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Neuroimage. 2015 Sep;118:662-6. doi: 10.1016/j.neuroimage.2015.05.047. Epub 2015 May 27.

Comparison of PCA approaches for very large group ICA.

Author information

1
The Mind Research Network & LBERI, Albuquerque, NM 87106, USA; Dept. of ECE, The University of New Mexico, Albuquerque, NM 87106, USA. Electronic address: vcalhoun@unm.edu.
2
The Mind Research Network & LBERI, Albuquerque, NM 87106, USA; Dept. of ECE, The University of New Mexico, Albuquerque, NM 87106, USA.
3
Dept. of CSEE, University of Maryland, Baltimore County, Baltimore, MD 21250, USA.
4
The Mind Research Network & LBERI, Albuquerque, NM 87106, USA.

Abstract

Large data sets are becoming more common in fMRI and, with the advent of faster pulse sequences, memory efficient strategies for data reduction via principal component analysis (PCA) turn out to be extremely useful, especially for widely used approaches like group independent component analysis (ICA). In this commentary, we discuss results and limitations from a recent paper on the topic and attempt to provide a more complete perspective on available approaches as well as discussing various issues to consider related to PCA for very large group ICA. We also provide an analysis of computation time, memory use, and number of dataloads for a variety of approaches under multiple scenarios of small and extremely large data sets.

KEYWORDS:

Independent component analysis; Memory; Principal component analysis; RAM

PMID:
26021216
PMCID:
PMC4554805
DOI:
10.1016/j.neuroimage.2015.05.047
[Indexed for MEDLINE]
Free PMC Article

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