Robust Group-Level Inference in Neuroimaging Genetic Studies
Year: 2013
Type: article
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.
Authors: Virgile Fritsch, Benoit Da Mota, Vincent Frouin, Eva Loth, Jean-baptiste Poline, Hal Id Hal, Bertrand Thirion
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W4299588008