Break up of heavy fermions at an antiferromagnetic instability

We present results of high-resolution, low-temperature measurements of the Hall coefficient, thermopower, and specific heat on stoichiometric YbRh2Si2. They support earlier conclusions of an electronic (Kondo-breakdown) quantum critical point concurring with a field induced antiferromagnetic one. We also discuss the detachment of the two instabilities under chemical pressure. Volume compression/expansion (via substituting Rh by Co/Ir) results in a stabilization/weakening of magnetic order. Moderate Ir substitution leads to a non-Fermi-liquid phase, in which the magnetic moments are neither ordered nor screened by the Kondo effect. The so-derived zero-temperature global phase diagram promises future studies to explore the nature of the Kondo breakdown quantum critical point without any interfering magnetism.Comment: minor changes, accepted for publication in JPS

Cognitive Impairment in Neuromyelitis Optica Spectrum Disorders: A Review of Clinical and Neuroradiological Features

Neuromyelitis optica spectrum disorders (NMOSD) are mostly relapsing autoimmune inflammatory disorders of the central nervous system (CNS) with optic neuritis, myelitis, and brainstem syndromes as clinical hallmarks. With a reported prevalence of up to 70%, cognitive impairment is frequent, but often unrecognized and an insufficiently treated burden of the disease. The most common cognitive dysfunctions are decline in attention andmemory performance.Magnetic resonance imaging can be used to access structural correlates of neuropsychological disorders. Cognitive impairment is not only a highly underestimated symptom in patients with NMOSD, but potentially also a clinical correlate of attack-independent changes in NMOSD, which are currently under debate. This article reviews cognitive impairment in NMOSD and discusses associations between structural changes of the CNS and cognitive deficits

Causal Influence of Linguistic Learning on Perceptual and Conceptual Processing: A Brain-Constrained Deep Neural Network Study of Proper Names and Category Terms

Language influences cognitive and conceptual processing, but the mechanisms through which such causal effects are realized in the human brain remain unknown. Here, we use a brain-constrained deep neural network model of category formation and symbol learning and analyze the emergent model’s internal mechanisms at the neural circuit level. In one set of simulations, the network was presented with similar patterns of neural activity indexing instances of objects and actions belonging to the same categories. Biologically realistic Hebbian learning led to the formation of instance-specific neurons distributed across multiple areas of the network, and, in addition, to cell assembly circuits of “shared” neurons responding to all category instances—the network correlates of conceptual categories. In two separate sets of simulations, the network learned the same patterns together with symbols for individual instances [“proper names” (PN)] or symbols related to classes of instances sharing common features [“category terms” (CT)]. Learning CT remarkably increased the number of shared neurons in the network, thereby making category representations more robust while reducing the number of neurons of instance-specific ones. In contrast, proper name learning prevented a substantial reduction of instance-specific neurons and blocked the overgrowth of category general cells. Representational similarity analysis further confirmed that the neural activity patterns of category instances became more similar to each other after category-term learning, relative to both learning with PN and without any symbols. These network-based mechanisms for concepts, PN, and CT explain why and how symbol learning changes object perception and memory, as revealed by experimental studies

Causal Influence of Linguistic Learning on Perceptual and Conceptual Processing: A Brain-Constrained Deep Neural Network Study of Proper Names and Category Terms.

Language influences cognitive and conceptual processing, but the mechanisms through which such causal effects are realized in the human brain remain unknown. Here, we use a brain-constrained deep neural network model of category formation and symbol learning and analyze the emergent model\u27s internal mechanisms at the neural circuit level. In one set of simulations, the network was presented with similar patterns of neural activity indexing instances of objects and actions belonging to the same categories. Biologically realistic Hebbian learning led to the formation of instance-specific neurons distributed across multiple areas of the network, and, in addition, to cell assembly circuits of shared neurons responding to all category instances-the network correlates of conceptual categories. In two separate sets of simulations, the network learned the same patterns together with symbols for individual instances [ proper names (PN)] or symbols related to classes of instances sharing common features [ category terms (CT)]. Learning CT remarkably increased the number of shared neurons in the network, thereby making category representations more robust while reducing the number of neurons of instance-specific ones. In contrast, proper name learning prevented a substantial reduction of instance-specific neurons and blocked the overgrowth of category general cells. Representational similarity analysis further confirmed that the neural activity patterns of category instances became more similar to each other after category-term learning, relative to both learning with PN and without any symbols. These network-based mechanisms for concepts, PN, and CT explain why and how symbol learning changes object perception and memory, as revealed by experimental studies

Active contour method for ILM segmentation in ONH volume scans in retinal OCT

The optic nerve head (ONH) is affected by many neurodegenerative and autoimmune inflammatory conditions. Optical coherence tomography can acquire high-resolution 3D ONH scans. However, the ONH's complex anatomy and pathology make image segmentation challenging. This paper proposes a robust approach to segment the inner limiting membrane (ILM) in ONH volume scans based on an active contour method of Chan-Vese type, which can work in challenging topological structures. A local intensity fitting energy is added in order to handle very inhomogeneous image intensities. A suitable boundary potential is introduced to avoid structures belonging to outer retinal layers being detected as part of the segmentation. The average intensities in the inner and outer region are then resealed locally to account for different brightness values occurring among the ONH center. The appropriate values for the parameters used in the complex computational model are found using an optimization based on the differential evolution algorithm. The evaluation of results showed that the proposed framework significantly improved segmentation results compared to the commercial solution

Imaging markers of disability in aquaporin-4 immunoglobulin G seropositive neuromyelitis optica: a graph theory study

Neuromyelitis optica spectrum disorders lack imaging biomarkers associated with disease course and supporting prognosis. This complex and heterogeneous set of disorders affects many regions of the central nervous system, including the spinal cord and visual pathway. Here, we use graph theory-based multimodal network analysis to investigate hypothesis-free mixed networks and associations between clinical disease with neuroimaging markers in 40 aquaporin-4-immunoglobulin G antibody seropositive patients (age = 48.16 ± 14.3 years, female:male = 36:4) and 31 healthy controls (age = 45.92 ± 13.3 years, female:male = 24:7). Magnetic resonance imaging measures included total brain and deep grey matter volumes, cortical thickness and spinal cord atrophy. Optical coherence tomography measures of the retina and clinical measures comprised of clinical attack types and expanded disability status scale were also utilized. For multimodal network analysis, all measures were introduced as nodes and tested for directed connectivity from clinical attack types and disease duration to systematic imaging and clinical disability measures. Analysis of variance, with group interactions, gave weights and significance for each nodal association (hyperedges). Connectivity matrices from 80% and 95% F-distribution networks were analyzed and revealed the number of combined attack types and disease duration as the most connected nodes, directly affecting changes in several regions of the central nervous system. Subsequent multivariable regression models, including interaction effects with clinical parameters, identified associations between decreased nucleus accumbens (β = −0.85, P = 0.021) and caudate nucleus (β = −0.61, P = 0.011) volumes with higher combined attack type count and longer disease duration, respectively. We also confirmed previously reported associations between spinal cord atrophy with increased number of clinical myelitis attacks. Age was the most important factor associated with normalized brain volume, pallidum volume, cortical thickness and the expanded disability status scale score. The identified imaging biomarker candidates warrant further investigation in larger-scale studies. Graph theory-based multimodal networks allow for connectivity and interaction analysis, where this method may be applied in other complex heterogeneous disease investigations with different outcome measures

Evidence for a Kondo destroying quantum critical point in YbRh2Si2

The heavy-fermion metal YbRh$_{2}$Si$_{2}$ is a weak antiferromagnet below $T_{N} = 0.07$ K. Application of a low magnetic field $B_{c} = 0.06$ T ($\perp c$) is sufficient to continuously suppress the antiferromagnetic (AF) order. Below $T \approx 10$ K, the Sommerfeld coefficient of the electronic specific heat $\gamma(T)$ exhibits a logarithmic divergence. At $T < 0.3$ K, $\gamma(T) \sim T^{-\epsilon}$ ($\epsilon: 0.3 - 0.4$), while the electrical resistivity $\rho(T) = \rho_{0} + aT$ ($\rho_{0}$: residual resistivity). Upon extrapolating finite-$T$ data of transport and thermodynamic quantities to $T = 0$, one observes (i) a vanishing of the "Fermi surface crossover" scale $T^{*}(B)$, (ii) an abrupt jump of the initial Hall coefficient $R_{H}(B)$ and (iii) a violation of the Wiedemann Franz law at $B = B_{c}$, the field-induced quantum critical point (QCP). These observations are interpreted as evidence of a critical destruction of the heavy quasiparticles, i.e., propagating Kondo singlets, at the QCP of this material.Comment: 20 pages, 8 figures, SCES 201

Interplay between Kondo suppression and Lifshitz transitions in YbRh2_2Si2_2 at high magnetic fields

We investigate the magnetic field dependent thermopower, thermal conductivity, resistivity and Hall effect in the heavy fermion metal YbRh2Si2. In contrast to reports on thermodynamic measurements, we find in total three transitions at high fields, rather than a single one at 10 T. Using the Mott formula together with renormalized band calculations, we identify Lifshitz transitions as their origin. The predictions of the calculations show that all experimental results rely on an interplay of a smooth suppression of the Kondo effect and the spin splitting of the flat hybridized bands.Comment: 5 pages, 4 figure

Reliability of Intra-Retinal Layer Thickness Estimates

Purpose Measurement of intra-retinal layer thickness using optical coherence tomography (OCT) has become increasingly prominent in multiple sclerosis (MS) research. Nevertheless, the approaches used for determining the mean layer thicknesses vary greatly. Insufficient data exist on the reliability of different thickness estimates, which is crucial for their application in clinical studies. This study addresses this lack by evaluating the repeatability of different thickness estimates. Methods Studies that used intra-retinal layer segmentation of macular OCT scans in patients with MS were retrieved from PubMed. To investigate the repeatability of previously applied layer estimation approaches, we generated datasets of repeating measurements of 15 healthy subjects and 13 multiple sclerosis patients using two OCT devices (Cirrus HD-OCT and Spectralis SD-OCT). We calculated each thickness estimate in each repeated session and analyzed repeatability using intra-class correlation coefficients and coefficients of repeatability. Results We identified 27 articles, eleven of them used the Spectralis SD-OCT, nine Cirrus HD-OCT, two studies used both devices and two studies applied RTVue-100. Topcon OCT-1000, Stratus OCT and a research device were used in one study each. In the studies that used the Spectralis, ten different thickness estimates were identified, while thickness estimates of the Cirrus OCT were based on two different scan settings. In the simulation dataset, thickness estimates averaging larger areas showed an excellent repeatability for all retinal layers except the outer plexiform layer (OPL). Conclusions Given the good reliability, the thickness estimate of the 6mm-diameter area around the fovea should be favored when OCT is used in clinical research. Assessment of the OPL was weak in general and needs further investigation before OPL thickness can be used as a reliable parameter

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation

Machine learning-based imaging diagnostics has recently reached or even superseded the level of clinical experts in several clinical domains. However, classification decisions of a trained machine learning system are typically non-transparent, a major hindrance for clinical integration, error tracking or knowledge discovery. In this study, we present a transparent deep learning framework relying on convolutional neural networks (CNNs) and layer-wise relevance propagation (LRP) for diagnosing multiple sclerosis (MS). MS is commonly diagnosed utilizing a combination of clinical presentation and conventional magnetic resonance imaging (MRI), specifically the occurrence and presentation of white matter lesions in T2-weighted images. We hypothesized that using LRP in a naive predictive model would enable us to uncover relevant image features that a trained CNN uses for decision-making. Since imaging markers in MS are well-established this would enable us to validate the respective CNN model. First, we pre-trained a CNN on MRI data from the Alzheimer's Disease Neuroimaging Initiative (n = 921), afterwards specializing the CNN to discriminate between MS patients and healthy controls (n = 147). Using LRP, we then produced a heatmap for each subject in the holdout set depicting the voxel-wise relevance for a particular classification decision. The resulting CNN model resulted in a balanced accuracy of 87.04% and an area under the curve of 96.08% in a receiver operating characteristic curve. The subsequent LRP visualization revealed that the CNN model focuses indeed on individual lesions, but also incorporates additional information such as lesion location, non-lesional white matter or gray matter areas such as the thalamus, which are established conventional and advanced MRI markers in MS. We conclude that LRP and the proposed framework have the capability to make diagnostic decisions of..