A1 Journal article (refereed)
Consistency of Independent Component Analysis for FMRI (2021)


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


JYU authors or editors


Publication details

All authors or editors: Zhao, Wei; Li, Huanjie; Hu, Guoqiang; Hao, Yuxing; Zhang, Qing; Wu, Jianlin; Frederick, Blaise B.; Cong, Fengyu

Journal or series: Journal of Neuroscience Methods

ISSN: 0165-0270

eISSN: 1872-678X

Publication year: 2021

Volume: 351

Article number: 109013

Publisher: Elsevier BV

Publication country: Netherlands

Publication language: English

DOI: https://doi.org/10.1016/j.jneumeth.2020.109013

Publication open access: Not open

Publication channel open access:

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/73899


Abstract

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.


Keywords: functional magnetic resonance imaging; signal analysis; signal processing

Free keywords: consistency; model order; ICA; fMRI


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2021

Preliminary JUFO rating: 1


Last updated on 2021-20-09 at 16:20