Social Cognition, Face Processing, and Oxytocin Receptor Single Nucleotide Polymorphisms in Typically Developing Children

Recent research has provided evidence of a link between behavioral measures of social cognition (SC) and neural and genetic correlates. Differences in face processing and variations in the oxytocin receptor (OXTR) gene have been associated with SC deficits and autism spectrum disorder (ASD) traits. Much work has examined the qualitative differences between those with ASD and typically developing (TD) individuals, but very little has been done to quantify the natural variation in ASD-like traits in the typical population. The present study examines this variation in TD children using a multidimensional perspective involving behavior assessment, neural electroencephalogram (EEG) testing, and OXTR genotyping. Children completed a series of neurocognitive assessments, provided saliva samples for sequencing, and completed a face processing task while connected to an EEG. No clear pattern emerged for EEG covariates or genotypes for individual OXTR single nucleotide polymorphisms (SNPs). However, SNPs rs2254298 and rs53576 consistently interacted such that the AG/GG allele combination of these SNPs was associated with poorer performance on neurocognitive measures. These results suggest that neither SNP in isolation is risk-conferring, but rather that the combination of rs2254298(A/G) and rs53576(G/G) confers a deleterious effect on SC across several neurocognitive measures. Copyright 2014. Published by Elsevier Ltd.

Transient rhythmic network activity in the somatosensory cortex evoked by distributed input in vitro

The initiation and maintenance of physiological and pathophysiological oscillatory activity depends on the synaptic interactions within neuronal networks. We studied the mechanisms underlying evoked transient network oscillation in acute slices of the adolescent rat somatosensory cortex and modeled its underpinning mechanisms. Oscillations were evoked by brief spatially distributed noisy extracellular stimulation, delivered via bipolar electrodes. Evoked transient network oscillation was detected with multi-neuron patch-clamp recordings under different pharmacological conditions. The observed oscillations are in the frequency range of 2-5 Hz and consist of 4-12 mV large, 40-150 ms wide compound synaptic events with rare overlying action potentials. This evoked transient network oscillation is only weakly expressed in the somatosensory cortex and requires increased [K+]o of 6.25 mM and decreased [Ca2+]o of 1.5 mM and [Mg2+]o of 0.5 mM. A peak in the cross-correlation among membrane potential in layers II/III, IV and V neurons reflects the underlying network-driven basis of the evoked transient network oscillation. The initiation of the evoked transient network oscillation is accompanied by an increased [K+]o and can be prevented by the K+ channel blocker quinidine. In addition, a shift of the chloride reversal potential takes place during stimulation, resulting in a depolarizing type A GABA (GABAA) receptor response. Blockade of alpha-amino-3-hydroxy-5-methyl-4-isoxazole-proprionate (AMPA), N-methyl-D-aspartate (NMDA), or GABA(A) receptors as well as gap junctions prevents evoked transient network oscillation while a reduction of AMPA or GABA(A) receptor desensitization increases its duration and amplitude. The apparent reversal potential of -27 mV of the evoked transient network oscillation, its pharmacological profile, as well as the modeling results suggest a mixed contribution of glutamatergic, excitatory GABAergic, and gap junctional conductances in initiation and maintenance of this oscillatory activity. With these properties, evoked transient network oscillation resembles epileptic afterdischarges more than any other form of physiological or pathophysiological neocortical oscillatory activity.

DISTRIBUTED REPRODUCIBLE RESEARCH USING CACHED COMPUTATIONS

The ability to make scientific findings reproducible is increasingly important in areas where substantive results are the product of complex statistical computations. Reproducibility can allow others to verify the published findings and conduct alternate analyses of the same data. A question that arises naturally is how can one conduct and distribute reproducible research? This question is relevant from the point of view of both the authors who want to make their research reproducible and readers who want to reproduce relevant findings reported in the scientific literature. We present a framework in which reproducible research can be conducted and distributed via cached computations and describe specific tools for both authors and readers. As a prototype implementation we introduce three software packages written in the R language. The cacheSweave and stashR packages together provide tools for caching computational results in a key-value style database which can be published to a public repository for readers to download. The SRPM package provides tools for generating and interacting with "shared reproducibility packages" (SRPs) which can facilitate the distribution of the data and code. As a case study we demonstrate the use of the toolkit on a national study of air pollution exposure and mortality.

Bayesian Hierarchical Distributed Lag Models for Summer Ozone Exposure and Cardio-Respiratory Mortality

In this paper, we develop Bayesian hierarchical distributed lag models for estimating associations between daily variations in summer ozone levels and daily variations in cardiovascular and respiratory (CVDRESP) mortality counts for 19 U.S. large cities included in the National Morbidity Mortality Air Pollution Study (NMMAPS) for the period 1987 - 1994. At the first stage, we define a semi-parametric distributed lag Poisson regression model to estimate city-specific relative rates of CVDRESP associated with short-term exposure to summer ozone. At the second stage, we specify a class of distributions for the true city-specific relative rates to estimate an overall effect by taking into account the variability within and across cities. We perform the calculations with respect to several random effects distributions (normal, t-student, and mixture of normal), thus relaxing the common assumption of a two-stage normal-normal hierarchical model. We assess the sensitivity of the results to: 1) lag structure for ozone exposure; 2) degree of adjustment for long-term trends; 3) inclusion of other pollutants in the model;4) heat waves; 5) random effects distributions; and 6) prior hyperparameters. On average across cities, we found that a 10ppb increase in summer ozone level for every day in the previous week is associated with 1.25 percent increase in CVDRESP mortality (95% posterior regions: 0.47, 2.03). The relative rate estimates are also positive and statistically significant at lags 0, 1, and 2. We found that associations between summer ozone and CVDRESP mortality are sensitive to the confounding adjustment for PM_10, but are robust to: 1) the adjustment for long-term trends, other gaseous pollutants (NO_2, SO_2, and CO); 2) the distributional assumptions at the second stage of the hierarchical model; and 3) the prior distributions on all unknown parameters. Bayesian hierarchical distributed lag models and their application to the NMMAPS data allow us estimation of an acute health effect associated with exposure to ambient air pollution in the last few days on average across several locations. The application of these methods and the systematic assessment of the sensitivity of findings to model assumptions provide important epidemiological evidence for future air quality regulations.

A BAYESIAN HIERARCHICAL MODEL FOR CONSTRAINED DISTRIBUTED LAG FUNCTIONS: ESTIMATING THE TIME COURSE OF HOSPITALIZATION ASSOCIATED WITH AIR POLLUTION EXPOSURE

Numerous time series studies have provided strong evidence of an association between increased levels of ambient air pollution and increased levels of hospital admissions, typically at 0, 1, or 2 days after an air pollution episode. An important research aim is to extend existing statistical models so that a more detailed understanding of the time course of hospitalization after exposure to air pollution can be obtained. Information about this time course, combined with prior knowledge about biological mechanisms, could provide the basis for hypotheses concerning the mechanism by which air pollution causes disease. Previous studies have identified two important methodological questions: (1) How can we estimate the shape of the distributed lag between increased air pollution exposure and increased mortality or morbidity? and (2) How should we estimate the cumulative population health risk from short-term exposure to air pollution? Distributed lag models are appropriate tools for estimating air pollution health effects that may be spread over several days. However, estimation for distributed lag models in air pollution and health applications is hampered by the substantial noise in the data and the inherently weak signal that is the target of investigation. We introduce an hierarchical Bayesian distributed lag model that incorporates prior information about the time course of pollution effects and combines information across multiple locations. The model has a connection to penalized spline smoothing using a special type of penalty matrix. We apply the model to estimating the distributed lag between exposure to particulate matter air pollution and hospitalization for cardiovascular and respiratory disease using data from a large United States air pollution and hospitalization database of Medicare enrollees in 94 counties covering the years 1999-2002.

The zebrafish, brain-specific, aromatase cyp19a2 is neither expressed nor distributed in a sexually dimorphic manner during sexual differentiation

Differential cyp19 aromatase expression during development leads to sexual dimorphisms in the mammalian brain. Whether this is also true for fish is unknown. The aim of the current study has been to follow the expression of the brain-specific aromatase cyp19a2 in the brains of sexually differentiating zebrafish. To assess the role of cyp19a2 in the zebrafish brain during gonadal differentiation, we used quantitative reverse transcriptase-polymerase chain reaction and immunohistochemistry to detect differences in the transcript or protein levels and/or expression pattern in juvenile fish, histology to monitor the gonadal status, and double immunofluorescence with neuronal or radial glial markers to characterize aromatase-positive cells. Our data show that cyp19a2 expression levels during zebrafish sexual differentiation cannot be assigned to a particular sex; the expression pattern in the brain is similar in both sexes and aromatase-positive cells appear to be mostly of radial glial nature.

The feeling of fluent perception: A single experience from multiple asynchronous sources

Zeki and co-workers recently proposed that perception can best be described as locally distributed, asynchronous processes that each create a kind of microconsciousness, which condense into an experienced percept. The present article is aimed at extending this theory to metacognitive feelings. We present evidence that perceptual fluency-the subjective feeling of ease during perceptual processing-is based on speed of processing at different stages of the perceptual process. Specifically, detection of briefly presented stimuli was influenced by figure-ground contrast, but not by symmetry (Experiment 1) or the font (Experiment 2) of the stimuli. Conversely, discrimination of these stimuli was influenced by whether they were symmetric (Experiment 1) and by the font they were presented in (Experiment 2), but not by figure-ground contrast. Both tasks however were related with the subjective experience of fluency (Experiments 1 and 2). We conclude that subjective fluency is the conscious phenomenal correlate of different processing stages in visual perception.

Detection of complex point targets with distributed assets in a MIMO radar system

The report explores the problem of detecting complex point target models in a MIMO radar system. A complex point target is a mathematical and statistical model for a radar target that is not resolved in space, but exhibits varying complex reflectivity across the different bistatic view angles. The complex reflectivity can be modeled as a complex stochastic process whose index set is the set of all the bistatic view angles, and the parameters of the stochastic process follow from an analysis of a target model comprising a number of ideal point scatterers randomly located within some radius of the targets center of mass. The proposed complex point targets may be applicable to statistical inference in multistatic or MIMO radar system. Six different target models are summarized here – three 2-dimensional (Gaussian, Uniform Square, and Uniform Circle) and three 3-dimensional (Gaussian, Uniform Cube, and Uniform Sphere). They are assumed to have different distributions on the location of the point scatterers within the target. We develop data models for the received signals from such targets in the MIMO radar system with distributed assets and partially correlated signals, and consider the resulting detection problem which reduces to the familiar Gauss-Gauss detection problem. We illustrate that the target parameter and transmit signal have an influence on the detector performance through target extent and the SNR respectively. A series of the receiver operator characteristic (ROC) curves are generated to notice the impact on the detector for varying SNR. Kullback–Leibler (KL) divergence is applied to obtain the approximate mean difference between density functions the scatterers assume inside the target models to show the change in the performance of the detector with target extent of the point scatterers.