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

作者：Shi, Yuhu; Zeng, Weiming^*; Tang, Xiaoyan; Kong, Wei; Yin, Jun

来源：Medical & Biological Engineering & Computing, 2018, 56(4): 683-694.

DOI：10.1007/s11517-017-1716-9

• fMRI
• GICA
• CICA
• Temporal a priori information
• Multi-objective optimization

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.

• 出版日期2018-4
• 单位中国人民解放军信息工程大学; 上海海事大学