Visualization of nonlinear kernel models in neuroimaging by sensitivity maps

By Peter Mondrup Rasmussen, Kristoffer Hougaard Madsen, Torben Ellegaard Lund, Lars Kai Hansen. 2011

75 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Cognitive Neuroscience

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Citations in the corpus

Interpreting support vector machine models for multivariate group wise analysis in neuroimaging (2015, positive): “While the weight map itself has been used for interpreting SVM models Rasmussen et al. (2011) Guyon et al. (2002), it has been noted that using SVM weights can assign relatively low weights to significant features and relatively larger weights to irrelevant features Hardin et al. (2004) Cuingnet et…”
Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps (2012, background): “…within the field of neuroimaging in linear discriminant analysis (Kjems et al., 2002), quadratic discriminant analysis (Yourganov et al., 2010), and nonlinear kernel models (Rasmussen et al., 2011), and in skin cancer detection by Raman spectroscopy using neural networks (Sigurdsson et al., 2004).”
Deep convolutional neural networks for interpretable analysis of EEG sleep stage scoring (2017, methodology): “In the current study, we resort to sensitivity analysis [23, 24, 25] as a visualisation tool to better understand the decisions made by our network.”
Revealing the co-existence of written and spoken language coding neural populations in the visual word form area (2025, unknown): “The advantage of non-linear over linear classifiers could be due to their flexible decision boundary ( Kragel et al., 2012; Rasmussen et al., 2011).”