SCGICAR: Spatial concatenation based group ICA with reference for fMRI data analysis. (September 2017)

• Record Type: Journal Article
• Authors: Shi, Yuhu; Zeng, Weiming; Wang, Nizhuan
• Is Part Of: Computer methods and programs in biomedicine. Volume 148(2017)
• Page Start: 137
• Page End: 151
• Publication Date: 2017-09
• Subjects: fMRI -- ICA -- PCA -- Spatial concatenation -- Multi-objective optimization -- Post-processing; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2017.07.001
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A novel SCGICAR method was proposed based on multi-objective optimization. PCA was used to extract the temporal priori information from group subjects. The post-processing means were adopted to obtain group spatial component and individual temporal component. The functional connectivity detection ability of SCGICAR has been improved on both single-subject and group-subjects levels. Abstract: Background and Objective: With the rapid development of big data, the functional magnetic resonance imaging (fMRI) data analysis of multi-subject is becoming more and more important. As a kind of blind source separation technique, group independent component analysis (GICA) has been widely applied for the multi-subject fMRI data analysis. However, spatial concatenated GICA is rarely used compared with temporal concatenated GICA due to its disadvantages. Methods: In this paper, in order to overcome these issues and to consider that the ability of GICA for fMRI data analysis can be improved by adding a priori information, we propose a novel spatial concatenation based GICA with reference (SCGICAR) method to take advantage of the priori information extracted from the group subjects, and then the multi-objective optimization strategy is used to implement this method. Finally, the post-processing means of principal component analysis and anti-reconstruction are used to obtain group spatial component and individual temporal component in the group, respectively. Results: The experimental results show that the proposed SCGICAR method has a better performance on both single-subject and multi-subject fMRI data analysis compared with classical methods. It not only can detect more accurate spatial and temporal component for each subject of the group, but also can obtain a better group component on both temporal and spatial domains. Conclusions: These results demonstrate that the proposed SCGICAR method has its own advantages in comparison with classical methods, and it can better reflect the commonness of subjects in the group.