  The shared response model provides a simple but effective framework to
analyse fMRI data of subjects exposed to naturalistic stimuli. However when the
number of subjects or runs is large, fitting the model requires a large amount
of memory and computational power, which limits its use in practice. In this
work, we introduce the FastSRM algorithm that relies on an intermediate
atlas-based representation. It provides considerable speed-up in time and
memory usage, hence it allows easy and fast large-scale analysis of
naturalistic-stimulus fMRI data. Using four different datasets, we show that
our method matches the performance of the original SRM algorithm while being
about 5x faster and 20x to 40x more memory efficient. Based on this
contribution, we use FastSRM to predict age from movie watching data on the
CamCAN sample. Besides delivering accurate predictions (mean absolute error of
7.5 years), FastSRM extracts topographic patterns that are predictive of age,
demonstrating that brain activity during free perception reflects age.
