Visualization of nonlinear kernel models in neuroimaging by sensitivity maps

Peter Mondrup Rasmussen, Kristoffer Hougaard Madsen, Torben Ellegaard Lund, Lars Kai Hansen

    Research output: Contribution to journalJournal articleResearchpeer-review


    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


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


    Dive into the research topics of 'Visualization of nonlinear kernel models in neuroimaging by sensitivity maps'. Together they form a unique fingerprint.

    Cite this