Suchergebnisse

Background: There is a higher incidence of psychotic disorders in neighbourhoods of greater social deprivation. However, it is not known whether this represents a causal relationship, as the stage at which social deprivation exerts its influence on the development of psychotic disorders is yet to be elucidated. We aimed to investigate the association between neighbourhood-level social deprivation and the rate of identification of individuals at Ultra-High Risk for psychosis (UHR), as well as the risk of transition to psychosis in UHR individuals. Methods: The cohort included all young people aged 15 to 24 identified as UHR attending an Early Intervention clinic in northwestern Melbourne over a 5-year period (2012–2016). Australian census data were used to obtain the at-risk population and social deprivation information according to the postcode of residence. Levels of social deprivation were arranged into quartiles. Poisson regression was used to calculate rate ratios and Cox regression analysis determined hazard ratios. Results: Of the 461 young people identified as UHR, 11.1% ( n = 49) lived in the most affluent neighbourhoods (Quartile 1) compared to 36.7% ( n = 162) in the most deprived neighbourhoods (Quartile 4). There was a 35% higher rate of identification of young people who were UHR from the most deprived neighbourhoods (aIRR = 1.35, 95% CI [0.98, 1.86]). Over a median follow-up of approximately 10 months (308 days (IQR: 188–557), 17.5% ( n = 77) were known to have transitioned to a full-threshold psychotic disorder. Residing in a neighbourhood of above average deprivation had a hazard ratio of 2.05 (95% CI [0.88, 4.80]) for risk of transition, when controlling for age, sex and substance use. Conclusions: These findings provide more support that EI services should be funded as per the expected incidence of psychotic disorders.

Altres ajuts: The study received grant 08-MNP-007 from the French government Agence Nationale de la Recherche and grant AOM-07-118 (Influence of Cannabis Psychopathological Outcome in At-risk Mental State [ICAAR study]) from the French Health Ministry Programme Hospitalier de Recherche Clinique. The Sainte-Anne Hospital Center promoted the study. Dr Kempton was supported by a Medical Research Council Fellowship grant MR/J008915/1. Dr Pantelis was supported by Australia's National Health and Medical Research Council Senior Principal Research Fellowship (ID: 628386 & 1105825) and by grant R246-2016-3237 from the Lundbeck Foundation. Dr Modinos was supported by a Sir Henry Dale Fellowship #202397/Z/16/Z, jointly funded by The Wellcome Trust and the Royal Society. ; This case-control study analyzes emotion recognition and neuroimaging data as well as clinical and functional outcomes for individuals at risk for transition to psychosis and those without psychiatric or neurological disorders. Is altered emotion recognition associated with adverse clinical and functional outcomes in people at clinical high risk for psychosis? In this case-control study of 213 individuals at clinical high risk for psychosis and 52 healthy participants, abnormalities in the recognition of negative emotion at baseline were associated with neuroanatomical alterations in the medial prefrontal cortex and hippocampus and with a low level of functioning at a 12-month follow-up. This study found that, in people with high risk for developing psychosis, functional outcomes are associated with the degree to which their emotion processing is altered. The development of adverse clinical outcomes in patients with psychosis has been associated with behavioral and neuroanatomical deficits related to emotion processing. However, the association between alterations in brain regions subserving emotion processing and clinical outcomes remains unclear. To examine the association between alterations in emotion processing and regional gray matter volumes ...

PSYSCAN Consortium. ; In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuroimaging has yet to deliver. This is in part explained by the use of univariate analytical techniques, small samples and lack of statistical power, lack of external validation of potential biomarkers, and lack of integration of nonimaging measures (eg, genetic, clinical, cognitive data). PSYSCAN is an international, longitudinal, multicenter study on the early stages of psychosis which uses machine learning techniques to analyze imaging, clinical, cognitive, and biological data with the aim of facilitating the prediction of psychosis onset and outcome. In this article, we provide an overview of the PSYSCAN protocol and we discuss benefits and methodological challenges of large multicenter studies that employ neuroimaging measures. ; The PSYSCAN Project is supported by grant agreement no. 603196 under the European Union's Seventh Framework Programme. ; Peer reviewed

PSYSCAN Consortium. ; In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuroimaging has yet to deliver. This is in part explained by the use of univariate analytical techniques, small samples and lack of statistical power, lack of external validation of potential biomarkers, and lack of integration of nonimaging measures (eg, genetic, clinical, cognitive data). PSYSCAN is an international, longitudinal, multicenter study on the early stages of psychosis which uses machine learning techniques to analyze imaging, clinical, cognitive, and biological data with the aim of facilitating the prediction of psychosis onset and outcome. In this article, we provide an overview of the PSYSCAN protocol and we discuss benefits and methodological challenges of large multicenter studies that employ neuroimaging measures. ; The PSYSCAN Project is supported by grant agreement no. 603196 under the European Union's Seventh Framework Programme.