1,826 research outputs found
The colour-magnitude relation of Globular Clusters in Centaurus and Hydra - Constraints on star cluster self-enrichment with a link to massive Milky Way GCs
We investigate the colour-magnitude relation of metal-poor globular clusters,
the 'blue tilt', in the Hydra and Centaurus galaxy clusters and constrain the
primordial conditions for star cluster self-enrichment. We analyse U,I
photometry for about 2500 globular clusters in the central regions of Hydra and
Centaurus, based on FORS1@VLT data. We convert the measured colour-magnitude
relations into mass-metallicity space and obtain a scaling of Z \propto M^{0.27
\pm 0.05} for Centaurus GCs and Z \propto M^{0.40 \pm 0.06} for Hydra GCs,
consistent with results in other environments. We find that the GC
mass-metallicity relation already sets in at present-day masses of a few 10^5
solar masses and is well established in the luminosity range of massive MW
clusters like omega Centauri. We compare the mass-metallicity relation with
predictions from the star cluster self-enrichment model by Bailin & Harris
(2009). For this we include effects of dynamical and stellar evolution and a
physically well motivated primordial mass-radius scaling. The self-enrichment
model reproduces the observed relations well for average primordial half-light
radii r_h ~ 1-1.5 pc, star formation efficiencies f_* ~ 0.3-0.4, and
pre-enrichment levels of [Fe/H] ~ -1.7 dex. Within the self-enrichment
scenario, the observed blue tilt implies a correlation between GC mass and
width of the stellar metallicity distribution. We find that this implied
correlation matches the trend of width with GC mass measured in Galactic GCs,
including extreme cases like omega Cen and M54. We conclude that 1. A
primordial star cluster mass-radius relation provides a significant improvement
to the self-enrichment model fits. 2. Broadenend metallicity distributions as
found in some massive MW globular clusters may have arisen naturally from
self-enrichment processes, without the need of a dwarf galaxy progenitor.Comment: 15 pages, 13 figures. Language edited version of paper accepted for
publication in Astronomy & Astrophysics. Colour-composite in Figure 1 reduced
in resolutio
Conservative and disruptive modes of adolescent change in human brain functional connectivity
Adolescent changes in human brain function are not entirely understood. Here, we used multiecho functional MRI (fMRI) to measure developmental change in functional connectivity (FC) of resting-state oscillations between pairs of 330 cortical regions and 16 subcortical regions in 298 healthy adolescents scanned 520 times. Participants were aged 14 to 26 y and were scanned on 1 to 3 occasions at least 6 mo apart. We found 2 distinct modes of age-related change in FC: âconservativeâ and âdisruptive.â Conservative development was characteristic of primary cortex, which was strongly connected at 14 y and became even more connected in the period from 14 to 26 y. Disruptive development was characteristic of association cortex and subcortical regions, where connectivity was remodeled: connections that were weak at 14 y became stronger during adolescence, and connections that were strong at 14 y became weaker. These modes of development were quantified using the maturational index (MI), estimated as Spearmanâs correlation between edgewise baseline FC (at 14 y, FC14) and adolescent change in FC (ÎFC14â26), at each region. Disruptive systems (with negative MI) were activated by social cognition and autobiographical memory tasks in prior fMRI data and significantly colocated with prior maps of aerobic glycolysis (AG), AG-related gene expression, postnatal cortical surface expansion, and adolescent shrinkage of cortical thickness. The presence of these 2 modes of development was robust to numerous sensitivity analyses. We conclude that human brain organization is disrupted during adolescence by remodeling of FC between association cortical and subcortical areas
Study of bound states in 12Be through low-energy 11Be(d,p)-transfer reactions
The bound states of 12Be have been studied through a 11Be(d,p)12Be transfer
reaction experiment in inverse kinematics. A 2.8 MeV/u beam of 11Be was
produced using the REX-ISOLDE facility at CERN. The outgoing protons were
detected with the T-REX silicon detector array. The MINIBALL germanium array
was used to detect gamma rays from the excited states in 12Be. The gamma-ray
detection enabled a clear identification of the four known bound states in
12Be, and each of the states has been studied individually. Differential cross
sections over a large angular range have been extracted. Spectroscopic factors
for each of the states have been determined from DWBA calculations and have
been compared to previous experimental and theoretical results
Low-energy Coulomb excitation of Fe and Mn following in-beam decay of Mn
Sub-barrier Coulomb-excitation was performed on a mixed beam of Mn and
Fe, following in-trap decay of Mn at REX-ISOLDE,
CERN. The trapping and charge breeding times were varied in order to alter the
composition of the beam, which was measured by means of an ionisation chamber
at the zero-angle position of the Miniball array. A new transition was observed
at 418~keV, which has been tentatively associated to a
transition. This fixes the relative
positions of the -decaying and states in Mn for
the first time. Population of the state was observed in Fe
and the cross-section determined by normalisation to the Ag target
excitation, confirming the value measured in recoil-distance lifetime
experiments.Comment: 9 pages, 10 figure
Brain charts for the human lifespan
Over the past few decades, neuroimaging has become a ubiquitous tool in basic research and clinical studies of the human brain. However, no reference standards currently exist to quantify individual differences in neuroimaging metrics over time, in contrast to growth charts for anthropometric traits such as height and weight1. Here we assemble an interactive open resource to benchmark brain morphology derived from any current or future sample of MRI data (http://www.brainchart.io/). With the goal of basing these reference charts on the largest and most inclusive dataset available, acknowledging limitations due to known biases of MRI studies relative to the diversity of the global population, we aggregated 123,984 MRI scans, across more than 100 primary studies, from 101,457 human participants between 115 days post-conception to 100 years of age. MRI metrics were quantified by centile scores, relative to non-linear trajectories2 of brain structural changes, and rates of change, over the lifespan. Brain charts identified previously unreported neurodevelopmental milestones3, showed high stability of individuals across longitudinal assessments, and demonstrated robustness to technical and methodological differences between primary studies. Centile scores showed increased heritability compared with non-centiled MRI phenotypes, and provided a standardized measure of atypical brain structure that revealed patterns of neuroanatomical variation across neurological and psychiatric disorders. In summary, brain charts are an essential step towards robust quantification of individual variation benchmarked to normative trajectories in multiple, commonly used neuroimaging phenotypes
Clinically feasible brain morphometric similarity network construction approaches with restricted magnetic resonance imaging acquisitions
Morphometric similarity networks (MSNs) estimate organization of the cortex as a biologically meaningful set of similarities between anatomical features at the macro-and microstructural level, derived from multiple structural MRI (sMRI) sequences. These networks are clinically relevant, predicting 40% variance in IQ. However, the sequences required (T1w, T2w, DWI) to produce these networks are longer acquisitions, less feasible in some populations. Thus, estimating MSNs using features from T1w sMRI is attractive to clinical and developmental neuroscience. We studied whether reduced-feature approaches approximate the original MSN model as a potential tool to investigate brain structure. In a large, homogenous dataset of healthy young adults (from the Human Connectome Project, HCP), we extended previous investigations of reduced-feature MSNs by comparing not only T1w-derived networks, but also additional MSNs generated with fewer MR sequences, to their full acquisition counterparts. We produce MSNs that are highly similar at the edge level to those generated with multimodal imaging; however, the nodal topology of the networks differed. These networks had limited predictive validity of generalized cognitive ability. Overall, when multimodal imaging is not available or appropriate, T1w-restricted MSN construction is feasible, provides an appropriate estimate of the MSN, and could be a useful approach to examine outcomes in future studies
Brain charts for the human lifespan
Over the past few decades, neuroimaging has become a ubiquitous tool in basic
research and clinical studies of the human brain. However, no reference standards
currently exist to quantify individual diferences in neuroimaging metrics over time,
in contrast to growth charts for anthropometric traits such as height and weight1
.
Here we assemble an interactive open resource to benchmark brain morphology
derived from any current or future sample of MRI data (http://www.brainchart.io/).
With the goal of basing these reference charts on the largest and most inclusive
dataset available, acknowledging limitations due to known biases of MRI studies
relative to the diversity of the global population, we aggregated 123,984 MRI scans,
across more than 100 primary studies, from 101,457 human participants between 115
days post-conception to 100 years of age. MRI metrics were quantifed by centile
scores, relative to non-linear trajectories2
of brain structural changes, and rates of
change, over the lifespan. Brain charts identifed previously unreported neurodevelo pmental milestones3
, showed high stability of individuals across longitudinal
assessments, and demonstrated robustness to technical and methodological
diferences between primary studies. Centile scores showed increased heritability
compared with non-centiled MRI phenotypes, and provided a standardized measure
of atypical brain structure that revealed patterns of neuroanatomical variation across
neurological and psychiatric disorders. In summary, brain charts are an essential step
towards robust quantifcation of individual variation benchmarked to normative
trajectories in multiple, commonly used neuroimaging phenotypes
Structural covariance networks are coupled to expression of genes enriched in supragranular layers of the human cortex.
Complex network topology is characteristic of many biological systems, including anatomical and functional brain networks (connectomes). Here, we first constructed a structural covariance network from MRI measures of cortical thickness on 296 healthy volunteers, aged 14-24 years. Next, we designed a new algorithm for matching sample locations from the Allen Brain Atlas to the nodes of the SCN. Subsequently we used this to define, transcriptomic brain networks by estimating gene co-expression between pairs of cortical regions. Finally, we explored the hypothesis that transcriptional networks and structural MRI connectomes are coupled. A transcriptional brain network (TBN) and a structural covariance network (SCN) were correlated across connection weights and showed qualitatively similar complex topological properties: assortativity, small-worldness, modularity, and a rich-club. In both networks, the weight of an edge was inversely related to the anatomical (Euclidean) distance between regions. There were differences between networks in degree and distance distributions: the transcriptional network had a less fat-tailed degree distribution and a less positively skewed distance distribution than the SCN. However, cortical areas connected to each other within modules of the SCN had significantly higher levels of whole genome co-expression than expected by chance. Nodes connected in the SCN had especially high levels of expression and co-expression of a human supragranular enriched (HSE) gene set that has been specifically located to supragranular layers of human cerebral cortex and is known to be important for large-scale, long-distance cortico-cortical connectivity. This coupling of brain transcriptome and connectome topologies was largely but not entirely accounted for by the common constraint of physical distance on both networks
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