Tensor clustering on outer-product of coefficient and component matrices of independent component analysis for reliable functional magnetic resonance imaging data decomposition

Guoqiang Hu; Qing Zhang; Abigail B Waters; Huanjie Li; Chi Zhang; Jianlin Wu; Fengyu Cong; Lisa D Nickerson

doi:10.1016/j.jneumeth.2019.108359

Tensor clustering on outer-product of coefficient and component matrices of independent component analysis for reliable functional magnetic resonance imaging data decomposition

J Neurosci Methods. 2019 Sep 1:325:108359. doi: 10.1016/j.jneumeth.2019.108359. Epub 2019 Jul 12.

Authors

Guoqiang Hu¹, Qing Zhang², Abigail B Waters³, Huanjie Li⁴, Chi Zhang⁴, Jianlin Wu⁵, Fengyu Cong⁶, Lisa D Nickerson⁷

Affiliations

¹ School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China; Department of Psychiatry, Harvard Medical School, Harvard University, Boston, MA, USA.
² Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China.
³ Department of Psychology, Suffolk University, Boston, MA, USA; Applied Neuroimaging Statistics Lab, Mclean Hospital, Belmont, MA, USA.
⁴ School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.
⁵ Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, China. Electronic address: cjr.wujianlin@vip.163.com.
⁶ School of Biomedical Engineering, Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China; Faculty of Information Technology, University of Jyvaskyla, Jyvaskyla, Finland. Electronic address: cong@dlut.edu.cn.
⁷ Applied Neuroimaging Statistics Lab, Mclean Hospital, Belmont, MA, USA; Department of Psychiatry, Harvard Medical School, Harvard University, Boston, MA, USA.

PMID: 31306718
DOI: 10.1016/j.jneumeth.2019.108359

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: Independent component analysis (ICA); Model order; Stability; Tensor clustering; fMRI.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't

MeSH terms

Adult
Algorithms
Cerebral Cortex / diagnostic imaging*
Computer Simulation
Female
Functional Neuroimaging / methods*
Humans
Image Processing, Computer-Assisted / methods*
Magnetic Resonance Imaging / methods*
Male
Models, Statistical
Nerve Net / diagnostic imaging*
Principal Component Analysis

Abstract

Publication types

MeSH terms

Grants and funding