Comparing the reliability of different ICA algorithms for fMRI analysis

Pengxu Wei, Ruixue Bao and Yubo Fan

PLOS ONE, 2022, vol. 17, issue 6, 1-12

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.

Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0270556

DOI: 10.1371/journal.pone.0270556

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