DIMENSIONALITY BASED SCALE SELECTION IN 3D LIDAR POINT CLOUDS

International audienceThis papers presents a multi-scale method that computes robust geometric features on lidar point clouds in order to retrieve the optimal neighborhood size for each point. Three dimensionality features are calculated on spherical neighborhoods at various radius sizes. Based on combinations of the eigenvalues of the local structure tensor, they describe the shape of the neighborhood, indicating whether the local geometry is more linear (1D), planar (2D) or volumetric (3D). Two radius-selection criteria have been tested and compared for finding automatically the optimal neighborhood radius for each point. Besides, such procedure allows a dimensionality labelling, giving significant hints for classification and segmentation purposes. The method is successfully applied to 3D point clouds from airborne, terrestrial, and mobile mapping systems since no a priori knowledge on the distribution of the 3D points is required. Extracted dimensionality features and labellings are then favorably compared to those computed from constant size neighborhoods

High and low levels of an NTRK2-driven genetic profile affect motor- and cognition-associated frontal gray matter in prodromal Huntington’s disease

This study assessed how BDNF (brain-derived neurotrophic factor) and other genes involved in its signaling influence brain structure and clinical functioning in pre-diagnosis Huntington’s disease (HD). Parallel independent component analysis (pICA), a multivariate method for identifying correlated patterns in multimodal datasets, was applied to gray matter concentration (GMC) and genomic data from a sizeable PREDICT-HD prodromal cohort (N = 715). pICA identified a genetic component highlighting NTRK2, which encodes BDNF’s TrkB receptor, that correlated with a GMC component including supplementary motor, precentral/premotor cortex, and other frontal areas (p < 0.001); this association appeared to be driven by participants with high or low levels of the genetic profile. The frontal GMC profile correlated with cognitive and motor variables (Trail Making Test A (p = 0.03); Stroop Color (p = 0.017); Stroop Interference (p = 0.04); Symbol Digit Modalities Test (p = 0.031); Total Motor Score (p = 0.01)). A top-weighted NTRK2 variant (rs2277193) was protectively associated with Trail Making Test B (p = 0.007); greater minor allele numbers were linked to a better performance. These results support the idea of a protective role of NTRK2 in prodromal HD, particularly in individuals with certain genotypes, and suggest that this gene may influence the preservation of frontal gray matter that is important for clinical functioning.This project was supported by 1U01NS082074 (V.C. and J.T., co-principal investigators) from the National Institutes of Health, National Institute of Neurological Disorders and Stroke. The PREDICT-HD study was supported by NIH/NINDS grant 5R01NS040068 awarded to J.P.; CHDI Foundation, Inc., A3917 and 6266 awarded to J.P.; Cognitive and Functional Brain Changes in Preclinical Huntington’s Disease (HD) 5R01NS054893 awarded to J.P.; 4D Shape Analysis for Modeling Spatiotemporal Change Trajectories in Huntington’s 1U01NS082086; Functional Connectivity in Premanifest Huntington’s Disease 1U01NS082083; and Basal Ganglia Shape Analysis and Circuitry in Huntington’s Disease 1U01NS082085 awarded to Christopher A. Ross

Clinical and biomarker changes in premanifest Huntington disease show trial feasibility: A decade of the PREDICT-HD study

There is growing consensus that intervention and treatment of Huntington disease (HD) should occur at the earliest stage possible. Various early-intervention methods for this fatal neurodegenerative disease have been identified, but preventive clinical trials for HD are limited by a lack of knowledge of the natural history of the disease and a dearth of appropriate outcome measures. Objectives of the current study are to document the natural history of premanifest HD progression in the largest cohort ever studied and to develop a battery of imaging and clinical markers of premanifest HD progression that can be used as outcome measures in preventive clinical trials. Neurobiological predictors of Huntington’s disease is a 32-site, international, observational study of premanifest HD, with annual examination of 1013 participants with premanifest HD and 301 gene-expansion negative controls between 2001 and 2012. Findings document 39 variables representing imaging, motor, cognitive, functional, and psychiatric domains, showing different rates of decline between premanifest HD and controls. Required sample size and models of premanifest HD are presented to inform future design of clinical and preclinical research. Preventive clinical trials in premanifest HD with participants who have a medium or high probability of motor onset are calculated to be as resource-effective as those conducted in diagnosed HD and could interrupt disease 7–12years earlier. Methods and measures for preventive clinical trials in premanifest HD more than a dozen years from motor onset are also feasible. These findings represent the most thorough documentation of a clinical battery for experimental therapeutics in stages of premanifest HD, the time period for which effective intervention may provide the most positive possible outcome for patients and their families affected by this devastating disease

Controlled-Atmosphere Flame Fusion Single-Crystal Growth of Non-Noble fcc, hcp, and bcc Metals Using Copper, Cobalt, and Iron

The growth of noble-metal single crystals via the flame fusion method was developed in the 1980s. Since then, there have been no major advancements to the technique until the recent development of the controlled-atmosphere flame fusion (CAFF) method to grow non-noble Ni single crystals. Herein, we demonstrate the generality of this method with the first preparation of fcc Cu as well as the first hcp and bcc single crystals of Co and Fe, respectively. The high quality of the single crystals was verified using scanning electron microscopy and Laue X-ray backscattering. Based on Wulff constructions, the equilibrium shapes of the single-crystal particles were studied, confirming the symmetry of the fcc, hcp, and bcc single-crystal lattices. The low cost of the CAFF method makes all kinds of high-quality non-noble single crystals independent of their lattice accessible for use in electrocatalysis, electrochemistry, surface science, and materials science.Funding Agencies|Natural Sciences and Engineering Research Council of Canada (NSERC)Natural Sciences and Engineering Research Council of Canada [RGPNM 477963-2015]; MITACS through the Globalink Research Award; German Research Foundation (DFG)German Research Foundation (DFG) [390874152, Sonderforschungsbereich SFB 1316]; Alexander von Humboldt foundationAlexander von Humboldt Foundation; Swedish Government Strategic Research Area in Materials Science on Functional Materials at Linkcping University (Faculty Grant SFO-MatLiU) [2009 00971]; DAADDeutscher Akademischer Austausch Dienst (DAAD)</p

Prefrontal brain network connectivity indicates degree of both schizophrenia risk and cognitive dysfunction

Objective:Cognitive dysfunction is a core feature of schizophrenia, and persons at risk for schizophrenia may show subtle deficits in attention and working memory. In this study, we investigated the relationship between integrity of functional brain networks and performance in attention and working memory tasks as well as schizophrenia risk.Methods:A total of 235 adults representing 3 levels of risk (102 outpatients with schizophrenia, 70 unaffected first-degree relatives of persons with schizophrenia, and 63 unrelated healthy controls [HCs]) completed resting-state functional magnetic resonance imaging and a battery of attention and working memory tasks (Brief Test of Attention, Hopkins Verbal Learning Test, and Brief Visuospatial Memory Test) on the same day. Functional networks were defined based on coupling with seeds in the dorsal anterior cingulate cortex, dorsolateral prefrontal cortex (DLPFC), medial prefrontal cortex (MPFC), and primary visual cortex. Networks were then dissected into regional clusters of connectivity that were used to generate individual interaction matrices representing functional connectivity within each network.Results:Both patients with schizophrenia and their first-degree relatives showed cognitive dysfunction compared with HCs. First canonicals indicated an inverse relationship between cognitive performance and connectivity within the DLPFC and MPFC networks. Multivariate analysis of variance revealed multivariate main effects of higher schizophrenia risk status on increased connectivity within the DLPFC and MPFC networks.Conclusions:These data suggest that excessive connectivity within brain networks coupled to the DLPFC and MPFC, respectively, accompany cognitive deficits in persons at risk for schizophrenia. This might reflect compensatory reactions in neural systems required for cognitive processing of attention and working memory tasks to brain changes associated with schizophrenia