[Collected during February to May 2014]

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Take a look!

The goal of this section is to have a look at references from non-medical librarian journals, but interesting for medical librarians

[Collected during January to February 2016]

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[Collected during November 2013 to February 2014]

Abstrac

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

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

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

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