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

TL;DR

This paper introduces PCA leverage and robust distance methods for detecting outliers in high-dimensional fMRI data, improving quality control and reliability of brain network analyses.

Contribution

It proposes novel outlier detection techniques tailored for high-dimensional fMRI data, with validation through simulations and real data applications.

Findings

PCA leverage effectively identifies outlying time points in fMRI.

Robust distance is less sensitive to outliers and has desirable statistical properties.

Outlier removal enhances the reliability of resting-state network estimation.

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 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 interest in fMRI data. We also propose an alternative measure, PCA robust distance, which is less sensitive to outliers and has controllable statistical properties. The proposed methods are validated through simulation studies and are shown to be highly accurate. We also conduct a reliability study using resting-state fMRI data from the Autism Brain Imaging Data Exchange and find that removal of outliers using the proposed methods results in more reliable estimation of subject-level resting-state networks using ICA.