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 language | English (US) |
|---|---|
| Pages (from-to) | 270-283 |
| Number of pages | 14 |
| Journal | NeuroImage |
| Volume | 78 |
| DOIs | |
| State | Published - Sep 2013 |
| Externally published | Yes |
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Keywords
- Neuroimaging analysis
- Statistical inference
- SVM
ASJC Scopus subject areas
- Cognitive Neuroscience
- Neurology
Cite this
Analytic estimation of statistical significance maps for support vector machine based multi-variate image analysis and classification. / Gaonkar, Bilwaj; Davatzikos, Christos.
In: NeuroImage, Vol. 78, 09.2013, p. 270-283.Research output: Contribution to journal › Article
}
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 - Statistical inference
KW - SVM
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U2 - 10.1016/j.neuroimage.2013.03.066
DO - 10.1016/j.neuroimage.2013.03.066
M3 - Article
VL - 78
SP - 270
EP - 283
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
ER -