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: “conservative” and “disruptive.” 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 Spearman’s correlation between edgewise baseline FC (at 14 y, FC14) and adolescent change in FC (ΔFC14−26), 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 62^{62}Fe and 62^{62}Mn following in-beam decay of 62^{62}Mn

Sub-barrier Coulomb-excitation was performed on a mixed beam of $^{62}$Mn and $^{62}$Fe, following in-trap $\beta^{-}$ decay of $^{62}$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 $(2^{+},3^{+})\rightarrow1^{+}_{g.s.}$ transition. This fixes the relative positions of the $\beta$-decaying $4^{+}$ and $1^{+}$ states in $^{62}$Mn for the first time. Population of the $2^{+}_{1}$ state was observed in $^{62}$Fe and the cross-section determined by normalisation to the $^{109}$Ag target excitation, confirming the $B(E2)$ 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