Detection of brain functional-connectivity difference in post-stroke patients using group-level covariance modeling

Functional brain connectivity, as revealed through distant correlations in the signals measured by functional Magnetic Resonance Imaging (fMRI), is a promising source of biomarkers of brain pathologies. However, establishing and using diagnostic markers requires probabilistic inter-subject comparisons. Principled comparison of functional-connectivity structures is still a challenging issue. We give a new matrix-variate probabilistic model suitable for inter-subject comparison of functional connectivity matrices on the manifold of Symmetric Positive Definite (SPD) matrices. We show that this model leads to a new algorithm for principled comparison of connectivity coefficients between pairs of regions. We apply this model to comparing separately post-stroke patients to a group of healthy controls. We find neurologically-relevant connection differences and show that our model is more sensitive that the standard procedure. To the best of our knowledge, these results are the first report of functional connectivity differences between a single-patient and a group and thus establish an important step toward using functional connectivity as a diagnostic tool

Estimated glomerular filtration rate is a poor predictor of the concentration of middle molecular weight uremic solutes in chronic kidney disease

Background: Uremic solute concentration increases as Glomerular Filtration Rate (GFR) declines. Weak associations were demonstrated between estimated GFR (eGFR) and the concentrations of several small water-soluble and protein-bound uremic solutes (MW500Da). Materials and Methods: In 95 CKD-patients (CKD-stage 2-5 not on dialysis), associations between different eGFR-formulae (creatinine, CystatinC-based or both) and the natural logarithm of the concentration of several LMWP's were analyzed: i.e. parathyroid hormone (PTH), Cystatin C (CystC), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-alpha), leptin, retinol binding protein (RbP), immunoglobin light chains kappa and lambda (Ig-kappa and Ig-lambda), beta-2-microglobulin (beta M-2), myoglobin and fibroblast growth factor-23 (FGF-23)). Results: The regression coefficients (R-2) between eGFR, based on the CKD-EPI-Crea-CystC-formula as reference, and the examined LMWP's could be divided into three groups. Most of the LMWP's associated weakly (R-2 0.7). Almost identical R-2-values were found per LMWP for all eGFR-formulae, with exception of CystC and beta M-2 which showed weaker associations with creatinine-based than with CystC-based eGFR. Conclusion: The association between eGFR and the concentration of several LMWP's is inconsistent, with in general low R-2-values. Thus, the use of eGFR to evaluate kidney function does not reflect the concentration of several LMWP's with proven toxic impact in CKD

High resolution whole brain diffusion imaging at 7 T for the Human Connectome Project

Mapping structural connectivity in healthy adults for the Human Connectome Project (HCP) benefits from high quality, high resolution, multiband (MB)-accelerated whole brain diffusion MRI (dMRI). Acquiring such data at ultrahigh fields (7 T and above) can improve intrinsic signal-to-noise ratio (SNR), but suffers from shorter T2 and T2⁎ relaxation times, increased B1+ inhomogeneity (resulting in signal loss in cerebellar and temporal lobe regions), and increased power deposition (i.e. specific absorption rate (SAR)), thereby limiting our ability to reduce the repetition time (TR). Here, we present recent developments and optimizations in 7 T image acquisitions for the HCP that allow us to efficiently obtain high quality, high resolution whole brain in-vivo dMRI data at 7 T. These data show spatial details typically seen only in ex-vivo studies and complement already very high quality 3 T HCP data in the same subjects. The advances are the result of intensive pilot studies aimed at mitigating the limitations of dMRI at 7 T. The data quality and methods described here are representative of the datasets that will be made freely available to the community in 2015

Manganese pigmented anodized copper as solar selective absorber

The study concerns the optical and structural properties of layers obtained by a new efficient surface treatment totally free of chromium species. The process is made up of an anodic oxidation of copper in an alkaline solution followed by an alkaline potassium permanganate dipping post-treatment. Coatings, obtained at the lab and pilot scales, are stable up to 220 °C in air and vacuum, present low emissivity (0.14 at 70 °C) and high solar absorptivity (0.96), i.e. a suitable thermal efficiency (0.84 at 70 °C)

Mortality Rates above Emergency Threshold in Population Affected by Conflict in North Kivu, Democratic Republic of Congo, July 2012-April 2013

The area of Walikale in North Kivu, Democratic Republic of Congo, is intensely affected by conflict and population displacement. Médecins-Sans-Frontières (MSF) returned to provide primary healthcare in July 2012. To better understand the impact of the ongoing conflict and displacement on the population, a retrospective mortality survey was conducted in April 2013. A two-stage randomized cluster survey using 31 clusters of 21 households was conducted. Heads of households provided information on their household make-up, ownership of non-food items (NFIs), access to healthcare and information on deaths and occurrence of self-reported disease in the household during the recall period. The recall period was of 325 days (July 2012-April 2013). In total, 173 deaths were reported during the recall period. The crude mortality rate (CMR) was of 1.4/10,000 persons/day (CI95%: 1.2-1.7) and the under-five- mortality rate (U5MR) of 1.9/10,000 persons per day (CI95%: 1.3-2.5). The most frequently reported cause of death was fever/malaria 34.1% (CI95%: 25.4-42.9). Thirteen deaths were due to intentional violence. Over 70% of all households had been displaced at some time during the recall period. Out of households with someone sick in the last two weeks, 63.8% sought health care; the main reason not to seek health care was the lack of money (n = 134, 63.8%, CI95%: 52.2-75.4). Non Food Items (NFI) ownership was low: 69.0% (CI95%: 53.1-79.7) at least one 10 liter jerry can, 30.1% (CI95%: 24.3-36.5) of households with visible soap available and 1.6 bednets per household. The results from this survey in Walikale clearly illustrate the impact that ongoing conflict and displacement are having on the population in this part of DRC. The gravity of their health status was highlighted by a CMR that was well above the emergency threshold of 1 person/10,000/day and an U5MR that approaches the 2 children/10,000/day threshold for the recall period

Finsler geometry on higher order tensor fields and applications to high angular resolution diffusion imaging.

We study 3D-multidirectional images, using Finsler geometry. The application considered here is in medical image analysis, specifically in High Angular Resolution Diffusion Imaging (HARDI) (Tuch et al. in Magn. Reson. Med. 48(6):1358–1372, 2004) of the brain. The goal is to reveal the architecture of the neural fibers in brain white matter. To the variety of existing techniques, we wish to add novel approaches that exploit differential geometry and tensor calculus. In Diffusion Tensor Imaging (DTI), the diffusion of water is modeled by a symmetric positive definite second order tensor, leading naturally to a Riemannian geometric framework. A limitation is that it is based on the assumption that there exists a single dominant direction of fibers restricting the thermal motion of water molecules. Using HARDI data and higher order tensor models, we can extract multiple relevant directions, and Finsler geometry provides the natural geometric generalization appropriate for multi-fiber analysis. In this paper we provide an exact criterion to determine whether a spherical function satisfies the strong convexity criterion essential for a Finsler norm. We also show a novel fiber tracking method in Finsler setting. Our model incorporates a scale parameter, which can be beneficial in view of the noisy nature of the data. We demonstrate our methods on analytic as well as simulated and real HARDI data

Brain connectivity using geodesics in HARDI

International audienceWe develop an algorithm for brain connectivity assessment using geodesics in HARDI (high angular resolution diffusion imaging). We propose to recast the problem of finding fibers bundles and connectivity maps to the calculation of shortest paths on a Riemannian manifold defined from fiber ODFs computed from HARDI measurements. Several experiments on real data show that out method is able to segment fibers bundles that are not easily recovered by other existing methods

Quality Control of Motor Unit Number Index (MUNIX) Measurements in 6 Muscles in a Single-Subject “Round-Robin” Setup

Background Motor Unit Number Index (MUNIX) is a neurophysiological measure that provides an index of the number of lower motor neurons in a muscle. Its performance across centres in healthy subjects and patients with Amyotrophic Lateral Sclerosis (ALS) has been established, but inter-rater variability between multiple raters in one single subject has not been investigated. Objective To assess reliability in a set of 6 muscles in a single subject among 12 examiners (6 experienced with MUNIX, 6 less experienced) and to determine variables associated with variability of measurements. Methods Twelve raters applied MUNIX in six different muscles (abductor pollicis brevis (APB), abductor digiti minimi (ADM), biceps brachii (BB), tibialis anterior (TA), extensor dig. brevis (EDB), abductor hallucis (AH)) twice in one single volunteer on consecutive days. All raters visited at least one training course prior to measurements. Intra- and inter-rater variability as determined by the coefficient of variation (COV) between different raters and their levels of experience with MUNIX were compared. Results Mean intra-rater COV of MUNIX was 14.0% (±6.4) ranging from 5.8 (APB) to 30.3% (EDB). Mean inter-rater COV was 18.1 (±5.4) ranging from 8.0 (BB) to 31.7 (AH). No significant differences of variability between experienced and less experienced raters were detected. Conclusion We provide evidence that quality control for neurophysiological methods can be performed with similar standards as in laboratory medicine. Intra- and inter-rater variability of MUNIX is muscle-dependent and mainly below 20%. Experienced neurophysiologists can easily adopt MUNIX and adequate teaching ensures reliable utilization of this method

Statistical Computing on Non-Linear Spaces for Computational Anatomy

International audienceComputational anatomy is an emerging discipline that aims at analyzing and modeling the individual anatomy of organs and their biological variability across a population. However, understanding and modeling the shape of organs is made difficult by the absence of physical models for comparing different subjects, the complexity of shapes, and the high number of degrees of freedom implied. Moreover, the geometric nature of the anatomical features usually extracted raises the need for statistics on objects like curves, surfaces and deformations that do not belong to standard Euclidean spaces. We explain in this chapter how the Riemannian structure can provide a powerful framework to build generic statistical computing tools. We show that few computational tools derive for each Riemannian metric can be used in practice as the basic atoms to build more complex generic algorithms such as interpolation, filtering and anisotropic diffusion on fields of geometric features. This computational framework is illustrated with the analysis of the shape of the scoliotic spine and the modeling of the brain variability from sulcal lines where the results suggest new anatomical findings