A projection pursuit algorithm to classify individuals using fMRI data: Application to schizophrenia

Oguz Demirci, Vincent P. Clark, Vince D. Calhoun

Research output: Contribution to journalArticlepeer-review

80 Scopus citations


Schizophrenia is diagnosed based largely upon behavioral symptoms. Currently, no quantitative, biologically based diagnostic technique has yet been developed to identify patients with schizophrenia. Classification of individuals into patient with schizophrenia and healthy control groups based on quantitative biologically based data is of great interest to support and refine psychiatric diagnoses. We applied a novel projection pursuit technique on various components obtained with independent component analysis (ICA) of 70 subjects' fMRI activation maps obtained during an auditory oddball task. The validity of the technique was tested with a leave-one-out method and the detection performance varied between 80% and 90%. The findings suggest that the proposed data reduction algorithm is effective in classifying individuals into schizophrenia and healthy control groups and may eventually prove useful as a diagnostic tool.

Original languageEnglish (US)
Pages (from-to)1774-1782
Number of pages9
Issue number4
StatePublished - Feb 15 2008
Externally publishedYes


  • Classification
  • Functional
  • ICA
  • Independent component analysis
  • PCA
  • Principal component analysis
  • Projection pursuit
  • Schizophrenia
  • fMRI

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience


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