  In neuroscience, understanding inter-individual differences has recently
emerged as a major challenge, for which functional magnetic resonance imaging
(fMRI) has proven invaluable. For this, neuroscientists rely on basic methods
such as univariate linear correlations between single brain features and a
score that quantifies either the severity of a disease or the subject's
performance in a cognitive task. However, to this date, task-fMRI and
resting-state fMRI have been exploited separately for this question, because of
the lack of methods to effectively combine them. In this paper, we introduce a
novel machine learning method which allows combining the activation-and
connectivity-based information respectively measured through these two fMRI
protocols to identify markers of individual differences in the functional
organization of the brain. It combines a multi-view deep autoencoder which is
designed to fuse the two fMRI modalities into a joint representation space
within which a predictive model is trained to guess a scalar score that
characterizes the patient. Our experimental results demonstrate the ability of
the proposed method to outperform competitive approaches and to produce
interpretable and biologically plausible results.
