Impact of eV-mass sterile neutrinos on neutrino-driven supernova outflows

Motivated by recent hints for sterile neutrinos from the reactor anomaly, we study active-sterile conversions in a three-flavor scenario (2 active + 1 sterile families) for three different representative times during the neutrino-cooling evolution of the proto-neutron star born in an electron-capture supernova. In our "early model" (0.5 s post bounce), the nu_e-nu_s MSW effect driven by Delta m^2=2.35 eV^2 is dominated by ordinary matter and leads to a complete nu_e-nu_s swap with little or no trace of collective flavor oscillations. In our "intermediate" (2.9 s p.b.) and "late models" (6.5 s p.b.), neutrinos themselves significantly modify the nu_e-nu_s matter effect, and, in particular in the late model, nu-nu refraction strongly reduces the matter effect, largely suppressing the overall nu_e-nu_s MSW conversion. This phenomenon has not been reported in previous studies of active-sterile supernova neutrino oscillations. We always include the feedback effect on the electron fraction Y_e due to neutrino oscillations. In all examples, Y_e is reduced and therefore the presence of sterile neutrinos can affect the conditions for heavy-element formation in the supernova ejecta, even if probably not enabling the r-process in the investigated outflows of an electron-capture supernova. The impact of neutrino-neutrino refraction is strong but complicated, leaving open the possibility that with a more complete treatment, or for other supernova models, active-sterile neutrino oscillations could generate conditions suitable for the r-process.Comment: 23 pages, including 14 figures and 2 tables (minor changes in the text). Matches published version in JCA

A Bayesian view of the current status of dark matter direct searches

Bayesian statistical methods offer a simple and consistent framework for incorporating uncertainties into a multi-parameter inference problem. In this work we apply these methods to a selection of current direct dark matter searches. We consider the simplest scenario of spin-independent elastic WIMP scattering, and infer the WIMP mass and cross-section from the experimental data with the essential systematic uncertainties folded into the analysis. We find that when uncertainties in the scintillation efficiency of Xenon100 have been accounted for, the resulting exclusion limit is not sufficiently constraining to rule out the CoGeNT preferred parameter region, contrary to previous claims. In the same vein, we also investigate the impact of astrophysical uncertainties on the preferred WIMP parameters. We find that within the class of smooth and isotropic WIMP velocity distributions, it is difficult to reconcile the DAMA and the CoGeNT preferred regions by tweaking the astrophysics parameters alone. If we demand compatibility between these experiments, then the inference process naturally concludes that a high value for the sodium quenching factor for DAMA is preferred.Comment: 37 pages, 14 figures and 7 tables. Replacement for matching the version accepted for publicatio

Probing the Local Velocity Distribution of WIMP Dark Matter with Directional Detectors

We explore the ability of directional nuclear-recoil detectors to constrain the local velocity distribution of weakly interacting massive particle (WIMP) dark matter by performing Bayesian parameter estimation on simulated recoil-event data sets. We discuss in detail how directional information, when combined with measurements of the recoil-energy spectrum, helps break degeneracies in the velocity-distribution parameters. We also consider the possibility that velocity structures such as cold tidal streams or a dark disk may also be present in addition to the Galactic halo. Assuming a carbon-tetrafluoride detector with a 30-kg-yr exposure, a 50-GeV WIMP mass, and a WIMP-nucleon spin-dependent cross-section of 0.001 pb, we show that the properties of a cold tidal stream may be well constrained. However, measurement of the parameters of a dark-disk component with a low lag speed of ~50 km/s may be challenging unless energy thresholds are improved.Comment: 38 pages, 15 figure

Stochastic Phase Space Localization for a Single Particle

We propose a feedback scheme to control the vibrational motion of a single trapped particle based on indirect measurements of its position. It results the possibility of a motional phase space uncertainty contraction, correponding to cool the particle close to the motional ground state.Comment: 9 pages, 1 figure. Concluding section and figure revised. In press on Phys. rev.

Dynamical mean-field approach to materials with strong electronic correlations

We review recent results on the properties of materials with correlated electrons obtained within the LDA+DMFT approach, a combination of a conventional band structure approach based on the local density approximation (LDA) and the dynamical mean-field theory (DMFT). The application to four outstanding problems in this field is discussed: (i) we compute the full valence band structure of the charge-transfer insulator NiO by explicitly including the p-d hybridization, (ii) we explain the origin for the simultaneously occuring metal-insulator transition and collapse of the magnetic moment in MnO and Fe2O3, (iii) we describe a novel GGA+DMFT scheme in terms of plane-wave pseudopotentials which allows us to compute the orbital order and cooperative Jahn-Teller distortion in KCuF3 and LaMnO3, and (iv) we provide a general explanation for the appearance of kinks in the effective dispersion of correlated electrons in systems with a pronounced three-peak spectral function without having to resort to the coupling of electrons to bosonic excitations. These results provide a considerable progress in the fully microscopic investigations of correlated electron materials.Comment: 24 pages, 14 figures, final version, submitted to Eur. Phys. J. for publication in the Special Topics volume "Cooperative Phenomena in Solids: Metal-Insulator Transitions and Ordering of Microscopic Degrees of Freedom

Progressive reconfiguration of resting-state brain networks as psychosis develops: Preliminary results from the North American Prodrome Longitudinal Study (NAPLS) consortium

Mounting evidence has shown disrupted brain network architecture across the psychosis spectrum. However, whether these changes relate to the development of psychosis is unclear. Here, we used graph theoretical analysis to investigate longitudinal changes in resting-state brain networks in samples of 72 subjects at clinical high risk (including 8 cases who converted to full psychosis) and 48 healthy controls drawn from the North American Prodrome Longitudinal Study (NAPLS) consortium. We observed progressive reduction in global efficiency (P = 0.006) and increase in network diversity (P = 0.001) in converters compared with non-converters and controls. More refined analysis separating nodes into nine key brain networks demonstrated that these alterations were primarily driven by progressively diminished local efficiency in the default-mode network (P = 0.004) and progressively enhanced node diversity across all networks (P < 0.05). The change rates of network efficiency and network diversity were significantly correlated (P = 0.003), suggesting these changes may reflect shared neural mechanisms. In addition, change rates of global efficiency and node diversity were significantly correlated with change rate of cortical thinning in the prefrontal cortex in converters (P < 0.03) and could be predicted by visuospatial memory scores at baseline (P < 0.04). These results provide preliminary evidence for longitudinal reconfiguration of resting-state brain networks during psychosis development and suggest that decreased network efficiency, reflecting an increase in path length between nodes, and increased network diversity, reflecting a decrease in the consistency of functional network organization, may be implicated in the progression to full psychosis

Altered brain activation during memory retrieval precedes and predicts conversion to psychosis in individuals at clinical high risk

Memory deficits are a hallmark of psychotic disorders such as schizophrenia. However, whether the neural dysfunction underlying these deficits is present before the onset of illness and potentially predicts conversion to psychosis is unclear. In this study, we investigated brain functional alterations during memory processing in a sample of 155 individuals at clinical high risk (including 18 subjects who later converted to full psychosis) and 108 healthy controls drawn from the second phase of the North American Prodrome Longitudinal Study (NAPLS-2). All participants underwent functional magnetic resonance imaging with a paired-associate memory paradigm at the point of recruitment and were clinically followed up for approximately 2 years. We found that at baseline, subjects at high risk showed significantly higher activation during memory retrieval in the prefrontal, parietal, and bilateral temporal cortices (PFWE &lt; .035). This effect was more pronounced in converters than nonconverters and was particularly manifested in unmedicated subjects (P &lt; .001). The hyperactivation was significantly correlated with retrieval reaction time during scan in converters (P = .009) but not in nonconverters and controls, suggesting an exaggerated retrieval effort. These findings suggest that hyperactivation during memory retrieval may mark processes associated with conversion to psychosis, and such measures have potential as biomarkers for psychosis prediction

Cross-paradigm connectivity: reliability, stability, and utility

While functional neuroimaging studies typically focus on a particular paradigm to investigate network connectivity, the human brain appears to possess an intrinsic “trait” architecture that is independent of any given paradigm. We have previously proposed the use of “cross-paradigm connectivity (CPC)” to quantify shared connectivity patterns across multiple paradigms and have demonstrated the utility of such measures in clinical studies. Here, using generalizability theory and connectome fingerprinting, we examined the reliability, stability, and individual identifiability of CPC in a group of highly-sampled healthy traveling subjects who received fMRI scans with a battery of five paradigms across multiple sites and days. Compared with single-paradigm connectivity matrices, the CPC matrices showed higher reliability in connectivity diversity, lower reliability in connectivity strength, higher stability, and higher individual identification accuracy. All of these assessments increased as a function of number of paradigms included in the CPC analysis. In comparisons involving different paradigm combinations and different brain atlases, we observed significantly higher reliability, stability, and identifiability for CPC matrices constructed from task-only data (versus those from both task and rest data), and higher identifiability but lower stability for CPC matrices constructed from the Power atlas (versus those from the AAL atlas). Moreover, we showed that multi-paradigm CPC matrices likely reflect the brain’s “trait” structure that cannot be fully achieved from single-paradigm data, even with multiple runs. The present results provide evidence for the feasibility and utility of CPC in the study of functional “trait” networks and offer some methodological implications for future CPC studies

Toward leveraging human connectomic data in large consortia: Generalizability of fmri-based brain graphs across sites, sessions, and paradigms

While graph theoretical modeling has dramatically advanced our understanding of complex brain systems, the feasibility of aggregating connectomic data in large imaging consortia remains unclear. Here, using a battery of cognitive, emotional and resting fMRI paradigms, we investigated the generalizability of functional connectomic measures across sites and sessions. Our results revealed overall fair to excellent reliability for a majority of measures during both rest and tasks, in particular for those quantifying connectivity strength, network segregation and network integration. Processing schemes such as node definition and global signal regression (GSR) significantly affected resulting reliability, with higher reliability detected for the Power atlas (vs. AAL atlas) and data without GSR. While network diagnostics for default-mode and sensori-motor systems were consistently reliable independently of paradigm, those for higher-order cognitive systems were reliable predominantly when challenged by task. In addition, based on our present sample and after accounting for observed reliability, satisfactory statistical power can be achieved in multisite research with sample size of approximately 250 when the effect size is moderate or larger. Our findings provide empirical evidence for the generalizability of brain functional graphs in large consortia, and encourage the aggregation of connectomic measures using multisite and multisession data

Cerebello-thalamo-cortical hyperconnectivity as a state-independent functional neural signature for psychosis prediction and characterization

Understanding the fundamental alterations in brain functioning that lead to psychotic disorders remains a major challenge in clinical neuroscience. In particular, it is unknown whether any state-independent biomarkers can potentially predict the onset of psychosis and distinguish patients from healthy controls, regardless of paradigm. Here, using multi-paradigm fMRI data from the North American Prodrome Longitudinal Study consortium, we show that individuals at clinical high risk for psychosis display an intrinsic “trait-like” abnormality in brain architecture characterized as increased connectivity in the cerebello–thalamo–cortical circuitry, a pattern that is significantly more pronounced among converters compared with non-converters. This alteration is significantly correlated with disorganization symptoms and predictive of time to conversion to psychosis. Moreover, using an independent clinical sample, we demonstrate that this hyperconnectivity pattern is reliably detected and specifically present in patients with schizophrenia. These findings implicate cerebello–thalamo–cortical hyperconnectivity as a robust state-independent neural signature for psychosis prediction and characterization