  For statistical analysis of functional Magnetic Resonance Imaging (fMRI) data
sets, we propose a data-driven approach based on Independent Component Analysis
(ICA) implemented in a new version of the AnalyzeFMRI R package. For fMRI data
sets, spatial dimension being much greater than temporal dimension, spatial ICA
is the tractable approach generally proposed. However, for some neuroscientific
applications, temporal independence of source signals can be assumed and
temporal ICA becomes then an attracting exploratory technique. In this work, we
use a classical linear algebra result ensuring the tractability of temporal
ICA. We report several experiments on synthetic data and real MRI data sets
that demonstrate the potential interest of our R package.
