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Erschienen in:
2012 | OriginalPaper | Buchkapitel
Graph-Based Inter-subject Classification of Local fMRI Patterns
verfasst von : Sylvain Takerkart, Guillaume Auzias, Bertrand Thirion, Daniele Schön, Liva Ralaivola
Erschienen in: Machine Learning in Medical Imaging
Verlag: Springer Berlin Heidelberg
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Classification of medical images in multi-subjects settings is a difficult challenge due to the variability that exists between individuals. Here we introduce a new graph-based framework designed to deal with inter-subject functional variability present in fMRI data. A graphical model is constructed to encode the functional, geometric and structural properties of local activation patterns. We then design a specific graph kernel, allowing to conduct SVM classification in graph space. Experiments conducted in an inter-subject classification task of patterns recorded in the auditory cortex show that it is the only approach to perform above chance level, among a wide range of tested methods.