Parallel group independent component analysis for massive fMRI data sets

Shaojie Chen, Lei Huang, Huitong Qiu, Mary Beth Nebel, Stewart H. Mostofsky, James J. Pekar, Martin A. Lindquist, Ani Eloyan, Brian S. Caffo

Research output: Contribution to journalArticlepeer-review

4 Scopus citations


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.

Original languageEnglish (US)
Article numbere0173496
JournalPloS one
Issue number3
StatePublished - Mar 2017

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)
  • General


Dive into the research topics of 'Parallel group independent component analysis for massive fMRI data sets'. Together they form a unique fingerprint.

Cite this