PCA leverage: outlier detection for high-dimensional functional magnetic resonance imaging data.

ABSTRACT: Outlier detection for high-dimensional (HD) data is a popular topic in modern statistical research. However, one source of HD data that has received relatively little attention is functional magnetic resonance images (fMRI), which consists of hundreds of thousands of measurements sampled at hundreds of time points. At a time when the availability of fMRI data is rapidly growing-primarily through large, publicly available grassroots datasets-automated quality control and outlier detection methods are greatly needed. We propose principal components analysis (PCA) leverage and demonstrate how it can be used to identify outlying time points in an fMRI run. Furthermore, PCA leverage is a measure of the influence of each observation on the estimation of principal components, which are often of i

SUBMITTER: Mejia AF 

PROVIDER: S-EPMC5862350 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature