Psychopathology runs in families and affects functioning of individuals and their family members. This study assessed the intergenerational transmission of psychopathology risk across three generations, and the extent to which social support factors may protect against this transmission from parents to their offspring. This study was embedded in Generation R, a multi-ethnic population-based cohort from fetal life onwards. Lifetime psychiatric disorders of grandparents were assessed with the Family Informant Schedule Criteria- updated for DSM-IV. Parental psychopathology was repeatedly measured by the Brief Symptom Inventory. Offspring psychopathology (ages 10 and 14) was assessed with the Brief Problem Monitor. Maternal and child social factors were assessed using questionnaire measures and a computerized peer nomination assessment. Our results show that the estimated additive interaction effect for the risk transmission of grandparental and pre- and postnatal parental psychopathology to offspring psychopathology was 23% (95% CI 19; 27). The joint effect of grandparental and parental psychopathology combined with maternal and child social support factors was 13% (95% CI 08; 17)], suggesting that social support factors diminished the intergenerational transmission of psychopathology from (grand)parents (G1 and G2) to offspring (G3). Transmission of psychopathology risk may have long-lasting developmental effects across generations. Social support factors reduced the vulnerability to the effects of psychopathology risk, underscoring the importance of the identification of buffering factors associated with good mental health in adolescents who are at high familial risk.

BACKGROUND: Childhood maltreatment contributes to a large mental health burden worldwide. Different measures of childhood maltreatment are not equivalent and may capture meaningful differences. In particular, prospective and retrospective measures of maltreatment identify different groups of individuals and are differentially associated with psychopathology. However, the reasons behind these discrepancies have not yet been comprehensively mapped.
METHODS: In this review, we draw on multi-disciplinary research and present an integrated framework to explain maltreatment measurement disagreement.
RESULTS: We identified three interrelated domains. First, methodological issues related to measurement and data collection methods. Second, the role of memory in influencing retrospective reports of maltreatment. Finally, the motivations individuals may have to disclose, withhold, or fabricate information about maltreatment.
CONCLUSIONS: A greater understanding of maltreatment measurement disagreement may point to new ways to conceptualise and assess maltreatment. Furthermore, it may help uncover mechanisms underlying maltreatment-related psychopathology and targets for novel interventions.

OBJECTIVE: Psychiatric comorbidities suggest that symptoms overlap across different diagnoses; the transdiagnostic network approach is valuable for studying psychopathology. Childhood trauma is a common transdiagnostic risk factor for psychiatric disorders, but the complex relationship between childhood trauma and psychopathology has seldom been investigated using a large cross-sectional transdiagnostic sample.
METHODS: This study recruited 869 patients with different diagnoses, including 418 schizophrenia, 215 bipolar disorder, and 236 major depressive disorder. Participants completed psychiatric interviews and self-report questionnaires. We constructed dimension- and item-level Least Absolute Shrinkage and Selection Operator-based (LASSO) networks to explore the relationship between childhood trauma, psychopathology, and duration of illness. Moreover, we constructed directed acyclic graphs (DAGs) to tentatively clarify the potential directions of associations among these variables. Network Comparison Tests (NCTs) were conducted for different diagnostic groups and gender-stratified groups.
RESULTS: The transdiagnostic LASSO networks showed that different types of childhood trauma exerted distinct impacts on various psychopathological dimensions. Emotional abuse was linked to depressive symptoms, physical abuse to excited symptoms, sexual abuse to positive and disorganized symptoms, emotional neglect to depressive symptoms and motivation and pleasure (MAP) deficits factor of negative symptoms, and physical neglect to MAP factor. The DAG findings generally concurred with the LASSO network. The NCT showed comparable networks.
CONCLUSIONS: Our findings suggest that childhood trauma is significantly associated with the development of psychopathology across different diagnostic groups. The affective pathway model suggests that early identification and tailored interventions would be needed for people with a history of childhood trauma.

BACKGROUND: Nurses are increasingly demanded to achieve gold-standards of care with fewer resources. Dealing effectively with stress experienced in their daily-work-life is thus crucial. This study is based on the Demands-Resources-and-Individual-Effects (DRIVE) Nurses Model and applied the person-centred approach with a twofold objective: 1. to identify patterns of coping strategies (Problem-Focused; Seek-Advice; Self-Blame; Wishful-Thinking; Escape/Avoidance) adopted by nurses to deal with perceived stress; 2. to explore potential differences in perceived Demands (Effort), Resources (Rewards, Job-Control, Social-Support), and Psychopathological Symptoms (Anxiety, Phobic-Anxiety, Obsessive-Compulsive, Somatization, Depression, Interpersonal-Sensitivity, Hostility, Psychoticism, Paranoid-Ideation) according to the emerged patterns.
METHODS: This cross-sectional study was reported by using the STROBE Checklist. Overall, 265 nursing professionals completed self-report measures. Non-hierarchical k-means-cluster-analysis was employed to derive patterns of coping. MANOVAs were used to test differences in Demands, Resources, and Psychopathological Symptoms according to the emerged patterns.
RESULTS: Three stable and meaningful patterns of coping were identified and labelled as Active/Solution-Oriented, Dysregulated/Emotion-focused, and Passive/Disengaged. Nurses belonging to Dysregulated/Emotion-focused group emerged to be at higher risk (higher effort/psychopathological suffering; lower resources) - followed by Passive/Disengaged group - in comparison with nurses belonging to Active/Solution-Oriented group.
CONCLUSIONS: Fostering nurses\' awareness of their latent coping patterns and supporting active approaches/emotional regulation strategies for stress management should represent a key goal when defining interventions promoting nurses\' health within/beyond the healthcare settings.

Co-occurrence between gambling disorder (GD) and other mental disorders is common, but its association with problematic pornography use (PPU) remains unexplored. This study aimed to investigate relationships between sociodemographic variables, personality measures, psychopathology, emotional regulation, and impulsivity and the co-occurrence of GD and PPU using structural equation modeling (SEM). The sample consisted of 359 adults seeking treatment for GD. The short version of the Problematic Pornography Consumption Scale (PPCS-6) identified patients with GD + PPU. Psychopathology, impulsivity, emotional regulation, and personality were also assessed. Higher impulsivity levels statistically predicted co-occurrence between GD and PPU. Impulsivity mediated the relationship between younger age, maladaptive personality features, and emotional dysregulation and co-occurrence. Psychopathological distress did not directly associate with GD + PPU co-occurrence. Impulsivity relates importantly to the co-occurrence of GD and PPU. Younger age, maladaptive personality, and emotional dysregulation contribute to increased impulsivity levels and co-occurrence. The findings highlight the importance of addressing impulsivity in understanding and treating co-occurring GD and PPU.

BACKGROUND: Facial expressions are a core aspect of non-verbal communication. Reduced emotional expressiveness of the face is a common negative symptom of schizophrenia, however, quantifying negative symptoms can be clinically challenging and involves a considerable element of rater subjectivity. We used computer vision to investigate if (i) automated assessment of facial expressions captures negative as well as positive and general symptom domains, and (ii) if automated assessments are associated with treatment response in initially antipsychotic-naïve patients with first-episode psychosis.
METHODS: We included 46 patients (mean age 25.4 (6.1); 65.2% males). Psychopathology was assessed at baseline and after 6 weeks of monotherapy with amisulpride using the Positive and Negative Syndrome Scale (PANSS). Baseline interview videos were recorded. Seventeen facial action units (AUs), that is, activation of muscles, from the Facial Action Coding System were extracted using OpenFace 2.0. A correlation matrix was calculated for each patient. Facial expressions were identified using spectral clustering at group-level. Associations between facial expressions and psychopathology were investigated using multiple linear regression.
RESULTS: Three clusters of facial expressions were identified related to different locations of the face. Cluster 1 was associated with positive and general symptoms at baseline, Cluster 2 was associated with all symptom domains, showing the strongest association with the negative domain, and Cluster 3 was only associated with general symptoms. Cluster 1 was significantly associated with the clinically rated improvement in positive and general symptoms after treatment, and Cluster 2 was significantly associated with clinical improvement in all domains.
CONCLUSIONS: Using automated computer vision of facial expressions during PANSS interviews did not only capture negative symptoms but also combinations of the three overall domains of psychopathology. Moreover, automated assessments of facial expressions at baseline were associated with initial antipsychotic treatment response. The findings underscore the clinical relevance of facial expressions and motivate further investigations of computer vision in clinical psychiatry.

OBJECTIVE: Previous neuroimaging research in alcohol use disorder (AUD) has found altered functional connectivity in the brain\'s salience, default mode, and central executive (CEN) networks (i.e. the triple network model), though their specific associations with AUD severity and heavy drinking remains unclear. This study utilized resting-state fMRI to examine functional connectivity in these networks and measures of alcohol misuse.
METHODS: Seventy-six adult heavy drinkers completed a 7-min resting-state functional MRI scan during visual fixation. Linear regression models tested if connectivity in the three target networks was associated with past 12-month AUD symptoms and number of heavy drinking days in the past 30 days. Exploratory analyses examined correlations between connectivity clusters and impulsivity and psychopathology measures.
RESULTS: Functional connectivity within the CEN network (right and left lateral prefrontal cortex [LPFC] seeds co-activating with 13 and 15 clusters, respectively) was significantly associated with AUD symptoms (right LPFC: β = .337, p-FDR = .016; left LPFC: β = .291, p-FDR = .028) but not heavy drinking (p-FDR > .749). Post-hoc tests revealed six clusters co-activating with the CEN network were associated with AUD symptoms-right middle frontal gyrus, right inferior parietal gyrus, left middle temporal gyrus, and left and right cerebellum. Neither the default mode nor the salience network was significantly associated with alcohol variables. Connectivity in the left LPFC was correlated with monetary delay discounting (r = .25, p = .03).
CONCLUSIONS: These findings support previous associations between connectivity within the CEN network and AUD severity, providing additional specificity to the relevance of the triple network model to AUD.

BACKGROUND: Depression represents one of the leading causes of disability due to illness worldwide. Previous studies have demonstrated the significant heterogeneity of the diagnosis of depression, making it necessary to develop new diagnostic approaches. Network analysis is a perspective that considers symptoms as constituents of the psychiatric disorder itself. The objective was to determine the structure of depressive symptoms using the CES-D and ZDS depression scales.
METHODS: Cross-sectional study of secondary analysis of 194 patients using the CES-D and ZDS scales. Correlation matrices and regularised partial correlation networks were constructed from the database. Centrality measures were estimated, and a network stability analysis was performed.
RESULTS: On the CES-D scale, the most central item was \"Sad\"; while on the ZDS scale, the most central items were \"Sad\" and \"Live\". On the CES-D scale, the connection between \"Enjoy\" and \"Happy\" was the strongest. On the ZDS scale, the strongest connection was between the items \"Live\" with \"Useful\". The item \"Morning\" was the least connected on the ZDS.
CONCLUSIONS: The most central symptom from the CES-D scale was sadness, while from the ZDS scale, was sadness and anhedonia.

Lacking biomarkers in psychiatry calls for a valid and reliable assessment of psychopathology across mental disorders that is easy to use, bridges research and clinical care, and that can capture clinician and patient perspectives. Herein we propose, a novel, brief, transdiagnostic tool to assess and visualize symptom severity in different psychiatric disorders. The Transdiagnostic Global Impression - Psychopathology scale (TGI-P) is based on the Clinical Global Impression - Severity scale (CGI-S), which was originally designed to measure global illness severity in one score. The TGI-P covers 10 transdiagnostic symptom domains and similar to the CGI-S, it is rated on a 7-point Likert-scale from 1 (normal) to 7 (extreme). These ten domains include positive symptoms, negative symptoms, manic symptoms, depressive symptoms, addiction symptoms, cognitive symptoms, anxiety symptoms, sleep symptoms, hostility symptoms, and self-harm symptoms. The results are visually presented, thus simplifying the monitoring of symptoms, and facilitating discussion with patients and caregivers. As part of the development process, the TGI-P was surveyed among 36 psychiatrists from 3 countries. Importantly, over 80 % of them was \"very positive\" or \"positive\" about the concept of the tool, and most of them (70 %) reported willingness to use it in their everyday practice. Further psychometric development and testing of the TGI-P is underway alongside future TGI scales covering adverse events, functioning and satisfaction.

BACKGROUND: Adverse Childhood Experiences (ACEs) are linked to the development of anxiety in adolescence. According to recent studies, the COVID-19 pandemic represents a novel ACE that is associated with anxiety among adolescents. This study investigates the relationship between ACEs, COVID-19, and anxiety in adolescents.
METHODS: A cross-sectional study was conducted using a community sample of 248 boys and girls ages 12 to 15 years (mean = 13.50 years) from five high schools in Eastern Attica. A total of four questionnaires were used: (1) Demographic Questionnaire, (2) State-Trait Anxiety Inventory for Children-STAIC, (3) Adverse Childhood Experiences Scale, and (4) COVID-19 Impact Scale.
RESULTS: The results demonstrated a moderate association between the total number of ACEs and anxiety (trait and state) in adolescence (trait anxiety: ρ = 0.37, p < 0.001; state anxiety: ρ = 0.29, p < 0.001). Girls scored significantly higher than boys on both trait anxiety (U = 4353, p < 0.001; mean difference = 5.5) and state anxiety (U = 5822.5, p = 0.014; mean difference = 2). The number of ACEs was found to be significantly related to the impact of COVID-19 (β = 0.025, p < 0.001).
CONCLUSIONS: This study highlights the significant link between ACEs and increased anxiety in adolescents, which is further exacerbated by the COVID-19 pandemic. The findings indicate that girls are more affected than boys. These results emphasize the need for targeted mental health interventions to enhance coping mechanisms, reduce stress, and address anxiety in adolescents, particularly during global crises like the pandemic. Developing such programs is essential for supporting the mental well-being of youth facing multiple stressors.