The network theory of mental disorders posits that associations between symptoms activate other symptoms to maintain a disorder over time. Network analytic approaches therefore may inform treatment targets. In the present study, we compared baseline OCD symptom networks among treatment responders to non-responders and examined how network structure and connectivity changed from before to after exposure and response prevention (ERP) treatment.

Community adults with OCD (n = 712) who underwent intensive outpatient treatment were assessed using the Yale-Brown Obsessive Compulsive Scale (YBOCS) at admission and discharge. Network comparison tests were used to (a) examine differences in baseline symptom network structures between treatment responders versus non-responders and (b) examine changes in network structures from pre- to post-treatment.

Pre-treatment network structures and global connectivity did not differ significantly between treatment responders and non-responders. However, post-treatment networks exhibited greater global strength (i.e., stronger associations between OCD symptoms) and significantly different network structure (i.e., different patterns of associations between OCD symptoms) relative to the pre-treatment network.

Findings showed that network structure and connectivity in OCD may be more informative as a marker of therapeutic change than in discriminating treatment responders from nonresponders using baseline symptoms. After ERP treatment, associations between obsessions and compulsions demonstrated significantly greater global network strength and altered network structure, thus underscoring the potential for network approaches to identify mechanisms of change throughout OCD treatment. Future studies incorporating session-by-session data may clarify when and how these network shifts occur over the course of therapy to help identify treatment targets.

Network analysis was employed to test whether the overall pattern of depressive–anxious symptom connections remains stable or whether specific symptom-to-symptom links shift from pregnancy to postpartum.

In a perinatal sample (n = 4,461 pregnant women, n = 5,711 postpartum women), depressive and anxiety symptoms were assessed with the Edinburgh Postnatal Depression Scale (EPDS) and Generalized Anxiety Disorder-7 (GAD-7). Phase-specific polychoric Gaussian graphical models were estimated with EBICglass. We examined strength and bridge centrality, community structure, and nodewise predictability, and compared networks using the network comparison test.

Depression and anxiety formed four reproducible communities (one GAD-7 worry/arousal and three EPDS affective/anhedonic, anxious–cognitive distress, and depressed affect/sleep–suicidality modules) with identical partitions across phases. Global strength was similar, but postpartum networks showed higher edge density and more negative partial correlations, suggesting localized changes in which symptom pairs were directly linked—and how strongly—across phases. Across phases, Sadness, Crying, Uncontrollable worrying, and Trouble relaxing were most central and predictable. Worry-, arousal-, and sleep-related symptoms (e.g., hard to sleep) showed the strongest bridge centrality postpartum, and Self-harm was a prominent bridge during pregnancy; several edges shifted between phases, including stronger Enjoyment–Self-harm and weaker Hard to sleep–Self-harm postpartum.

Perinatal depression and anxiety organize into cohesive yet partially distinct symptom networks that remain globally stable but show localized shifts in direct symptom-to-symptom connections from pregnancy to postpartum. Central affective and arousal nodes, particularly sadness, pathological worry, and sleep disturbance, may be high-yield targets for phase-tailored screening and intervention.

Network modeling of post-concussion symptoms following mild traumatic brain injury (mTBI) has emerged as a promising tool for understanding how cognitive, emotional, and somatic symptoms co-occur and interact. However, the generalizability of networks developed in individual studies remains unclear. This study aimed to develop the first-ever meta-analytic pooled between-persons network structure of post-concussion symptoms and systematically examine the between-study heterogeneity of these symptom networks.

Using the Meta-Analytic Gaussian Network Aggregation (MAGNA) framework, a single pooled network model was developed by aggregating data from 6 distinct samples, comprising a total of 5,776 participants. Additionally, this study quantitatively assessed the degree of heterogeneity across these studies.

Strong symptom clusters between cognitive, emotional, and somatic symptoms were identified. Concentration difficulty and slowed thinking were the most central symptoms in the pooled MAGNA network. Large between-study heterogeneity was observed.

Findings from this meta-analysis highlight cognitive symptoms as most important for defining the network structure after mTBI at a group level, potentially perpetuating and/or being perpetuated by symptoms in other domains. The large heterogeneity observed between studies underscores the need for an idiographic (person-specific) approach to studying post-concussion symptom networks to inform precision rehabilitation.

Coroners’ Prevention of Future Death reports (PFDRs, also known as Regulation 28 reports) provide an opportunity to understand factors contributing to mental health-related deaths.

To examine available mental health-related PFDRs, addressing three core questions: (a) What is the overall profile of these reports? (b) What relational patterns emerge from these reports? and (c) What concerns and preventive actions do coroners highlight in these reports, and how they evolved over time?

We collected all mental-health related public PFDRs available up to June 2025 (N = 586). Data extraction combined automated web scraping, optical character reading and large language model (LLM)-assisted (GPT-4o) parsing to capture demographics, settings, coroner areas, co-occurring categories, concerns and recommended actions. Descriptive statistics, category and recipient co-occurrence network analysis and thematic analysis were used to provide a comprehensive landscape of these reports.

Report numbers increased steadily from 2013, peaking in 2021 and then declined. Some jurisdictions, including Manchester South, East Sussex and East London, consistently had more PFDRs issued. The deceased were typically young, male and had died mainly outwith hospital, most often at home; 78.0% of reports included at least one formal response from recipients, whereas 22.0% had no corresponding response available. The network analyses suggested that PFDRs seldom identified isolated issues. Coroners’ concerns changed over time, from service access and resources to inter-agency coordination and then, more recently, to risk assessment and management.

Mental health-related deaths examined by coroners arise within complex, evolving multi-sector contexts and do not frequently identify single errors. Minimising such deaths may require coordinated strategies across healthcare, social care and justice systems. Analysis of PFDRs allows identification of patterns that may inform such actions. PFDRs should be analysed routinely and patterns followed over time.

Language impairments are common in affective and psychotic disorders, yet their patterns and underlying pathomechanisms remain insufficiently understood. A transdiagnostic perspective provides a framework for identifying shared and disorder-specific language alterations across diagnostic boundaries. Combining natural language processing (NLP) with network analysis enables the investigation of complex associations between linguistic, cognitive, and psychopathological features.

Spontaneous speech from N = 372 participants (119 MDD, 27 BD, 48 SSD and 178 HC) was elicited using four Thematic Apperception Test pictures (~12 min per participant). NLP models were applied to extract latent linguistic variables across various levels, including lexical diversity, syntactic complexity, semantic coherence, and disfluencies. Network analysis was used to relate linguistic variables, psychopathology (SAPS, SANS, HAM-A, HAM-D, YMRS, TLI, GAF), and cognitive performance (attention, verbal memory, recognition, and verbal fluency).

Linguistic variables formed the densest network cluster, with type–token ratio, mean length of utterance, and syntactic complexity emerging as central nodes. Psychopathology variables were less cohesive, while TLI “Impoverishment”, coherence mean, and executive functioning bridged linguistic, cognitive, and psychopathological domains. Network comparison tests revealed no significant differences in linguistic–cognitive network structure across HC, MDD, BD, and SSD.

Linguistic networks show high structural consistency across healthy individuals and patients, whereas psychopathological symptom networks reflect transdiagnostic profiles. These findings support a dimensional and transdiagnostic framework underscore shared language–cognition mechanisms, and highlight executive functioning as key cross-domain connection, which opens up new avenues for dimensional research into the pathophysiological and etiological mechanisms underlying language dysfunctions.

Compared with well-studied internal adaptive systems, there remains a lack of comprehensive exploration of external correlated factors of resilience, as well as the way in which each ingredient of resilience is influenced.

This study aims to explore the dimensional associations among resilience and several factors, including parenting rearing style, childhood trauma and negative life events.

A series of social demographic variables, parental rearing patterns, childhood trauma, negative life events and resilience were assessed. Multiple linear regression analysis was used to explore correlated factors of resilience, with all the above factors included in the model. Network analysis was conducted to identify the central factor and key associations, and to visualise complex interactions among resilience, parenting rearing style, childhood trauma and negative life events.

This cross-sectional study was conducted among 4302 freshmen (2388 females, 55.5%; mean 18.59; s.d. = 0.95) from three colleges between October and December 2020. Three key associations were discovered: ‘learning pressure and emotional control’ (r = −0.195, P < 0.05), ‘emotional neglect and family support’ (r = −0.129, P < 0.05) and ‘maternal care family support’ (r = 0.193, P < 0.05). ‘Emotional abuse’ (bridge expected influence, −0.588) was the core node of the estimated network.

This study found that learning pressure, emotional neglect and maternal care emerged as the most critical external correlates of resilience. Emotional abuse occupies the most central position in the external correlated network of resilience. Future longitudinal research should clarify the temporal impacts of these associations, and the key factors, in the dynamic resilience system.

In the ultra-high risk for psychosis (UHR) field, it is unknown whether understanding symptom relationships, beyond symptom severity alone, may hold prognostic value and inform preventive care. In this study, network analysis was performed to examine the interconnections between baseline symptoms in UHR youth who did and did not transition to psychosis over three years.

In a sample selected from the UHR1000+ cohort, positive and basic symptoms were assessed using the Comprehensive Assessment of At-Risk Mental States. Network analyses and network comparison tests were performed.

195 UHR youth transitioned to psychosis within three years and 346 did not. The two groups did not differ in the network structure, global strength (i.e., the overall level of connectivity between symptoms), or centrality of symptoms (i.e., their importance within networks). The transitioned group was characterized by unusual thought content not being connected to other symptoms; however, its centrality between networks was comparable. Across networks, impaired cognitive functioning connected disorganized speech to impaired emotional functioning, motor functioning, and tolerance to normal stress. Impaired bodily sensation connected perceptual abnormalities to other symptoms.

The networks of youth who transitioned and who did not transition were similar, indicating similar baseline symptom relationships. Across groups, unusual thought content, despite being traditionally associated with transition, had little to no interactions with other symptoms. Clinical manifestations that may need attention include impaired cognitive functioning, which connected several symptoms, and impaired bodily sensation. Future research using time series data may support progress toward individualized care.

How psychotic symptoms, depressive symptoms, cognitive deficits, and functional impairment may interact with one another in schizophrenia or bipolar disorder is unclear.

This study explored these interactions in a discovery sample of 339 Chinese, of whom 146 had first-episode schizophrenia and 193 had bipolar disorder. Psychotic symptoms were assessed using the Positive and Negative Symptom Scale; depressive symptoms, using the Hamilton Depression Rating Scale; cognitive deficits, using tests of processing speed, executive function, and logical memory; and functional impairment, using clinical assessments. Network models connecting the four types of variables were developed and compared between men and women and between disorders. Potential causal relationships among the variables were explored through directed acyclic graphing. The results in the discovery sample were compared to those obtained for a validation sample of 235 Chinese, of whom 138 had chronic schizophrenia and 97 had bipolar disorder.

In the discovery and validation cohorts, schizophrenia and bipolar disorder showed similar networks of associations, in which the central hubs included ‘disorganized’ symptoms, depressive symptoms, and deficits in processing speed during the digital symbol substitution test. Directed acyclic graphing suggested that disorganized symptoms were upstream drivers of cognitive impairment and functional decline, while core depressive symptoms (e.g. low mood) drove somatic and anxiety symptoms.

Our study advocates for transdiagnostic, network-informed strategies prioritizing the mitigation of disorganization and depressive symptoms to disrupt symptom cascades and improve functional outcomes in schizophrenia and bipolar disorder.