Detection of schizophrenia using fMRI data via projection pursuit

Oguz Demirci, Vince D. Calhoun

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Schizophrenia is currently diagnosed based upon symptoms and there is no quantitative, biologically based technique as yet. Classification of individuals into schizophrenia and control groups based on fMRI data is thus of great interest to support, psychiatric diagnoses. We applied a novel projection pursuit technique on the default mode component of 70 subjects' fMRI data obtained during an auditory odd-ball task. The validity of the technique was tested with a leave-one-out method and the detection performance varied between 80% and 90% applying different masks. The findings suggest that the proposed data reduction algorithm is effective in classifying individuals into schizophrenia and control groups and useful as a diagnostic tool.

Original languageEnglish (US)
Title of host publicationMachine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP
Pages199-204
Number of pages6
DOIs
StatePublished - Dec 1 2007
Externally publishedYes
Event17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007 - Thessaloniki, Greece
Duration: Aug 27 2007Aug 29 2007

Publication series

NameMachine Learning for Signal Processing 17 - Proceedings of the 2007 IEEE Signal Processing Society Workshop, MLSP

Other

Other17th IEEE International Workshop on Machine Learning for Signal Processing, MLSP-2007
CountryGreece
CityThessaloniki
Period8/27/078/29/07

ASJC Scopus subject areas

  • Computer Science(all)
  • Signal Processing

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