We present a method for fast resting-state fMRI spatial decomposi-tions of
very large datasets, based on the reduction of the temporal dimension before
applying dictionary learning on concatenated individual records from groups of
subjects. Introducing a measure of correspondence between spatial
decompositions of rest fMRI, we demonstrates that time-reduced dictionary
learning produces result as reliable as non-reduced decompositions. We also
show that this reduction significantly improves computational scalability
