Group-PCA for very large fMRI datasets

Abstract

Increasingly-large datasets (for example, the resting-state fMRI data from the Human Connectome Project) are demanding analyses that are problematic because of the sheer scale of the aggregate data. We present two approaches for applying group-level PCA; both give a close approximation to the output of PCA applied to full concatenation of all individual datasets, while having very low memory requirements regardless of the number of datasets being combined. Across a range of realistic simulations, we find that in most situations, both methods are more accurate than current popular approaches for analysis of multi-subject resting-state fMRI studies. The group-PCA output can be used to feed into a range of further analyses that are then rendered practical, such as the estimation of group-averaged voxelwise connectivity, group-level parcellation, and group-ICA.

Abstract of "Group-PCA for very large fMRI datasets" by Smith SM, Hyvärinen A, Varoquaux G et al., https://doi.org/10.1016/j.neuroimage.2014.07.051, used under its Creative Commons Attribution license (CC-BY); markup removed, otherwise unmodified.