Multivariate analysis of fMRI data by oriented partial least squares

William S. Rayens, Anders H. Andersen

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


Partial least squares (PLS) has been used in multivariate analysis of functional magnetic resonance imaging (fMRI) data as a way of incorporating information about the underlying experimental paradigm. In comparison, principal component analysis (PCA) extracts structure merely by summarizing variance and with no assurance that individual component structures are directly interpretable or that they represent salient and useful features. Oriented partial least squares (OrPLS) is a new PLS-like analysis paradigm in which extracted components can be oriented away from undesirable noise or confounds in the data and toward a desired targeted structure reflecting the fMRI experiment.

Original languageEnglish
Pages (from-to)953-958
Number of pages6
JournalMagnetic Resonance Imaging
Issue number7
StatePublished - Sep 2006


  • Data analysis
  • Multivariate
  • Structure seeking
  • fMRI

ASJC Scopus subject areas

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging


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