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Authors: Peter M. Rasmussen 1 ; Tanya Schmah 2 ; Kristoffer H. Madsen 3 ; Torben E. Lund 4 ; Grigori Yourganov 5 ; Stephen C. Strother 5 and Lars K. Hansen 6

Affiliations: 1 Technical University of Denmark and Aarhus University Hospital, Denmark ; 2 University of Toronto, Canada ; 3 Technical University of Denmark and Copenhagen University Hospital Hvidovre, Denmark ; 4 Aarhus University Hospital, Denmark ; 5 Baycrest Centre for Geriatric Care and University of Toronto, Canada ; 6 Technical University of Denmark, Denmark

ISBN: 978-989-8425-89-8

Keyword(s): Neuroimaging, Classification, Multivariate Analysis, Model Interpretation, Model Visualization, Sensitivity Map, NPAIRS Resampling, Functional Magnetic Resonance Imaging.

Related Ontology Subjects/Areas/Topics: Applications and Services ; Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computer Vision, Visualization and Computer Graphics ; Data Manipulation ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Medical Image Detection, Acquisition, Analysis and Processing ; Methodologies and Methods ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Soft Computing

Abstract: 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 v isualization procedure on a real data from a simple functional magnetic resonance imaging experiment. (More)

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Paper citation in several formats:
M. Rasmussen, P.; M. Rasmussen, P.; Schmah, T.; H. Madsen, K.; E. Lund, T.; Yourganov, G.; Yourganov, G.; C. Strother, S. and K. Hansen, L. (2012). VISUALIZATION OF NONLINEAR CLASSIFICATION MODELS IN NEUROIMAGING - Signed Sensitivity Maps.In Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012) ISBN 978-989-8425-89-8, pages 254-263. DOI: 10.5220/0003785602540263

@conference{biosignals12,
author={Peter M. Rasmussen. and Peter M. Rasmussen. and Tanya Schmah. and Kristoffer H. Madsen. and Torben E. Lund. and Grigori Yourganov. and Grigori Yourganov. and Stephen C. Strother. and Lars K. Hansen.},
title={VISUALIZATION OF NONLINEAR CLASSIFICATION MODELS IN NEUROIMAGING - Signed Sensitivity Maps},
booktitle={Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)},
year={2012},
pages={254-263},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003785602540263},
isbn={978-989-8425-89-8},
}

TY - CONF

JO - Proceedings of the International Conference on Bio-inspired Systems and Signal Processing - Volume 1: BIOSIGNALS, (BIOSTEC 2012)
TI - VISUALIZATION OF NONLINEAR CLASSIFICATION MODELS IN NEUROIMAGING - Signed Sensitivity Maps
SN - 978-989-8425-89-8
AU - M. Rasmussen, P.
AU - M. Rasmussen, P.
AU - Schmah, T.
AU - H. Madsen, K.
AU - E. Lund, T.
AU - Yourganov, G.
AU - Yourganov, G.
AU - C. Strother, S.
AU - K. Hansen, L.
PY - 2012
SP - 254
EP - 263
DO - 10.5220/0003785602540263

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