Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification

Bilwaj Gaonkar, Christos Davatzikos

Research output: Contribution to journalArticle

Abstract

Multivariate pattern analysis (MVPA) methods such as support vector machines (SVMs) have been increasingly applied to fMRI and sMRI analyses, enabling the detection of distinctive imaging patterns. However, identifying brain regions that significantly contribute to the classification/group separation requires computationally expensive permutation testing. In this paper we show that the results of SVM-permutation testing can be analytically approximated. This approximation leads to more than a thousandfold speedup of the permutation testing procedure, thereby rendering it feasible to perform such tests on standard computers. The speedup achieved makes SVM based group difference analysis competitive with standard univariate group difference analysis methods.

Original languageEnglish (US)
Pages (from-to)270-283
Number of pages14
JournalNeuroImage
Volume78
DOIs
StatePublished - Sep 1 2013

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Keywords

  • Neuroimaging analysis
  • SVM
  • Statistical inference

ASJC Scopus subject areas

  • Neurology
  • Cognitive Neuroscience

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