Although mental disorders have long been considered complex dynamic systems, our understanding of the mutual interactions and temporal patterns of their symptoms remains limited.

In this longitudinal study, we examined the structure and dynamics of four key mental health indicators – depression, anxiety, post-traumatic stress disorder, and insomnia – in a representative sample of the Slovak population (effective N = 3,874) over 10 waves spanning 3.5 years. For each construct, a longitudinal panel network model was estimated.

The temporal relationships between symptoms were mostly weak, with the autoregressive effects typically being stronger. In depression, anxiety, and insomnia, some causal chains and feedback loops were identified. In all constructs, both contemporaneous and between-person networks showed dense connections.

The findings provide critical insights into the complexity of mental health development, offering potential targets for intervention and prevention strategies.

Attachment style is widely recognized as influential in shaping responses to bereavement and prolonged grief disorder (PGD). Although theorized extensively, empirical clarity regarding how attachment styles specifically impact PGD symptoms and therapeutic implications remains limited. This study aimed to identify cognitive-behavioral mechanisms linking attachment styles to PGD symptoms.

Data were collected from a community sample of 695 bereaved adults. Network analysis explored interactions between attachment styles (anxious and avoidant) and various cognitive-behavioral factors associated with PGD, including appraisals, memory characteristics, maladaptive coping strategies, and a sense of social disconnection.

The findings reveal attachment styles as peripheral within the network, suggesting that their direct influence on PGD symptoms may be less central than previously theorized. However, anxious attachment correlated positively with injustice rumination and altered social self, while avoidant attachment was positively associated with perceived loss of future and relationships and preferences for solitude, and negatively associated with proximity-seeking behaviors and fear of losing connection to the deceased. Cognitive-behavioral factors, particularly memory characteristics and social disconnection, held central positions within the network, mediating relationships between attachment styles and PGD.

Attachment styles indirectly influence PGD through cognitive-behavioral pathways rather than exerting strong direct effects. By bridging the gap between attachment theory and cognitive-behavioral approaches to grief, this study offers a more nuanced understanding of its relationship with PGD and points toward potential new avenues for future interventions aimed at addressing attachment-related challenges in bereaved individuals.

Network analysis is a promising approach for elucidating the dynamics of the transition from psychopathology to well-being. Recently, symptom connectivity strength has been proposed as a measure of plasticity – the capacity to change disease severity. Yet, empirical findings remain inconsistent. We propose that this inconsistency can be resolved by recognizing that the interpretation of connectivity strength varies along the recovery process from depression, whether at baseline or during clinical change.

We analyzed 2,710 depressed patients from the STAR*D dataset, grouped by the magnitude of change in depressive score. Symptom network connectivity was estimated from QIDS-C items at three time points: (i) baseline, (ii) change – defined as when clinical change in depression score occurs, (iii) post-change - corresponding to when the maximum clinical change is achieved.

At baseline, connectivity strength predicts the maximum clinical change, inversely correlating with its magnitude (ρ = −0.95, p = 0.001). At the change time point, connectivity strength parallels clinical change (ρ = 0.92, p = 0.002). A direct and significant association between connectivity strength and depression severity emerges only at the change (ρ = 0.98, p = 0.0003) and post-change (ρ = 0.95, p = 0.001) time points.

The interpretation of connectivity strength for predicting depression trajectories varies by timepoint: at baseline, it measures plasticity -- the capacity for change -- whereas during clinical change, it indicates the magnitude of change in symptom severity. This framework supports the reliability of this prognostic marker for designing personalized therapeutic interventions in psychiatry.

The heterogeneity of chronic post-COVID neuropsychiatric symptoms (PCNPS), especially after infection by the Omicron strain, has not been adequately explored.

To explore the clustering pattern of chronic PCNPS in a cohort of patients having their first COVID infection during the ‘Omicron wave’ and discover phenotypes of patients based on their symptoms’ patterns using a pre-registered protocol.

We assessed 1205 eligible subjects in Hong Kong using app-based questionnaires and cognitive tasks.

Partial network analysis of chronic PCNPS in this cohort produced two major symptom clusters (cognitive complaint–fatigue and anxiety–depression) and a minor headache–dizziness cluster, like our pre-Omicron cohort. Participants with high numbers of symptoms could be further grouped into two distinct phenotypes: a cognitive complaint–fatigue predominant phenotype and another with symptoms across multiple clusters. Multiple logistic regression showed that both phenotypes were predicted by the level of pre-infection deprivation (adjusted P-values of 0.025 and 0.0054, respectively). The severity of acute COVID (adjusted P = 0.023) and the number of pre-existing medical conditions predicted only the cognitive complaint–fatigue predominant phenotype (adjusted P = 0.003), and past suicidal ideas predicted only the symptoms across multiple clusters phenotype (adjusted P < 0.001). Pre-infection vaccination status did not predict either phenotype.

Our findings suggest that we should pursue a phenotype-driven approach with holistic biopsychosocial perspectives in disentangling the heterogeneity under the umbrella of chronic PCNPS. Management of patients complaining of chronic PCNPS should be stratified according to their phenotypes. Clinicians should recognise that depression and anxiety cannot explain all chronic post-COVID cognitive symptoms.

Depression is a complex mental health disorder with highly heterogeneous symptoms that vary significantly across individuals, influenced by various factors, including sex and regional contexts. Network analysis is an analytical method that provides a robust framework for evaluating the heterogeneity of depressive symptoms and identifying their potential clinical implications.

To investigate sex-specific differences in the network structures of depressive symptoms in Asian patients diagnosed with depressive disorders, using data from the Research on Asian Psychotropic Prescription Patterns for Antidepressants, Phase 3, which was conducted in 2023.

A network analysis of 10 depressive symptoms defined according to the National Institute for Health and Care Excellence guidelines was performed. The sex-specific differences in the network structures of the depressive symptoms were examined using the Network Comparison Test. Subgroup analysis of the sex-specific differences in the network structures was performed according to geographical region classifications, including East Asia, Southeast Asia, and South or West Asia.

A total of 998 men and 1,915 women with depression were analysed in this study. The analyses showed that all 10 depressive symptoms were grouped into a single cluster. Low self-confidence and loss of interest emerged as the most central nodes for men and women, respectively. In addition, a significant difference in global strength invariance was observed between the networks. In the regional subgroup analysis, only East Asian men showed two distinct clustering patterns. In addition, significant differences in global strength and network structure were observed only between East Asian men and women.

The study highlights the sex-specific differences in depressive symptom networks across Asian countries. The results revealed that low self-confidence and loss of interest are the main symptoms of depression in Asian men and women, respectively. The network connections were more localised in men, whereas women showed a more diverse network. Among the Asian subgroups analysed, only East Asians exhibited significant differences in network structure. The considerable effects of neurovegetative symptoms in men may indicate potential neurobiological underpinnings of depression in the East Asian population.

Difficult-to-treat depression (DTD) is a common clinical challenge for major depressive disorder and bipolar disorders. Electro convulsive therapy (ECT) has proven to be one of the most effective treatments for this condition. Although several studies have investigated individually the clinical factors associated with the DTD response, the role of their interplay in the clinical response to ECT remains unknown. In the present study, we aimed to characterize the network of symptoms in DTD, evaluate the effects of ECT on the interrelationship of depressive symptoms, and identify the network characteristics that could predict the clinical response.

A network analysis of clinical and demographic data from 154 patients with DTD was performed to compare longitudinally the patterns of relationships among depressive symptoms after ECT treatment. Furthermore, we estimated the network structure at baseline associated with a greater clinical improvement (≥80% reduction at Montgomery–Åsberg Depression Rating Scale total score).

ECT modulated the network of depressive symptoms, with increased strength of the global network (p = 0.03, Cohen’s d = −0.98, 95% confidence interval = [−1.07, −0.88]). The strength of the edges between somatic symptoms (appetite and sleep) and cognitive-emotional symptoms (tension, lassitude, and pessimistic thoughts) was also increased. A stronger negative relationship between insomnia and pessimistic thoughts was associated with a greater improvement after ECT. Concentration difficulties and apparent sadness showed the greatest centrality.

In conclusion, ECT treatment may affect not only the severity of the symptoms but also their relationship; this may contribute to the response in DTD.

Heterogeneous symptoms in major depression contribute to unsuccessful antidepressant treatment, termed treatment-resistant depression (TRD). Psychometric network modeling conceptualizes depression as interplay of symptoms with potential benefits for treatment; however, a knowledge gap exists regarding networks in TRD.

Symptoms from 1,385 depressed patients, assessed by the Montgomery-Åsberg-depression rating scale (MADRS) as part of the “TRD-III” cohort of the multinational research consortium “Group for the Studies of Resistant Depression,” were used for Gaussian graphical network modeling. Networks were estimated for two timepoints, pretreatment and posttreatment, after the establishment of outcomes response, non-response, and TRD. Applying the network-comparison test, edge weights, and symptom centrality was assessed by bootstrapping. Applying the network-comparison test, outcome groups were compared cross-sectionally and longitudinally regarding the networks’ global strength, invariance, and centrality.

Pretreatment networks did not differ in global strength, but outcome groups showed distinct symptom connections. For both response and TRD, global strength was reduced posttreatment, leading to significant differences between each pair of networks posttreatment. Sadness, lassitude, inability-to-feel, and pessimistic thoughts ranked most centrally in unfavorable outcomes, while reduced-appetite and suicidal thoughts were more densely connected in response. Connections between central symptoms increased in strength following unsuccessful treatment, particularly regarding links involving pessimistic thoughts in TRD.

Treatment reduced global network strength across outcome groups. However, distinct symptom networks were found in patients showing response to treatment, non-response, and TRD. More easily targetable symptoms such as reduced-appetite were central to networks in patients with response, while pessimistic thoughts may be a key symptom upholding disease burden in TRD.

Multimorbidity, especially physical–mental multimorbidity, is an emerging global health challenge. However, the characteristics and patterns of physical–mental multimorbidity based on the diagnosis of mental disorders in Chinese adults remain unclear.

A cross-sectional study was conducted from November 2004 to April 2005 among 13,358 adults (ages 18–65years) residing in Liaoning Province, China, to evaluate the occurrence of physical–mental multimorbidity. Mental disorders were assessed using the Composite International Diagnostic Interview (version 1.0) with reference to the Diagnostic and Statistical Manual of Mental Disorders (3rd Edition Revised), while physical diseases were self-reported. Physical–mental multimorbidity was assessed based on a list of 16 physical and mental morbidities with prevalence ≥1% and was defined as the presence of one mental disorder and one physical disease. The chi-square test was used to calculate differences in the prevalence and comorbidity of different diseases between the sexes. A matrix heat map was generated of the absolute number of comorbidities for each disease. To identify complex associations and potential disease clustering patterns, a network analysis was performed, constructing a network to explore the relationships within and between various mental disorders and physical diseases.

Physical–mental multimorbidity was confirmed in 3.7% (498) of the participants, with a higher prevalence among women (4.2%, 282) than men (3.3%, 216). The top three diseases with the highest comorbidity rate and average number of comorbidities were dysphoric mood (86.3%; 2.86), social anxiety disorder (77.8%; 2.78) and major depressive disorder (77.1%; 2.53). A physical–mental multimorbidity network was visually divided into mental and physical domains. Additionally, four distinct multimorbidity patterns were identified: ‘Affective-addiction’, ‘Anxiety’, ‘Cardiometabolic’ and ‘Gastro-musculoskeletal-respiratory’, with the digestive-respiratory-musculoskeletal pattern being the most common among the total sample. The affective-addiction pattern was more prevalent in men and rural populations. The cardiometabolic pattern was more common in urban populations.

The physical–mental multimorbidity network structure and the four patterns identified in this study align with previous research, though we observed notable differences in the proportion of these patterns. These variations highlight the importance of tailored interventions that address specific multimorbidity patterns while maintaining broader applicability to diverse populations.