Consistency of Independent Component Analysis for FMRI
Zhao, W., Li, H., Hu, G., Hao, Y., Zhang, Q., Wu, J., Frederick, B. B., & Cong, F. (2021). Consistency of Independent Component Analysis for FMRI. Journal of Neuroscience Methods, 351, Article 109013. https://doi.org/10.1016/j.jneumeth.2020.109013
Published inJournal of Neuroscience Methods
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© 2020 Elsevier B.V.
Background Independent component analysis (ICA) has been widely used for blind source separation in the field of medical imaging. However, despite of previous substantial efforts, the stability of ICA components remains a critical issue which has not been adequately addressed, despite numerous previous efforts. Most critical is the inconsistency of some of the extracted components when ICA is run with different model orders (MOs). New Method In this study, a novel method of determining the consistency of component analysis (CoCA) is proposed to evaluate the consistency of extracted components with different model orders. In the method, “consistent components” (CCs) are defined as those which can be extracted repeatably over a range of model orders. Result The efficacy of the method was evaluated with simulation data and fMRI datasets. With our method, the simulation result showed a clear difference of consistency between ground truths and noise. Comparison with existing methods The information criteria were implemented to provide suggestions for the optimal model order, where some of the ICs were revealed inconsistent in our proposed method. Conclusions This method provided an objective protocol for choosing CCs of an ICA decomposition of a data matrix, independent of model order. This is especially useful with high model orders, where noise or other disturbances could possibly lead to an instability of the components. ...
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Additional information about fundingThis work was supported by National Natural Science Foundation of China (Grant Nos. 91748105 & 81601484), and National Foundation in China (Nos. JCKY 2019110B009 & 2020-JCJQ-JJ-252), and the scholarships from China Scholarship Council (No. 202006060130), and the Fundamental Research Funds for the Central Universities [DUT2019, DUT20LAB303] in Dalian University of Technology in China.
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