Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

Standard

Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps. / Rasmussen, Peter Mondrup; Schmah, Tanya; Madsen, Kristoffer Hougaard; Lund, Torben E.; Yourganov, Grigori; Strother, Stephen C.; Hansen, Lars Kai.

BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing. SciTePress, 2012. p. 254-263.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

Harvard

Rasmussen, PM, Schmah, T, Madsen, KH, Lund, TE, Yourganov, G, Strother, SC & Hansen, LK 2012, 'Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps'. in BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing. SciTePress, pp. 254-263.

APA

Rasmussen, P. M., Schmah, T., Madsen, K. H., Lund, T. E., Yourganov, G., Strother, S. C., & Hansen, L. K. (2012). Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps. In BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing. (pp. 254-263). SciTePress.

CBE

Rasmussen PM, Schmah T, Madsen KH, Lund TE, Yourganov G, Strother SC, Hansen LK. 2012. Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps. In BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing. SciTePress. pp. 254-263.

MLA

Rasmussen, Peter Mondrup et al. "Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps". BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing. SciTePress. 2012. 254-263.

Vancouver

Rasmussen PM, Schmah T, Madsen KH, Lund TE, Yourganov G, Strother SC et al. Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps. In BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing. SciTePress. 2012. p. 254-263.

Author

Rasmussen, Peter Mondrup; Schmah, Tanya; Madsen, Kristoffer Hougaard; Lund, Torben E.; Yourganov, Grigori; Strother, Stephen C.; Hansen, Lars Kai / Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps.

BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing. SciTePress, 2012. p. 254-263.

Publication: Research - peer-reviewArticle in proceedings – Annual report year: 2012

Bibtex

@inbook{2d74c35d211d40e98e02104b8a633d56,
title = "Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps",
keywords = "Magnetic resonance imaging, Neuroimaging, Neurophysiology, Signal processing, Visualization, Classification (of information)",
publisher = "SciTePress",
author = "Rasmussen, {Peter Mondrup} and Tanya Schmah and Madsen, {Kristoffer Hougaard} and Lund, {Torben E.} and Grigori Yourganov and Strother, {Stephen C.} and Hansen, {Lars Kai}",
year = "2012",
isbn = "978-989-8425-89-8",
pages = "254-263",
booktitle = "BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing",

}

RIS

TY - GEN

T1 - Visualization of Nonlinear Classification Models in Neuroimaging - Signed Sensitivity Maps

A1 - Rasmussen,Peter Mondrup

A1 - Schmah,Tanya

A1 - Madsen,Kristoffer Hougaard

A1 - Lund,Torben E.

A1 - Yourganov,Grigori

A1 - Strother,Stephen C.

A1 - Hansen,Lars Kai

AU - Rasmussen,Peter Mondrup

AU - Schmah,Tanya

AU - Madsen,Kristoffer Hougaard

AU - Lund,Torben E.

AU - Yourganov,Grigori

AU - Strother,Stephen C.

AU - Hansen,Lars Kai

PB - SciTePress

PY - 2012

Y1 - 2012

N2 - Classification models are becoming increasing popular tools in the analysis of neuroimaging data sets. Besides obtaining good prediction accuracy, a competing goal is to interpret how the classifier works. From a neuroscientific perspective, we are interested in the brain pattern reflecting the underlying neural encoding of an experiment defining multiple brain states. In this relation there is a great desire for the researcher to generate brain maps, that highlight brain locations of importance to the classifiers decisions. Based on sensitivity analysis, we develop further procedures for model visualization. Specifically we focus on the generation of summary maps of a nonlinear classifier, that reveal how the classifier works in different parts of the input domain. Each of the maps includes sign information, unlike earlier related methods. The sign information allows the researcher to assess in which direction the individual locations influence the classification. We illustrate the visualization procedure on a real data from a simple functional magnetic resonance imaging experiment.

AB - Classification models are becoming increasing popular tools in the analysis of neuroimaging data sets. Besides obtaining good prediction accuracy, a competing goal is to interpret how the classifier works. From a neuroscientific perspective, we are interested in the brain pattern reflecting the underlying neural encoding of an experiment defining multiple brain states. In this relation there is a great desire for the researcher to generate brain maps, that highlight brain locations of importance to the classifiers decisions. Based on sensitivity analysis, we develop further procedures for model visualization. Specifically we focus on the generation of summary maps of a nonlinear classifier, that reveal how the classifier works in different parts of the input domain. Each of the maps includes sign information, unlike earlier related methods. The sign information allows the researcher to assess in which direction the individual locations influence the classification. We illustrate the visualization procedure on a real data from a simple functional magnetic resonance imaging experiment.

KW - Magnetic resonance imaging

KW - Neuroimaging

KW - Neurophysiology

KW - Signal processing

KW - Visualization

KW - Classification (of information)

SN - 978-989-8425-89-8

BT - BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing

T2 - BIOSIGNALS 2012 - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing

SP - 254

EP - 263

ER -