Visualization of nonlinear kernel models in neuroimaging by sensitivity maps

Research output: Contribution to journalJournal article – Annual report year: 2011Researchpeer-review

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There is significant current interest in decoding mental states from neuroimages. In this context kernel methods, e.g., support vector machines (SVM) are frequently adopted to learn statistical relations between patterns of brain activation and experimental conditions. In this paper we focus on visualization of such nonlinear kernel models. Specifically, we investigate the sensitivity map as a technique for generation of global summary maps of kernel classification models. We illustrate the performance of the sensitivity map on functional magnetic resonance (fMRI) data based on visual stimuli. We show that the performance of linear models is reduced for certain scan labelings/categorizations in this data set, while the nonlinear models provide more flexibility. We show that the sensitivity map can be used to visualize nonlinear versions of kernel logistic regression, the kernel Fisher discriminant, and the SVM, and conclude that the sensitivity map is a versatile and computationally efficient tool for visualization of nonlinear kernel models in neuroimaging.
Original languageEnglish
Issue number3
Pages (from-to)1120-1131
Publication statusPublished - 2011
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Model visualization, Nonlinear modeling, Support vector machine, Neuroimaging, Multivariate analysis, Sensitivity map, Kernel methods, Machine learning, Pattern analysis
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ID: 5610861