Assessing Patient Experience and Orientation in the Emergency Department with Virtual Windows

Patients have benefitted from increasingly sophisticated diagnostic and therapeutic innovations over the years. However, the design of the physical hospital environment has garnered less attention. This may negatively impact a patient’s experience and health. In areas of the hospital, such as the emergency department (ED), patients may spend hours, or even days, in a windowless environment. Studies have highlighted the importance of natural light and imagery, as they are essential in providing important stimuli to regulate circadian rhythm and orientation, and to mitigate the onset of certain medical conditions. In hospital locations where standard windows may be infeasible, the use of a virtual window may simulate the benefits of an actual window. In this pilot study, we assessed patient experience and orientation with virtual windows in the ED. We demonstrated that virtual windows are an acceptable technology that may improve patient experience and orientation

The fraction of early-type galaxies in low redshift groups and clusters of galaxies

We examine the fraction of early-type (and spiral) galaxies found in groups and clusters of galaxies as a function of dark matter halo mass. We use morphological classifications from the Galaxy Zoo project matched to halo masses from both the C4 cluster catalogue and the Yang et al (2007) group catalogue. We find that the fraction of early-type (or spiral) galaxies remains constant (changing by less than 10%) over three orders of magnitude in halo mass (13<log MH/Msol/h<15.8). This result is insensitive to our choice of halo mass measure, from velocity dispersions or summed optical luminosity. Furthermore, we consider the morphology-halo mass relations in bins of galaxy stellar mass M*, and find that while the trend of constant fraction remains unchanged, the early-type fraction amongst the most massive galaxies (11<log M*/Msol/h <12) is a factor of three greater than lower mass galaxies (10<logM*/Msol/h<10.7). We compare our observational results with those of simulations presented in De Lucia et al (2011), as well as previous observational analyses, and semi-analytic bulge (or disc) dominated galaxies from the Millennium Simulation. We find the simulations recover similar trends as observed, but may over-predict the abundances of the most massive bulge dominated (early-type) galaxies. Our results suggest that most morphological transformation is happening on the group scale before groups merge into massive clusters. However, we show that within each halo a morphology-density relation remains: it is summing the total fraction to a self-similar scaled radius which results in a flat morphology-halo mass relationship.Comment: 9 page, 5 figures, modified to match accepted version (MNRAS

The ENIGMA Stroke Recovery Working Group: Big data neuroimaging to study brain–behavior relationships after stroke

The goal of the Enhancing Neuroimaging Genetics through Meta‐Analysis (ENIGMA) Stroke Recovery working group is to understand brain and behavior relationships using well‐powered meta‐ and mega‐analytic approaches. ENIGMA Stroke Recovery has data from over 2,100 stroke patients collected across 39 research studies and 10 countries around the world, comprising the largest multisite retrospective stroke data collaboration to date. This article outlines the efforts taken by the ENIGMA Stroke Recovery working group to develop neuroinformatics protocols and methods to manage multisite stroke brain magnetic resonance imaging, behavioral and demographics data. Specifically, the processes for scalable data intake and preprocessing, multisite data harmonization, and large‐scale stroke lesion analysis are described, and challenges unique to this type of big data collaboration in stroke research are discussed. Finally, future directions and limitations, as well as recommendations for improved data harmonization through prospective data collection and data management, are provided

The Rising Star-Formation Histories of Distant Galaxies and Implications for Gas Accretion with Time

Distant galaxies show correlations between their current star-formation rates (SFRs) and stellar masses, implying that their star-formation histories (SFHs) are highly similar. Moreover, observations show that the UV luminosities and stellar masses grow from z=8 to 3, implying that the SFRs increase with time. We compare the cosmologically averaged evolution in galaxies at 3 < z < 8 at constant comoving number density, n = 2 x 10^-4 Mpc^-3. This allows us to study the evolution of stellar mass and star formation in the galaxy predecessors and descendants in ways not possible using galaxies selected at constant stellar mass or SFR, quantities that evolve strongly in time. We show that the average SFH of these galaxies increase smoothly from z=8 to 3 as SFR ~ t^alpha with alpha = 1.7 +/- 0.2. This conflicts with assumptions that the SFR is either constant or declines exponentially in time. We show that the stellar mass growth in these galaxies is consistent with this derived SFH. This provides evidence that the slope of the high-mass end of the IMF is approximately Salpeter unless the duty cycle of star formation is much less than unity. We argue that these relations follow from gas accretion (either through accretion or delivered by mergers) coupled with galaxy disk growth under the assumption that the SFR depends on the local gas surface density. This predicts that gas fractions decrease from z=8 to 3 on average as f_gas ~ (1+z)^0.9 for galaxies with this number density. The implied galaxy gas accretion rates at z > 4 are as fast and may even exceed the SFR: this is the "gas accretion epoch". At z < 4 the SFR overtakes the implied gas accretion rate, indicating a period where galaxies consume gas faster than it is acquired. At z < 3, galaxies with this number density depart from these relations implying that star formation and gas accretion are slowed at later times.Comment: Accepted for publication in MNRAS. 13 pages, 7 figures. Comments welcome. Updated with MNRAS-accepted versio

Galaxy Zoo: The Environmental Dependence of Bars and Bulges in Disc Galaxies

We present an analysis of the environmental dependence of bars and bulges in disc galaxies, using a volume-limited catalogue of 15810 galaxies at z<0.06 from the Sloan Digital Sky Survey with visual morphologies from the Galaxy Zoo 2 project. We find that the likelihood of having a bar, or bulge, in disc galaxies increases when the galaxies have redder (optical) colours and larger stellar masses, and observe a transition in the bar and bulge likelihoods, such that massive disc galaxies are more likely to host bars and bulges. We use galaxy clustering methods to demonstrate statistically significant environmental correlations of barred, and bulge-dominated, galaxies, from projected separations of 150 kpc/h to 3 Mpc/h. These environmental correlations appear to be independent of each other: i.e., bulge-dominated disc galaxies exhibit a significant bar-environment correlation, and barred disc galaxies show a bulge-environment correlation. We demonstrate that approximately half (50 +/- 10%) of the bar-environment correlation can be explained by the fact that more massive dark matter haloes host redder disc galaxies, which are then more likely to have bars. Likewise, we show that the environmental dependence of stellar mass can only explain a small fraction (25 +/- 10%) of the bar-environment correlation. Therefore, a significant fraction of our observed environmental dependence of barred galaxies is not due to colour or stellar mass dependences, and hence could be due to another galaxy property. Finally, by analyzing the projected clustering of barred and unbarred disc galaxies with halo occupation models, we argue that barred galaxies are in slightly higher-mass haloes than unbarred ones, and some of them (approximately 25%) are satellite galaxies in groups. We also discuss implications about the effects of minor mergers and interactions on bar formation.Comment: 20 pages, 18 figures; references updated; published in MNRA

The Evolution of Early-type Red Galaxies with the GEMS Survey: Luminosity-size and Stellar Mass-size Relations Since z=1

We combine HST/ACS imaging from the GEMS survey with redshifts and rest-frame quantities from COMBO-17 to study the evolution of morphologically early-type galaxies with red colors since z=1. We use a new large sample of 728 galaxies with centrally-concentrated radial profiles (Sersic n>2.5) and rest-frame U-V colors on the red sequence. By appropriate comparison with the local relations from SDSS, we find that the luminosity-size (L-R) and stellar mass-size (M-R) relations evolve in a manner that is consistent with the passive aging of ancient stars. By itself, this result is consistent with a completely passive evolution of the red early-type galaxy population. If instead, as demonstrated by a number of recent surveys, the early-type galaxy population builds up in mass by a factor of 2 since z=1, our results imply that new additions to the early-type galaxy population follow similar L-R and M-R correlations, compared to the older subset of early-type galaxies. Adding early-type galaxies to the red sequence through disk fading appears to be consistent with the data. Through comparison with models, the role of dissipationless merging is limited to <1 major merger on average since z=1 for the most massive galaxies. Predictions from models of gas-rich mergers are not yet mature enough to allow a detailed comparison to our observations. We find tentative evidence that the amount of luminosity evolution depends on galaxy stellar mass, such that the least massive galaxies show stronger luminosity evolution compared to more massive early types. This could reflect a different origin of low-mass early-type galaxies and/or younger stellar populations; the present data is insufficient to discriminate between these possibilities. (abridged)Comment: Submitted to ApJ, 23 pages, Latex using emulateapj5.sty and onecolfloat.sty (included), 10 figures, version with full resolution figures at http://www.astro.umass.edu/~dmac/Papers/ETevol.hires.p

Neuroimaging-Based Classification of PTSD Using Data-Driven Computational Approaches:A Multisite Big Data Study from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Recent advances in data-driven computational approaches have been helpful in devising tools to objectively diagnose psychiatric disorders. However, current machine learning studies limited to small homogeneous samples, different methodologies, and different imaging collection protocols, limit the ability to directly compare and generalize their results. Here we aimed to classify individuals with PTSD versus controls and assess the generalizability using a large heterogeneous brain datasets from the ENIGMA-PGC PTSD Working group.METHODS: We analyzed brain MRI data from 3,477 structural-MRI; 2,495 resting state-fMRI; and 1,952 diffusion-MRI. First, we identified the brain features that best distinguish individuals with PTSD from controls using traditional machine learning methods. Second, we assessed the utility of the denoising variational autoencoder (DVAE) and evaluated its classification performance. Third, we assessed the generalizability and reproducibility of both models using leave-one-site-out cross-validation procedure for each modality.RESULTS: We found lower performance in classifying PTSD vs. controls with data from over 20 sites (60% test AUC for s-MRI, 59% for rs-fMRI and 56% for d-MRI), as compared to other studies run on single-site data. The performance increased when classifying PTSD from HC without trauma history in each modality (75% AUC). The classification performance remained intact when applying the DVAE framework, which reduced the number of features. Finally, we found that the DVAE framework achieved better generalization to unseen datasets compared with the traditional machine learning frameworks, albeit performance was slightly above chance.CONCLUSION: These results have the potential to provide a baseline classification performance for PTSD when using large scale neuroimaging datasets. Our findings show that the control group used can heavily affect classification performance. The DVAE framework provided better generalizability for the multi-site data. This may be more significant in clinical practice since the neuroimaging-based diagnostic DVAE classification models are much less site-specific, rendering them more generalizable.</p

A Comparison of Methods to Harmonize Cortical Thickness Measurements Across Scanners and Sites

Results of neuroimaging datasets aggregated from multiple sites may be biased by site-specific profiles in participants’ demographic and clinical characteristics, as well as MRI acquisition protocols and scanning platforms. We compared the impact of four different harmonization methods on results obtained from analyses of cortical thickness data: (1) linear mixed-effects model (LME) that models site-specific random intercepts (LME INT), (2) LME that models both site-specific random intercepts and age-related random slopes (LME INT+SLP), (3) ComBat, and (4) ComBat with a generalized additive model (ComBat-GAM). Our test case for comparing harmonization methods was cortical thickness data aggregated from 29 sites, which included 1,340 cases with posttraumatic stress disorder (PTSD) (6.2–81.8 years old) and 2,057 trauma-exposed controls without PTSD (6.3–85.2 years old). We found that, compared to the other data harmonization methods, data processed with ComBat-GAM was more sensitive to the detection of significant case-control differences (Χ 2(3) = 63.704, p < 0.001) as well as case-control differences in age-related cortical thinning (Χ 2(3) = 12.082, p = 0.007). Both ComBat and ComBat-GAM outperformed LME methods in detecting sex differences (Χ 2(3) = 9.114, p = 0.028) in regional cortical thickness. ComBat-GAM also led to stronger estimates of age-related declines in cortical thickness (corrected p-values < 0.001), stronger estimates of case-related cortical thickness reduction (corrected p-values < 0.001), weaker estimates of age-related declines in cortical thickness in cases than controls (corrected p-values < 0.001), stronger estimates of cortical thickness reduction in females than males (corrected p-values < 0.001), and stronger estimates of cortical thickness reduction in females relative to males in cases than controls (corrected p-values < 0.001). Our results support the use of ComBat-GAM to minimize confounds and increase statistical power when harmonizing data with non-linear effects, and the use of either ComBat or ComBat-GAM for harmonizing data with linear effects

Remodeling of the Cortical Structural Connectome in Posttraumatic Stress Disorder:Results from the ENIGMA-PGC PTSD Consortium

BACKGROUND: Posttraumatic stress disorder (PTSD) is accompanied by disrupted cortical neuroanatomy. We investigated alteration in covariance of structural networks associated with PTSD in regions that demonstrate the case-control differences in cortical thickness (CT) and surface area (SA). METHODS: Neuroimaging and clinical data were aggregated from 29 research sites in >1,300 PTSD cases and >2,000 trauma-exposed controls (age 6.2-85.2 years) by the ENIGMA-PGC PTSD working group. Cortical regions in the network were rank-ordered by effect size of PTSD-related cortical differences in CT and SA. The top-n (n = 2 to 148) regions with the largest effect size for PTSD > non-PTSD formed hypertrophic networks, the largest effect size for PTSD < non-PTSD formed atrophic networks, and the smallest effect size of between-group differences formed stable networks. The mean structural covariance (SC) of a given n-region network was the average of all positive pairwise correlations and was compared to the mean SC of 5,000 randomly generated n-region networks. RESULTS: Patients with PTSD, relative to non-PTSD controls, exhibited lower mean SC in CT-based and SA-based atrophic networks. Comorbid depression, sex and age modulated covariance differences of PTSD-related structural networks. CONCLUSIONS: Covariance of structural networks based on CT and cortical SA are affected by PTSD and further modulated by comorbid depression, sex, and age. The structural covariance networks that are perturbed in PTSD comport with converging evidence from resting state functional connectivity networks and networks impacted by inflammatory processes, and stress hormones in PTSD

Smaller total and subregional cerebellar volumes in posttraumatic stress disorder:a mega-analysis by the ENIGMA-PGC PTSD workgroup

Although the cerebellum contributes to higher-order cognitive and emotional functions relevant to posttraumatic stress disorder (PTSD), prior research on cerebellar volume in PTSD is scant, particularly when considering subregions that differentially map on to motor, cognitive, and affective functions. In a sample of 4215 adults (PTSD n = 1642; Control n = 2573) across 40 sites from the ENIGMA-PGC PTSD working group, we employed a new state-of-the-art deep-learning based approach for automatic cerebellar parcellation to obtain volumetric estimates for the total cerebellum and 28 subregions. Linear mixed effects models controlling for age, gender, intracranial volume, and site were used to compare cerebellum volumes in PTSD compared to healthy controls (88% trauma-exposed). PTSD was associated with significant grey and white matter reductions of the cerebellum. Compared to controls, people with PTSD demonstrated smaller total cerebellum volume, as well as reduced volume in subregions primarily within the posterior lobe (lobule VIIB, crus II), vermis (VI, VIII), flocculonodular lobe (lobule X), and corpus medullare (all p -FDR &lt; 0.05). Effects of PTSD on volume were consistent, and generally more robust, when examining symptom severity rather than diagnostic status. These findings implicate regionally specific cerebellar volumetric differences in the pathophysiology of PTSD. The cerebellum appears to play an important role in higher-order cognitive and emotional processes, far beyond its historical association with vestibulomotor function. Further examination of the cerebellum in trauma-related psychopathology will help to clarify how cerebellar structure and function may disrupt cognitive and affective processes at the center of translational models for PTSD.</p