Aims. We study the relationship between the local environment of galaxies and their star formation rate (SFR) in the Great Observatories Origins Deep Survey, GOODS, at z ∼ 1. Methods. We use ultradeep imaging at 24 µm with the MIPS camera onboard Spitzer to determine the contribution of obscured light to the SFR of galaxies over the redshift range 0.8 ≤ z ≤ 1.2. Accurate galaxy densities are measured thanks to the large sample of ∼1200 spectroscopic redshifts with high (∼70%) spectroscopic completeness. Morphology and stellar masses are derived from deep HST-ACS imaging, supplemented by ground based imaging programs and photometry from the IRAC camera onboard Spitzer. Results. We show that the star formation-density relation observed locally was reversed at z ∼ 1: the average SFR of an individual galaxy increased with local galaxy density when the universe was less than half its present age. Hierarchical galaxy formation models (simulated lightcones from the Millennium model) predicted such a reversal to occur only at earlier epochs (z > 2) and at a lower level. We present a remarkable structure at z ∼ 1.016, containing X-ray traced galaxy concentrations, which will eventually merge into a Virgo-like cluster. This structure illustrates how the individual SFR of galaxies increases with density and shows that it is the ∼1−2 Mpc scale that affects most the star formation in galaxies at z ∼ 1. The SFR of z ∼ 1 galaxies is found to correlate with stellar mass suggesting that mass plays a role in the observed star formation-density trend. However the specific SFR (=SFR/M ) decreases with stellar mass while it increases with galaxy density, which implies that the environment does directly affect the star formation activity of galaxies. Major mergers do not appear to be the unique or even major cause for this effect since nearly half (46%) of the luminous infrared galaxies (LIRGs) at z ∼ 1 present the HST-ACS morphology of spirals, while only a third present a clear signature of major mergers. The remaining galaxies are divided into compact (9%) and irregular (14%) galaxies. Moreover, the specific SFR of major mergers is only marginally stronger than that of spirals. Conclusions. These findings constrain the influence of the growth of large-scale structures on the star formation history of galaxies. Reproducing the SFR-density relation at z ∼ 1 is a new challenge for models, requiring a correct balance between mass assembly through mergers and in-situ star formation at early epochs.

Schizophrenia has often been conceived as a disorder of connectivity between components of large-scale brain networks. We tested this hypothesis by measuring aspects of both functional connectivity and functional network topology derived from resting-state fMRI time series acquired at 72 cerebral regions over 17 min from 15 healthy volunteers (14 male, 1 female) and 12 people diagnosed with schizophrenia (10 male, 2 female). We investigated between-group differences in strength and diversity of functional connectivity in the 0.06 -0.125 Hz frequency interval, and some topological properties of undirected graphs constructed from thresholded interregional correlation matrices. In people with schizophrenia, strength of functional connectivity was significantly decreased, whereas diversity of functional connections was increased. Topologically, functional brain networks had reduced clustering and small-worldness, reduced probability of high-degree hubs, and increased robustness in the schizophrenic group. Reduced degree and clustering were locally significant in medial parietal, premotor and cingulate, and right orbitofrontal cortical nodes of functional networks in schizophrenia. Functional connectivity and topological metrics were correlated with each other and with behavioral performance on a verbal fluency task. We conclude that people with schizophrenia tend to have a less strongly integrated, more diverse profile of brain functional connectivity, associated with a less hub-dominated configuration of complex brain functional networks. Alongside these behaviorally disadvantageous differences, however, brain networks in the schizophrenic group also showed a greater robustness to random attack, pointing to a possible benefit of the schizophrenia connectome, if less extremely expressed.

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.

Self-organized criticality is an attractive model for human brain dynamics, but there has been little direct evidence for its existence in large-scale systems measured by neuroimaging. In general, critical systems are associated with fractal or power law scaling, long-range correlations in space and time, and rapid reconfiguration in response to external inputs. Here, we consider two measures of phase synchronization: the phase-lock interval, or duration of coupling between a pair of (neurophysiological) processes, and the lability of global synchronization of a (brain functional) network. Using computational simulations of two mechanistically distinct systems displaying complex dynamics, the Ising model and the Kuramoto model, we show that both synchronization metrics have power law probability distributions specifically when these systems are in a critical state. We then demonstrate power law scaling of both pairwise and global synchronization metrics in functional MRI and magnetoencephalographic data recorded from normal volunteers under resting conditions. These results strongly suggest that human brain functional systems exist in an endogenous state of dynamical criticality, characterized by a greater than random probability of both prolonged periods of phase-locking and occurrence of large rapid changes in the state of global synchronization, analogous to the neuronal “avalanches” previously described in cellular systems. Moreover, evidence for critical dynamics was identified consistently in neurophysiological systems operating at frequency intervals ranging from 0.05–0.11 to 62.5–125 Hz, confirming that criticality is a property of human brain functional network organization at all frequency intervals in the brain's physiological bandwidth.

Long-range cortical functional connectivity is often reduced in autism spectrum disorders (ASD), but the nature of local cortical functional connectivity in ASD has remained elusive. We used magnetoencephalography to measure task-related local functional connectivity, as manifested by coupling between the phase of alpha oscillations and the amplitude of gamma oscillations, in the fusiform face area (FFA) of individuals diagnosed with ASD and typically developing individuals while they viewed neutral faces, emotional faces, and houses. We also measured task-related long-range functional connectivity between the FFA and the rest of the cortex during the same paradigm. In agreement with earlier studies, long-range functional connectivity between the FFA and three distant cortical regions was reduced in the ASD group. However, contrary to the prevailing hypothesis in the field, we found that local functional connectivity within the FFA was also reduced in individuals with ASD when viewing faces. Furthermore, the strength of long-range functional connectivity was directly correlated to the strength of local functional connectivity in both groups; thus, long-range and local connectivity were reduced proportionally in the ASD group. Finally, the magnitude of local functional connectivity correlated with ASD severity, and statistical classification using local and long-range functional connectivity data identified ASD diagnosis with 90% accuracy. These results suggest that failure to entrain neuronal assemblies fully both within and across cortical regions may be characteristic of ASD.coherence | parvalbumin O scillations in the brain underlie many key cortical functions and coordinate activity between distant brain areas. These rhythmic oscillations are typically studied in distinct frequency bands, often segregated as delta (1-2 Hz), theta (3-7 Hz), alpha (8-12 Hz), beta (13-30 Hz), low gamma (30-60 Hz), and high gamma (>60 Hz). Each frequency band has been associated with specific cognitive and synaptic processes, with some overlaps among bands (1). Substantial evidence indicates that abnormalities in long-range interregional functional connectivity, usually mediated by theta and alpha oscillations, are common in neurodevelopmental disorders (2), including autism spectrum disorders (ASD) (3).The nature of functional connectivity in ASD has been under intense investigation since the publication of a 2004 report showing reduced long-range functional connectivity in ASD (4). The prevailing hypothesis in the field is that ASD is characterized by reduced long-range functional connectivity and increased local functional connectivity (5-7). The hypothesis that local functional connectivity is increased in ASD is motivated primarily by genetic and histological evidence of reduced neuronal inhibition in ASD (8-10). Although a large body of functional evidence supports the hypothesis of generally reduced long-range functional connectivity in ASD (11), neurophysiological signatures of local functional connectivity in ASD have r...

Effortful cognitive performance is theoretically expected to depend on the formation of a global neuronal workspace. We tested specific predictions of workspace theory, using graph theoretical measures of network topology and physical distance of synchronization, in magnetoencephalographic data recorded from healthy adult volunteers (N ϭ 13) during performance of a working memory task at several levels of difficulty. We found that greater cognitive effort caused emergence of a more globally efficient, less clustered, and less modular network configuration, with more long-distance synchronization between brain regions. This pattern of task-related workspace configuration was more salient in the ␤-band (16 -32 Hz) and ␥-band (32-63 Hz) networks, compared with both lower (␣-band; 8 -16 Hz) and higher (high ␥-band; 63-125 Hz) frequency intervals. Workspace configuration of ␤-band networks was also greater in faster performing participants (with correct response latency less than the sample median) compared with slower performing participants. Processes of workspace formation and relaxation in relation to time-varying demands for cognitive effort could be visualized occurring in the course of task trials lasting Ͻ2 s. These experimental results provide support for workspace theory in terms of complex network metrics and directly demonstrate how cognitive effort breaks modularity to make human brain functional networks transiently adopt a more efficient but less economical configuration.

Estimates of galaxy merger rates based on counts of close pairs typically assume that most of the observed systems will merge within a few hundred Myr (for projected pair separations ≤25 h −1 kpc). Here we investigate these assumptions using virtual galaxy catalogues derived from the Millennium Simulation, a very large N-body simulation of structure formation in the concordance cold dark matter ( CDM) cosmology. These catalogues have been shown to be at least roughly consistent with a wide range of properties of the observed galaxy population at both low and high redshift. Here we show that they also predict close pair abundances at low redshift which agree with those observed. They thus embed a realistic and realistically evolving galaxy population within the standard structure formation paradigm, and so are well suited to calibrate the relation between close galaxy pairs and mergers. We show that observational methods, when applied to our mock galaxy surveys, do indeed identify pairs which are physically close and due to merge. The sample-averaged merging time depends only weakly on the stellar mass and redshift of the pair. At z ≤ 2 this time-scale is T ≈ T 0 r 25 M −0.3 * , where r 25 is the maximum projected separation of the pair sample in units of 25 h −1 kpc, M * is the typical stellar mass of the pairs in units of 3 × 10 10 h −1 M and the coefficient T 0 is 1.1 Gyr for samples selected to have line-of-sight velocity difference smaller than 300 km s −1 and 1.6 Gyr for samples where this velocity difference is effectively unconstrained. These time-scales increase slightly with redshift and are longer than assumed in most observational studies, implying that merger rates have typically been overestimated.

Human functional magnetic resonance imaging (fMRI) brain networks have a complex topology comprising integrative components, e.g. long-distance inter-modular edges, that are theoretically associated with higher biological cost. Here, we estimated intra-modular degree, inter-modular degree and connection distance for each of 285 cortical nodes in multi-echo fMRI data from 38 healthy adults. We used the multivariate technique of partial least squares (PLS) to reduce the dimensionality of the relationships between these three nodal network parameters and prior microarray data on regional expression of 20 737 genes. The first PLS component defined a transcriptional profile associated with high intra-modular degree and short connection distance, whereas the second PLS component was associated with high inter-modular degree and long connection distance. Nodes in superior and lateral cortex with high inter-modular degree and long connection distance had local transcriptional profiles enriched for oxidative metabolism and mitochondria, and for genes specific to supragranular layers of human cortex. In contrast, primary and secondary sensory cortical nodes in posterior cortex with high intra-modular degree and short connection distance had transcriptional profiles enriched for RNA translation and nuclear components. We conclude that, as predicted, topologically integrative hubs, mediating long-distance connections between modules, are more costly in terms of mitochondrial glucose metabolism.This article is part of the themed issue ‘Interpreting BOLD: a dialogue between cognitive and cellular neuroscience’.