Spatial Independent Component Analysis (ICA) is an increasingly used
data-driven method to analyze functional Magnetic Resonance Imaging (fMRI)
data. To date, it has been used to extract meaningful patterns without prior
information. However, ICA is not robust to mild data variation and remains a
parameter-sensitive algorithm. The validity of the extracted patterns is hard
to establish, as well as the significance of differences between patterns
extracted from different groups of subjects. We start from a generative model
of the fMRI group data to introduce a probabilistic ICA pattern-extraction
algorithm, called CanICA (Canonical ICA). Thanks to an explicit noise model and
canonical correlation analysis, our method is auto-calibrated and identifies
the group-reproducible data subspace before performing ICA. We compare our
method to state-of-the-art multi-subject fMRI ICA methods and show that the
features extracted are more reproducible