  The growing use of neuroimaging technologies generates a massive amount of
biomedical data that exhibit high dimensionality. Tensor-based analysis of
brain imaging data has been proved quite effective in exploiting their multiway
nature. The advantages of tensorial methods over matrix-based approaches have
also been demonstrated in the characterization of functional magnetic resonance
imaging (fMRI) data, where the spatial (voxel) dimensions are commonly grouped
(unfolded) as a single way/mode of the 3-rd order array, the other two ways
corresponding to time and subjects. However, such methods are known to be
ineffective in more demanding scenarios, such as the ones with strong noise
and/or significant overlapping of activated regions. This paper aims at
investigating the possible gains from a better exploitation of the spatial
dimension, through a higher- (4 or 5) order tensor modeling of the fMRI signal.
In this context, and in order to increase the degrees of freedom of the
modeling process, a higher-order Block Term Decomposition (BTD) is applied, for
the first time in fMRI analysis. Its effectiveness is demonstrated via
extensive simulation results.
