TY - JOUR
T1 - Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification
AU - Gaonkar, Bilwaj
AU - Davatzikos, Christos
PY - 2013/9
Y1 - 2013/9
N2 - 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.
AB - 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.
KW - Neuroimaging analysis
KW - SVM
KW - Statistical inference
UR - http://www.scopus.com/inward/record.url?scp=84877323945&partnerID=8YFLogxK
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U2 - 10.1016/j.neuroimage.2013.03.066
DO - 10.1016/j.neuroimage.2013.03.066
M3 - Article
C2 - 23583748
AN - SCOPUS:84877323945
VL - 78
SP - 270
EP - 283
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
ER -