{"id":"doi:10.1016/j.neuroimage.2014.07.051","kind":"article","title":"Group-PCA for very large fMRI datasets","authors":["Smith SM","Hyvärinen A","Varoquaux G","Miller KL","Beckmann CF"],"author_count":5,"year":2014,"published":"2014-08-03","venue":"NeuroImage","cited_by":186,"doi":"10.1016/j.neuroimage.2014.07.051","arxiv_id":null,"nct_id":null,"source_url":"https://doi.org/10.1016/j.neuroimage.2014.07.051","archive_url":"https://space-kitty.com/papers/doi:10.1016/j.neuroimage.2014.07.051","updated":null,"pmid":"25094018","pmcid":"PMC4289914","openalex_id":null,"pdf_url":"https://europepmc.org/articles/PMC4289914/pdf","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_license":"CC-BY (article license as recorded by Europe PMC; abstract reproduced unmodified apart from markup removal)","license":"CC-BY","is_open_access":true,"retracted":false,"peer_reviewed":null,"categories":[],"details":null,"provenance":"europepmc","attribution":"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.","topics":[{"slug":"connectomics","name":"Connectomics & Brain Mapping","domain_slug":"neuroscience"}],"cited_in_dossiers":[]}