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Parallel group independent component analysis for massive fMRI data sets

Author

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

Abstract

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.

Suggested Citation

  • Shaojie Chen & Lei Huang & Huitong Qiu & Mary Beth Nebel & Stewart H Mostofsky & James J Pekar & Martin A Lindquist & Ani Eloyan & Brian S Caffo, 2017. "Parallel group independent component analysis for massive fMRI data sets," PLOS ONE, Public Library of Science, vol. 12(3), pages 1-17, March.
  • Handle: RePEc:plo:pone00:0173496
    DOI: 10.1371/journal.pone.0173496
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    Cited by:

    1. Yenny Webb-Vargas & Shaojie Chen & Aaron Fisher & Amanda Mejia & Yuting Xu & Ciprian Crainiceanu & Brian Caffo & Martin A. Lindquist, 2017. "Big Data and Neuroimaging," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 9(2), pages 543-558, December.

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