Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Heritability and polygenic predictionIn the EUR sample, the SNP-based heritability (h 2 SNP ) (that is, the proportion of variance in liability attributable to all measured SNPs)

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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.

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Background:There is robust evidence that schizophrenia is characterized by immune-inflammatory abnormalities, including variations on cytokine levels. The results of previous studies, however, are heterogeneous due to several confounding factors, such as the effects of antipsychotic drugs. Therefore, research on drug-naïve first-episode psychosis (FEP) patients is essential to elucidate the role of immune processes in that disorder.Methods:The aim of this study is to compare cytokine levels (IL-2, IL-10, IL-4, IL-6, IFN-γ, TNF-α, and IL-17) in drug-naïve FEP patients both before and after treatment with risperidone for 10 weeks, and to investigate possible associations between cytokine levels and clinical responses to treatment and presence of depressive symptoms. It this study, we included 55 drug-naïve FEP patients who had repeated measurements of cytokine levels and 57 healthy controls.Results:We found that FEP patients had significantly higher IL-6, IL-10 and TNF-α levels than healthy controls. After risperidone treatment, these three cytokines and additionally IL-4 decreased significantly. No significant difference was found between the post-treatment cytokine levels in FEP patients and in healthy controls, suggesting that these alterations in cytokine profiles are a state marker of FEP. No significant association was found between risperidone-induced changes in cytokines and the clinical response to treatment or the presence of depression. There was a significant inverse association between the risperidone-induced changes in IL-10 and the negative symptoms.Conclusions:In conclusion, our results show a specific cytokine profile in FEP patients (monocytic and regulatory T-cell activation) and suggest immunoregulatory effects of risperidone treatment, characterized by suppressant effects on monocytic, Th2, and T-regulatory functions.

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The findings of this study reinforce that SCZ is associated with a pro-inflammatory profile and suggest that some immune mediators may be used as reliable biomarkers for the diagnosis of SCZ and treatment resistance.

Objective: To conduct a comprehensive review of current evidence on factors for nonadherence to treatment in individuals with first-episode psychosis (FEP). Methods: MEDLINE, LILACS, PsycINFO, and SciELO databases were searched with the keywords first episode psychosis, factor, adherence, nonadherence, engagement, disengagement, compliance, and intervention. References of selected studies were consulted for relevant articles. Results: A total of 157 articles were screened, of which 33 articles were retained for full review. The factors related to nonadherence were: a) patient-related (e.g., lower education level, persistent substance use, forensic history, unemployment, history of physical abuse); b) environment-related (e.g., no family involved in treatment, social adjustment difficulties); c) medication-related (e.g., rapid remission of negative symptoms when starting treatment, therapeutic alliance); and d) illness-related (e.g., more positive symptoms, more relapses). Treatment factors that improve adherence include a good therapeutic alliance and a voluntary first admission when hospitalization occurs. Conclusion:The results of this review suggest that nonadherence to treatment in FEP is multifactorial. Many of these factors are modifiable and can be specifically targeted in early intervention programs. Very few studies have assessed strategies to raise adherence in FEP.