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Neuroimage. 2014 Nov 1;101:738-49. doi: 10.1016/j.neuroimage.2014.07.051. Epub 2014 Aug 3.

Group-PCA for very large fMRI datasets.

Author information

1
FMRIB (Oxford University Centre for Functional MRI of the Brain), University of Oxford, UK. Electronic address: steve@fmrib.ox.ac.uk.
2
Dept of Computer Science, University of Helsinki, Finland.
3
Parietal Team, INRIA Saclay-Ile-de-France, Saclay, France.
4
FMRIB (Oxford University Centre for Functional MRI of the Brain), University of Oxford, UK.
5
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.

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.

KEYWORDS:

Big data; ICA; PCA; fMRI

PMID:
25094018
PMCID:
PMC4289914
DOI:
10.1016/j.neuroimage.2014.07.051
[Indexed for MEDLINE]
Free PMC Article

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