TY - JOUR
T1 - Parallel group independent component analysis for massive fMRI data sets
AU - Chen, Shaojie
AU - Huang, Lei
AU - Qiu, Huitong
AU - Nebel, Mary Beth
AU - Mostofsky, Stewart H.
AU - Pekar, James J.
AU - Lindquist, Martin A.
AU - Eloyan, Ani
AU - Caffo, Brian S.
N1 - Funding Information:
The project was supported by the NIH grant RO1 EB012547 from the National Institute of Biomedical Imaging And Bioengineering.
Funding Information:
This research was supported by the NIH grant P41 EB015909 from the National Institute of Biomedical Imaging And Bioengineering.
Funding Information:
The project was supported by the NIH grant RO1 NS060910 from the National Institute of Neurological Disorders and Stroke.
Publisher Copyright:
© 2017 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
PY - 2017/3
Y1 - 2017/3
N2 - Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively.
AB - Independent component analysis (ICA) is widely used in the field of functional neuroimaging to decompose data into spatio-temporal patterns of co-activation. In particular, ICA has found wide usage in the analysis of resting state fMRI (rs-fMRI) data. Recently, a number of large-scale data sets have become publicly available that consist of rs-fMRI scans from thousands of subjects. As a result, efficient ICA algorithms that scale well to the increased number of subjects are required. To address this problem, we propose a two-stage likelihood-based algorithm for performing group ICA, which we denote Parallel Group Independent Component Analysis (PGICA). By utilizing the sequential nature of the algorithm and parallel computing techniques, we are able to efficiently analyze data sets from large numbers of subjects. We illustrate the efficacy of PGICA, which has been implemented in R and is freely available through the Comprehensive R Archive Network, through simulation studies and application to rs-fMRI data from two large multi-subject data sets, consisting of 301 and 779 subjects respectively.
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U2 - 10.1371/journal.pone.0173496
DO - 10.1371/journal.pone.0173496
M3 - Article
C2 - 28278208
AN - SCOPUS:85014930973
SN - 1932-6203
VL - 12
JO - PLoS One
JF - PLoS One
IS - 3
M1 - e0173496
ER -