identified potential targets for ameliorating these symptoms, such as the central, bridge, and upstream symptoms. Future intervention programs should prioritize these potential targets. Through the implementation of interventions like mindfulness-based stress reduction, cognitive behavioral therapy, and group psychotherapy, tailored to address these targeted symptoms, it is feasible to effectively mitigate both sleep disturbances and perceived stress in firefighters, and ultimately improve the mental health of this particular occupational group.

Sensory stimulation of the brain reverberates in its recurrent neural networks. However, current computational models of brain activity do not separate immediate sensory responses from this intrinsic dynamic. We apply a vector-autoregressive model with external input (VARX), combining the concepts of "functional connectivity" and "encoding models", to intracranial recordings in humans. This model captures the extrinsic effect of the stimulus and separates that from the intrinsic effect of the recurrent brain dynamic. We find that the intrinsic dynamic enhances and prolongs the neural responses to scene cuts, eye movements, and sounds. Failing to account for these extrinsic inputs, leads to spurious recurrent connections that govern the intrinsic dynamic. We also find that the recurrent connectivity during rest is reduced during movie watching. The model shows that an external stimulus can reduce intrinsic noise. It also shows that sensory areas have mostly outward, whereas higher-order brain areas mostly incoming connections. We conclude that the response to an external audiovisual stimulus can largely be attributed to the intrinsic dynamic of the brain, already observed during rest.

Depression and social anxiety are highly comorbid in young adults, posing significant harm. This study aimed to identify central and bridge symptoms of depression and social anxiety in medical students, providing insights for intervention strategies. This cross-sectional study recruited 456 medical students. Depression symptoms were measured using the nine-item Patient Health Questionnaire (PHQ-9), while social anxiety was assessed using the Interaction Anxiousness Scale (IAS). Network analysis was utilised to examine symptom-level associations between depression and social anxiety. The study identified 'anxious at parties' and 'nervous with authority figures' as the most central symptoms, while 'nervous in informal gatherings' and 'fatigue' were the most bridge symptoms in the network of depression and social anxiety symptoms among medical students. These symptoms were crucial in the development of depression and social anxiety, indicating the target for interventions. The network analysis perspective suggests that the development of social anxiety in medical students is closely related to career development, while its comorbidity with depression is associated with social and emotional support. Distinct intervention strategies could target central and bridge symptoms to address social anxiety and its comorbidity with depression among medical students.

The two-polarity model of personality development posits that personality vulnerabilities, specifically dependency and self-criticism, play a critical role in adolescent psychopathology risk. This theoretical framework emphasizes the importance of understanding how these vulnerabilities manifest and develop during adolescence, as adolescents face distinct developmental challenges at different stages. However, existing research overlooks the impact of stage-specific challenges on the development of personality vulnerabilities.

This study included 24,946 Chinese adolescents (Mage ± SD = 15.50 ± 2.03; 46.50 % girls) divided into three age groups: early (10-13 years, N = 4652), middle (14-17 years, N = 15,065), and late (18-20 years, N = 5229). We employed network analysis to investigate the symptom structure of self-criticism and dependency through different adolescent stages, focusing on core symptoms (highly connected nodes), bridges (links between dependency and self-criticism), and potential causal relationships.

Feelings of disappointing others and loneliness after arguments are consistently central to self-criticism and dependency, respectively. Connections between these vulnerabilities change across stages. Evidence from the directed acyclic graph suggests that self-criticism gradually replaces dependency as the key to triggering experiences of personality vulnerability from early to late adolescence.

This study identified the personality vulnerability network structure across three stages of adolescents. The results highlight a developmental shift in the triggering sequences of personality vulnerability, moving from predominantly dependency in early adolescence to increased self-criticism in later stages. These insights underscore the significance of the developmental context in shaping personality vulnerabilities across adolescence and offer crucial directions for stage-specific interventions.

In response to concerns about excessive homework and off-campus tutoring among Chinese adolescents, the Chinese government recently implemented the nationwide "Double Reduction" (DR) policy to ease their academic burdens. However, the potential protective effects of the DR policy on adolescent mental health remain unclear. This study applied a network approach to examine relationships between several changes post-policy implementation (e.g., reduced homework, more extracurricular activities, increased physical activity, more time with parents, and reduced academic stress) and multiple mental health symptoms (e.g., symptoms of anxiety, depression, and insomnia) in 45,573 adolescents across two waves (i.e., three and eight months post-implementation). Results found consistent network structures over time, with "increased extracurricular activities" and "reduced homework" identified as the most central nodes. The former was most strongly associated with a depressive symptom named "energy loss," while the latter was most strongly linked to an anxiety symptom named "trouble relaxing." These findings provide preliminary evidence for the protective effect of the DR policy on adolescent mental health, emphasizing the importance of reducing homework and expanding extracurricular opportunities for adolescents' well-being.

Body dissatisfaction is a normative experience with the potential to impact women's mental and physical health. It is closely related to self-objectification, where the body is viewed as an object for aesthetic evaluation rather than for its functional attributes. Self-objectification not only affects women's body image but is associated with diminished subjective awareness of, and attention towards, internal bodily states (i.e., interoceptive sensibility). This study uses network analysis and network comparison to investigate the relationships between features of interoceptive sensibility and self-objectification in women with high (N = 348) and low (N = 354) body dissatisfaction. The results firstly revealed significant differences in the connections between interoceptive sensibility and self-objectification in women with high and low body dissatisfaction (p = 0.026). High body dissatisfaction in women was characterised by lower levels of body listening, self-regulation, and body trusting. Alternatively, high emotional awareness was most central to women with low body dissatisfaction. This study highlights the central role of interoceptive sensibility in body dissatisfaction and identifies key features of interoceptive sensibility to target for improving body dissatisfaction.

Cancers are shaped by somatic mutations, microenvironment, and patient background, each altering gene expression and regulation in complex ways, resulting in heterogeneous cellular states and dynamics. Inferring gene regulatory networks (GRNs) from expression data can help characterize this regulation-driven heterogeneity, but network inference requires many statistical samples, limiting GRNs to cluster-level analyses that ignore intracluster heterogeneity. We propose to move beyond coarse analyses of predefined subgroups by using contextualized learning, a multitask learning paradigm that uses multiview contexts including phenotypic, molecular, and environmental information to infer personalized models. With sample-specific contexts, contextualization enables sample-specific models and even generalizes at test time to predict network models for entirely unseen contexts. We unify three network model classes (Correlation, Markov, and Neighborhood Selection) and estimate context-specific GRNs for 7,997 tumors across 25 tumor types, using copy number and driver mutation profiles, tumor microenvironment, and patient demographics as model context. Our generative modeling approach allows us to predict GRNs for unseen tumor types based on a pan-cancer model of how somatic mutations affect gene regulation. Finally, contextualized networks enable GRN-based precision oncology by providing a structured view of expression dynamics at sample-specific resolution, explaining known biomarkers in terms of network-mediated effects and leading to subtypings that improve survival prognosis. We provide a SKLearn-style Python package https://contextualized.ml for learning and analyzing contextualized models, as well as interactive plotting tools for pan-cancer data exploration at https://github.com/cnellington/CancerContextualized.

Self-focused attention, cost/probability bias, and avoidance behavior are maintaining factors of social anxiety. The manipulation of dispositional mindfulness has been shown to reduce social anxiety and its maintaining factors. This study examined the associations among dispositional mindfulness, self-focused attention, cost/probability bias, avoidance behavior, and social anxiety to explore the mechanism of mindfulness and its relationship with social anxiety in patients with social anxiety disorder (SAD) and university students. Data from 412 patients with SAD and 367 students were analyzed using psychometric network analysis. Both groups completed self-report measures assessing dispositional mindfulness, self-focused attention, cost/probability bias, avoidance behavior, and social anxiety. A weak negative association was found between non-judging in dispositional mindfulness and self-focused attention to one's behavior in the SAD patients' network. In the students' network, a weak negative association was observed between non-judging and self-focused attention to one's behaviors, as well as between describing and social anxiety. In the SAD patients' network, nodes with the highest strength centrality were probability bias and social anxiety. In the students' network, nodes with the highest strength centrality were social anxiety and cost bias. The global network structure and connectivity differed between patients with SAD and students. The findings support the cognitive-behavioral model of SAD and highlight the heterogeneity of social anxiety, necessitating tailored intervention strategies.

Depression has emerged as a leading contribution of the global mental health burden, particularly among underrepresented adolescents. Despite the World Health Organization's promotion of primary mental healthcare as a critical solution, its real-world effectiveness in low- and middle-income countries remains debated due to high costs and insufficient follow-up. This study aimed to explore the impact of primary mental healthcare on the core symptoms of adolescent depression using network analysis, while examining the influence of demographic factors such as gender, age, and family support, to identify more precise and targeted healthcare strategies, improving its effectiveness.

A citywide, multi-center, longitudinal cohort study was conducted in Nanchong, Sichuan Province, China, involving 73,750 adolescents (34,606 girls and 39,144 boys) with median age of 14.00 years old. The Comprehensive Primary Healthcare for Adolescents Program (CPHG) involved two rounds of psychological screening and early intervention. Depressive symptoms were assessed using the Center for Epidemiological Studies Depression Scale (CES-D). Network analysis was employed to map the interrelations between depressive symptoms and evaluate the healthcare's impact.

The CPHG program significantly reduced CES-D median scores from 6.00 to 2.00 (p < 0.001). Network analysis revealed changes in the structure and centrality of depressive symptoms post-intervention, with specific symptoms such as sadness (C18) showing consistent reductions across subgroups. Gender disparities were evident, with female adolescents exhibiting stronger symptom interconnectivity. Junior high school students also demonstrated a more robust symptom network compared to senior high school students. Adolescents living in social welfare institutions exhibited higher global expected influence of depressive symptoms than those living with both parents.

Primary mental healthcare effectively modifies the network structure of depressive symptoms in adolescents, with specific symptoms like sadness being critical targets. Gender and grade-level differences highlight the need for tailored mental healthcare strategies. The findings underscore the importance of addressing both core and peripheral symptoms to enhance treatment efficacy and reduce the severity and recurrence of depression among underrepresented adolescents.

Negative affect may trigger a variety of psychological and behavioral problems, while physical activity has been shown to reduce negative affect. However, the underlying mechanisms remain unclear. The aim of this study was to investigate the relationship between physical activity and negative affect, specially examining the role of sleep quality in the relationship.

Cross-sectional design was adopted in this study. A total of 557 adolescents participated in the study, completing self-reported questionnaires on physical activity, sleep quality, and negative affect.

Sleep quality mediated the relationship between physical activity and negative affect. There was a positive correlation between "exercise" and "sleep quality" and a negative correlation between "sleep restfulness" and "feeling bored".

This study uncovered the underlying mechanism of physical activity associated with negative affect, offering significance for the prevention and intervention of depression and anxiety disorders.

ObjectivesTraumatic experiences constitute a risk factor for developing different psychopathologies, such as post-traumatic stress disorder (PTSD), complex PTSD (CPTSD), and positive symptoms of psychosis. However, on the symptom level, it is still unclear how CPTSD and positive symptoms of psychosis associate with each other. The present study aimed to shed light on these dynamics by investigating the symptoms network of CPTSD and positive symptoms of psychosis.MethodsA network analysis was performed on CPTSD and psychosis symptoms among a Canadian community sample with a history of traumatic life events (n = 747). Measures included the international trauma questionnaire and the mPRIME screen.ResultsIn total, 4.8% of the sample reached the criteria of probable PTSD and 7% fulfilled the criteria of probable CPTSD. PTSD and CPTSD groups had a significantly higher severity of positive symptoms of psychosis compared to the no-disorder group. Network analysis revealed 3 distinct communities of symptoms of PTSD, disturbances in self-organization, and psychosis. Affective dysregulation served as the bridging symptom between the communities. Hearing one's own thoughts aloud was the most central symptom in the network.ConclusionsFindings show that positive symptoms of psychosis can be considered trauma-related responses. Furthermore, interventions targeting affective dysregulation as well as the experience and distress associated with hearing one's own thoughts aloud may contribute to symptom reduction and improved functioning.

Previous studies have not found cross-cultural full measurement invariance for the Level of Personality Functioning Scale - Brief Form 2.0 (LPFS-BF 2.0). While invariance analysis ensures valid cross-cultural comparisons, it may not capture all possible cultural impacts. One possibility is to employ network analysis to compare countries regarding connections and centrality indicators. This study aimed to validate the cross-cultural applicability of the LPFS-BF 2.0 across eight countries: Australia, Brazil, the United Kingdom, Iran, Italy, Jordan, Saudi Arabia, and the United States. The sample comprised 4,310 adults (aged between 18 to 84); the majority of the total sample reported being women (72.9%), being Caucasian (56.6%) who completed the LPFS-BF 2.0. Measurement invariance was assessed using multi-group confirmatory factor analysis (MGCFA), while network analysis explored the centrality and interrelationships of LPFS-BF 2.0 items within each country. Our findings mark the first time full invariance across different cultures was observed with the LPFS-BF 2.0. Network analysis revealed unique cultural patterns in item centrality, suggesting the need for further refinement of specific items to address cultural differences. The study highlights the LPFS-BF 2.0 as a robust tool for assessing personality functioning globally and underscores the importance of culturally sensitive adaptations in personality assessment. Network analysis offers an innovative method for assessing cultural differences in personality, and identifying stable and variable aspects of pathology to inform clinical interventions.

In survival analysis, researchers commonly focus on variable selection issues in real-world data, particularly when complex network structures exist among covariates. Additionally, due to factors such as data collection costs and delayed entry, real-world data often exhibit censoring and truncation phenomena.This paper addresses left-truncated current status data by employing a copula-based approach to model the relationship between censoring time and failure time. Based on this, we investigate the problem of variable selection in the context of complex network structures among covariates. To this end, we integrate Markov Random Field (MRF) with the Proportional Hazards (PH) model, and extend the latter to more flexibly characterize the correlation structure among covariates. For solving the constructed model, we propose a penalized optimization method and utilize spline functions to estimate the baseline hazard function. Through numerical simulation experiments and case studies of clinical trial data, we comprehensively evaluate the effectiveness and performance of the proposed model and its parameter inference strategy. This evaluation not only demonstrates the robustness of the proposed model in handling complex disease data but also further verifies the high precision and reliability of the parameter estimation method.

The aim of this study was to utilize network analysis to explore the interconnections among anxiety, depressive, and insomnia symptoms in depressed patients in China.

The study included two surveys, the baseline survey was conducted from May 18, 2020 to June 18, 2020, and the follow-up survey was conducted 5 months later. A total of 4476 patients completed the baseline survey, and 1877 of them completed the follow-up survey. Depression symptoms were evaluated using the 9-item Patient Health Questionnaire-9 (PHQ-9), anxiety symptoms were evaluated using the 7-item Generalized Anxiety Disorder (GAD-7), and insomnia symptoms were evaluated using the 7-item Insomnia Severity Index (ISI). The centrality indices are utilized in the network analysis, and using Network Comparison Test (NCT) to evaluate the differences between the network structures at two different time points.

Network analysis revealed that the central symptom value was ISI5 ("Interfere with your daily functioning") in the baseline networks and ISI4 ("Worried/distressed") in the follow-up networks, the symptom with the bridge symptom value in both networks was PHQ9-3 ("Sleep"). The NCT results revealed no significant differences in edge weights and global strength among participants who completed both baseline and follow-up surveys.

Our results suggest that central symptom (e.g., "Interfere with your daily functioning","Worried/distressed") and bridge symptom PHQ9-3 ("Sleep") can be prioritized as a target for intervention and treatment in patients with depression.

Older adults frequently face a myriad of physical and mental health challenges, which can contribute to feelings of stress and subsequent depression. Nevertheless, with age often comes a wealth of life experience and resilience. Perceived stress commonly predicts depression across all age groups, while meditation has been associated with lower levels of depression. Additionally, adherence to the Five Precepts is a prevalent practice among Thai individuals, particularly among older adults. However, it remains unclear how the combination of meditation and the Buddhist Five Precepts influences depression levels. This study aims to explore the predictive roles of meditation, the practice of the Five Precepts, and perceived stress on depression among older adults, with comparisons drawn to younger adults. A sample of 1472 individuals (232 were older adults and 1240 were adults) participated in the study. All completed the questionnaires for depression, perceived stress, meditation, and five precepts using the core symptom index, perceived stress scale, and inner strength-based inventory. Moderation model and mediation model analyses were employed to analyze the relationship between the perceived stress scores and the symptoms of depression for both older and younger samples. In the older adults group, 59.9% were female, with a mean age of 67.96 years (SD 6.8). In the adult group, 71.3% were male, with a mean age of 29.04 years (SD 10.5). The findings revealed that among the older adults' group, precepts, meditation, and their interaction significantly predicted a lower level of depressive symptoms (estimated coefficient = - 0.1082, 95% CI = - 0.1865, - 0.03). However, this association was not observed in the younger adults' group (estimated coefficient = -0.0199, 95% CI = - 0.0465, 0.0066). The variance explained in depressive symptoms changed from 24.9% in the linear model of perceived stress to 31.8% in the moderated moderation model, representing a 27.7% increase. Conversely, meditation and the five precepts mediated the relationship between stress and depressive symptoms in younger people but not in older adults. The indirect effect of perceived stress was significant only through the five precepts (estimated coefficient = - 0.3173, 95% CI = - 0.4787, - 0.1558; p = .0001). The variance explained in depressive symptoms changed from 42.2% in the linear model of perceived stress to 43.2% in the mediation model, representing a 2.5% increase. This study emphasizes that older adults may experience enhanced benefits from meditation and adherence to the Five Precepts compared to younger adults. Additionally, the effectiveness of meditation appears to be influenced by the extent of precept practice. Older individuals who actively engage in both high levels of precept adherence and meditation demonstrate a more significant buffering effect on the relationship between stress and depression. These findings suggest that lifestyle factors, such as religious practices like meditation and adherence to precepts, may have a differential impact on older adults compared to younger counterparts. The implications for older individuals are promising and warrant encouragement, while further research is needed to explore relevant factors contributing to reduced depression among younger populations.

Patient safety culture (PSC) is crucial for reducing medical errors and improving patient outcomes globally. This study aims to identify key improvement targets in China's PSC to promote a safer healthcare environment.

Data were extracted from two national PSC surveys conducted in 2016 and 2020 and were analyzed using the 12-dimensional Hospital Survey on Patient Safety Culture (HSOPSC) 1.0 questionnaire. Central targets were identified through strength, closeness, and betweenness centrality. Network stability was assessed using the case dropping bootstrap method.

A total of 24,529 responses were included, with an average positive response rate of 63.92%. Teamwork within units had the highest rate, and nonpunitive response to error the lowest. Feedback and communication about errors showed the greatest strength (1.302), closeness (0.008), and betweenness (22), occupying a core node position in both genders and correlating strongly with communication openness. After the COVID-19 pandemic, the core node position of management support for patient safety has become more prominent.

While teamwork is a notable strength, there is room to enhance the nonpunitive response to errors. Improving feedback and communication practices can further bolster openness and collaboration within teams, leading to an overall healthier work environment.

Image-based sexual abuse (IBSA) is a growing global issue with significant psychological impacts, yet few validated tools assess childhood experiences. This study provides information on a newly developed IBSA Scale designed to capture the accumulating burden of multiple distinct dimensions of IBSA. The study included 6226 U.S. participants aged 18-28, with an oversampling of IBSA experiences (prior to age of 18) and of sexual and gender minority individuals. Psychometric evaluation involved Exploratory Graph Analysis (EGA) and Confirmatory Factor Analysis (CFA) were conducted to assess the scale's structure. Convergent validity was tested using peer norms and online risky behaviors, and discriminant validity with social connectedness. EGA confirmed a cohesive structure and 100% stability of all items, confirmed by CFA (χ2(9) = 102.17, p < .01, CFI = .99, RMSEA = .041). Convergent validity was demonstrated by moderate correlations with peer norms (rmean = .41) and online risky behaviors (rmean = .24). Discriminant validity was confirmed with low correlations with social connectedness (rmean = -.06). Sociodemographic analysis revealed higher IBSA scores among females, sexual and gender minority individuals, and participants with lower-income (p < .001). The IBSA scale is a promising tool for measuring the cumulative burden of different IBSA experiences.

Trajectories of fundamental movement skill (FMS) development start diverging in females and males in early childhood, with determinants of this divergence spanning from individual to social and environmental factors. The present cross-sectional study focuses on the role of free outdoor play and aims to investigate whether sex differences in FMS typically observed in early childhood are associated with participation in free outdoor play.

One hundred and forty-two children aged 4.3 ± 0.8 yrs were evaluated for locomotor and object control skills (TGMD-3), weight status (BMI), and free outdoor play (parent-reported). Motor skill competence scores were submitted to moderated regression analyses to evaluate the individual and joint effects of sex, outdoor play, age, and BMI; interrelations among these variables were also estimated with network analysis.

Results of the moderated regression showed, beyond the expected prediction of motor skill competence (overall and object control skills) by sex and age, also a significant sex x outdoor play interaction, with higher motor skills being predicted by more frequent outdoor play in males only. The network analysis confirmed a positive association between outdoor play and motor skill competence in males but not in females.

Males might capitalize on free outdoor play opportunities as early as preschool age to engage in activities that promote their motor and especially object control skill development. Longitudinal studies are needed to test causality and derive practical indications for enabling both males and females to fully exploit the opportunities provided by free outdoor play to exercise both locomotor and object control skills.

The current study compared symptom networks between individuals exhibiting resilience and non-resilience trajectories of adaptation two years after the COVID-19 outbreak.

A population-representative sample (N = 906) reported symptoms of anxiety and depression in February-July 2020 (T1), March-August 2021 (T2), and September 2021-February 2022 (T3), as well as symptoms of post-traumatic stress disorder (PTSD) and adjustment disorder (AD) at T3. After differentiating between individuals with resilience and non-resilience trajectories using growth mixture modeling, network analyses were conducted to investigate group differences in T3 network symptoms (undirected and directed).

Despite non-significant group differences (M = 0.184, p = .380; S = 0.096, p = .681), distinctive qualitative characteristics were observed between networks. Difficulty relaxing was identified as the single root cause in the more diffused resilience network, with anxiety and depressive symptoms as additional starting points in the non-resilience network, which was more interconnected into clusters with clear-cut diagnostic boundaries. Sad mood demonstrated a transdiagnostic communicative role across common mental disorders.

Our results contribute to the understanding of anxiety-depression-PTSD-AD symptom networks in resilient and non-resilient individuals by highlighting the consequences of heterogeneity in adaptation capacity in the development of pandemic-related psychopathology.

First-episode schizophrenia represents a critical period for intervention in the treatment of schizophrenia. Understanding the intricate relationships between psychiatric symptoms and cognitive functions is vital for early precise intervention and predicting illness outcomes. Previous research has largely overlooked this issue, and traditional analytical methods based on pre-established theoretical assumptions are insufficient. This study aims to address this gap by utilizing graph theory-based network analysis.

The study employed the Positive and Negative Syndrome Scale (PANSS) to assess psychiatric symptoms. Cognitive functions were evaluated using the Digit Symbol and Information subtests from the Wechsler Adult Intelligence Scale (WAIS), which measure information processing efficiency and general knowledge, respectively. A network of psychiatric symptoms and cognitive functions was constructed based on these assessments.

The network analysis revealed that negative symptom nodes were central. Notably, node N1 (Blunted affect) showed a negative correlation with the Digit Symbol node, being the only psychiatric symptom node linked to cognitive functions. Community detection analysis indicated that cognitive, positive symptom, and negative symptom nodes tended to cluster within their respective categories, while general psychopathology nodes showed a tendency to cluster with various types of nodes. Some general psychopathology nodes were isolated, reflecting the concealed nature of certain psychiatric symptoms in first-episode schizophrenia patients.

This study innovatively applies network analysis to explore the characteristics of the psychiatric symptom-cognitive function network in Chinese patients with first-episode schizophrenia. The findings provide valuable theoretical insights for targeted symptom-based interventions and for predicting disease outcomes in first-episode schizophrenia.

Antisocial Personality Disorder (ASPD) is a personality disorder that entails significant impairments and/or costs at the individual, interpersonal, and community levels. Given its clinical relevance, scientific research is placing a significant focus on the study of the central characteristics of this condition to guide prevention and clinical practice. Within this framework, the present study aimed to investigate the associations and centrality of ASPD criteria and maladaptive trait domains in mental health professionals' conceptualization of the disorder, thus taking into account both categorical and dimensional approaches to personality pathology. The research involved 322 mental health professionals who reviewed the importance of ASPD criteria (Section II) and maladaptive trait domains (Criterion B of Section III). Data were analyzed using a network analysis approach. Both edge weights and node centrality were investigated. Within the criteria network, all centrality indices unanimously highlighted the role of lack of remorse. Regarding the trait domain network, the contributions of antagonism, detachment, and disinhibition were emphasized. The findings of this research collect and systematize the mental health professionals' perspective through the network analysis approach to provide further understanding of ASPD's central features. Such data may have useful practical implications for both research and clinical practice.

Brain health is a dynamic state involving cognitive, emotional, and motor domains. Measuring brain health is a challenge owing to the uncertainty as to whether it is one or many constructs. This study aimed to contribute evidence for brain health as a unified construct by estimating the strength of relationships between and among patient-reported items related to the brain health construct in a population with brain vulnerability owing to HIV.

Data for this cross-sectional analysis came from a Canadian cohort of people aging with HIV. The sample included 710 men recruited between 2014 and 2016 from five Canadian cities. A network analysis was conducted with 30 items selected from the brain-related domains of fatigue, cognition, depression, sleep, anxiety, and motivation. Node centrality measures were used to determine the most critical items in the network.

The network showed small-world properties, that is, most nodes can be reached from other nodes with few hops," indicating strong connectivity. The most central symptoms were "How much do you enjoy life?" and "How often do you have negative feelings?".

The small-world properties of the network structure indicate that brain health items are interconnected and may be influenced by shared underlying factors. The centrality indices suggest that items related to enjoyment of life and negative feelings may be particularly important for understanding brain health in this population. Future research should aim to replicate these findings in larger and more diverse samples to confirm their robustness and generalizability.

The manuscript considers multivariate functional data analysis with a known graphical model among the functional variables representing their conditional relationships (e.g., brain region-level fMRI data with a prespecified connectivity graph among brain regions). Functional Gaussian graphical models (GGM) used for analyzing multivariate functional data customarily estimate an unknown graphical model, and cannot preserve knowledge of a given graph. We propose a method for multivariate functional analysis that exactly conforms to a given inter-variable graph. We first show the equivalence between partially separable functional GGM and graphical Gaussian processes (GP), proposed recently for constructing optimal multivariate covariance functions that retain a given graphical model. The theoretical connection helps to design a new algorithm that leverages Dempster's covariance selection for obtaining the maximum likelihood estimate of the covariance function for multivariate functional data under graphical constraints. We also show that the finite term truncation of functional GGM basis expansion used in practice is equivalent to a low-rank graphical GP, which is known to oversmooth marginal distributions. To remedy this, we extend our algorithm to better preserve marginal distributions while respecting the graph and retaining computational scalability. The benefits of the proposed algorithms are illustrated using empirical experiments and a neuroimaging application.

In emotional eating (EE), affective states influence desire to eat and/or eating behavior. Most research on EE focuses on over-/under-eating in response to negative emotions, the former of which is related to higher weight and binge-spectrum eating disorder (ED) symptoms while the latter has been implicated in low weight and restrictive-only ED. Individuals endorsing both forms of negative EE are more impaired than those reporting unidirectional negative EE. Less is known about the influence of positive emotions on eating, in part due to a lack of self-report measures of positive over- and under-eating as separate constructs. The current study presents the development, including item reduction and initial factorial validity (primary aims) and convergent validity (secondary aims) of the Comprehensive Emotional Eating Scale (CEES), a four-factor measure of positive and negative over- and under-eating. The initial 134-item pool for the CEES was based on a review of extant emotional eating scales and emotion words from the Positive and Negative Affect Schedule. The item pool was reduced and four factors identified using exploratory graphical analysis. The structure of the final set of 40 items was evaluated using confirmatory factor analysis. Convergent validity of the negative over- and under-eating scales was established using measures of body image-focused eating disorder and ARFID symptoms, and exploratory data on the correlates of positive over- and under eating were reported. The CEES showed preliminary evidence of factorial and convergent validity in a US-based convenience sample. It allows for classification on all four quadrants of emotional eating.

In the last half of the twentieth century, psychology and neuroscience have experienced a renewed interest in intraindividual variation. To date, there are few quantitative methods to evaluate whether a population (between-person) structure is likely to hold for individual people, often referred to as ergodicity. We introduce a new network information theoretic metric, the ergodicity information index (EII), that quantifies the amount of information lost by representing all individuals with a between-person structure. A Monte Carlo simulation demonstrated that EII can effectively delineate between ergodic and nonergodic systems. A bootstrap test is derived to statistically determine whether the empirical data is likely generated from an ergodic process. When a process is identified as nonergodic, then it's possible that a mixture of groups exist. To evaluate whether groups exist, we develop an information theoretic clustering method to detect groups. Finally, two empirical examples are presented using intensive longitudinal data from personality and neuroscience domains. Both datasets were found to be nonergodic, and meaningful groupings were identified in each dataset. Subsequent analysis showed that some of these groups are ergodic, meaning that the individuals can be represented with a single population structure without significant loss of information. Notably, in the neuroscience data, we could correctly identify two clusters of individuals (young vs. older adults) measured by a pattern separation task that were related to hippocampal connectivity to the default mode network.

Network analysis has become an essential tool in biological and biomedical research, providing insights into complex biological mechanisms. Since biological systems are inherently time-dependent, incorporating time-varying methods is crucial for capturing temporal changes, adaptive interactions, and evolving dependencies within networks. Our study explores key time-varying methodologies for network structure estimation and network inference based on observed structures. We begin by discussing approaches for estimating network structures from data, focusing on the time-varying Gaussian graphical model, dynamic Bayesian network, and vector autoregression-based causal analysis. Next, we examine analytical techniques that leverage pre-specified or observed networks, including other autoregression-based methods and latent variable models. Furthermore, we explore practical applications and computational tools designed for these methods. By synthesizing these approaches, our study provides a comprehensive evaluation of their strengths and limitations in the context of biological data analysis.

The glymphatic system is a glial-based perivascular network that facilitates the clearance of metabolic waste from the brain. Dysfunction of the glymphatic system, along with neurological symptoms such as cognitive deficits and olfactory dysfunction, has been reported in patients with coronavirus disease (COVID-19). However, the link between these neurological symptoms and alterations in the glymphatic system remains unclear. In this study, we aimed to evaluate magnetic resonance imaging (MRI)-based measures of the glymphatic system in patients recovered from COVID-19 with and without neurological symptoms.

This study included 89 patients who recovered from respiratory infections, of whom 71 had confirmed COVID-19 (20 experienced anosmia and 41 had cognitive symptoms). Three MRI-based measures were quantified and compared: the dilated perivascular spaces (dPVS), free water (FW) fraction, and diffusion tensor image analysis along the perivascular spaces (DTI-ALPS). A partial correlation network was used to assess the relationships between COVID-19 infection, neurological symptoms, and glymphatic measures.

COVID-19 patients with anosmia had increased FW in the left orbitofrontal area compared to those without anosmia (mean difference: 0.01, p = 0.48), while patients with cognitive symptoms showed decreased left-sided DTI-ALPS (mean difference: 0.06, p = 0.40). Neurological symptoms mediate the relationship between COVID-19 and glymphatic system measures.

Our findings imply that neurological symptoms accompanied by COVID-19 are linked to distinct alterations in the glymphatic system, suggesting a potential association between neuroinvasion and neuroinflammatory processes related to COVID-19.

Non-suicidal self-injury (NSSI) in adolescents has a complex etiology and a wide range of negative consequences. This study aimed to assess the interactions between NSSI and sleep quality and cytokines and explore the association of these factors with cognitive flexibility.

A cross-sectional survey was conducted from January 2022 to September 2024 in Qingdao, China. Adolescent Non-Suicidal Self-Injury Assessment Questionnaire, Pittsburgh Sleep Quality Index, and Wisconsin Card Sorting Test 128 card version were used to assess the NSSI, sleep quality, and cognitive flexibility. Levels of 12 serum cytokines were measured. Network analysis was performed by R software (version 4.4.1) to identify the central nodes and bridging symptoms of the network and all nodes' association with cognitive flexibility.

A total of 337 adolescents with depressive disorders were included in the study. In the NSSI-Sleep Quality-Cytokines Network "Intentional scratches", "IL-12p70", and "Intentionally hitting oneself with fists or harder objects" were central nodes in the network. Furthermore, sleep-related variables such as "Sleep disturbance" and "Sleep duration" were identified as bridge symptoms. No direct association between NSSI and cognitive flexibility was observed.

The cross-sectional design, reliance on self-reported data, and restricted geographic sample limit the ability to establish causal relationships and generalize the findings.

IL-12p70 plays a significant role in the development of NSSI among adolescents with depressive disorders. Sleep problems facilitate the interaction between NSSI and cytokines. Cognitive flexibility may be related to NSSI through indirect pathways.

Smartphone addiction, which represents a behavioral concern, has been found to correlate with a diverse range of mental health problems among the undergraduate students. Meanwhile, the modes of behavioral inhibition/activation showed specific characteristics in smartphone addiction individuals. Therefore, clarifying the interaction of mental health and behavioral modes with smartphone addiction is urgent. Here, we used a cross-sectional design, aiming to explore the network interactions among smartphone addiction, mental health (depression, anxiety, stress, loneliness and life satisfaction), and the behavioral inhibition/activation system (BIS/BAS) in undergraduate students.

We employed network analysis and community detection in R, to investigate the centrality and edge connections, which are the mainly index of key factors and interaction effects between factors respectively. The study was carried out among 701 participants with an average age of 18.27 ± 1.57 years old, all of whom had completed self-report scales.

The network analysis results suggested that depression and anxiety, the behavioral activation of fun seeking, reward responsiveness and behavioral inhibition had significantly higher centrality than the other mental health and BIS/BAS factors respectively. When combining the connections of network analysis and community detection, the stronger connections mainly present in the inner domain of mental health factors and the BIS/BAS, respectively. The findings regarding the transdomain connections imply that stress is likely to serve as a mediator in the relationships between smartphone addiction and both depression as well as BAS-Drive.

Therefore, the current study suggests that although common psychological and behavioral system problems may play a dominant role in smartphone addiction among undergraduate students, it is still necessary to consider whether other factors such as stress may play a potential moderating role. The new insight will help to enhance mental health and facilitating proper smartphone use management to avoid the aggravation of addiction problems among undergraduate student.

Many studies have aimed to understand the impact of the COVID-19 pandemic on mental health. However, less research has focused on the changes in symptom clusters of comorbid disorders. To understand the impact of the COVID-19 pandemic it is necessary to evaluate the relationships between symptoms of comorbid disorders. This was the first study to compare comorbidity networks of depression, anxiety and eating disorder (ED) symptoms to investigate the overall connectivity of symptoms before and during the onset of the pandemic.

Self-report questionnaire data from 1361 adolescent psychiatric inpatients (Mage = 15.32, SD = 1.47) were used for this study. A network analysis was conducted including 52 questionnaire items of depression, anxiety and eating disorder to identify and compare core symptoms and bridge symptoms in a pre and a peri pandemic sample.

A significantly higher network density and overall connectivity were found in the peri pandemic sample. Links between feelings of failure in the depression cluster and worry what other people think in the anxiety cluster as well as between difficulties getting rid of bad/ silly thoughts in the anxiety cluster and suicidal thoughts in the depression cluster emerged as the strongest pathways in both networks. Body image disturbance emerged as the strongest bridge symptom for eating disorders in both networks. There were no significant differences in the most prominent core and bridge symptoms between the networks, indicating a high stability of core symptoms and pathways across circumstances.

Our findings suggest a multidimensional relationship between symptoms of depression, anxiety, and eating disorders. The persistence of symptom pathways after the onset of the pandemic implies that these pathways may be responsible for the occurrence of comorbidity and should be primary targets of psychotherapy for affected patients. Addressing core and bridge symptoms in the therapy of comorbid disorders should be a priority and may be more effective than conventional treatment strategies.

Depression in middle-aged individuals is influenced by multiple factors; however, research focusing on this demographic remains limited. The social-ecological system framework explores how interactions among individual characteristics, social relationships, and environmental contexts contribute to health outcomes. This study aimed to assess the prevalence, core manifestations, and influencing factors of depression through a comprehensive model grounded in the social-ecological system.

Using data from the 2018 Chinese Labor Dynamic Survey, a total of 3,799 middle-aged individuals (mean age: 52.35 years; 53.75% female) were included. Depression was assessed using the Center for Epidemiological Studies Depression Scale, with a cut-off score of 36. We used a graphical gaussian model to identify the measurement network and core symptoms of midlife depression, and logistic regression to identify influencing factors. A social-ecological model of middle-aged depression was constructed through a Mixed Graphical Model.

23.74% of participants exhibited clinically significant depression. Feelings of disgust (Strength = 2.18, Expected Influence = 1.30) and incapacity (Strength = 1.29, Expected Influence = 1.57) emerged as the most central symptoms. Higher global strength (GS = 8.89, p < 0.001) of the network suggested stronger associations and mutual exacerbation of symptoms. Logistic regression showed that education, exercise, and subjective feeling were associated with depression risk (p < 0.05). The social-ecological system emphasized the importance of education (Strength = 2.50, Expected Influence = 2.50) and complex interaction between subjective and objective influencing factors.

This study found middle-aged individuals had a high prevalence of depression, characterized by feelings of disgust and incapacity. Influencing factors spanned individual, social and environmental levels. These results emphasize the role of education in depressive individuals, providing guidance for potential future interventions.

Post-stroke depression (PSD) is a common neuropsychiatric problem associated with a high disease burden and reduced quality of life (QoL). To date, few studies have examined the network structure of depressive symptoms and their relationships with QoL in stroke survivors.

This study aimed to explore the network structure of depressive symptoms in PSD and investigate the interrelationships between specific depressive symptoms and QoL among older stroke survivors.

This study was based on the 2017-2018 collection of data from a large national survey in China. Depressive symptoms were assessed using the 10-item Centre for Epidemiological Studies Depression Scale (CESD), while QoL was measured with the World Health Organization Quality of Life-brief version. Network analysis was employed to explore the structure of PSD, using expected influence (EI) to identify the most central symptoms and the flow function to investigate the association between depressive symptoms and QoL.

A total of 1123 stroke survivors were included, with an overall prevalence of depression of 34.3% (n=385; 95% confidence interval 31.5% to 37.2%). A higher risk of PSD was significantly associated with limited activities of daily living (odds ratio (OR)=1.340; p=0.048), presence of heart diseases (OR=1.589; p=0.002) and more severe anxiety symptoms (OR=1.472; p<0.001). In the network model of depression, the most central symptoms were CESD3 ('feeling blue/depressed', EI: 1.180), CESD6 ('feeling nervous/fearful', EI: 0.864) and CESD8 ('loneliness', EI: 0.843). In addition, CESD5 ('hopelessness', EI: -0.195), CESD10 ('sleep disturbances', EI: -0.169) and CESD4 ('everything was an effort', EI: -0.150) had strong negative associations with QoL.

This study found that PSD was common among older Chinese stroke survivors. Given its negative impact on QoL, appropriate interventions targeting central symptoms and those associated with QoL should be developed and implemented for stroke survivors with PSD.

Chinese older adults frequently encounter limitations in activities of daily living (ADL) and experience depression. Prior research has not deeply explored the interconnectedness of these factors through network analysis.

The study utilized data from 2,137 older adults aged 65 and older, sourced from the 2018 China Health and Retirement Longitudinal Study (CHARLS). The ADL scale and CESD-10 were employed to assess ability to perform ADL and depression, respectively. We conducted network modeling and bridge expected influence (BEI) evaluations to investigate the relationships between these ADL and depression.

Our network analysis revealed robust connections between ADL and depressive symptoms. Specifically, somatic symptoms emerged as significant predictors of depression risk with the highest BEI of 0.21, whereas positive symptoms exhibited a protective effect with the highest BEI of 0.13. Notably, toileting with the highest BEI of 0.04 among the ADL was identified as a pivotal node linking ADL to depression.

This study illuminated the complex interplay between ADL and depression in Chinese older adults, with toileting serving as a crucial connecting point. Our findings offer valuable insights that can inform efforts to enhance mental health and improve the quality of life for this population.

Insomnia and hypersomnolence symptoms are prevalent among university students, yet their assessment methods face limitations, and the relationship between these symptoms remains underexplored. We examined the structural invariance of the Sleep Condition Indicator (SCI) and the Idiopathic Hypersomnia Severity Scale (IHSS) in university students. In addition, we proposed a network approach to the insomnia and hypersomnolence symptoms. A total of 433 university students underwent a clinical interview for sleep and socio-demographics and completed the SCI and the IHSS. Confirmatory factorial and network analyses were conducted. The SCI demonstrated a two-factor structure, while the IHSS exhibited a three-factor structure. Over 70% of students scored above the IHSS clinical threshold, while 36.5% were diagnosed with insomnia. For the SCI, a threshold of ≤ 13 demonstrated the highest predictive value for diagnosing insomnia. Network analyses highlighted the central role of cognitive consequences of insomnia and hypersomnolence. Cognitive and emotional consequences of insomnia and hypersomnolence were moderately interconnected. Higher cognitive complaints related to insomnia were associated with increased feelings of insufficient sleep and more pronounced sleep inertia. Additionally, greater wakefulness after sleep onset was linked to both a shorter ideal night-time sleep duration and increased difficulty staying awake during low-stimulation activities throughout the day. The SCI and IHSS showed structural invariance in university students when compared to the general population. Insomnia and hypersomnolence represent critical clinical issues among French students. We underscored the intricate relationship between insomnia and hypersomnolence, highlighting the urgent need for targeted interventions that address both daytime and nighttime sleep-wake disturbances.

Poor life-satisfaction, feelings of loneliness, depression, and anxiety are commonly reported in the Japanese general population. The aim of this study was to examine the network structure of these common mental health problems in a Japanese sample and to explore differences between males and females. Participants (N = 476; 235 males, 241 females) were recruited from a Japanese general population and completed questionnaires to measure levels of depression, anxiety, loneliness, and life satisfaction. A psychometric network analysis was conducted using a Gaussian Graphical Model, and a network comparison test (NCT) was performed to evaluate overall consistency and differences in global network connectivity. The network analysis identified problems relaxing, sad mood, feelings of isolation, and poor life satisfaction as central nodes in the overall network. The NCT revealed no significant differences between the male and female networks in the global network structure as well as connectivity. However, the strength values for restlessness and tiredness were significantly higher in the network for males than in the network for females. There were no differences in the global network structure and connectivity of mental health psychopathology between males and females. Problems relaxing, feelings of depression, being isolated from others, and poor life satisfaction contribute to poor mental health and may be potential intervention targets.