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Conference Papers Year : 2013

Robust Group-Level Inference in Neuroimaging Genetic Studies

Abstract

Gene-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.

Domains

Medical Imaging
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Dates and versions

hal-00833953 , version 1 (13-06-2013)

Identifiers

  • HAL Id : hal-00833953 , version 1

Cite

Virgile Fritsch, Benoit da Mota, Gaël Varoquaux, Vincent Frouin, Eva Loth, et al.. Robust Group-Level Inference in Neuroimaging Genetic Studies. Pattern Recognition in Neuroimaging, Jun 2013, Philadelphie, United States. ⟨hal-00833953⟩
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