  Inferring the functional specificity of brain regions from functional
Magnetic Resonance Images (fMRI) data is a challenging statistical problem.
While the General Linear Model (GLM) remains the standard approach for brain
mapping, supervised learning techniques (a.k.a.} decoding) have proven to be
useful to capture multivariate statistical effects distributed across voxels
and brain regions. Up to now, much effort has been made to improve decoding by
incorporating prior knowledge in the form of a particular regularization term.
In this paper we demonstrate that further improvement can be made by accounting
for non-linearities using a ranking approach rather than the commonly used
least-square regression. Through simulation, we compare the recovery properties
of our approach to linear models commonly used in fMRI based decoding. We
demonstrate the superiority of ranking with a real fMRI dataset.
