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Comparing the reliability of different ICA algorithms for fMRI analysis

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  • Pengxu Wei
  • Ruixue Bao
  • Yubo Fan

Abstract

Independent component analysis (ICA) has been shown to be a powerful blind source separation technique for analyzing functional magnetic resonance imaging (fMRI) data sets. ICA can extract independent spatial maps and their corresponding time courses from fMRI data without a priori specification of time courses. Some popular ICA algorithms such as Infomax or FastICA generate different results after repeated analysis from the same data volume, which is generally acknowledged as a drawback for ICA approaches. The reliability of some ICA algorithms has been explored by methods such as ICASSO and RAICAR (ranking and averaging independent component analysis by reproducibility). However, the exact algorithmic reliability of different ICA algorithms has not been examined and compared with each other. Here, the quality index generated with ICASSO and spatial correlation coefficients were used to examine the reliability of different ICA algorithms. The results demonstrated that Infomax running 10 times with ICASSO could generate consistent independent components from fMRI data sets.

Suggested Citation

  • Pengxu Wei & Ruixue Bao & Yubo Fan, 2022. "Comparing the reliability of different ICA algorithms for fMRI analysis," PLOS ONE, Public Library of Science, vol. 17(6), pages 1-12, June.
    • Handle: RePEc:plo:pone00:0270556
    DOI: 10.1371/journal.pone.0270556
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    References listed on IDEAS

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    1. Gautam V Pendse & David Borsook & Lino Becerra, 2011. "A Simple and Objective Method for Reproducible Resting State Network (RSN) Detection in fMRI," PLOS ONE, Public Library of Science, vol. 6(12), pages 1-20, December.
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