Support vector machines for neuroimage analysis: Interpretation from discrimination

Support vector machines (SVMs) have been widely used in neuroimage analysis as an effective multivariate analysis tool for group comparison. As neuroimage analysis is often an exploratory research, it is an important issue to characterize the group difference captured by SVM with anatomically interpretable patterns, which provides insights into the unknown mechanism of the brain. In this chapter, SVM-based methods and applications are introduced for neuroimage analysis from this point of view. The discriminative patterns are decoded from SVMs through distinctive feature selection, SVM decision boundary interpretation, and discriminative learning of generative models.