High rates of comorbidities and poor validity of disorder diagnostic criteria for mental disorders hamper advances in mental health research. Recent work has suggested the utility of continuous cross-cutting dimensions, including general psychopathology and specific factors of externalizing and internalizing (e.g., distress and fear) syndromes. The current study evaluated the reliability of competing structural models of psychopathology and examined external validity of the best fitting model on the basis of family risk and child global executive function (EF). A community sample of 8,012 families from Brazil with children ages 6-12 years completed structured interviews about the child and parental psychiatric syndromes, and a subsample of 2,395 children completed tasks assessing EF (i.e., working memory, inhibitory control, and time processing). Confirmatory factor analyses tested a series of structural models of psychopathology in both parents and children. The model with a general psychopathology factor ("P factor") with 3 specific factors (fear, distress, and externalizing) exhibited the best fit. The general P factor accounted for most of the variance in all models, with little residual variance explained by each of the 3 specific factors. In addition, associations between child and parental factors were mainly significant for the P factors and nonsignificant for the specific factors from the respective models. Likewise, the child P factor-but not the specific factors-was significantly associated with global child EF. Overall, our results provide support for a latent overarching P factor characterizing child psychopathology, supported by familial associations and child EF. (PsycINFO Database Record

Early-onset chronic cannabis users exhibited poorer cognitive performance than controls and late-onset users in executive functioning. Chronic cannabis use, when started before age 15, may have more deleterious effects on neurocognitive functioning.

The objective of this study is to present the rationale, methods, design and preliminary results from the High Risk Cohort Study for the Development of Childhood Psychiatric Disorders. We describe the sample selection and the components of each phases of the study, its instruments, tasks and procedures. Preliminary results are limited to the baseline phase and encompass: (i) the efficacy of the oversampling procedure used to increase the frequency of both child and family psychopathology; (ii) interrater reliability and (iii) the role of differential participation rate. A total of 9937 children from 57 schools participated in the screening procedures. From those 2512 (random = 958; high risk = 1554) were further evaluated with diagnostic instruments. The prevalence of any child mental disorder in the random strata and high-risk strata was 19.9% and 29.7%. The oversampling procedure was successful in selecting a sample with higher family rates of any mental disorders according to diagnostic instruments. Interrater reliability (kappa) for the main diagnostic instrument range from 0.72 (hyperkinetic disorders) to 0.84 (emotional disorders). The screening instrument was successful in selecting a sub-sample with "high risk" for developing mental disorders. This study may help advance the field of child psychiatry and ultimately provide useful clinical information.

No abstract

BackgroundViolence and other traumatic events, as well as psychiatric disorders are frequent in developing countries, but there are few population studies to show the actual impact of traumatic events in the psychiatric morbidity in low and middle-income countries (LMIC).AimsTo study the relationship between traumatic events and prevalence of mental disorders in São Paulo and Rio de Janeiro, Brazil.MethodsCross-sectional survey carried out in 2007–2008 with a probabilistic representative sample of 15- to 75-year-old residents in Sao Paulo and Rio de Janeiro, Brazil, using the Composite International Diagnostic Interview.ResultsThe sample comprised 3744 interviews. Nearly 90% of participants faced lifetime traumatic events. Lifetime prevalence of any disorders was 44% in Sao Paulo and 42.1% in Rio de Janeiro. One-year estimates were 32.5% and 31.2%. One-year prevalence of traumatic events was higher in Rio de Janeiro than Sao Paulo (35.1 vs. 21.7; p<0.001). Participants from Rio de Janeiro were less likely to have alcohol dependence (OR = 0.55; p = 0.027), depression (OR = 0.6; p = 0.006) generalized anxiety (OR = 0.59; p = 0.021) and post-traumatic stress disorder (OR = 0.62; p = 0.027). Traumatic events correlated with all diagnoses – e.g. assaultive violence with alcohol dependence (OR = 5.7; p<0.001) and with depression (OR = 1.7; p = 0.001).ConclusionOur findings show that psychiatric disorders and traumatic events, especially violence, are extremely common in Sao Paulo and Rio de Janeiro, supporting the idea that neuropsychiatric disorders and external causes have become a major public health priority, as they are amongst the leading causes of burden of disease in low and middle-income countries. The comparison between the two cities regarding patterns of violence and psychiatric morbidity suggests that environmental factors may buffer the negative impacts of traumatic events. Identifying such factors might guide the implementation of interventions to improve mental health and quality of life in LMIC urban centers.

BackgroundPatients with schizophrenia have lower longevity than the general population as a consequence of a combination of risk factors connected to the disease, lifestyle and the use of medications, which are related to weight gain.MethodsA multicentric, randomized, controlled-trial was conducted to test the efficacy of a 12-week group Lifestyle Wellness Program (LWP). The program consists of a one-hour weekly session to discuss topics like dietary choices, lifestyle, physical activity and self-esteem with patients and their relatives. Patients were randomized into two groups: standard care (SC) and standard care plus intervention (LWP). Primary outcome was defined as the weight and body mass index (BMI).Results160 patients participated in the study (81 in the intervention group and 79 in the SC group). On an intent to treat analysis, after three months the patients in the intervention group presented a decrease of 0.48 kg (CI 95% -0.65 to 1.13) while the standard care group showed an increase of 0.48 kg (CI 95% 0.13 to 0.83; p=0.055). At six-month follow-up, there was a significant weight decrease of −1.15 kg, (CI 95% -2.11 to 0.19) in the intervention group compared to a weight increase in the standard care group (+0.5 kg, CI 95% -0.42–1.42, p=0.017).ConclusionIn conclusion, this was a multicentric randomized clinical trial with a lifestyle intervention for individuals with schizophrenia, where the intervention group maintained weight and presented a tendency to decrease weight after 6 months. It is reasonable to suppose that lifestyle interventions may be important long-term strategies to avoid the tendency of these individuals to increase weight.Clinicaltrials.gov identifierNCT01368406

Neuroimaging-based models contribute to increasing our understanding of schizophrenia pathophysiology and can reveal the underlying characteristics of this and other clinical conditions. However, the considerable variability in reported neuroimaging results mirrors the heterogeneity of the disorder. Machine learning methods capable of representing invariant features could circumvent this problem. In this structural MRI study, we trained a deep learning model known as deep belief network (DBN) to extract features from brain morphometry data and investigated its performance in discriminating between healthy controls (N = 83) and patients with schizophrenia (N = 143). We further analysed performance in classifying patients with a first-episode psychosis (N = 32). The DBN highlighted differences between classes, especially in the frontal, temporal, parietal, and insular cortices, and in some subcortical regions, including the corpus callosum, putamen, and cerebellum. The DBN was slightly more accurate as a classifier (accuracy = 73.6%) than the support vector machine (accuracy = 68.1%). Finally, the error rate of the DBN in classifying first-episode patients was 56.3%, indicating that the representations learned from patients with schizophrenia and healthy controls were not suitable to define these patients. Our data suggest that deep learning could improve our understanding of psychiatric disorders such as schizophrenia by improving neuromorphometric analyses.

Recent years have seen considerable progress in epidemiological and molecular genetic research into environmental and genetic factors in schizophrenia, but methodological uncertainties remain with regard to validating environmental exposures, and the population risk conferred by individual molecular genetic variants is small. There are now also a limited number of studies that have investigated molecular genetic candidate gene-environment interactions (G × E), however, so far, thorough replication of findings is rare and G × E research still faces several conceptual and methodological challenges. In this article, we aim to review these recent developments and illustrate how integrated, large-scale investigations may overcome contemporary challenges in G × E research, drawing on the example of a large, international, multi-center study into the identification and translational application of G × E in schizophrenia. While such investigations are now well underway, new challenges emerge for G × E research from late-breaking evidence that genetic variation and environmental exposures are, to a significant degree, shared across a range of psychiatric disorders, with potential overlap in phenotype.