title: Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification.
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.
authors.affiliation: Section for Biomedical image analysis, University of Pennsylvania, 3600 Market St., Suite 380, Philadelphia, PA 19104, USA. bilwaj@gmail.com
authors.forename: Bilwaj
authors.initials: B
authors.lastname: Gaonkar
authors.forename: Christos
authors.initials: C
authors.lastname: Davatzikos
mesh_terms: D001931:Brain Mapping / Q000379:methods*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging; D015999:Multivariate Analysis; D060388:Support Vector Machine