A selective overview of feature screening methods with applications to neuroimaging data

: In neuroimaging studies, regression models are frequently used to identify the association of the imaging features and clinical outcome, where the number of imaging features (e.g., hundreds of thousands of voxel-level predictors) much outweighs the number of subjects in the studies. Classical best subset selection or penalized variable selection methods that perform well for low- or moderate-dimensional data do not scale to ultrahigh-dimensional neuroimaging data. To reduce the dimensionality, variable screening has emerged as a powerful tool for feature selection in neuroimaging studies. We present a selective review of the recent developments in ultrahigh-dimensional variable screening, with a focus on their practical performance on the analysis of neuroimaging data with complex spatial correlation structures and high-dimensionality. We conduct extensive simulation studies to compare the performance on selection accuracy and computational costs between the different methods. We present analyses of resting-state functional magnetic resonance imaging data in the Autism Brain Imaging Data Exchange study. This article is categorized under: Applications of Computational Statistics > Computational and Molecular BiologyStatistical Learning and Exploratory Methods of the Data Sciences > Image Data MiningStatistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data.