Networks of major depressive disorder: A systematic review

Social Anxiety and Its Comorbidity With Depression in Medical Students: A Network Analysis

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.

A cell-type-specific circuit of somatostatin neurons in the habenula encodes antidepressant action in male mice

Major depression is characterized by an array of negative experiences, including hopelessness and anhedonia. We hypothesize that inhibition of negative experiences or aversion may generate antidepressant action. To directly test this hypothesis, we perform multimodal behavioral screenings in male mice and identify somatostatin (SST)-expressing neurons in the region X (HBX) between the lateral and medial habenula as a specific type of antidepressant neuron. SST neuronal activity modulation dynamically regulates antidepressant induction and relief. We also explore the circuit basis for encoding these modulations using single-unit recordings. We find that SST neurons receive inhibitory synaptic inputs directly from cholecystokinin-expressing neurons in the bed nucleus of the stria terminalis and project excitatory axon terminals onto proenkephalin-expressing neurons in the interpeduncular nucleus. This study reveals a cell-type-specific circuit of SST neurons in the HBX that encodes antidepressant action, and the control of the circuit may contribute to improving well-being.

Network Analysis of Basic Psychological Need Frustration and Depressive Symptoms Among Adolescents: A Longitudinal Study

Studies have shown a reciprocal relationship between basic psychological need frustration and depressive symptoms in adolescents. However, the underlying pattern of this relationship remains unclear. This longitudinal study employed network analysis to examine the central nodes, bridge nodes, and dynamic changes in the network structure of psychological need frustration and depressive symptoms among 1,381 Chinese adolescents (51.85% girls; Mage = 15.17, SD = 0.49, range: 13-16). The results revealed that competence frustration, mood, fatigue, and worthlessness were central nodes, exhibiting strong connections with other nodes in the network. Furthermore, competence frustration and worthlessness functioned as bridge nodes, linking psychological need frustration with depressive symptoms. Notably, the network structure remained stable over time, with no significant changes observed. These findings highlight the critical role of competence frustration and worthlessness in the co-occurrence of psychological need frustration and depressive symptoms. Targeting these nodes in interventions may help reduce adolescent distress and improve mental health outcomes.

Prediction of electroconvulsive therapy outcome: A network analysis approach

OBJECTIVE

While electroconvulsive therapy (ECT) for the treatment of major depressive disorder is effective, individual response is variable and difficult to predict. These difficulties may in part result from heterogeneity at the symptom level. We aim to predict remission using baseline depression symptoms, taking the associations among symptoms into account, by using a network analysis approach.

METHOD

We combined individual patient data from two randomized controlled trials (total N = 161) and estimated a Mixed Graphical Model to estimate which baseline depression symptoms (corresponding to HRSD-17 items) uniquely predicted remission (defined as either HRSD≤7 or MADRS<10). We included study as moderator to evaluate study heterogeneity. For symptoms directly predictive of remission we computed odds ratios.

RESULTS

Three baseline symptoms were uniquely predictive of remission: suicidality negatively predicted remission (OR = 0.75; bootstrapped confidence interval (bCI) = 0.44-1.00) whereas retardation (OR = 1.21; bCI = 1.00-2.02) and hypochondriasis (OR = 1.31; bCI = 1.00-2.25) positively predicted remission. The estimated effects did not differ across trials as no moderation effects were found.

CONCLUSION

By using a network analysis approach this study identified that the presence of suicidal ideation predicts an overall worse treatment outcome. Psychomotor retardation and hypochondriasis, on the other hand, seem to be associated with a better outcome.

The Dialectical Relationship Between Burnout and Work Engagement: A Network Approach

Based on previous empirical evidence, scholars have argued for a dialectical relationship between burnout and work engagement. However, these conclusions largely rely on latent variable models, which have inherent limitations due to strong hierarchical assumptions about data. As a result, previous findings should be considered preliminary indications rather than conclusive evidence. In this study, we introduce network analysis to investigate the relationship between burnout and work engagement. We demonstrate its utility by comparing the fit indices of the network model and three factor models in a cross-national sample with 2469 employees from Norway and 879 employees from the US and UK. Based on theory and fit indices, we conclude that the network model was preferred in both samples. Using this model, we uncovered positive relationships between two work engagement variables and the exhaustion component of burnout. Theoretically, this study broadens our understanding and show support for the dialectical relationship between burnout and work engagement. Furthermore, by introducing the network model to empirical research, we provide a novel approach that contribute to nuance and ideas for research on burnout and work engagement. Practically, our results offer insightful data on possible points for intervention between burnout and work engagement variables.

Longitudinal associations of depressive symptoms in father-mother-child triads: A cross-lagged panel network analysis

BACKGROUND

The current study aimed to test symptom-level associations underlying the concordance of depressive symptoms in father-mother-child triads. We used network analysis to examine central and bridge symptoms in the contemporaneous depressive network of triads and additionally assessed prospective relationships in temporal depressive networks.

METHODS

We included 881 father-mother-child triads with children aged 10 to 14 years from the China Family Panel Studies. Depressive symptoms were assessed by the short version of the Center for Epidemiologic Studies Depression Scale (CESD) across three different time points from 2012 to 2018. Contemporaneous and temporal networks (2012 → 2016 and 2016 → 2018) were estimated to examine the cross-sectional and longitudinal relationships between symptoms.

RESULTS

Within the contemporaneous network, "feeling depressed" was the most central symptom. Parental "could not get going" was identified as the bridge symptom across almost all cross-sectional networks. In the temporal network (2012 → 2016), fathers' symptoms were likely to influence mothers' symptoms. Over time (2016 → 2018), offspring symptoms (such as "could not get going") began to affect their parents. Certain symptoms were more influential than others: for instance, fathers' "could not get going" significantly predicted mothers' "bad life" and feeling that "everything was an effort" in 2016. Fathers' "could not get going" in 2016 significantly predicted children's "bad life" and "lack of happiness" in 2018.

LIMITATIONS

A self-reported scale other than clinical diagnosis was used to assess depressive symptoms.

CONCLUSIONS

The current study demonstrates that family members mutually influence each other in specific depressive symptoms. Therefore, family-based treatments that combat depression in youth should also involve both parents and target core depressive symptoms to disrupt the cycle of depression within the family context.

Demoralization's link to depression and anxiety symptoms: A network analysis

Demoralization represents a clinical syndrome conceptualized as maladaptive coping to a stressor associated with discouragement, feelings of hopelessness, helplessness, and a loss of meaning in life. It is a prevalent comorbidity in individuals with severe physical illnesses and affects a substantial proportion of the general population when facing global stressors like the COVID-19 pandemic. The main aim of the study was to test whether demoralization and features of depression and anxiety might reflect distinct entities within the general population, specifically in older adults, and to explore symptom interconnections, using a network psychometrics approach. The revised demoralization scale (DS-II) and the patient health questionnaire 4 (PHQ-4) were applied to a representative sample (N = 2434) from the German general population. Network structures were analyzed using exploratory graph analysis (EGA) to identify the most distinct symptom groupings and their relationships. Stability of networks and symptom groupings was tested using bootstrap procedures. EGA revealed unidimensionality within younger adulthood and a four-factor solution within older adults, reliably distinguishing PHQ-4 and DS-II items. The most central features of the network were worthlessness, pointlessness, helplessness, feeling trapped and low mood. Suicidal ideation was more closely related to DS-II worthlessness than to PHQ-4 items. The cross-sectional design and using PHQ-4 instead of more comprehensive measures of depression and anxiety were limitations. In conclusion, in general population, demoralization symptoms can be distinguished from lack of interest or pleasure and low mood. It might represent a valid psychological construct beyond clinical populations. Further investigation of diagnostic implications is encouraged.

Alterations in orbitofrontal cortex communication relate to suicidal attempts in patients with major depressive disorder

BACKGROUND

Investigating how the interaction between the orbitofrontal cortex (OFC) and various brain regions/functional networks in major depressive disorder (MDD) patients with a history of suicide attempt (SA) holds importance for understanding the neurobiology of this population.

METHODS

We employed resting-state functional magnetic resonance imaging (rs-fMRI) to analyze the OFC's functional segregation in 586 healthy individuals. A network analysis framework was then applied to rs-fMRI data from 86 MDD-SA patients and 85 MDD-Control patients, utilizing seed mappings of OFC subregions and a multi-connectivity-indicator strategy involving cross-correlation, total interdependencies, Granger causality, and machine learning.

RESULTS

Four functional subregions of left and right OFC, were designated as seed regions of interest. Relative to the MDD-Control group, the MDD-SA group exhibited enhanced functional connectivity (FC) and attenuated interaction between the OFC and the sensorimotor network, imbalanced communication between the OFC and the default mode network, enhanced FC and interaction between the OFC and the ventral attention network, enhanced interaction between the OFC and the salience network, and attenuated FC between the OFC and the frontoparietal network.

LIMITATIONS

The medication and treatment condition of patients with MDD was not controlled, so the medication effect on the alteration model cannot be affirmed.

CONCLUSION

The findings suggest an imbalanced interaction pattern between the OFC subregions and a set of cognition- and emotion-related functional networks/regions in the MDD-SA group.

Network Structure of Transdiagnostic Dimensions of Emotional Disorders in Adolescents With Subthreshold Anxiety and Depression: Links With Psychopathology and Socio-Emotional Adjustment

BACKGROUND

The transdiagnostic and network model approaches to psychopathology have emerged as an alternative to taxonomic systems. The main goal was to examine the network structure of the transdiagnostic dimensions of emotional disorders in adolescents with subclinical emotional symptoms. In addition, cross-sectional network analyses were conducted to investigate the relationships between transdiagnostic dimensions, psychopathology, and socio-emotional adjustment.

METHOD

The sample consisted of a total of 476 adolescents from 85 schools from different provinces in Spain with subthreshold anxiety and depression. The mean age was 13.77 years (SD = 1.43, range: 10-18 years), 73.9% were female. Several questionnaires assessing transdiagnostic dimensions of emotional disorders, mental health difficulties, self-esteem, perceived social support, sense of belonging at school, and prosocial behaviour were used.

RESULTS

A strong interrelation was found between the transdiagnostic variables, except with the positive temperament dimension. The dimensions with the greatest weight were those of traumatic re-experiencing, intrusive cognitions, avoidance, neuroticism, and depressed mood. Negative relationships were found between the transdiagnostic dimensions of emotional disorders and the protective factors included in the study. Stability analyses indicated that the networks were accurately estimated.

CONCLUSION

The present paper points to the value of conceptualising emotional disorders from a transdiagnostic and network model perspective. In addition, the work provides new insights into the nature of the relationships between transdiagnostic dimensions of emotional disorders, and the role played by risk and protective factors.

Gender Difference in the Onset of Adolescent Depressive Symptoms: A Cross-Lagged Panel Network Analysis

Depressive symptoms are prevalent in adolescents, especially girls, underscoring the need for early detection and targeted interventions. Identifying initial symptoms and their temporal associations is vital for such interventions. This study used cross-lagged panel network (CLPN) analysis to examine the central depressive symptoms and their interconnections within a national cohort derived from the China Family Panel Study (CFPS). The participants included 2524 adolescents (45.8% girls), with depressive symptoms assessed using the Epidemiological Studies Depression Scale (CES-D-8) in 2016 (Mage = 12.30) and 2018 (Mage = 14.25). The CLPN model showed that "loneliness" and "not getting going (fatigue)" at T1 were the strongest predictors of subsequent depressive symptoms at T2, after controlling for demographic variables and depressive symptoms at T1. Conversely, depressed mood and anhedonia at T2 were most likely to be influenced by other symptoms at T1. Gender-stratified analyses identified "loneliness" as the initial symptom in girls and "fatigue" for boys. Additionally, girls exhibited stronger reciprocal associations among depressive symptoms than boys. The findings suggest that addressing interpersonal loneliness is crucial for adolescent girls, whereas somatic fatigue should be a focus for adolescent boys, highlighting the need for gender-specific approaches in early intervention strategies. This research provides insights into the distinct gendered networks of depressive symptomatology in adolescents, informing tailored prevention and intervention efforts.

Pinocembrin alleviates LPS-induced depressive-like behavior in mice via the NLRP3/DCC signaling pathway

OBJECTIVE

Depression, a prevalent and severe mental disorder, continues to be a significant area of research concerning its pathogenesis and therapeutic approaches. Conventional antidepressants are often limited by delayed therapeutic effects and notable adverse reactions, necessitating the development of innovative and efficacious treatment modalities. Multiple lines of evidence suggest that peripheral and central inflammation play a role in depression, and that anti-inflammatory drugs can ameliorate depressive symptoms in patients with inflammation-related depression. Pinocembrin (PB), a natural bioactive compound, is renowned for its anti-inflammatory and antioxidant properties, while the effect and mechanism of PB are still unclear. Consequently, this study employs PB as an intervention to investigate its effects on depression in mice model, with the objective of establishing a novel therapeutic strategy and foundational data for the treatment of depression.

METHODS

(1) The acute inflammation model used lipopolysaccharide (LPS) to induce depression-like behavior in mice by injecting LPS intraperitoneally at a dose of 0.83 mg/kg. The effects of PB (20 mg/kg, i.p.) and the NLRP3 inflammasome inhibitor MCC950 (10 mg/kg, i.p.) on improving depression behavior in mice were evaluated. (2) To explore the specific mechanism of PB in improving depression-like behavior in LPS mice by regulating NLRP3 and Netrin-1/DCC pathway.

RESULTS

The results showed that after intraperitoneal injection of LPS, the mice exhibited a significant decrease in body weight, sucrose preference score, and a significant increase in tail suspension immobility time. Treatment with PB and MCC950 increased the sucrose preference score and decreased the tail suspension immobility time. Besides, PB and MCC950 could inhibit the expression of NLRP3 related neuroinflammation, down-regulated the Netrin-1/DCC signaling pathway, and improved hippocampal neuroplasticity in mice.

CONCLUSION

In conclusion, PB significantly improved LPS-induced depression-like behavior in mice by reducing the expression of hippocampal NLRP3 inflammasome and down-regulating the Netrin-1/DCC signaling pathway. Additionally, PB was found to regulate α-amino-3-hydroxy-5-methyl-4 isoxazole receptor (AMPAR) and postsynaptic density 95 (PSD95), protecting excitatory synaptic transmission and enhancing synaptic plasticity. This study demonstrates the effectiveness of PB in improving depressive symptoms induced by LPS and provides a new strategy for the clinical treatment of depression.

The same but different too: Depression profiles in young adults without a history of psychiatric treatment identified using Bayesian and partial correlation networks

Depression is a heterogenous diagnostic construct; however, dynamic interactions between specific depressive symptoms across their qualitatively different profiles remain largely unknown. The study aimed to recognize the most prevalent profiles of depressive symptoms and assess their dynamics in young adults without a history of psychiatric treatment. Depressive symptoms were recorded using the Patient Health Questionnaire-9 (PHQ-9). The data were assessed for all theoretical and empirical combinations of depressive symptoms in participants with a positive screening for depression. The profiles identified in the majority of participants were analyzed using partial correlation and Bayesian networks. Data from 3583 individuals with a positive screening for depression were analyzed. Out of 382 theoretical profiles, 150 profiles (39.3%) were present in this dataset. The majority of participants (56.8%) showed 4 profiles of depressive symptoms including the profile with all depressive symptoms present, the profile without suicidal ideation, the profile without psychomotor impairment, and the profile without both psychomotor impairment and suicidal ideation. The profiles differed largely in terms of their dynamics and symptoms that are necessary to activate the whole network. The network characteristics within specific profiles did not differ significantly across the level of difficulties attributable to depressive symptoms. Our findings indicate that depression emerging in young adults shows a limited number of symptom profiles. However, dynamics of depressive symptoms differs largely between specific profiles regardless of functional impairment indicating the need to personalize therapeutic approaches. Future studies should further disentangle the heterogeneity of depressive symptoms, e.g., by dissecting the symptoms that are combined together by single PHQ-9 items (i.e., hypersomnia and insomnia; psychomotor agitation and retardation).

The Global and Local Patterns of Reading-Related Cognitive and Ecological Variables in Chinese First-Grade Children: A Cross-Sectional Network Analysis

In the current study, we tested a network model of reading difficulty by using state-of-the-art psychological network analysis. Four hundred and fifty-three Chinese first-grade children (about 38% female, mean age = 7.00, SD = 0.41) were divided into good (n = 154), competent (n = 147), and struggling readers (n = 152) based on their scores of Chinese character reading. The Extended Bayesian Information Criterion graphical lasso (EBICglasso) method was applied to estimate cross-sectional networks for the three groups. Each network included four cognitive nodes (homophone awareness, morphological structure awareness, phonological awareness, and vocabulary) and two ecological nodes (family socioeconomic status and the number of books at home). Chronological age and nonverbal intelligence were also included in the estimated networks. The global (i.e., global structure and global connectivity) and local patterns (i.e., the most important edges and nodes) in each network were reported. The network comparison results showed that global connectivity was significantly lower among struggling readers than for good readers, implying that a holistic impairment of bidirectional connections among multiple variables relates to the difficulty in learning to read. The theoretical and empirical implications and the significance of applying the network approach to reading research are discussed.

Machine learning and Bayesian network analyses identifies associations with insomnia in a national sample of 31,285 treatment-seeking college students

BACKGROUND

A better understanding of the relationships between insomnia and anxiety, mood, eating, and alcohol-use disorders is needed given its prevalence among young adults. Supervised machine learning provides the ability to evaluate which mental disorder is most associated with heightened insomnia among U.S. college students. Combined with Bayesian network analysis, probable directional relationships between insomnia and interacting symptoms may be illuminated.

METHODS

The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. We used a 4-step statistical approach: (1) at the disorder level, an elastic net regularization model examined the relative importance of the association between insomnia and 7 mental disorders (major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder); (2) This model was evaluated within a hold-out sample. (3) at the symptom level, a completed partially directed acyclic graph (CPDAG) was computed via a Bayesian hill-climbing algorithm to estimate potential directionality among insomnia and its most associated disorder [based on SHAP (SHapley Additive exPlanations) values)]; (4) the CPDAG was then tested for generalizability by assessing (in)equality within a hold-out sample using structural hamming distance (SHD).

RESULTS

Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .44 (.01); RMSE = 5.00 (0.08)], with comparable performance in the hold-out sample (R2 = .33; RMSE = 5.47). SHAP values indicated that the presence of any mental disorder was associated with higher insomnia scores, with major depressive disorder as the most important disorder associated with heightened insomnia (mean |SHAP|= 3.18). The training CPDAG and hold-out CPDAG (SHD = 7) suggested depression symptoms presupposed insomnia with depressed mood, fatigue, and self-esteem as key parent nodes.

CONCLUSION

These findings provide insights into the associations between insomnia and mental disorders among college students and warrant further investigation into the potential direction of causality between insomnia and depression.

TRIAL REGISTRATION

Trial was registered on the National Institute of Health RePORTER website (R01MH115128 || 23/08/2018).

Multi-scale modeling to investigate the effects of transcranial magnetic stimulation on morphologically-realistic neuron with depression

Transcranial magnetic stimulation (TMS) is a non-invasive neuromodulation technique to activate or inhibit the activity of neurons, and thereby regulate their excitability. This technique has demonstrated potential in the treatment of neuropsychiatric disorders, such as depression. However, the effect of TMS on neurons with different severity of depression is still unclear, limiting the development of efficient and personalized clinical application parameters. In this study, a multi-scale computational model was developed to investigate and quantify the differences in neuronal responses to TMS with different degrees of depression. The microscale neuronal models we constructed represent the hippocampal CA1 region in rats under normal conditions and with varying severities of depression (mild, moderate, and major depressive disorder). These models were then coupled to a macroscopic TMS-induced E-Fields model of a rat head comprising multiple types of tissue. Our results demonstrate alterations in neuronal membrane potential and calcium concentration across varying levels of depression severity. As depression severity increases, the peak membrane potential and polarization degree of neuronal soma and dendrites gradually decline, while the peak calcium concentration decreases and the peak arrival time prolongs. Concurrently, the electric fields thresholds and amplification coefficient gradually rise, indicating an increasing difficulty in activating neurons with depression. This study offers novel insights into the mechanisms of magnetic stimulation in depression treatment using multi-scale computational models. It underscores the importance of considering depression severity in treatment strategies, promising to optimize TMS therapeutic approaches.

Understanding the relationship between childhood abuse and affective symptoms in bipolar disorder: New insights from a network analysis

The impact of childhood abuse on the presentation of bipolar disorder could be further elucidated by comparing the networks of affective symptoms among individuals with and with no history of childhood abuse. Data from 476 participants in the Clinical Health Outcomes Initiative in Comparative Effectiveness for Bipolar Disorder study were used to fit several regularised Gaussian Graphical Models. Differences in the presentation of depressive and manic symptoms were uncovered: only among participants with a history of childhood abuse, inadequacy and pessimism were central symptoms in the network of depressive symptoms, while racing thoughts was an important symptom in the network of manic symptoms. Following network theory, focusing treatments at the symptom-level and on central symptoms - like inadequacy, pessimism, and racing thoughts - could be an effective approach for managing affective symptoms among the sizeable proportion of people with bipolar disorder who have experienced childhood abuse. This study contributes a thorough investigation of the networks of affective symptoms among participants with and with no history of childhood abuse, albeit limited by the use of a binary, self-report measure of childhood abuse, thereby emphasising the importance of assessing for childhood abuse and taking needed steps towards identifying novel targets for treating bipolar disorder.

At-home, telehealth-supported ketamine treatment for depression: Findings from longitudinal, machine learning and symptom network analysis of real-world data

BACKGROUND

Improving safe and effective access to ketamine therapy is of high priority given the growing burden of mental illness. Telehealth-supported administration of sublingual ketamine is being explored toward this goal.

METHODS

In this longitudinal study, moderately-to-severely depressed patients received four doses of ketamine at home over four weeks within a supportive digital health context. Treatment was structured to resemble methods of therapeutic psychedelic trials. Patients receiving a second course of treatment were also examined. Symptoms were assessed using the Patient Health Questionnaire (PHQ-9) for depression. We conducted preregistered machine learning and symptom network analyses to investigate outcomes (osf.io/v2rpx).

RESULTS

A sample of 11,441 patients was analyzed, demonstrating a modal antidepressant response from both non-severe (n = 6384, 55.8 %) and severe (n = 2070, 18.1 %) baseline depression levels. Adverse events were detected in 3.0-4.8 % of participants and predominantly neurologic or psychiatric in nature. A second course of treatment helped extend improvements in patients who responded favorably to initial treatment. Improvement was most strongly predicted by lower depression scores and age at baseline. Symptoms of Depressed mood and Anhedonia sustained depression despite ongoing treatment.

LIMITATIONS

This study was limited by the absence of comparison or control groups and lack of a fixed-dose procedure for ketamine administration.

CONCLUSIONS

At-home, telehealth-supported ketamine administration was largely safe, well-tolerated, and associated with improvement in patients with depression. Strategies for combining psychedelic-oriented therapies with rigorous telehealth models, as explored here, may uniquely address barriers to mental health treatment.

Symptom network connectivity and interaction among people with HIV in China: secondary analysis based on a cross-sectional survey

BACKGROUND

The symptom burden in people with HIV (PWH) is considerable. Nonetheless, the identification of a central symptom, or bridge symptom, among the myriad symptoms experienced by PWH remains unclear. This study seeks to establish networks of symptom experiences within different clusters and investigate the relationships and interconnectedness between these symptoms in PWH.

METHODS

A multicenter, cross-sectional descriptive design was carried out in China over two periods: November 2021 to January 2022 and April 2022 to May 2022. A total of 711 PWH completed online questionnaires, providing information on demographics and the 27-item Self-Report Symptom Scale. The symptom network was analyzed using Network/Graph theory, allowing for the exploration of connections between physical, cognitive, and psychological symptoms. This analysis was based on data from a subset of 493 individuals out of the total 711 PWH.

RESULTS

A total of 493 PWH who exhibited symptoms out of a total of 711 PWH were analyzed. The average number of symptoms reported was 5.367. The most prevalent symptom was sleep disturbance (37.98%). In the node centrality analysis, a cognitive symptom, 'becoming confusing', emerged as the most central symptom with significant values for node centrality (strength = 1.437, betweenness = 140.000, closeness = 0.003). Fever was identified as the bridge symptom with the highest bridge strength (0.547), bridge closeness (0.053), lower bridge betweenness (23.000), and bridge expectedinfluence (0.285). Overall, our network displayed good accuracy and stability.

CONCLUSION

Early identification and assessment of the central or bridge symptoms should be emphasized in clinical practice. According to the findings from network analysis, healthcare providers should proactively explore intervention strategies or bundle care to alleviate the burden of symptoms and enable anticipatory care.

Unveiling Transitions in Disease States: Study of Depressive and Anxiety Symptom Networks over Time

Background

Major depressive disorder (MDD) and anxiety disorders (AD) have high degrees of comorbidity and show great overlap in symptoms. The analysis of covarying depressive- and anxiety symptoms in longitudinal, sparse data panels has received limited attention. Dynamic time warping (DTW) analysis may help to provide new insights into symptom network properties based on diagnostic- and disease-state stability criteria.

Materials and Methods

In the Netherlands Study of Depression and Anxiety depressive-, anxiety-, and worry symptoms were assessed four or five times over the course of 9 years using self-report questionnaires. The sample included 1,649 participants at baseline, comprising controls (n = 360), AD patients (n = 158), MDD patients (n = 265), and comorbid AD-MDD patients (n = 866). With DTW, 1,649 distance matrices were calculated, which yielded symptom networks and enabling comparison of network densities among subgroups.

Results

The mean age of the sample was 41.5 years (standard deviations, 13.2), of whom 66.4% were female. The largest distance was between worry symptoms and physiological arousal symptoms, implicating the most dissimilar dynamics over time. The network density in the groups, from lowest to highest, followed the order: controls, AD, MDD, and comorbid AD-MDD. The comorbid group showed strongly connected mood and cognitive symptoms, which contrasted with the more strongly connected somatic and arousal symptoms in the AD and MDD groups. Groups that showed more transitions in disease states over follow-up, regardless of the diagnoses, had the highest network density compared to more stable states of health or disease (beta for quadratic term = -0.095; P  < 0.001).

Conclusions

Symptom networks over time can be visualized by applying DTW methods on sparse panel data. Network density was highest in patients with comorbid anxiety and depressive disorders and those with more instability in disease states, suggesting that a stronger internal connectivity may facilitate "critical transitions" within the complex systems framework.

Eveningness is associated with more severe depressive symptoms independently of sleep quality and fatigue level only in first-episode but not recurrent depression in major depressive disorder

People with an evening chronotype have an increased risk of experiencing a major depressive disorder (MDD). It is unclear if this effect is predominantly related to the initial development of MDD or also present in recurrent episodes. The current study aimed to investigate if the association between chronotype and depressive severity in MDD patients is comparable in MDD patients with first and recurrent episodes. 386 MDD patients, 70.7% females and aged between 16 and 64, participated in the study. The Morningness - Eveningness Questionnaire (MEQ), Pittsburgh Sleep Quality Index (PSQI), Multidimensional Fatigue Inventory (MFI20), and Quick Inventory of Depressive Symptomatology (QIDS-SR16) were administered to participants to determine chronotype, sleep quality, fatigue level, and depressive severity, respectively. Multivariate regression models were utilized to analyze how chronotype influences depressive severity. The study showed that chronotype, sleep quality, and fatigue level were all associated with depressive severity. Eveningness significantly predicted an increase in depressive severity independently of sleep quality and fatigue level only in patients with the first episode (-0.068, p = 0.010), but not in patients with recurrent episodes (0.013, p = 0.594). Circadian-focused treatment should be considered in first-episode depression only.

Non-invasive vagus nerve stimulation conditions increased invigoration and wanting in depression

BACKGROUND

Major depressive disorder (MDD) is often marked by impaired motivation and reward processing, known as anhedonia. Many patients do not respond to first-line treatments, and improvements in motivation can be slow, creating an urgent need for rapid interventions. Recently, we demonstrated that transcutaneous auricular vagus nerve stimulation (taVNS) acutely boosts effort invigoration in healthy participants, but its effects on depression remain unclear.

OBJECTIVE

To assess the impact of taVNS on effort invigoration and maintenance in a sample that includes patients with MDD, evaluating the generalizability of our findings.

METHODS

We used a single-blind, randomized crossover design in 30 patients with MDD and 29 matched (age, sex, and BMI) healthy control participants (HCP).

RESULTS

Consistent with prior findings, taVNS increased effort invigoration for rewards in both groups during Session 1 (p = .040), particularly for less wanted rewards in HCP (pboot < 0.001). However, invigoration remained elevated in all participants, and no acute changes were observed in Session 2 (Δinvigoration = 3.3, p = .12). Crucially, throughout Session 1, we found taVNS-induced increases in effort invigoration (pboot = 0.008) and wanting (pboot = 0.010) in patients with MDD, with gains in wanting maintained across sessions (Δwanting = 0.06, p = .97).

CONCLUSIONS

Our study replicates the invigorating effects of taVNS in Session 1 and reveals its generalizability to depression. Furthermore, we expand upon previous research by showing taVNS-induced conditioning effects on invigoration and wanting within Session 1 in patients that were largely sustained. While enduring motivational improvements present challenges for crossover designs, they are highly desirable in interventions and warrant further follow-up research.

Mastery is central: an examination of complex interrelationships between physical health, stress and adaptive cognition, and social connection with depression and anxiety symptoms

Background

Research has established the link between physical health, stress and cognition, and social connection with depression and anxiety. Nevertheless, an understanding of the comorbidity of depression and anxiety symptoms and their complex interrelationships with relevant factors remains still limited. This study investigated the complex pattern of interplay between depression and anxiety symptoms and pertinent physical, cognitive, and social factors and potential gender differences.

Methods

Using a sample of 600 middle-aged men and women, depression and anxiety as well as physical health, perceived stress and mastery, and social connection were assessed. The network structure of depression and anxiety symptoms and risk and resilience factors were characterized by examining interrelationships and the centrality indices of Strength and Bridge Strength. Gender differences were examined using the Network Comparison Test.

Results

Perceived stress and mastery were central bridge factors influencing comorbid depression and anxiety symptoms, and perceived stress, anhedonia, and mastery exhibited strong inter-connections to each other. The connections of physical health-anhedonia and sleep disturbance-irritability were stronger in men than in women, while social connection with family was linked to interpersonal problems only in women.

Conclusions

The results underscore that prevention and interventions targeting reducing perceptions of stress and promoting mastery would prevent onset or recurrence of depression and anxiety symptoms among middle-aged men and women. Engaging in behavioral activities to maintain physical health and ensuring adequate sleep could be particularly beneficial for men in reducing overall symptom severity.

Post-stroke Depressive Symptoms and Cognitive Performances: A Network Analysis

OBJECTIVE

To examine the relationships between post-stroke depression and cognition using network analysis. In particular, we identified central depressive symptoms, central cognitive performances, and bridge components that connect these 2 constructs.

DESIGN

An observational study. We applied network analysis to analyze baseline data to visualize and quantify the relationships between depression and cognition.

SETTING

Home and Community.

PARTICIPANTS

202 participants with mild-to-moderate stroke (N=202; mean age: 59.7 years; 55% men; 55% Whites; 90% ischemic stroke).

INTERVENTION

Not applicable.

MAIN OUTCOME MEASURES

Patient Health Questionnaire (PHQ-8) for depressive symptoms and the NIH Toolbox Cognitive Battery for cognitive performances.

RESULTS

Depressive symptoms were positively intercorrelated with the network, with symptoms from similar domains clustered together. Mood (expected influence=1.58), concentration (expected influence=0.67), and guilt (expected influence=0.63) were the top 3 central depressive symptoms. Cognitive performances also showed similar network patterns, with executive function (expected influence=0.89), expressive language (expected influence=0.68), and processing speed (expected influence=0.48) identified as the top 3 central cognitive performances. Psychomotor functioning (bridge expected influence=2.49) and attention (bridge expected influence=1.10) were the components connecting depression and cognition.

CONCLUSIONS

The central and bridge components identified in this study might serve as targets for interventions against these deficits. Future trials are needed to compare the effectiveness of interventions targeting the central and bridge components vs general interventions treating depression and cognitive impairment as a homogenous clinical syndrome.

"I'm always going to be tired": a qualitative exploration of adolescents' experiences of fatigue in depression

Adolescent depression is a prevalent and disabling condition, but current psychological treatments are only moderately effective. One way to enhance outcomes is to further our understanding of adolescent depression and improve our capacity to target the most frequently reported and problematic symptoms. A common but often neglected symptom of depression is fatigue, which is associated with considerable impairment and has the potential to interfere with adolescents' engagement in psychological therapies. Despite this, the experience of fatigue in adolescent depression and how we target it in treatment is currently poorly understood. Therefore, we aimed to explore adolescents' experiences and understandings of fatigue in depression, recruiting from clinical and community settings. Semi-structured interviews were conducted with 19 UK-based adolescents aged 14-18 years old with elevated symptoms of depression. Using reflexive thematic analysis, three themes were generated. Fatigue is a complex concept explored adolescents' understanding of fatigue as a dynamic, multifaceted symptom which had mental and physical components. Trapped in a cycle of fatigue considered the complex and reciprocal relationship between fatigue and other depressive symptoms, and the subsequent impact of limited energy on engagement with everyday activities. Finally, stigma as a barrier to help-seeking highlighted how adolescents were reluctant to seek help due to experienced stigma and the perception that fatigue was not a serious enough symptom. Findings from this study suggest that fatigue should be viewed as a psychological as well as somatic symptom of depression, with implications regarding the identification and treatment of fatigue in depression in routine clinical practice.

Sex differences of post-Covid patients undergoing outpatient pulmonary rehabilitation

BACKGROUND

Following years of pandemic severe acute respiratory syndrome coronavirus 2 infections labelled Covid-19, long lasting impairment summarized as post-Covid syndrome (PCS) challenges worldwide healthcare. Patients benefit from rehabilitation programs, but sex specific aspects of improvement remain little understood. The aim of the study was to assess whether women and men differ in response to outpatient pulmonary rehabilitation for PCS.

METHODS

263 (54.4% female) patients partaking in outpatient pulmonary rehabilitation (OPR) due to PCS between March 2020 and July 2022 were included in a prospective observational cohort study. Outcomes were assessed at baseline and before discharge from OPR and included six-minute walking distance (6MWD), 1-second forced expiratory volume (FEV1), diffusion capacity for carbon monoxide, maximal inspiratory pressure (MIP), dyspnea (medical research council scale), and post-Covid functional status scale (PCFS). Sexspecific changes in outcomes following OPR were assessed by linear mixed model and presented as mean differences (MD) with 95% confidence intervals. Linear regression was applied to test whether 6MWD correlates with PCFS and the minimal clinically important difference (MCID) in 6MWD regarding an improvement of at least one point in PCFS was computed with logistic regression.

RESULTS

Significant improvement throughout OPR was observed for all outcomes (all p < 0.0001). Despite less severe Covid-19 infections, PCFS scores remained higher in females after OPR (p = 0.004) and only 19.4% of women compared to 38.5% of men achieved remission of functional impairment. At baseline as well as after OPR, females showed higher symptom load compared to men in dyspnea (p = 0.0027) and scored lower in FEV1 (p = 0.009) and MIP (p = 0.0006) assessment. Performance in 6MWD was comparable between men and women. An increase of 35 m in 6MWD was computed as minimal clinically important difference to improve functional impairment.

CONCLUSION

Both subjective symptoms such as fatigue and dyspnea and objective impairment in performance in pulmonary function were more frequently observed among women. Despite improvement throughout OPR in both women and men, the sex-gap in symptom load could not be closed as women less often achieved remission from functional impairment due to PCS. Intensified treatment of these symptoms should be considered in women undergoing rehabilitation for PCS.

Hazardous drinking in young adults with co-occurring PTSD and psychosis symptoms: A network analysis

BACKGROUND

Existing literature suggests the co-occurrence of post-traumatic stress disorder (PTSD) and psychosis among young adults is related to hazardous drinking. However, the influencing mechanisms among these co-occurrences are inconclusive. Thus, this study aimed to investigate the symptomatic associations between PTSD, psychosis, and hazardous drinking.

METHODS

This study included 96,218 young Chinese adults, divided into three groups (PTSD, Psychosis, and co-occurring PTSD-Psychosis). PTSD, psychosis, and hazardous drinking were measured by the ten-item Trauma Screening Questionnaire, the seven-item Psychosis Screener Scale, and the four-item Alcohol Use Disorders Identification Test, respectively. Network analysis was utilized to explore and compare the symptomatic correlation between PTSD, psychosis, and hazardous drinking.

RESULTS

In this study, the most crucial symptom (both central and bridge) was "delusion of control" among the three networks. Hazardous drinking was another main bridge symptom. Compared to the Psychosis group and the co-occurring PTSD-Psychosis group, "Delusion of reference or persecution" to "Grandiose delusion" was the strongest edge in "the network structure of the PTSD group".

LIMITATIONS

The cross-sectional study cannot determine the causal relationship. Applying self-reporting questionnaires may cause inherent bias. Young adult participants limited the generalization of the results to other groups.

CONCLUSIONS

Among the three network structures, delusion of control was the most crucial symptom, and hazardous drinking was another bridge symptom; the edge of delusion of reference or persecution and grandiose delusion was strongest in the PTSD group's network. Efforts should be taken to develop diverse targeted interventions for these core symptoms to relieve PTSD, psychosis, and hazardous drinking in young adults.

Comparing psychological versus pharmacological treatment in emotional disorders: A network analysis

Transdiagnostic group cognitive behavioural therapy (TD-GCBT) is more effective in improving symptoms and severity of emotional disorders (EDs) than treatment as usual (TAU; usually pharmacological treatment). However, there is little research that has examined the effects of these treatments on specific symptoms. This study used Network Intervention Analysis (NIA) to investigate the direct and differential effects of TD-GCBT + TAU and TAU on specific symptoms of anxiety and depression. Data are from a multicentre randomised clinical trial (N = 1061) comparing TD-GCBT + TAU versus TAU alone for EDs. The networks included items from the PHQ-9 (depression) and GAD-7 (anxiety) questionnaire and mixed graphical models were estimated at pre-treatment, post-treatment and 3-, 6- and 12-month follow-up. Results revealed that TD-GCBT + TAU was associated with direct effects, mainly on several anxiety symptoms and depressed mood after treatment. New direct effects on other depressive symptoms emerged during the follow-up period promoted by TD-GCBT compared to TAU. Our results suggest that the improvement of anxiety symptoms after treatment might precipitate a wave of changes that favour a decrease in depressive symptomatology. NIA is a methodology that can provide fine-grained insight into the likely pathways through which treatments exert their effects.

Looking at individual symptoms: the dynamic network structure of depressive symptoms in cancer survivors and their preferences for psychological care

PURPOSE

The majority of depressed cancer survivors do not receive psychological care, possibly because offered care does not align with their experiences and preferences. We examined (1) which depressive symptoms cancer survivors would like to receive psychological care for; (2) how distinct depressive symptoms are related to each other in the contemporaneous and temporal network of depressive symptoms; and (3) whether survivors' care needs correspond to the interconnectedness of these specific symptoms.

METHOD

Fifty-two cancer survivors suffering from at least mild depressive symptoms and were not receiving psychological care filled out a baseline questionnaire about their care needs for distinct depressive symptoms, followed by ecological momentary assessments (EMA) assessing depressive symptoms (14 days, five times a day). Multi-level vector autoregression analysis was used to estimate associations between distinct depressive symptoms as well as their centrality within the network.

RESULTS

Cancer survivors most strongly preferred to receive care for fatigue, feeling down, little enjoyment, and sleep problems. Fatigue, together with worry and lack of concentration, most strongly predicted the onset of other symptoms. Little enjoyment and feeling down were two of the most central symptoms (i.e., strongly connected to other symptoms) in the contemporaneous network and were most strongly influenced by other symptoms in the temporal network.

CONCLUSIONS

Clinicians can offer specific interventions that target fatigue, as these played an important role in the onset of symptoms and would align with survivors' needs.

IMPLICATIONS FOR CANCER SURVIVORS

Offering such symptom-specific care may increase the uptake of psychological interventions in cancer survivors.

Aberrant reward learning, but not negative reinforcement learning, is related to depressive symptoms: an attentional perspective

BACKGROUND

Aberrant reward functioning is implicated in depression. While attention precedes behavior and guides higher-order cognitive processes, reward learning from an attentional perspective - the effects of prior reward-learning on subsequent attention allocation - has been mainly overlooked.

METHODS

The present study explored the effects of reward-based attentional learning in depression using two separate, yet complimentary, studies. In study 1, participants with high (HD) and low (LD) levels of depression symptoms were trained to divert their gaze toward one type of stimuli over another using a novel gaze-contingent music reward paradigm - music played when fixating the desired stimulus type and stopped when gazing the alternate one. Attention allocation was assessed before, during, and following training. In study 2, using negative reinforcement, the same attention allocation pattern was trained while substituting the appetitive music reward for gazing the desired stimulus type with the removal of an aversive sound (i.e. white noise).

RESULTS

In study 1 both groups showed the intended shift in attention allocation during training (online reward learning), while generalization of learning at post-training was only evident among LD participants. Conversely, in study 2 both groups showed post-training generalization. Results were maintained when introducing anxiety as a covariate, and when using a more powerful sensitivity analysis. Finally, HD participants showed higher learning speed than LD participants during initial online learning, but only when using negative, not positive, reinforcement.

CONCLUSIONS

Deficient generalization of learning characterizes the attentional system of HD individuals, but only when using reward-based positive reinforcement, not negative reinforcement.

A network model of depressive and anxiety symptoms: a statistical evaluation

BACKGROUND

Although network analysis studies of psychiatric syndromes have increased in recent years, most have emphasized centrality symptoms and robust edges. Broadening the focus to include bridge symptoms within a systematic review could help to elucidate symptoms having the strongest links in network models of psychiatric syndromes. We conducted this systematic review and statistical evaluation of network analyses on depressive and anxiety symptoms to identify the most central symptoms and bridge symptoms, as well as the most robust edge indices of networks.

METHODS

A systematic literature search was performed in PubMed, PsycINFO, Web of Science, and EMBASE databases from their inception to May 25, 2022. To determine the most influential symptoms and connections, we analyzed centrality and bridge centrality rankings and aggregated the most robust symptom connections into a summary network. After determining the most central symptoms and bridge symptoms across network models, heterogeneity across studies was examined using linear logistic regression.

RESULTS

Thirty-three studies with 78,721 participants were included in this systematic review. Seventeen studies with 23 cross-sectional networks based on the Patient Health Questionnaire (PHQ) and Generalized Anxiety Disorder (GAD-7) assessments of clinical and community samples were examined using centrality scores. Twelve cross-sectional networks based on the PHQ and GAD-7 assessments were examined using bridge centrality scores. We found substantial variability between study samples and network features. 'Sad mood', 'Uncontrollable worry', and 'Worrying too much' were the most central symptoms, while 'Sad mood', 'Restlessness', and 'Motor disturbance' were the most frequent bridge centrality symptoms. In addition, the connection between 'Sleep' and 'Fatigue' was the most frequent edge for the depressive and anxiety symptoms network model.

CONCLUSION

Central symptoms, bridge symptoms and robust edges identified in this systematic review can be viewed as potential intervention targets. We also identified gaps in the literature and future directions for network analysis of comorbid depression and anxiety.

Centrality measures in psychological networks: A simulation study on identifying effective treatment targets

The network theory of psychopathology suggests that symptoms in a disorder form a network and that identifying central symptoms within this network might be important for an effective and personalized treatment. However, recent evidence has been inconclusive. We analyzed contemporaneous idiographic networks of depression and anxiety symptoms. Two approaches were compared: a cascade-based attack where symptoms were deactivated in decreasing centrality order, and a normal attack where symptoms were deactivated based on original centrality estimates. Results showed that centrality measures significantly affected the attack's magnitude, particularly the number of components and average path length in both normal and cascade attacks. Degree centrality consistently had the highest impact on the network properties. This study emphasizes the importance of considering centrality measures when identifying treatment targets in psychological networks. Further research is needed to better understand the causal relationships and predictive capabilities of centrality measures in personalized treatments for mental disorders.

Machine Learning and Bayesian Network Analyses Identifies Psychiatric Disorders and Symptom Associations with Insomnia in a national sample of 31,285 Treatment-Seeking College Students

Background

A better understanding of the structure of relations among insomnia and anxiety, mood, eating, and alcohol-use disorders is needed, given its prevalence among young adults. Supervised machine learning provides the ability to evaluate the discriminative accuracy of psychiatric disorders associated with insomnia. Combined with Bayesian network analysis, the directionality between symptoms and their associations may be illuminated.

Methods

The current exploratory analyses utilized a national sample of college students across 26 U.S. colleges and universities collected during population-level screening before entering a randomized controlled trial. Firstly, an elastic net regularization model was trained to predict, via repeated 10-fold cross-validation, which psychiatric disorders were associated with insomnia severity. Seven disorders were included: major depressive disorder, generalized anxiety disorder, social anxiety disorder, panic disorder, post-traumatic stress disorder, anorexia nervosa, and alcohol use disorder. Secondly, using a Bayesian network approach, completed partially directed acyclic graphs (CPDAG) built on training and holdout samples were computed via a Bayesian hill-climbing algorithm to determine symptom-level interactions of disorders most associated with insomnia [based on SHAP (SHapley Additive exPlanations) values)] and were evaluated for stability across networks.

Results

Of 31,285 participants, 20,597 were women (65.8%); mean (standard deviation) age was 22.96 (4.52) years. The elastic net model demonstrated clinical significance in predicting insomnia severity in the training sample [R2 = .449 (.016); RMSE = 5.00 [.081]), with comparable performance in accounting for variance explained in the holdout sample [R2 = .33; RMSE = 5.47). SHAP indicated the presence of any psychiatric disorder was associated with higher insomnia severity, with major depressive disorder demonstrated to be the most associated disorder. CPDAGs showed excellent fit in the holdout sample and suggested that depressed mood, fatigue, and self-esteem were the most important depression symptoms that presupposed insomnia.

Conclusion

These findings offer insights into associations between psychiatric disorders and insomnia among college students and encourage future investigation into the potential direction of causality between insomnia and major depressive disorder.

Trial registration

Trial may be found on the National Institute of Health RePORTER website: Project Number: R01MH115128-05.

A neuroimaging study of brain activity alterations in treatment-resistant depression after a dual target accelerated transcranial magnetic stimulation

In this study, we designed a new transcranial magnetic stimulation (TMS) protocol using a dual-target accelerated transcranial magnetic stimulation (aTMS) for patients with treatment resistant depression (TRD). There are 58 TRD patients were recruited from the Second People's Hospital of Guizhou Province, who were, respectively, received dual-target (real continuous theta burst stimulation (cTBS) at right orbitofrontal cortex (OFC) and real repetitive transcranial magnetic stimulation (rTMS) at left dorsolateral prefrontal cortex (DLPFC)), single- target (sham cTBS at right OFC and real rTMS at left DLPFC), and sham stimulation (sham cTBS at right OFC and sham rTMS at left DLPFC). Resting-state functional magnetic resonance imaging (rs-fMRI) was acquired before and after aTMS treatment to compare characteristics of brain activities by use of amplitude of low-frequency fluctuations (ALFF), fractional low-frequency fluctuations (fALFF) and functional connectivity (FC). At the same time, Hamilton Depression Scale-24 (HAMD24) were conducted to assess the effect. HAMD24 scores reduced significantly in dual group comparing to the single and sham group. Dual-target stimulation decreased not only the ALFF values of right fusiform gyrus (FG) and fALFF values of the left superior temporal gyrus (STG), but also the FC between the right FG and the bilateral middle frontal gyrus (MFG), left triangular part of inferior frontal gyrus (IFG). Higher fALFF value in left STG at baseline may predict better reaction for bilateral arTMS. Dual-targe stimulation can significantly change resting-state brain activities and help to improve depressive symptoms.

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.

Depression and anxiety among caregivers of psychiatric patients during the late stage of the COVID-19 pandemic: A perspective from network analysis

BACKGROUND

Depressive and anxiety symptoms (depression and anxiety hereafter) are common among psychiatric patients and their caregivers during the COVID-19 pandemic. Network analysis is a novel method to assess the associations between psychiatric syndromes/disorders at the symptom level. This study examined depression and anxiety among caregivers of psychiatric inpatients during the late stage of the COVID-19 pandemic from the perspective of network analysis.

METHODS

A total of 1101 caregivers of psychiatric inpatients were included in this study. The severity of depression was assessed using the nine-item Patient Health Questionnaire (PHQ-9), while anxiety was assessed with the seven-item Generalized Anxiety Disorder Scale (GAD-7). The expected index (EI) and bridge EI index were used to identify the central and bridge symptoms, respectively. The stability of the network was evaluated via a case-dropping bootstrap procedure.

RESULTS

The prevalence of depression and anxiety were 32.4 % (95%CI: 29.7 %-35.3 %) and 28.0 % (95%CI: 25.4 %-30.7 %), respectively while the prevalence of comorbid depression and anxiety was 24.9 % (95%CI: 22.4 %-27.6 %). The most central symptom was "Fatigue", followed by "Trouble Relaxing" and "Restlessness". The highest bridge symptom was "Restlessness", followed by "Uncontrollable worry" and "Suicide ideation". The bootstrap test indicated that the whole network model was stable, and no network difference was detected between genders and between different education levels.

CONCLUSIONS

Depression, anxiety, and comorbid depression and anxiety were common among caregivers of psychiatric inpatients during the late stage of the COVID-19 pandemic. Central and bridge symptoms identified in this network analysis should be considered key target symptoms to address in caregivers of patients.

Understanding of Depressive Symptomatology across Major Depressive Disorder and Bipolar Disorder: A Network Analysis

Background and Objectives: Depressive symptoms are prominent in both major depressive disorder (MDD) and bipolar disorder (BD). However, comparative research on the network structure of depressive symptoms in these two diagnostic groups has been limited. This study aims to compare the network structure of depressive symptoms in MDD and BD, providing a deeper understanding of the depressive symptomatology of each disorder. Materials and Methods: The Zung Self-Rating Depressive Scale, a 20-item questionnaire, was administered to assess the depressive symptoms in individuals with MDD (n = 322) and BD (n = 516). A network analysis was conducted using exploratory graph analysis (EGA), and the network structure was analyzed using regularized partial correlation models. To validate the dimensionality of the Zung SDS, principal component analysis (PCA) was adopted. Centrality measures of the depressive symptoms within each group were assessed, followed by a network comparison test between the two groups. Results: In both diagnostic groups, the network analysis revealed four distinct categories, aligning closely with the PCA results. "Depressed affect" emerged as the most central symptom in both MDD and BD. Furthermore, non-core symptoms, "Personal devaluation" in MDD and "Confusion" in BD, displayed strong centrality. The network comparison test did not reveal significant differences in the network structure between MDD and BD. Conclusions: The absence of significant differences in the network structures between MDD and BD suggests that the underlying mechanisms of depressive symptoms may be similar across these disorders. The identified central symptoms, including "Depressed affect", in both disorders and the distinct non-core symptoms in each highlight the complexity of the depressive symptomatology. Future research should focus on validating these symptoms as therapeutic targets and incorporate various methodologies, including non-metric dimension reduction techniques or canonical analysis.

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.

Sex differences in the symptom network structure of depression: Findings from a nationwide sample of the Spanish adult population

BACKGROUND

Sex differences in the prevalence and clinical features of depression have been widely described. However, some authors argue that categorical diagnostic systems do not adequately capture the complexity of depression. The aim of this study was to examine sex differences in the symptom network structure of depressive symptoms among individuals with a major depressive episode.

METHODS

The study sample consisted of 510 participants (age 62.17 ± 14.43, 71.96 % women) from a nationwide study of the Spanish non-institutionalised adult population (Edad con Salud). To estimate the presence of a 12-month major depressive episode according to DSM-IV criteria, participants were administered an adapted version of the Composite International Diagnostic Interview (CIDI 3.0). A network analysis was carried out to determine possible interrelationships between different depressive symptoms by sex.

RESULTS

Men and women showed a similar overall structure and network strength. However, sex-specific variations emerged in relation to individual symptom associations and symptom centrality. Specifically, for individual symptom associations "loss of confidence" and "suicide attempts" were more strongly related in women, and "suicidal ideation" and "impaired thinking" in men. For symptom centrality, "anxiety" played a central role in men's symptomatology, whereas "hopelessness", "loss of confidence", "distress" and "slowness of movement" were the most central symptoms in the women's group.

LIMITATIONS

Reliance on cross-sectional data precludes us from determining the direction and temporality of the association between different symptoms.

CONCLUSIONS

This study suggests that specific symptoms should be prioritised in the prevention, diagnosis assessment and treatment of depressed patients based on sex.

Crying over spilled milk? A network analysis of aversive well-being comparison, brooding rumination and depressive symptoms

BACKGROUND

Comparative thinking about one's well-being is ubiquitous. Comparisons that threaten an individual's self-motives are aversive and interact with rumination and depression. Aversive well-being comparisons include upward social, past temporal, counterfactual, and criteria-based comparisons, as well as downward prospective temporal comparisons. Although the frequency, discrepancy, and affective impact of aversive comparison total scores have been associated with brooding rumination and depression, no study has investigated the interaction of specific comparison standards (e.g., social or counterfactual) with symptom cascades of brooding and depressive symptoms.

METHODS

To examine this interaction, we conducted network analyses on the interplay between aversive well-being comparisons, brooding rumination, and depression. Specifically, we conducted a cross-sectional study in N = 500 dysphoric individuals and a longitudinal study in N = 921 participants at two timepoints, three months apart. Participants completed measures of depression, brooding, and the Comparison Standards Scale for Well-being, which assessed the frequency, perceived discrepancy, and affective impact of aversive well-being comparisons.

RESULTS

Feelings of worthlessness emerged as the most central attribute in the networks of the dysphoric sample. Longitudinally, brooding and depressive symptoms predicted aversive comparisons, but not the other way around, which accounted for social and other-referent counterfactual comparisons to a greater degree than for other comparison types.

LIMITATIONS

We used nonclinical samples.

CONCLUSIONS

The findings highlight the critical role of comparison standards in depression. Further research is warranted to detect potential intervention targets for mitigating negative effects of negative self-evaluation.

A cross-sectional online survey of depression symptoms among New Zealand's Asian community in the first 10 months of the COVID-19 pandemic

The COVID-19 pandemic has elevated levels of distress and resulted in anti-Asian discrimination in many countries. We aimed to determine the 10-month prevalence of depression symptoms in Asian adults in New Zealand during the pandemic and to see if this was related to experience of racism. An online survey was conducted and a stratified sample of 402 respondents completed the brief Centre for Epidemiological Studies-Depression (CES-D) scale. Analyses included: descriptive statistics, depression scores by age/gender, factor analysis of the 10 item CES-D and partial correlation network analysis of CES-D items together with questions about experience of racism. Results show that half of the sample reported clinically significant symptoms of depression. Depression was higher among younger participants but there was no gender difference. Internal consistency was high (α = 0.85) for the CES-D which revealed a clear two-factor structure. Network analysis suggested that sleeping problems might be the bridge between experiences of racism and depression. The prevalence of low mood was high with clinically significant levels of depressive symptoms. Depression was higher in younger people and had a modest positive correlation with personal experience of racism.

Network modeling of major depressive disorder symptoms in adult women

BACKGROUND

Major depressive disorder (MDD) is one of the growing human mental health challenges facing the global health care system. In this study, the structural connectivity between symptoms of MDD is explored using two different network modeling approaches.

METHODS

Data are from 'the Virginia Adult Twin Study of Psychiatric and Substance Use Disorders (VATSPSUD)'. A cohort of N = 2163 American Caucasian female-female twins was assessed as part of the VATSPSUD study. MDD symptoms were assessed using personal structured clinical interviews. Two network analyses were conducted. First, an undirected network model was estimated to explore the connectivity between the MDD symptoms. Then, using a Bayesian network, we computed a directed acyclic graph (DAG) to investigate possible directional relationships between symptoms.

RESULTS

Based on the results of the undirected network, the depressed mood symptom had the highest centrality value, indicating its importance in the overall network of MDD symptoms. Bayesian network analysis indicated that depressed mood emerged as a plausible driving symptom for activating other symptoms. These results are consistent with DSM-5 guidelines for MDD. Also, somatic weight and appetite symptoms appeared as the strongest connections in both networks.

CONCLUSIONS

We discuss how the findings of our study might help future research to detect clinically relevant symptoms and possible directional relationships between MDD symptoms defining major depression episodes, which would help identify potential tailored interventions. This is the first study to investigate the network structure of VATSPSUD data using both undirected and directed network models.

Association between overt aggression and anhedonia in patients with major depressive disorder during the acute phase

OBJECTIVE

To explore the factors influencing anhedonia at baseline and use them as confounding factors. To further investigate the correlation between overt aggression and anhedonia during the acute phase of major depressive disorder.

METHODS

In this eight-week prospective study, 384 major depressive disorder patients were recruited from the outpatient section of Shanghai Mental Health Center from May 1, 2017, to October 30, 2018. Standard treatments were performed with escitalopram or venlafaxine for participants. Depressive symptoms, overt aggression, and anhedonia were assessed using the 17-item Hamilton Rating Scale for Depression, Modified Overt Aggression Scale, and Snaith-Hamilton Pleasure Scale at baseline, and in the 4th and 8th weeks.

RESULTS

Obsessive-compulsive symptoms and the duration of untreated psychosis were positively associated with aggression (P < 0.05). Patients with aggressive behaviour had worse cognitive impairment and severe anhedonia of pleasurable sensory experiences (P < 0.05). For anhedonia, being female (tau_b = -0.23, P = 0.012) was a protective factor, while number of recurrent, melancholic features, current obsessions, previous combination drug therapies, depressive symptoms, and aggressive behaviour were risk factors (P < 0.05). Social anhedonia related to interests/pastimes, and pleasurable sensory experiences were more severe in major depressive disorder patients with aggressive behaviour in the acute phase (P < 0.05).

CONCLUSIONS

Anhedonia persisted in major depressive disorder patients with aggressive behaviour after standardized treatment during the acute phase. Being female protected the pleasures from social interaction and sensory experience. However, the number of depressive episodes, melancholic features, current obsessive symptoms, previous combination drug therapies, depressive symptoms, and aggressive behaviour was positively associated with anhedonia.

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.

A network analysis of the long-term quality of life and mental distress of COVID-19 survivors 1 year after hospital discharge

Objectives

COVID-19 survivors suffer from persistent mental distress and impaired quality of life (QOL) after recovery from the infection. However, the symptom-symptom interaction between these psychological variables remained unexplored. The present study aimed to determine the symptom network of mental distress (depression, anxiety, sleep disturbance, fatigue, and post-traumatic stress disorder) and their association with QOL among 535 COVID-19 survivors 1 year after hospital discharge.

Methods

9-item Patient Health Questionnaire, 7-item Generalized Anxiety Disorder Scale, Chalder fatigue scale, Impact of Event Scale-Revised, Pittsburgh Sleep Quality Index, and 36-Item Short-Form Health Survey were applied to measure depression, anxiety, fatigue, PTSD, sleep disturbances, and QOL, respectively. Two networks were estimated using Gaussian graphical model. Network 1 consisted of mental symptoms to determine the central and bridge symptoms. Network 2 additionally included QOL to determine which mental symptoms were mostly related to QOL.

Results

60% of the COVID-19 survivors experienced mental distress 1 year after hospital discharge. Uncontrollable and excessive worry, psychomotor symptoms, intrusion, and daytime dysfunction were the most central symptoms. Daytime dysfunction and fatigue (especially mental fatigue and loss of energy) served as the bridge symptoms across the mental distress network and exhibited the most substantial association with QOL.

Conclusion

Our study demonstrated several key symptoms that played a vital role in mental distress and QOL among COVID-19 survivors. Prompt screening and targeted interventions for these symptoms might hold great promise in preventing mental distress and improving QOL in COVID-19 survivors.

The Longitudinal Features of Depressive Symptoms During the COVID-19 Pandemic Among Chinese College Students: A Network Perspective

There is substantial evidence that the Corona Virus Disease 2019 (COVID-19) pandemic increased the risk of depressive symptoms among college students, but the long-term features of depressive symptoms on a symptom level have been poorly described. The current study investigated interaction patterns between depressive symptoms via network analysis. In this longitudinal study, participants included 860 Chinese college students (65.8% female; M_age = 20.6, SD_age = 1.8, range: 17-27) who completed a questionnaire at three-time points three months apart. Results demonstrated that fatigue was the most influential symptom, and the occurrence of fatigue could give rise to other depressive symptoms. In addition to predicting other symptoms, fatigue could be predicted by other symptoms in the measurement. The network structures were similar across time, suggesting that the overall interaction pattern of depressive symptoms was stable over the longitudinal course. These findings suggest that depressive symptoms during the COVID-19 period are associated with the presence of fatigue.

Sex differences in childhood maltreatment, inflammation, and adulthood depression: A network analysis

Background

Efforts to improve treatment for adults with major depression (MD) and childhood maltreatment (CM) have identified inflammation as a potential target to improve health. Network models have emerged as a new way to understand the relationship between depressive symptoms and inflammation. However, none have accounted for the role of childhood maltreatment in the link between depressive symptoms and inflammation, or sex differences commonly found in these constructs.

Methods

Data from two waves of the Midlife Development in the United States study were used in this study (N = 1917). The Center for Epidemiological Studies Depression (CES-D) scale and Childhood Trauma Questionnaire, and six inflammation markers served as nodes in an undirected psychometric network analysis. Edges between nodes were calculated using partial Spearman's correlation. Separate networks were modeled for males and females.

Results

The total network revealed several associations between nodes of CM, MD, and inflammation, with emotional abuse having a strong association with somatic complaints. Network comparison testing revealed male-female network invariance, with several edge differences between male and female networks. Males and females showed differences in associations across inflammatory markers and depressive symptom clusters, particularly among somatic complaints and interpersonal difficulties.

Conclusions

Specific associations between dimensions of inflammation, CM, and MD may represent important targets for treatment. Network models disaggregated by sex showed that males and females may have fundamentally different associations between these constructs, suggesting that future studies should consider sex-specific interventions.

Proteomics reveals mitochondrial dysfunction and energy metabolism disturbance of intestine in a nonhuman primate model of depression

BACKGROUND

The gut-brain axis has been shown to play an important role in depression. However, few studies have examined proteomic changes in the intestine of the nonhuman primate model of depression.

METHODS

We investigated the intestinal proteome of macaques (Macaca fascicularis) with depression-like (DL) behaviors by data-independent acquisition techniques. We also performed integration analyses of proteomic changes, previous metabolomic and microbiotic data. Moreover, we confirmed the gene expressions of key proteins.

RESULTS

Sixty-five differentially expressed proteins (DEPs) were identified, of which fifty-four DEPs were down-regulated and the others were altered conversely in DL macaques compared with the control group. Pathway analysis indicated that mitochondrial function and energy metabolism were representative functions of DEPs. The key DEPs were significantly associated with glycerophospholipid metabolism and imbalances of gut microbe. We confirmed that key molecules (NDUFB4, UQCR10, PISD) were significantly inhibited, which may disturb the energy transformation of the electron respiratory chain and the homeostasis of the mitochondrial membrane.

LIMITATIONS

Further research is warranted to determine the effects of depression on other peripheral organs.

CONCLUSIONS

These findings suggest the functional disorder of intestinal mitochondria in DL macaques. The disturbances of glycerophospholipid metabolism and gut microbiota may exacerbate disruptions of energy metabolism. Taking together, our study provides new clues to the relationship between depression and intestinal proteome.

The Structure of Compulsive Sexual Behavior: A Network Analysis Study

Compulsive sexual behavior is a phenomenon characterized by a persistent failure to control intense, repetitive sexual impulses or urges, resulting in repetitive sexual behavior that causes marked distress or impairment in personal, familial, social, educational, or occupational areas of functioning. Despite its major impact on mental health and quality of life, little is known about its internal structure and whether this phenomenon differs across genders, age groups, and risk status. By considering a large online sample (n = 3186; 68.3% males), ranging from 14 to 64 years old, compulsive sexual behavior was explored by means of network analysis. State-of-the-art analytical techniques were adopted to investigate the pattern of association among the different elements of compulsive sexual behavior, identify possible communities of nodes, pinpoint the most central nodes, and detect differences between males and females, among different age groups, as well as between individuals at low and high risk of developing a full-blown disorder. The analyses revealed that the network was characterized by three communities, namely Consequence, Preoccupation, and Perceived Dyscontrol, and that the most central node was related to (perceived) impulse dyscontrol. No substantial differences were found between males and females and across age. Failing to meet one's own commitments and responsibilities was more central in individuals at high risk of developing a full-blown disorder than in those at low risk.

Depression networks: a systematic review of the network paradigm causal assumptions

The network paradigm for psychiatric disorder nosology was proposed based on the hypothesis that mental disorders are caused by networks of symptoms that are themselves causally related. Researchers have widely applied and integrated this paradigm to examine a variety of mental disorders, particularly depression. Existing studies generally focus on the correlation structure of symptoms, inferring causal relationships. Thus, presumption of causality may not be justified. The goal of this review was to examine the assumptions necessary for causal inference in network studies of depression. Specifically, we examined whether and how network studies address common violations of causal assumptions (i.e. no measurement error, exchangeability, and positivity). Of the 41 studies reviewed, five (12%) studies discussed sources of confounding unrelated to measurement error; none discussed positivity; and five conducted post-hoc analysis for measurement error. Depression network studies, in principle, are conducted under the assumption that symptom relationships are causal. Yet, in practice, studies seldomly discussed or adequately tested assumptions required to infer causality. Researchers continue to design studies that are unable to support the credibility of the network paradigm for the study of depression. There is a critical need to ensure scientific efforts cease to perpetuate problematic designs and findings to a potentially unsubstantiated paradigm.