# Group-PCA for very large fMRI datasets

Article · Smith SM, Hyvärinen A, Varoquaux G, Miller KL, Beckmann CF

- Archive id: doi:10.1016/j.neuroimage.2014.07.051
- Published: 2014-08-03
- Venue: NeuroImage
- DOI: https://doi.org/10.1016/j.neuroimage.2014.07.051
- PMID: 25094018
- PMCID: PMC4289914
- Citations: 186
- Source URL: https://doi.org/10.1016/j.neuroimage.2014.07.051
- PDF: https://europepmc.org/articles/PMC4289914/pdf
- Provenance: europepmc

## 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 reproduced under CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)._

Topics: [Connectomics & Brain Mapping](https://space-kitty.com/topics/connectomics)

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