Pair Approximation Models for Disease Spread

We consider a Susceptible-Infective-Recovered (SIR) model, where the mechanism for the renewal of susceptibles is demographic, on a ring with next nearest neighbour interactions, and a family of correlated pair approximations (CPA), parametrized by a measure of the relative contributions of loops and open triplets of the sites involved in the infection process. We have found that the phase diagram of the CPA, at fixed coordination number, changes qualitatively as the relative weight of the loops increases, from the phase diagram of the uncorrelated pair approximation to phase diagrams typical of one-dimensional systems. In addition, we have performed computer simulations of the same model and shown that while the CPA with a constant correlation parameter cannot describe the global behaviour of the model, a reasonable description of the endemic equilibria as well as of the phase diagram may be obtained by allowing the parameter to depend on the demographic rate.Comment: 6 pages, 3 figures, LaTeX2e+SVJour+AmSLaTeX, NEXTSigmaPhi 2005; metadata title corrected wrt paper titl

Enhancing the Reproducibility of Group Analysis with Randomized Brain Parcellations

International audienceNeuroimaging group analyses are used to compare the inter-subject variability observed in brain organization with behavioural or genetic variables and to assess risks factors of brain diseases. The lack of stability and of sensitivity of current voxel-based analysis schemes may however lead to non-reproducible results. A new approach is introduced to overcome the limitations of standard methods, in which active voxels are detected according to a consensus on several random parcellations of the brain images, while a permutation test controls the false positive risk. Both on syntetic and real data, this approach shows higher sensitivity, better recovery and higher reproducibility than standard methods and succeeds in detecting a significant association in an imaging-genetic study between a genetic variant next to the COMT gene and a region in the left thalamus on a functional Magnetic Resonance Imaging contrast

Transformation kinetics and microstructures of Ti17 titanium alloy during continuous cooling

International audienceWe have investigated the microstructure evolutions in the Ti17 near Click to view the MathML source titanium alloy during heat treatments. The phase transformation has first been studied experimentally by combining X-ray diffraction analysis, electrical resistivity and microscopy observations. From a series of isothermal treatments, a IT diagram has been determined, which takes into account the different morphologies. Then, a Johnson–Mehl–Avrami–Kolmogorov (JMAK) model has been successfully used to describe the phase transformation kinetics during either isothermal or cooling treatments. Finally, the coupling of the JMAK model to the finite element software ZeBuLoN allowed us to investigate the evolution of the spatial distribution of the different morphologies during the cooling of an aircraft engine shaft disk after forging

Robust Group-Level Inference in Neuroimaging Genetic Studies

International audienceGene-neuroimaging studies involve high-dimensional data that have a complex statistical structure and that are likely to be contaminated with outliers. Robust, outlier-resistant methods are an alternative to prior outliers removal, which is a difficult task under high-dimensional unsupervised settings. In this work, we consider robust regression and its application to neuroimaging through an example gene-neuroimaging study on a large cohort of 300 subjects. We use randomized brain parcellation to sample a set of adapted low-dimensional spatial models to analyse the data. We combine this approach with robust regression in an analysis method that we show is outperforming state-of-the-art neuroimaging analysis methods

A-Brain: Using the Cloud to Understand the Impact of Genetic Variability on the Brain

International audienceJoint genetic and neuroimaging data analysis on large cohorts of subjects is a new approach used to assess and understand the variability that exists between individuals. This approach has remained poorly understood so far and brings forward very significant challenges, as progress in this field can open pioneering directions in biology and medicine. As both neuroimaging- and genetic-domain observations represent a huge amount of variables (of the order of 106 ), performing statistically rigorous analyses on such Big Data represents a computational challenge that cannot be addressed with conventional computational techniques. In the A-Brain project, we address this computational problem using cloud computing techniques on Microsoft Azure, relying on our complementary expertise in the area of scalable cloud data management and in the field of neuroimaging and genetics data analysis

Robust regression for large-scale neuroimaging studies

Multi-subject datasets used in neuroimaging group studies have a complex structure, as they exhibit non-stationary statistical properties across regions and display various artifacts. While studies with small sample sizes can rarely be shown to deviate from standard hypotheses (such as the normality of the residuals) due to the poor sensitivity of normality tests with low degrees of freedom, large-scale studies (e.g. > 100 subjects) exhibit more obvious deviations from these hypotheses and call for more refined models for statistical inference. Here, we demonstrate the benefits of robust regression as a tool for analyzing large neuroimaging cohorts. First, we use an analytic test based on robust parameter estimates; based on simulations, this procedure is shown to provide an accurate statistical control without resorting to permutations. Second, we show that robust regression yields more detections than standard algorithms using as an example an imaging genetics study with 392 subjects. Third, we show that robust regression can avoid false positives in a large-scale analysis of brain–behavior relationships with over 1500 subjects. Finally we embed robust regression in the Randomized Parcellation Based Inference (RPBI) method and demonstrate that this combination further improves the sensitivity of tests carried out across the whole brain. Altogether, our results show that robust procedures provide important advantages in large-scale neuroimaging group studies

A MapReduce Approach for Ridge Regression in Neuroimaging-Genetic Studies

International audienceIn order to understand the large between-subject variability observed in brain organization and assess factor risks of brain diseases, massive efforts have been made in the last few years to acquire high-dimensional neuroimaging and genetic data on large cohorts of subjects. The statistical analysis of such high-dimensional and complex data is carried out with increasingly sophisticated techniques and represents a great computational challenge. To be fully exploited, the concurrent increase of computational power then requires designing new parallel algorithms. The MapReduce framework coupled with efficient algorithms permits to deliver a scalable analysis tool that deals with high-dimensional data and hundreds of permutations in a few hours. On a real functional MRI dataset, this tool shows promising results

A fast computational framework for genome-wide association studies with neuroimaging data

International audienceIn the last few years, it has become possible to acquire high-dimensional neuroimaging and genetic data on relatively large cohorts of subjects, which provides novel means to understand the large between-subject variability observed in brain organization. Genetic association studies aim at unveiling correlations between the genetic variants and the numerous phenotypes extracted from brain images and thus face a dire multiple comparisons issue. While these statistics can be accumulated across the brain volume for the sake of sensitivity, the significance of the resulting summary statistics can only be assessed through permutations. Fortunately, the increase of computational power can be exploited, but this requires designing new parallel algorithms. The MapReduce framework coupled with efficient algorithms permits to deliver a scalable analysis tool that deals with high-dimensional data and thousands of permutations in a few hours. On a real functional MRI dataset, this tool shows promising results with a genetic variant that survives the very strict correction for multiple testing