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

CanICA: Model-based extraction of reproducible group-level ICA patterns from fMRI time series