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Many neuropsychiatric illnesses are associated with psychosis, i.e., hallucinations (perceptions in the absence of causative stimuli) and delusions (irrational, often bizarre beliefs). Current models of brain function view perception as a combination of two distinct sources of information: bottom-up sensory input and top-down influences from prior knowledge. This framework may explain hallucinations and delusions. Here, we characterized the balance between visual bottom-up and top-down processing in people with early psychosis (study 1) and in psychosis-prone, healthy individuals (study 2) to elucidate the mechanisms that might contribute to the emergence of psychotic experiences. Through a specialized mental-health service, we identified unmedicated individuals who experience early psychotic symptoms but fall below the threshold for a categorical diagnosis. We observed that, in early psychosis, there was a shift in information processing favoring prior knowledge over incoming sensory evidence. In the complementary study, we capitalized on subtle variations in perception and belief in the general population that exhibit graded similarity with psychotic experiences (schizotypy). We observed that the degree of psychosis proneness in healthy individuals, and, specifically, the presence of subtle perceptual alterations, is also associated with stronger reliance on prior knowledge. Although, in the current experimental studies, this shift conferred a performance benefit, under most natural viewing situations, it may provoke anomalous perceptual experiences. Overall, we show that early psychosis and psychosis proneness both entail a basic shift in visual information processing, favoring prior knowledge over incoming sensory evidence. The studies provide complementary insights to a mechanism by which psychotic symptoms may emerge.

ObjectivesTo assess the potential impacts of successive lockdown-easing measures in England, at a point in the COVID-19 pandemic when community transmission levels were relatively high.DesignWe developed a Bayesian model to infer incident cases and reproduction number (R) in England, from incident death data. We then used this to forecast excess cases and deaths in multiple plausible scenarios in which R increases at one or more time points.SettingEngland.ParticipantsPublicly available national incident death data for COVID-19 were examined.Primary outcomeExcess cumulative cases and deaths forecast at 90 days, in simulated scenarios of plausible increases in R after successive easing of lockdown in England, compared with a baseline scenario where R remained constant.ResultsOur model inferred an R of 0.75 on 13 May when England first started easing lockdown. In the most conservative scenario modelled where R increased to 0.80 as lockdown was eased further on 1 June and then remained constant, the model predicted an excess 257 (95% CI 108 to 492) deaths and 26 447 (95% CI 11 105 to 50 549) cumulative cases over 90 days. In the scenario with maximal increases in R (but staying ≤1), the model predicts 3174 (95% CI 1334 to 6060) excess cumulative deaths and 421 310 (95% CI 177 012 to 804 811) cases. Observed data from the forecasting period aligned most closely to the scenario in which R increased to 0.85 on 1 June, and 0.9 on 4 July.ConclusionsWhen levels of transmission are high, even small changes in R with easing of lockdown can have significant impacts on expected cases and deaths, even if R remains ≤1. This will have a major impact on population health, tracing systems and healthcare services in England. Following an elimination strategy rather than one of maintenance of R ≤1 would substantially mitigate the impact of the COVID-19 epidemic within England.

Noradrenaline plays an integral role in learning by optimising behavioural strategies and facilitating choice execution. Testing the noradrenergic framework of learning in the context of human diseases offers a test bed for current normative neuroscience theories and may also indicate therapeutic potential. Parkinson’s disease is often considered as a model of dopamine deficits, including dopamine’s role in reinforcement learning. However, noradrenergic function is also severely impaired by Parkinson’s disease, contributing to cognitive deficits. Using a single dose of the noradrenaline reuptake inhibitor atomoxetine in people with Parkinson’s disease (in a randomised double-blind placebo-controlled crossover design), we show improvements in learning compared to placebo. Computational cognitive modelling confirmed a substantial shift in the decision noise parameter, indicative of more exploitative choices. This response pattern closely resembled that of age-matched controls and simulations of optimal response strategies. Pupillometry revealed increased baseline pupil diameter under atomoxetine, which correlated with behavioural improvements, and a heightened phasic pupillary response to feedback. Our findings confirm the noradrenergic contribution to reinforcement learning, and in doing so they challenge the simple interpretation of tonic-phasic locus coeruleus firing patterns based on pupillometry. Noradrenergic modulation is a potential treatment strategy for cognitive symptoms in Parkinson’s disease and related disorders. Using pharmacology, pupillometry and cognitive modelling, evidence for a noradrenergic contribution to reinforcement learning is shown in Parkinson’s disease.

The A118G single-nucleotide polymorphism (SNP rs1799971) in the μ-opioid receptor gene, OPRM1, has been much studied in relation to alcohol use disorders. The reported effects of allelic variation at this SNP on alcohol-related behaviors, and on opioid receptor antagonist treatments, have been inconsistent. We investigated the pharmacogenetic interaction between A118G variation and the effects of two μ-opioid receptor antagonists in a clinical lab setting. Fifty-six overweight and moderate-heavy drinkers were prospectively stratified by genotype (29 AA homozygotes, 27 carriers of at least 1 G allele) in a double-blind placebo-controlled, three-period crossover design with naltrexone (NTX; 25 mg OD for 2 days, then 50 mg OD for 3 days) and GSK1521498 (10 mg OD for 5 days). The primary end point was regional brain activation by the contrast between alcohol and neutral tastes measured using functional magnetic resonance imaging (fMRI). Secondary end points included other fMRI contrasts, subjective responses to intravenous alcohol challenge, and food intake. GSK1521498 (but not NTX) significantly attenuated fMRI activation by appetitive tastes in the midbrain and amygdala. GSK1521498 (and NTX to a lesser extent) significantly affected self-reported responses to alcohol infusion. Both drugs reduced food intake. Across all end points, there was less robust evidence for significant effects of OPRM1 allelic variation, or for pharmacogenetic interactions between genotype and drug treatment. These results do not support strong modulatory effects of OPRM1 genetic variation on opioid receptor antagonist attenuation of alcohol- and food-related behaviors. However, they do support further investigation of GSK1521498 as a potential therapeutic for alcohol use and eating disorders.

CamBAfx is a workflow application designed for both researchers who use workflows to process data (consumers) and those who design them (designers). It provides a front-end (user interface) optimized for data processing designed in a way familiar to consumers. The back-end uses a pipeline model to represent workflows since this is a common and useful metaphor used by designers and is easy to manipulate compared to other representations like programming scripts. As an Eclipse Rich Client Platform application, CamBAfx's pipelines and functions can be bundled with the software or downloaded post-installation. The user interface contains all the workflow facilities expected by consumers. Using the Eclipse Extension Mechanism designers are encouraged to customize CamBAfx for their own pipelines. CamBAfx wraps a workflow facility around neuroinformatics software without modification. CamBAfx's design, licensing and Eclipse Branding Mechanism allow it to be used as the user interface for other software, facilitating exchange of innovative computational tools between originating labs.

This corrects the article DOI: 10.1038/npp.2016.60

The standard Bayesian observer model (Geisler and Kersten, 2002) embodies the assumption that perception is based on two sources of information: prior beliefs about the state of the world are updated by incoming sensory information; perceptual decisions are then based on the updated beliefs. Specifically, the observer's belief about the statistical regularities of the world is modeled by a probability distribution (the prior); the observer's noisy sensory processing is reflected in a measurement distribution, which specifies a likelihood function; the combination of prior and likelihood according to Bayes' theorem models the combination of belief and sensory information; this results in an a posteriori probability distribution (the posterior), representing the space on which the perceptual decision is made. [...]the center piece of Pellicano and Burr's proposal, emphasized in the title and the abstract, is the assertion that due to a “broadening” of the prior, autistic perception is more accurate in the sense that it is closer to physical reality. The possible consistency, however, would not rectify the problems that, we believe, attend the central conceptual tenet of the paper. [...]it is noteworthy that some of the examples are vulnerable to empirical criticism.

The development of an alcohol use disorder in adolescence is associated with increased risk of future alcohol dependence. The differential associations of risk factors with alcohol use over the course of 8 years are important for preventive measures. To determine the differential associations of risk-taking aspects of personality, social factors, brain functioning, and familial risk with hazardous alcohol use in adolescents over the course of 8 years. The IMAGEN multicenter longitudinal cohort study included adolescents recruited from European schools in Germany, the UK, France, and Ireland from January 2008 to January 2019. Eligible participants included those with available neuropsychological, self-report, imaging, and genetic data at baseline. Adolescents who were ineligible for magnetic resonance imaging or had serious medical conditions were excluded. Data analysis was conducted from July 2021 to September 2022. Personality testing, psychosocial factors, brain functioning, and familial risk of alcohol misuse. Hazardous alcohol use as measured with the Alcohol Use Disorders Identification Test scores, a main planned outcome of the IMAGEN study. Alcohol misuse trajectories at ages 14, 16, 19, and 22 years were modeled using latent growth curve models. A total of 2240 adolescents (1110 female [49.6%] and 1130 male [50.4%]) were included in the study. There was a significant negative association of psychosocial resources (β = -0.29; SE = 0.03; P < .001) with the general risk of alcohol misuse as well as a significant positive association of the risk-taking aspects of personality with the intercept (β = 0.19; SE = 0.04; P < .001). Furthermore, there were significant positive associations of the social domain (β = 0.13; SE = 0.02; P < .001) and the personality domain (β = 0.07; SE = 0.02; P < .001) with trajectories of alcohol misuse development over time (slope). Family history of substance misuse was negatively associated with general risk of alcohol misuse (β = -0.04; SE = 0.02; P = .045) and its development over time (β = -0.03; SE = 0.01; P = .01). Brain functioning showed no significant association with intercept or slope of alcohol misuse in the model. The findings of this cohort study suggest known risk factors of adolescent drinking may contribute differentially to future alcohol misuse. This approach may inform more individualized preventive interventions.

The A118G single-nucleotide polymorphism (SNP rs1799971) in the μ-opioid receptor gene, OPRM1, has been much studied in relation to alcohol use disorders. The reported effects of allelic variation at this SNP on alcohol-related behaviors, and on opioid receptor antagonist treatments, have been inconsistent. We investigated the pharmacogenetic interaction between A118G variation and the effects of two μ-opioid receptor antagonists in a clinical lab setting. Fifty-six overweight and moderate-heavy drinkers were prospectively stratified by genotype (29 AA homozygotes, 27 carriers of at least 1 G allele) in a double-blind placebo-controlled, three-period crossover design with naltrexone (NTX; 25 mg OD for 2 days, then 50 mg OD for 3 days) and GSK1521498 (10 mg OD for 5 days). The primary end point was regional brain activation by the contrast between alcohol and neutral tastes measured using functional magnetic resonance imaging (fMRI). Secondary end points included other fMRI contrasts, subjective responses to intravenous alcohol challenge, and food intake. GSK1521498 (but not NTX) significantly attenuated fMRI activation by appetitive tastes in the midbrain and amygdala. GSK1521498 (and NTX to a lesser extent) significantly affected self-reported responses to alcohol infusion. Both drugs reduced food intake. Across all end points, there was less robust evidence for significant effects of OPRM1 allelic variation, or for pharmacogenetic interactions between genotype and drug treatment. These results do not support strong modulatory effects of OPRM1 genetic variation on opioid receptor antagonist attenuation of alcohol- and food-related behaviors. However, they do support further investigation of GSK1521498 as a potential therapeutic for alcohol use and eating disorders.