  While medical imaging typically provides massive amounts of data, the
extraction of relevant information for predictive diagnosis remains a difficult
challenge. Functional MRI (fMRI) data, that provide an indirect measure of
task-related or spontaneous neuronal activity, are classically analyzed in a
mass-univariate procedure yielding statistical parametric maps. This analysis
framework disregards some important principles of brain organization:
population coding, distributed and overlapping representations. Multivariate
pattern analysis, i.e., the prediction of behavioural variables from brain
activation patterns better captures this structure. To cope with the high
dimensionality of the data, the learning method has to be regularized. However,
the spatial structure of the image is not taken into account in standard
regularization methods, so that the extracted features are often hard to
interpret. More informative and interpretable results can be obtained with the
l_1 norm of the image gradient, a.k.a. its Total Variation (TV), as
regularization. We apply for the first time this method to fMRI data, and show
that TV regularization is well suited to the purpose of brain mapping while
being a powerful tool for brain decoding. Moreover, this article presents the
first use of TV regularization for classification.
