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Published in: Medical & Biological Engineering & Computing 4/2018

02-09-2017 | Original Article

An improved multi-objective optimization-based CICA method with data-driver temporal reference for group fMRI data analysis

Authors: Yuhu Shi, Weiming Zeng, Xiaoyan Tang, Wei Kong, Jun Yin

Published in: Medical & Biological Engineering & Computing | Issue 4/2018

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Abstract

Group independent component analysis (GICA) has been successfully applied to study multi-subject functional magnetic resonance imaging (fMRI) data, and the group independent component (GIC) represents the commonality of all subjects in the group. However, some studies show that the performance of GICA can be improved by incorporating a priori information, which is not always considered when looking for GICs in existing GICA methods. In this paper, we propose an improved multi-objective optimization-based constrained independent component analysis (CICA) method to take advantage of the temporal a priori information extracted from all subjects in the group by incorporating it into the computational process of GICA for group fMRI data analysis. The experimental results of simulated and real data show that the activated regions and the time course detected by the improved CICA method are more accurate in some sense. Moreover, the GIC computed by the improved CICA method has a higher correlation with the corresponding independent component of each subject in the group, which means that the improved CICA method with the temporal a priori information extracted from the group can better reflect the commonality of the subjects. These results demonstrate that the improved CICA method has its own advantages in fMRI data analysis.

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Appendix
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Metadata
Title
An improved multi-objective optimization-based CICA method with data-driver temporal reference for group fMRI data analysis
Authors
Yuhu Shi
Weiming Zeng
Xiaoyan Tang
Wei Kong
Jun Yin
Publication date
02-09-2017
Publisher
Springer Berlin Heidelberg
Published in
Medical & Biological Engineering & Computing / Issue 4/2018
Print ISSN: 0140-0118
Electronic ISSN: 1741-0444
DOI
https://doi.org/10.1007/s11517-017-1716-9

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