Matter Clustering Research Articles

History of Peripartum Depression Moderates the Association Between Estradiol Polygenic Risk Scores and Basal Ganglia Volumes in Major Depressive Disorder

The neurobiological differences between women who have experienced a peripartum episode and those who have only had episodes outside of this period are not well understood. 64 parous female patients with major depressive disorder that have either a positive (n=30) or negative (n=34) history of peripartum depression (PPD) underwent MRI acquisition to obtain structural brain images. An independent two-sample t-test comparing patients with and without a history of PPD was performed using voxel-based morphometry analysis (VBM). Additionally, polygenic risk scores (PRSs) for estradiol were calculated and a moderation analysis was conducted between 3 estradiol PRSs and PPD history status on extracted cluster volumes using IBM SPSS PROCESS macro. The VBM analysis identified larger grey matter volumes in bilateral clusters encompassing the putamen, pallidum, caudate, and thalamus in patients with PPD history compared to patients without a history. The moderation analysis identified a significant interaction of 2 estradiol PRSs and PPD history on grey matter cluster volumes with a positive effect in PPD women and a negative effect in women with no history of PPD. Our findings demonstrate that women who have experienced a peripartum episode are neurobiologically distinct from women who have no history of PPD in a cluster within the basal ganglia, an area important for motivation, decision-making, and emotional processing. Furthermore, we show that the genetic load for estradiol has a differing effect in this area based on PPD status which supports the claim that PPD is associated with sensitivity to sex steroid hormones.

Galaxy clustering in modified gravity from full-physics simulations – I. Two-point correlation functions

ABSTRACT We present an in-depth investigation of galaxy clustering based on a new suite of realistic large-box galaxy formation simulations in $f(R)$ gravity, with a subgrid physics model that has been recalibrated to reproduce various observed stellar and gas properties. We focus on the two-point correlation functions of the luminous red galaxies (LRGs) and emission line galaxies (ELGs), which are primary targets of ongoing and future galaxy surveys such as Dark Energy Spectroscopic Instrument (DESI). One surprising result is that, due to several non-trivial effects of modified gravity on matter clustering and the galaxy–halo connection, the clustering signal does not depend monotonically on the fifth-force strength. For LRGs, this complicated behaviour poses a challenge to meaningfully constraining this model. For ELGs, in contrast, this can be straightforwardly explained by the time evolution of the fifth force, which means that weaker $f(R)$ models can display nearly the same – up to 25 per cent – deviations from Lambda cold dark matter model as the strongest ones, albeit at lower redshifts. This implies that even very weak $f(R)$ models can be strongly constrained, unlike with most other observations. Our results show that galaxy formation acquires a significant environment dependence in $f(R)$ gravity, which, if not properly accounted for, may lead to biased constraints on the model. This highlights the essential role of hydrodynamical simulations in future tests of gravity exploring precision galaxy-clustering data from the likes of DESI and Euclid.

Integrating supervised and unsupervised learning approaches to unveil critical process inputs

This study introduces a machine learning framework tailored to large-scale industrial processes characterized by a plethora of numerical and categorical inputs. The framework aims to (i) discern critical parameters that influence the output and (ii) generate accurate out-of-sample qualitative and quantitative predictions of production outcomes. Specifically, we address the pivotal question of the significance of each input in shaping the process outcome, using an industrial Chemical Vapor Deposition (CVD) process as an example. The initial objective involves merging subject matter expertise and clustering techniques exclusively on the process output, here, coating thickness measurements at various positions in the reactor. This approach identifies groups of production runs that share similar qualitative characteristics, such as film mean thickness and standard deviation. In particular, the differences of the outcomes represented by the different clusters can be attributed to differences in specific inputs, indicating that these inputs are potentially critical to the production outcome. Shapley value analysis corroborates the formed hypotheses. Leveraging this insight, we subsequently implement supervised classification and regression methods using the identified critical process inputs. The proposed methodology proves to be valuable in scenarios with a multitude of inputs and insufficient data for the direct application of deep learning techniques, providing meaningful insights into the underlying processes.

Circadian rapid eye movement sleep expression is associated with brain microstructural integrity in older adults

Rapid eye movement sleep (REMS) is increasingly suggested as a discriminant sleep state for subtle signs of age-related neurodegeneration. While REMS expression is under strong circadian control and circadian dysregulation increases with age, the association between brain aging and circadian REMS regulation has not yet been assessed. Here, we measure the circadian amplitude of REMS through a 40-h in-lab multiple nap protocol in controlled laboratory conditions, and brain microstructural integrity with quantitative multi-parameter mapping (MPM) imaging in 86 older individuals. We show that reduced circadian REMS amplitude is related to lower magnetization transfer saturation (MTsat), longitudinal relaxation rate (R1) and effective transverse relaxation rate (R2*) values in several white matter regions mostly located around the lateral ventricles, and with lower R1 values in grey matter clusters encompassing the hippocampus, parahippocampus, thalamus and hypothalamus. Our results further highlight the importance of considering circadian regulation for understanding the association between sleep and brain structure in older individuals.

Distinct biological property of tau in tau-first cognitive proteinopathy: Evidence by longitudinal clinical neuroimaging profiles and compared with late-onset Alzheimer disease.

Tau-first cognitive proteinopathy (TCP) denotes a clinical phenotype of Alzheimer disease (AD) showing Florzolotau(18F) positron emission tomography (PET) positivity but a negative amyloid status. We explored the biological property of tau using longitudinal cognitive and neuroimaging data in TCP and compared with late-onset AD (LOAD). We enrolled 56 patients with LOAD, 34 patients with TCP, and 26 cognitive unimpaired controls. All of the participants had historical data of 2 to 4 three-dimensional T1 images and 2 to 6 annual cognitive evaluations over a follow-up period of 7 years. Tau topography was measured using Florzolotau(18F) PET. In the LOAD and TCP groups, we constructed tau or gray matter clusters covarying with the cognitive measurements. We used mediator analysis to explore the regional tau load as predictor, gray matter partitions as mediators, and significant cognitive test scores as outcomes. Longitudinal cognitive decline and cortical thickness degeneration pattern were analyzed using a linear mixed-effects model. The TCP group had longitudinal declines in nonexecutive domains. The deterministic factor predicting the short-term memory score in TCP was the hippocampal volume and not directly via the medial and lateral temporal tau load. These features formed the conceptual differences with LOAD. The biological properties of tau and the longitudinal cognitive-imaging trajectory support the conceptual distinction between TCP and LOAD. TCP represents one specific entity featuring salient short-term memory impairment, declines in nonexecutive domains, a slower gray matter degenerative pattern, and a restricted impact of tau.

ICM-SHOX. I. Methodology Overview and Discovery of a Gas–Dark Matter Velocity Decoupling in the MACS J0018.5+1626 Merger

Galaxy cluster mergers are rich sources of information to test cluster astrophysics and cosmology. However, cluster mergers produce complex projected signals that are difficult to interpret physically from individual observational probes. Multi-probe constraints on the gas and dark matter (DM) cluster components are necessary to infer merger parameters that are otherwise degenerate. We present Improved Constraints on Mergers with SZ, Hydrodynamical simulations, Optical, and X-ray (ICM-SHOX), a systematic framework to jointly infer multiple merger parameters quantitatively via a pipeline that directly compares a novel combination of multi-probe observables to mock observables derived from hydrodynamical simulations. We report a first application of the ICM-SHOX pipeline to MACS J0018.5+1626, wherein we systematically examine simulated snapshots characterized by a wide range of initial parameters to constrain the MACS J0018.5+1626 merger geometry. We constrain the epoch of MACS J0018.5+1626 to the range 0–60 Myr post-pericenter passage, and the viewing angle is inclined ≈27°–40° from the merger axis. We obtain constraints for the impact parameter (≲250 kpc), mass ratio (≈1.5–3.0), and initial relative velocity when the clusters are separated by 3 Mpc (≈1700–3000 km s−1). The primary and secondary clusters initially (at 3 Mpc) have gas distributions that are moderately and strongly disturbed, respectively. We discover a velocity space decoupling of the DM and gas distributions in MACS J0018.5+1626, traced by cluster-member galaxy velocities and the kinematic Sunyaev–Zel'dovich effect, respectively. Our simulations indicate this decoupling is dependent on the different collisional properties of the two distributions for particular merger epochs, geometries, and viewing angles.

Euclid preparation. XLIV. Modelling spectroscopic clustering on mildly nonlinear scales in beyond-LCDM models

The space satellite mission will measure the large-scale clustering of galaxies at an unprecedented precision, providing a unique probe of modifications to the Lambda CDM model. We investigated the approximations needed to efficiently predict the large-scale clustering of matter and dark matter halos in the context of modified gravity and exotic dark energy scenarios. We examined the normal branch of the Dvali--Gabadadze--Porrati model, the Hu--Sawicki $f(R)$ model, a slowly evolving dark energy model, an interacting dark energy model, and massive neutrinos. For each, we tested approximations for the perturbative kernel calculations, including the omission of screening terms and the use of perturbative kernels based on the Einstein--de Sitter universe; we explored different infrared-resummation schemes, tracer bias models and a linear treatment of massive neutrinos; we investigated various approaches for dealing with redshift-space distortions and modelling the mildly nonlinear scales, namely the Taruya--Nishimishi--Saito prescription and the effective field theory of large-scale structure. This work provides a first validation of the various codes being considered by for the spectroscopic clustering probe in beyond-Lambda CDM scenarios. We calculated and compared the $ statistic to assess the different modelling choices. This was done by fitting the spectroscopic clustering predictions to measurements from numerical simulations and perturbation theory-based mock data. We compared the behaviour of this statistic in the beyond-Lambda CDM cases, as a function of the maximum scale included in the fit, to the baseline Lambda CDM case. We find that the Einstein--de Sitter approximation without screening is surprisingly accurate for the modified gravity cases when comparing to the halo clustering monopole and quadrupole obtained from simulations and mock data. Further, we find the same goodness-of-fit for both cases -- the one including and the one omitting non-standard physics in the predictions. Our results suggest that the inclusion of multiple redshift bins, higher-order multipoles, higher-order clustering statistics (such as the bispectrum), and photometric probes such as weak lensing, will be essential to extract information on massive neutrinos, modified gravity and dark energy. Additionally, we show that the three codes used in our analysis, namely PBJ Pybird and MG-Copter exhibit sub-percent agreement for $k Mpc $ across all the models. This consistency underscores their value as reliable tools.

Analysis of cognitive dysfunction in Parkinson's disease using voxel based morphometry and radiomics.

Cognitive impairment in Parkinson's disease (PD) is associated with changes in the brain anatomical structures. The objective of this study, is to identify the atrophy patterns based on the severity of cognitive decline and evaluate the disease progression. In this study, gray matter alterations are analysed in 135 PD subjects under 3 cognitive domains (91 Cognitively normal PD (NC-PD), 25 PD with Mild Cognitive Impairment (PD-MCI) and 19 PD with Dementia (PD-D)) by comparing them with 58 Healthy Control (HC) subjects. Voxel Based Morphometry (VBM) is used to segment the gray matter regions in magnetic resonance images and analyse the atrophy patterns statistically. Significant patterns of gray matter variations observed in the middle temporal and medial frontal region differentiate between HC and PD subject groups based on the severity of cognitive decline. Abnormalities in gray matter is substantiated through radiomic features extracted from the significant gray matter clusters. Significant radiomic features of the clusters are able to differentiate between the HC and PD-D subjects with an accuracy of 81.82%. Higher atrophy levels identified in PD-D subjects compared to NC-PD and PD-MCI group enables early diagnosis and treatment procedures. The combined and comprehensive analysis of gray matter alterations through VBM and radiomic features gives better assessment of cognitive impairment in PD.

Clustering of dark matter in the cosmic web as a probe of massive neutrinos

ABSTRACT The large-scale structure of the Universe is distributed in a cosmic web. Studying the distribution and clustering of dark matter particles and haloes may open up a new horizon for studying the physics of the dark Universe. In this work, we investigate the nearest neighbour statistics and spherical contact function in cosmological models with massive neutrinos. For this task, we use the relativistic N-body code, gevolution, and study particle snapshots at three different redshifts. In each snapshot, we find the haloes and evaluate the letter functions for them. We show that a generic behaviour can be found in the nearest neighbour, G(r), and spherical contact functions, F(r), which makes these statistics promising tools to constrain the total neutrino mass.

A double take on early and interacting dark energy from JWST

The very first light captured by the James Webb Space Telescope (JWST) revealed a population of galaxies at very high redshifts more massive than expected in the canonical ΛCDM model of structure formation. Barring, among others, a systematic origin of the issue, in this paper, we test alternative cosmological perturbation histories. We argue that models with a larger matter component Ω m and/or a larger scalar spectral index ns can substantially improve the fit to JWST measurements. In this regard, phenomenological extensions related to the dark energy sector of the theory are appealing alternatives, with Early Dark Energy emerging as an excellent candidate to explain (at least in part) the unexpected JWST preference for larger stellar mass densities. Conversely, Interacting Dark Energy models, despite producing higher values of matter clustering parameters such as σ 8, are generally disfavored by JWST measurements. This is due to the energy-momentum flow from the dark matter to the dark energy sector, implying a smaller matter energy density. Upcoming observations may either strengthen the evidence or falsify some of these appealing phenomenological alternatives to the simplest ΛCDM picture.

Cosmological baryon spread and impact on matter clustering in CAMELS

ABSTRACT We quantify the cosmological spread of baryons relative to their initial neighbouring dark matter distribution using thousands of state-of-the-art simulations from the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project. We show that dark matter particles spread relative to their initial neighbouring distribution owing to chaotic gravitational dynamics on spatial scales comparable to their host dark matter halo. In contrast, gas in hydrodynamic simulations spreads much further from the initial neighbouring dark matter owing to feedback from supernovae (SNe) and active galactic nuclei (AGN). We show that large-scale baryon spread is very sensitive to model implementation details, with the fiducial simba model spreading ∼40 per cent of baryons >1 Mpc away compared to ∼10 per cent for the IllustrisTNG and astrid models. Increasing the efficiency of AGN-driven outflows greatly increases baryon spread while increasing the strength of SNe-driven winds can decrease spreading due to non-linear coupling of stellar and AGN feedback. We compare total matter power spectra between hydrodynamic and paired N-body simulations and demonstrate that the baryonic spread metric broadly captures the global impact of feedback on matter clustering over variations of cosmological and astrophysical parameters, initial conditions, and (to a lesser extent) galaxy formation models. Using symbolic regression, we find a function that reproduces the suppression of power by feedback as a function of wave number (k) and baryonic spread up to $k \sim 10\, h$ Mpc−1 in SIMBA while highlighting the challenge of developing models robust to variations in galaxy formation physics implementation.

21 cm intensity mapping cross-correlation with galaxy surveys: Current and forecasted cosmological parameters estimation for the SKAO

ABSTRACT We present a comprehensive set of forecasts for the cross-correlation signal between 21 cm intensity mapping and galaxy redshift surveys. We focus on the data sets that will be provided by the SKAO for the 21 cm signal, DESI and Euclid for galaxy clustering. We build a likelihood which takes into account the effect of the beam for the radio observations, the Alcock–Paczynski effect, a simple parametrization of astrophysical nuisances, and fully exploit the tomographic power of such observations in the range z = 0.7–1.8 at linear and mildly non-linear scales (k < 0.25h Mpc−1). The forecasted constraints, obtained with Monte Carlo Markov Chains techniques in a Bayesian framework, in terms of the six base parameters of the standard ΛCDM model, are promising. The predicted signal-to-noise ratio for the cross-correlation can reach ∼50 for z ∼ 1 and k ∼ 0.1h Mpc−1. When the cross-correlation signal is combined with current Cosmic Microwave Background (CMB) data from Planck, the error bar on $\Omega _{\rm c}\, h^2$ and H0 is reduced by factors 3 and 6, respectively, compared to CMB only data, due to the measurement of matter clustering provided by the two observables. The cross-correlation signal has a constraining power that is comparable to the autocorrelation one and combining all the clustering measurements a sub-per cent error bar of 0.33 per cent on H0 can be achieved, which is about a factor 2 better than CMB only measurements. Finally, as a proof of concept, we test the full pipeline on the real data measured by the MeerKat collaboration (Cunnington et al. 2022) presenting some (weak) constraints on cosmological parameters.

Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study

The Adolescent Brain Cognitive Development (ABCD) Study® has collected data from over 10,000 children across 21 sites, providing insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a dataset of harmonized and processed ABCD dMRI data (from release 3) has been created, comprising quality-controlled imaging data from 9,345 subjects, focusing exclusively on the baseline session, i.e., the first time point of the study. This resource required substantial computational time (approx. 50,000 CPU hours) for harmonization, whole-brain tractography, and white matter parcellation. The dataset includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts in full and low resolution, and 804 different dMRI-derived measures per subject (72.3 TB total size). Accessible via the NIMH Data Archive, it offers a large-scale dMRI dataset for studying structural connectivity in child and adolescent neurodevelopment. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.

Early dark energy constraints with late-time expansion marginalization

Early dark energy (EDE) is an extension to the ΛCDM model that includes an additional energy density contribution near recombination. The model was proposed to reduce the tension between the measurements of the Hubble constant H 0 from the cosmic microwave background (CMB) and from the local cosmic distance ladder. Some analyses in the recent literature have shown intriguing hints for EDE. However, this model increases the tension in the derived clustering of galaxies (as measured by the so-called S 8 parameter) between CMB and large scale structure (LSS) measurements. This new tension limits the contribution of EDE during recombination, and thus its effect on the Hubble tension. In this work, we investigate whether the inclusion of a general, smooth late-time dark energy modification can increase back the EDE contribution when LSS data is included in the analysis. In order to generalize the late expansion with respect to the ΛCDM model, we substitute the cosmological constant by a late dark energy fluid model with a piecewise constant equation of state w(z) in redshift bins. We show that, when analysing this generalized model with combinations of CMB, LSS and type Ia supernovae data from several experiments no significant changes on S 8 and EDE parameter constraints is found. The contribution to the EDE fraction constraint with late-time expansion marginalization is f EDE = 0.067+0.019 -0.027 using 3 redshift bins, with similar results for 5 and 10 redshift bins. This work shows that in order to solve simultaneously the Hubble and S 8 tensions, one needs a mechanism for increasing the clustering of matter at late times different from a simple change in the background evolution of late dark energy.

Model independent dark matter properties from cosmic growth

Dark matter dominates the matter budget of the universe but its nature is unknown. Deviations from the standard model, where dark matter clusters with the same gravitational strength as baryons, and has the same pressureless equation of state as baryons, can be tested by cosmic growth measurements. We take a model independent approach, allowing deviations in bins of redshift, and compute the constraints enabled by ongoing cosmic structure surveys through redshift space distortions and peculiar velocities. These can produce constraints at the 3-14% level in four independent redshift bins over z = [0,4].

The contribution of massive haloes to the matter power spectrum in the presence of AGN feedback

ABSTRACT The clustering of matter, as measured by the matter power spectrum, informs us about cosmology, dark matter, and baryonic effects on the distribution of matter in the universe. Using cosmological hydrodynamical simulations from the cosmo-OWLS and BAHAMAS simulation projects, we investigate the contribution of power in haloes with various masses, to the full power spectrum, as well as the power ratio between baryonic and dark matter only (DMO) simulations for a matched (between simulations) and an unmatched set of haloes. We find that the presence of AGN feedback suppresses the power on all scales for haloes of all masses examined (1011.25 ≤ M500, crit ≤ $10^{14.75}\, \mathrm{M_\odot }/h$), by ejecting matter from within $r_{500,\mathrm{c}}\,$ to $r_{200,\mathrm{m}}\,$ and potentially beyond in massive haloes (M500, crit ≳ $10^{{13}}\, \mathrm{M_\odot }/h$), and likely impeding the growth of lower-mass haloes as a consequence. A lower AGN feedback temperature changes the behaviour of high-mass haloes (M500, crit ≥ $10^{{13.25}}\, \mathrm{M_\odot }/h$), damping the effects of AGN feedback at small scales, $k\, {{\gtrsim }}\, {{4}}\, h\mathrm{\, Mpc^{-1}}$. For $k\, {{\lesssim }}\, {{3}}\, h\mathrm{\, Mpc^{-1}}$, group-sized haloes ($10^{{14\pm 0.25}}\, \mathrm{M_\odot }/h$) dominate the power spectrum, while on smaller scales the combined contributions of lower-mass haloes to the full power spectrum rise above that of the group-sized haloes. Finally, we present a model for the power suppression due to feedback, which combines observed mean halo baryon fractions with halo mass fractions and halo-matter cross-spectra extracted from DMO simulations to predict the power suppression to per cent level accuracy down to $k\, {{\approx }}\, {{10}}\, h\mathrm{\, Mpc^{-1}}$ without any free parameters.

Thermodynamics of quark matter with multiquark clusters in an effective Beth-Uhlenbeck type approach

We describe multiquark clusters in quark matter within a Beth-Uhlenbeck approach in a background gluon field coupled to the underlying chiral quark dynamics using the Polyakov gauge. An effective potential for the traced Polyakov loop is used to establish the center symmetry of the SU(3) color which suppresses colored states and its dynamical breaking as an aspect of the confinement/deconfinement transition. Quark confinement is modeled by a large quark mass in vacuum which is motivated by a confining density functional approach. A multiquark cluster containing n quarks and antiquarks is described as a binary composite of smaller subclusters n1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n_1$$\\end{document} and n2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n_2$$\\end{document} (n1+n2=n\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$n_1+n_2=n$$\\end{document}). It has a spectrum consisting of a bound state and a scattering state continuum. For the corresponding cluster-cluster phase shifts we use simple ansätze that capture the Mott dissociation of clusters as a function of temperature and chemical potential. We go beyond the simple “step-up-step-down” model that ignores continuum correlations and introduce an improved model that includes them in a generic form. In order to explain the model, we restrict ourselves here to the cases where the cluster size is 1≤n≤6\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1 \\le n \\le 6$$\\end{document}. A striking result is the suppression of the abundance of colored multiquark clusters at low temperatures by the coupling to the Polyakov loop and their importance for a quantitative description of lattice QCD thermodynamics at non-vanishing baryochemical potentials. An important ingredient are Polyakov-loop generalized distribution functions of n-quark clusters which are derived here for the first time. Within our approach we calculate thermodynamic properties such as baryon density and entropy. We demonstrate that the limits of a hadron resonance gas at low temperatures and O(g2)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\mathcal {O}(g^2)$$\\end{document} perturbative QCD at high temperatures are correctly reproduced. A comparison with lattice calculations shows that our model is able to give a unified, systematic approach to describe properties of the quark-gluon-hadron system.

Mapping gray and white matter volume abnormalities in early-onset psychosis: an ENIGMA multicenter voxel-based morphometry study

IntroductionRegional gray matter (GM) alterations have been reported in early-onset psychosis (EOP, onset before age 18), but previous studies have yielded conflicting results, likely due to small sample sizes and the different brain regions examined. In this study, we conducted a whole brain voxel-based morphometry (VBM) analysis in a large sample of individuals with EOP, using the newly developed ENIGMA-VBM tool.Methods15 independent cohorts from the ENIGMA-EOP working group participated in the study. The overall sample comprised T1-weighted MRI data from 482 individuals with EOP and 469 healthy controls. Each site performed the VBM analysis locally using the standardized ENIGMA-VBM tool. Statistical parametric T-maps were generated from each cohort and meta-analyzed to reveal voxel-wise differences between EOP and healthy controls as well as the individual-based association between GM volume and age of onset, chlorpromazine (CPZ) equivalent dose, and other clinical variables.ResultsCompared with healthy controls, individuals with EOP showed widespread lower GM volume encompassing most of the cortex, with the most marked effect in the left median cingulate (Hedges’ g = 0.55, p = 0.001 corrected), as well as small clusters of lower white matter (WM), whereas no regional GM or WM volumes were higher in EOP. Lower GM volume in the cerebellum, thalamus and left inferior parietal gyrus was associated with older age of onset. Deficits in GM in the left inferior frontal gyrus, right insula, right precentral gyrus and right superior frontal gyrus were also associated with higher CPZ equivalent doses.ConclusionEOP is associated with widespread reductions in cortical GM volume, while WM is affected to a smaller extent. GM volume alterations are associated with age of onset and CPZ equivalent dose but these effects are small compared to case-control differences. Mapping anatomical abnormalities in EOP may lead to a better understanding of the role of psychosis in brain development during childhood and adolescence.

Dense Cold Quark–Gluon Matter Clusters and Their Study at the NICA Collider

In this paper, the production of particles outside the region of nucleon–nucleon kinematics due to interactions involving dense cold clusters of quark-gluon matter in nuclei is calculated. The possibility of observing this process in the region of central rapidities and large transverse momenta in heavy ion collisions at low energies with MPD detector at the NICA collider is demonstrated.

Dynamic Human Brain Imaging with a Portable PET Camera: Comparison to a Standard Scanner.

Portable, cost-effective PET cameras can radically expand the applicability of PET. We present here a within-participant comparison of fully quantified [18F]FDG dynamic scans in healthy volunteers using the standard Biograph mCT scanner and portable CerePET scanner. Methods: Each of 20 healthy volunteers underwent dynamic [18F]FDG imaging with both scanners (1-154 d apart) and concurrent arterial blood sampling. Tracer SUV, net influx rate (Ki), and the corresponding cerebral metabolic rate of glucose (CMRglu) were quantified at regional and voxel levels. Results: At the regional level, CerePET outcome measure estimates within participants robustly correlated with Biograph mCT estimates in the neocortex, wherein the average Pearson correlation coefficients across participants ± SD were 0.83 ± 0.07 (SUV) and 0.85 ± 0.08 (Ki and CMRglu). There was also strong agreement between CerePET and Biograph mCT estimates, wherein the average regression slopes across participants were 0.84 ± 0.17 (SUV), 0.83 ± 0.17 (Ki), and 0.85 ± 0.18 (CMRglu). There was similar bias across participants but higher correlation and less variability in subcortical regions than in cortical regions. Pearson correlation coefficients for subcortical regions equaled 0.97 ± 0.02 (SUV) and 0.97 ± 0.03 (Ki and CMRglu), and average regression slopes equaled 0.79 ± 0.14 (SUV), 0.83 ± 0.11 (Ki), and 0.86 ± 0.11 (CMRglu). In voxelwise assessment, CerePET and Biograph mCT estimates across outcome measures were significantly different only in a cluster of left frontal white matter. Conclusion: Our results indicate robust correlation and agreement between semi- and fully quantitative brain glucose metabolism measurements from portable CerePET and standard Biograph mCT scanners. The results obtained with a portable PET scanner in this comparison in humans require follow-up but lend confidence to the feasibility of more flexible and portable brain imaging with PET.