Group-PCA for very large fMRI datasets

Journal article

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.

Authors: Smith, S; Hyva rinen, A; Varoquaux, G; Miller, K; Beckmann, C

• Publisher: Academic Press Inc.
• Journal: NeuroImage
• Volume: 101
• Pages: 738-749
• Publication date: 2014-11-01
• DOI: 10.1016/j.neuroimage.2014.07.051
• EISSN: 1095-9572
• ISSN: 1053-8119
• Language: English
• Keywords: PCA; Big data; ICA; FMRI