A1 Journal article (refereed)
Tensor clustering on outer-product of coefficient and component matrices of independent component analysis for reliable functional magnetic resonance imaging data decomposition (2019)


Hu, G., Zhang, Q., Waters, A. B., Li, H., Zhang, C., Wu, J., Cong, F., & Nickerson, L. D. (2019). Tensor clustering on outer-product of coefficient and component matrices of independent component analysis for reliable functional magnetic resonance imaging data decomposition. Journal of Neuroscience Methods, 325, Article 108359. https://doi.org/10.1016/j.jneumeth.2019.108359


JYU authors or editors


Publication details

All authors or editors: Hu, Guoqiang; Zhang, Qing; Waters, Abigail B.; Li, Huanjie; Zhang, Chi; Wu, Jianlin; Cong, Fengyu; Nickerson, Lisa D.

Journal or series: Journal of Neuroscience Methods

ISSN: 0165-0270

eISSN: 1872-678X

Publication year: 2019

Volume: 325

Article number: 108359

Publisher: Elsevier BV

Publication country: Netherlands

Publication language: English

DOI: https://doi.org/10.1016/j.jneumeth.2019.108359

Publication open access: Openly available

Publication channel open access: Partially open access channel

Publication is parallel published (JYX): https://jyx.jyu.fi/handle/123456789/65140


Abstract

Background. Stability of spatial components is frequently used as a post-hoc selection criteria for choosing the dimensionality of an independent component analysis (ICA) of functional magnetic resonance imaging (fMRI) data. Although the stability of the ICA temporal courses differs from that of spatial components, temporal stability has not been considered during dimensionality decisions.
New method. The current study aims to (1) develop an algorithm to incorporate temporal course stability into dimensionality selection and (2) test the impact of temporal course on the stability of the ICA decomposition of fMRI data via tensor clustering. Resting state fMRI data were analyzed with two popular ICA algorithms, InfomaxICA and FastICA, using our new method and results were compared with model order selection based on spatial or temporal criteria alone.
Results. Hierarchical clustering indicated that the stability of the ICA decomposition incorporating spatiotemporal tensor information performed similarly when compared to current best practice. However, we found that component spatiotemporal stability and convergence of the model varied significantly with model order. Considering both may lead to methodological improvements for determining ICA model order. Selected components were also significantly associated with relevant behavioral variables.
Comparison with Existing Method: The Kullback–Leibler information criterion algorithm suggests the optimal model order for group ICA is 40, compared to the proposed method with an optimal model order of 20.
Conclusion. The current study sheds new light on the importance of temporal course variability in ICA of fMRI data.


Keywords: functional magnetic resonance imaging; signal analysis

Free keywords: independent component analysis (ICA); fMRI; tensor clustering; stability; model order


Contributing organizations


Ministry reporting: Yes

Reporting Year: 2019

JUFO rating: 1


Last updated on 2024-08-01 at 18:54