88 research outputs found

    Autistic traits, but not schizotypy, predict increased weighting of sensory information in Bayesian visual integration

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    Recent theories propose that schizophrenia/schizotypy and autistic spectrum disorder are related to impairments in Bayesian inference that is, how the brain integrates sensory information (likelihoods) with prior knowledge. However existing accounts fail to clarify: (i) how proposed theories differ in accounts of ASD vs. schizophrenia and (ii) whether the impairments result from weaker priors or enhanced likelihoods. Here, we directly address these issues by characterizing how 91 healthy participants, scored for autistic and schizotypal traits, implicitly learned and combined priors with sensory information. This was accomplished through a visual statistical learning paradigm designed to quantitatively assess variations in individuals' likelihoods and priors. The acquisition of the priors was found to be intact along both traits spectra. However, autistic traits were associated with more veridical perception and weaker influence of expectations. Bayesian modeling revealed that this was due, not to weaker prior expectations, but to more precise sensory representations

    The aetiology of social deficits within mental health disorders:The role of the immune system and endogenous opioids

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    The American National Institute for Mental Health (NIMH) has put out a set of research goals that include a long-term plan to identify more reliable endogenous explanations for a wide variety of mental health disorders (Insel, 2013). In response to this, we have identified a major symptom that underlies multiple mental health disorders – social bonding dysfunction. We suggest that endogenous opioid abnormalities can lead to altered social bonding, which is a symptom of various mental health disorders, including depression, schizophrenia and ASD. This article first outlines how endogenous opioids play a role in social bonding. Then we show their association with the body’s inflammation immune function, and review recent literature linking inflammation to mental health ‘immunophenotypes’. We finish by explaining how these immunophenotypes may be caused by alterations in the endogenous opioid system. This is the first overview of the role of inflammation across multiple disorders where we provide a biochemical explanation for why immunophenotypes might exist across diagnoses. We propose a novel mechanism of how the immune system may be causing ‘sickness-type’ behaviours (fatigue, appetite change, social withdrawal and inhibited motivation) in those who have these immunophenotypes. We hope that this novel aetiology can be used as a basis for future research in mental health

    Fishing for mammals: Landscape-level monitoring of terrestrial and semi-aquatic communities using eDNA from riverine systems

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    Environmental DNA (eDNA) metabarcoding has revolutionized biomonitoring in both marine and freshwater ecosystems. However, for semi-aquatic and terrestrial animals, the application of this technique remains relatively untested. We first assess the efficiency of eDNA metabarcoding in detecting semi-aquatic and terrestrial mammals in natural lotic ecosystems in the UK by comparing sequence data recovered from water and sediment samples to the mammalian communities expected from historical data. Secondly, using occupancy modelling we compared the detection efficiency of eDNA metabarcoding to multiple conventional non-invasive survey methods (latrine surveys and camera trapping). eDNA metabarcoding detected a large proportion of the expected mammalian community within each area. Common species in the areas were detected at the majority of sites. Several key species of conservation concern in the UK were detected by eDNA sampling in areas where authenticated records do not currently exist, but potential false positives were also identified. Water-based eDNA metabarcoding provided comparable results to conventional survey methods in per unit of survey effort for three species (water vole, field vole and red deer) using occupancy models. The comparison between survey ‘effort’ to reach a detection probability of ≥.95 revealed that 3–6 water replicates would be equivalent to 3–5 latrine surveys and 5–30 weeks of single camera deployment, depending on the species. Synthesis and applications. eDNA metabarcoding can be used to generate an initial ‘distribution map’ of mammalian diversity at the landscape level. If conducted during times of peak abundance, carefully chosen sampling points along multiple river courses provide a reliable snapshot of the species that are present in a catchment area. In order to fully capture solitary, rare and invasive species, we would currently recommend the use of eDNA metabarcoding alongside other non-invasive surveying methods (i.e. camera traps) to maximize monitoring efforts. © 2020 British Ecological Societ

    Atypical processing of uncertainty in individuals at risk for psychosis

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    Current theories of psychosis highlight the role of abnormal learning signals, i.e., prediction errors (PEs) and uncertainty, in the formation of delusional beliefs. We employed computational analyses of behaviour and functional magnetic resonance imaging (fMRI) to examine whether such abnormalities are evident in clinical high risk (CHR) individuals. Non-medicated CHR individuals (n = 13) and control participants (n = 13) performed a probabilistic learning paradigm during fMRI data acquisition. We used a hierarchical Bayesian model to infer subject-specific computations from behaviour \u2013 with a focus on PEs and uncertainty (or its inverse, precision) at different levels, including environmental \u2018volatility\u2019 \u2013 and used these computational quantities for analyses of fMRI data. Computational modelling of CHR individuals\u2019 behaviour indicated volatility estimates converged to significantly higher levels than in controls. Model-based fMRI demonstrated increased activity in prefrontal and insular regions of CHR individuals in response to precision-weighted low-level outcome PEs, while activations of prefrontal, orbitofrontal and anterior insula cortex by higher-level PEs (that serve to update volatility estimates) were reduced. Additionally, prefrontal cortical activity in response to outcome PEs in CHR was negatively associated with clinical measures of global functioning. Our results suggest a multi-faceted learning abnormality in CHR individuals under conditions of environmental uncertainty, comprising higher levels of volatility estimates combined with reduced cortical activation, and abnormally high activations in prefrontal and insular areas by precision-weighted outcome PEs. This atypical representation of high- and low-level learning signals might reflect a predisposition to delusion formation

    Evidence of Segregated Spawning in a Single Marine Fish Stock: Sympatric Divergence of Ecotypes in Icelandic Cod?

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    There is increasing recognition of intraspecific diversity and population structure within marine fish species, yet there is little direct evidence of the isolating mechanisms that maintain it or documentation of its ecological extent. We analyzed depth and temperature histories collected by electronic data storage tags retrieved from 104 Atlantic cod at liberty ≥1 year to evaluate a possible isolating mechanisms maintaining population structure within the Icelandic cod stock. This stock consists of two distinct behavioral types, resident coastal cod and migratory frontal cod, each occurring within two geographically distinct populations. Despite being captured together on the same spawning grounds, we show the behavioral types seem reproductively isolated by fine-scale differences in spawning habitat selection, primarily depth. Additionally, the different groups occupied distinct seasonal thermal and bathymetric niches that generally demonstrated low levels of overlap throughout the year. Our results indicate that isolating mechanisms, such as differential habitat selection during spawning, might contribute to maintaining diversity and fine-scale population structure in broadcast-spawning marine fishes

    Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

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    Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)

    Studies of jet mass in dijet and W/Z plus jet events

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    This is the pre-print version of the final published paper that is available from the link below.Invariant mass spectra for jets reconstructed using the anti-kT and Cambridge-Aachen algorithms are studied for different jet “grooming” techniques in data corresponding to an integrated luminosity of 5 fb-1, recorded with the CMS detector in proton-proton collisions at the LHC at a center-of-mass energy of 7TeV. Leading-order QCD predictions for inclusive dijet and W/Z+jet production combined with parton-shower Monte Carlo models are found to agree overall with the data, and the agreement improves with the implementation of jet grooming methods used to distinguish merged jets of large transverse momentum from softer QCD gluon radiation
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