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

Amanda F. Mejia, Mary Beth Nebel, Ani Eloyan, Brian Caffo, Martin A. Lindquist

Research output: Contribution to journalArticlepeer-review


Outlier detection for high-dimensional (HD) datais apopular topicinmodern 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 interest in fMRI data. We also propose analternative measure, PCA robust distance, which is less sensitive tooutliers 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 independent components analysis.

Original languageEnglish (US)
Pages (from-to)521-536
Number of pages16
Issue number3
StatePublished - Jul 1 2017


  • FMRI
  • High-dimensional statistics
  • Image analysis
  • Leverage
  • Outlier detection
  • Principal component analysis
  • Robust statistics

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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