Partial least squares for discrimination in fMRI data

Anders H. Andersen, William S. Rayens, Yushu Liu, Charles D. Smith

Research output: Contribution to journalArticlepeer-review

43 Scopus citations


Multivariate methods for discrimination were used in the comparison of brain activation patterns between groups of cognitively normal women who are at either high or low Alzheimer's disease risk based on family history and apolipoprotein-E4 status. Linear discriminant analysis (LDA) was preceded by dimension reduction using principal component analysis (PCA), partial least squares (PLS) or a new oriented partial least squares (OrPLS) method. The aim was to identify a spatial pattern of functionally connected brain regions that was differentially expressed by the risk groups and yielded optimal classification accuracy. Multivariate dimension reduction is required prior to LDA when the data contain more feature variables than there are observations on individual subjects. Whereas PCA has been commonly used to identify covariance patterns in neuroimaging data, this approach only identifies gross variability and is not capable of distinguishing among-groups from within-groups variability. PLS and OrPLS provide a more focused dimension reduction by incorporating information on class structure and therefore lead to more parsimonious models for discrimination. Performance was evaluated in terms of the cross-validated misclassification rates. The results support the potential of using functional magnetic resonance imaging as an imaging biomarker or diagnostic tool to discriminate individuals with disease or high risk.

Original languageEnglish
Pages (from-to)446-452
Number of pages7
JournalMagnetic Resonance Imaging
Issue number3
StatePublished - Apr 2012

Bibliographical note

Funding Information:
This work was supported by a grant from the National Institute of Neurological Disorders and Stroke ( R01-NS036660 ).


  • Alzheimer's disease
  • Biomarker
  • Classification
  • Imaging
  • Neuro
  • Pattern

ASJC Scopus subject areas

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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