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Visualization of nonlinear kernel models in neuroimaging by sensitivity maps

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Visualization of nonlinear kernel models in neuroimaging by sensitivity maps. / Rasmussen, Peter Mondrup; Madsen, Kristoffer Hougaard; Lund, Torben Ellegaard; Hansen, Lars Kai.

I: NeuroImage, Bind 55, Nr. 3, 04.2011, s. 1120-1131.

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningfagfællebedømt

Harvard

Rasmussen, PM, Madsen, KH, Lund, TE & Hansen, LK 2011, 'Visualization of nonlinear kernel models in neuroimaging by sensitivity maps', NeuroImage, bind 55, nr. 3, s. 1120-1131. https://doi.org/10.1016/j.neuroimage.2010.12.035

APA

Rasmussen, P. M., Madsen, K. H., Lund, T. E., & Hansen, L. K. (2011). Visualization of nonlinear kernel models in neuroimaging by sensitivity maps. NeuroImage, 55(3), 1120-1131. https://doi.org/10.1016/j.neuroimage.2010.12.035

Vancouver

Rasmussen PM, Madsen KH, Lund TE, Hansen LK. Visualization of nonlinear kernel models in neuroimaging by sensitivity maps. NeuroImage. 2011 apr;55(3):1120-1131. https://doi.org/10.1016/j.neuroimage.2010.12.035

Author

Rasmussen, Peter Mondrup ; Madsen, Kristoffer Hougaard ; Lund, Torben Ellegaard ; Hansen, Lars Kai. / Visualization of nonlinear kernel models in neuroimaging by sensitivity maps. I: NeuroImage. 2011 ; Bind 55, Nr. 3. s. 1120-1131.

Bibtex

@article{ec5cd15500ef4f078cb71babf2a25d6c,
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.",
keywords = "Algorithms, Artificial Intelligence, Brain, Brain Mapping, Discriminant Analysis, Humans, Image Processing, Computer-Assisted, Linear Models, Logistic Models, Magnetic Resonance Imaging, Models, Neurological, Models, Statistical, Nonlinear Dynamics, Pattern Recognition, Automated, Principal Component Analysis",
author = "Rasmussen, {Peter Mondrup} and Madsen, {Kristoffer Hougaard} and Lund, {Torben Ellegaard} and Hansen, {Lars Kai}",
note = "Copyright {\textcopyright} 2010 Elsevier Inc. All rights reserved.",
year = "2011",
month = apr,
doi = "10.1016/j.neuroimage.2010.12.035",
language = "English",
volume = "55",
pages = "1120--1131",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Elsevier",
number = "3",

}

RIS

TY - JOUR

T1 - Visualization of nonlinear kernel models in neuroimaging by sensitivity maps

AU - Rasmussen, Peter Mondrup

AU - Madsen, Kristoffer Hougaard

AU - Lund, Torben Ellegaard

AU - Hansen, Lars Kai

N1 - Copyright © 2010 Elsevier Inc. All rights reserved.

PY - 2011/4

Y1 - 2011/4

N2 - 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.

AB - 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.

KW - Algorithms

KW - Artificial Intelligence

KW - Brain

KW - Brain Mapping

KW - Discriminant Analysis

KW - Humans

KW - Image Processing, Computer-Assisted

KW - Linear Models

KW - Logistic Models

KW - Magnetic Resonance Imaging

KW - Models, Neurological

KW - Models, Statistical

KW - Nonlinear Dynamics

KW - Pattern Recognition, Automated

KW - Principal Component Analysis

U2 - 10.1016/j.neuroimage.2010.12.035

DO - 10.1016/j.neuroimage.2010.12.035

M3 - Journal article

C2 - 21168511

VL - 55

SP - 1120

EP - 1131

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

IS - 3

ER -

ID: 40226874