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Neuroanatomical abnormalities in first-episode psychosis (FEP) tend to be subtle and widespread. The vast majority of previous studies have used small samples, and therefore may have been underpowered. In addition, most studies have examined participants at a single research site, and therefore the results may be specific to the local sample investigated. Consequently, the findings reported in the existing literature are highly heterogeneous. This study aimed to overcome these issues by testing for neuroanatomical abnormalities in individuals with FEP that are expressed consistently across several independent samples.
Structural Magnetic Resonance Imaging data were acquired from a total of 572 FEP and 502 age and gender comparable healthy controls at five sites. Voxel-based morphometry was used to investigate differences in grey matter volume (GMV) between the two groups. Statistical inferences were made at p < 0.05 after family-wise error correction for multiple comparisons.
FEP showed a widespread pattern of decreased GMV in fronto-temporal, insular and occipital regions bilaterally; these decreases were not dependent on anti-psychotic medication. The region with the most pronounced decrease – gyrus rectus – was negatively correlated with the severity of positive and negative symptoms.
This study identified a consistent pattern of fronto-temporal, insular and occipital abnormalities in five independent FEP samples; furthermore, the extent of these alterations is dependent on the severity of symptoms and duration of illness. This provides evidence for reliable neuroanatomical alternations in FEP, expressed above and beyond site-related differences in anti-psychotic medication, scanning parameters and recruitment criteria.
As demonstrated by neuroimaging data, the human brain contains systems that control responses to threat. The revised Reinforcement Sensitivity Theory of personality predicts that individual differences in the reactivity of these brain systems produce anxiety and fear-related personality traits. Here we discuss some of the challenges in testing this theory and, as an example, present a pilot study that aimed to dissociate brain activity during pursuit by threat and goal conflict. We did this by translating the Mouse Defense Test Battery for human fMRI use. In this version, dubbed the Joystick Operated Runway Task (JORT), we repeatedly exposed 24 participants to pursuit and goal conflict, with and without threat of electric shock. The runway design of JORT allowed the effect of threat distance on brain activation to be evaluated independently of context. Goal conflict plus threat of electric shock caused deactivation in a network of brain areas that included the fusiform and middle temporal gyri, as well as the default mode network core, including medial frontal regions, precuneus and posterior cingulate gyrus, and laterally the inferior parietal and angular gyri. Consistent with earlier research, we also found that imminent threat activated the midbrain and that this effect was significantly stronger during the simple pursuit condition than during goal conflict. Also consistent with earlier research, we found significantly greater hippocampal activation during goal conflict than pursuit by imminent threat. In conclusion, our results contribute knowledge to theories linking anxiety disorders to altered functioning in defensive brain systems and also highlight challenges in this research domain.
There is no consensus as to whether magnetic resonance imaging (MRI) should be used as part of the initial clinical evaluation of patients with first-episode psychosis (FEP).
(a) To assess the logistical feasibility of routine MRI; (b) to define the clinical significance of radiological abnormalities in patients with FEP.
Radiological reports from MRI scans of two FEP samples were reviewed; one comprised 108 patients and 98 healthy controls recruited to a research study and the other comprised 241 patients scanned at initial clinical presentation plus 66 healthy controls.
In the great majority of patients, MRI was logistically feasible. Radiological abnormalities were reported in 6% of the research sample and in 15% of the clinical sample (odds ratio (OR) = 3.1, 95% CI 1.26–7.57, χ2(1) = 6.63, P = 0.01). None of the findings necessitated a change in clinical management.
Rates of neuroradiological abnormalities in FEP are likely to be underestimated in research samples that often exclude patients with organic abnormalities. However, the majority of findings do not require intervention.
To determine whether the seasonality of surgical site infections (SSIs) can be explained by changes in temperature.
Retrospective cohort analysis.
The National Inpatient Sample database.
All hospital discharges with a primary diagnosis of SSI from 1998 to 2011 were considered cases. Discharges with a primary or secondary diagnoses of specific surgeries commonly associated with SSIs from the previous and current month served as our “at risk” cohort.
We modeled the national monthly count of SSI cases both nationally and stratified by region, sex, age, and type of institution. We used data from the National Climatic Data Center to estimate the monthly average temperatures for all hospital locations. We modeled the odds of having a primary diagnosis of SSI as a function of demographics, payer, location, patient severity, admission month, year, and the average temperature in the month of admission.
SSI incidence is highly seasonal, with the highest SSI incidence in August and the lowest in January. During the study period, there were 26.5% more cases in August than in January (95% CI, 23.3–29.7). Controlling for demographic and hospital-level characteristics, the odds of a primary SSI admission increased by roughly 2.1% per 2.8°C (5°F) increase in the average monthly temperature. Specifically, the highest temperature group, >32.2°C (>90°F), was associated with an increase in the odds of an SSI admission of 28.9% (95% CI, 20.2–38.3) compared to temperatures <4.4°C (<40°F).
At population level, SSI risk is highly seasonal and is associated with warmer weather.
This white paper identifies knowledge gaps and new challenges in healthcare epidemiology research, assesses the progress made toward addressing research priorities, provides the Society for Healthcare Epidemiology of America (SHEA) Research Committee's recommendations for high-priority research topics, and proposes a road map for making progress toward these goals. It updates the 2010 SHEA Research Committee document, “Charting the Course for the Future of Science in Healthcare Epidemiology: Results of a Survey of the Membership of SHEA,” which called for a national approach to healthcare-associated infections (HAIs) and a prioritized research agenda. This paper highlights recent studies that have advanced our understanding of HAIs, the establishment of the SHEA Research Network as a collaborative infrastructure to address research questions, prevention initiatives at state and national levels, changes in reporting and payment requirements, and new patterns in antimicrobial resistance.
Machine-learning methods may be used to perform many tasks required in the analysis of astronomical data, including: data description and interpretation, pattern recognition, prediction, classification, compression, inference and many more. An intuitive and well-established approach to machine learning is the use of artificial neural networks (NNs), which consist of a group of interconnected nodes, each of which processes information that it receives and then passes this product on to other nodes via weighted connections. In particular, I discuss the first public release of the generic neural network training algorithm, called SkyNet, and demonstrate its application to astronomical problems focusing on its use in the BAMBI package for accelerated Bayesian inference in cosmology, and the identification of gamma-ray bursters. The SkyNet and BAMBI packages, which are fully parallelised using MPI, are available at http://www.mrao.cam.ac.uk/software/.