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
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
visualization procedure on a real data from a simple functional magnetic resonance imaging experiment.
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