title: 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.
authors.affiliation: FMRIB (Oxford University Centre for Functional MRI of the Brain), University of Oxford, UK. Electronic address: steve@fmrib.ox.ac.uk.
authors.forename: Stephen M
authors.initials: SM
authors.lastname: Smith
authors.affiliation: Dept of Computer Science, University of Helsinki, Finland.
authors.forename: Aapo
authors.initials: A
authors.lastname: Hyvärinen
authors.affiliation: Parietal Team, INRIA Saclay-Ile-de-France, Saclay, France.
authors.forename: Gaël
authors.initials: G
authors.lastname: Varoquaux
authors.affiliation: FMRIB (Oxford University Centre for Functional MRI of the Brain), University of Oxford, UK.
authors.forename: Karla L
authors.initials: KL
authors.lastname: Miller
authors.affiliation: FMRIB (Oxford University Centre for Functional MRI of the Brain), University of Oxford, UK; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, The Netherlands.
authors.forename: Christian F
authors.initials: CF
authors.lastname: Beckmann
mesh_terms: D016477:Artifacts; D003198:Computer Simulation; D063132:Connectome / Q000379:methods*; D003627:Data Interpretation, Statistical; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D025341:Principal Component Analysis / Q000379:methods*
keywords: Big data; ICA; PCA; fMRI
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