title: Visualization of nonlinear kernel models in neuroimaging by sensitivity maps.
abstract: 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.
authors.affiliation: DTU Informatics, Technical University of Denmark, Denmark. pmra@imm.dtu.dk
authors.forename: Peter Mondrup
authors.initials: PM
authors.lastname: Rasmussen
authors.forename: Kristoffer Hougaard
authors.initials: KH
authors.lastname: Madsen
authors.forename: Torben Ellegaard
authors.initials: TE
authors.lastname: Lund
authors.forename: Lars Kai
authors.initials: LK
authors.lastname: Hansen
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology; D001931:Brain Mapping / Q000379:methods*; D016002:Discriminant Analysis; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D016014:Linear Models; D016015:Logistic Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008959:Models, Neurological; D015233:Models, Statistical; D017711:Nonlinear Dynamics; D010363:Pattern Recognition, Automated / Q000379:methods; D025341:Principal Component Analysis