SCGICAR: Spatial concatenation based group ICA with reference for fMRI data analysis.BACKGROUND AND OBJECTIVE
 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.  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.  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.
SCGICAR
SCGICAR: Spatial concatenation based group ICA with reference for fMRI data analysis.BACKGROUND AND OBJECTIVE

Spatial concatenation based group ICA with reference for fMRI data analysis
SCGICAR: Spatial concatenation based group ICA with reference for fMRI data analysis.BACKGROUND AND OBJECTIVE
 As a kind of blind source separation technique, group independent component analysis (GICA) has been widely applied for the multi-subject fMRI data analysis.  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.  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. 
fMRI data analysis
As a kind of blind source separation technique, group independent component analysis (GICA) has been widely applied for the multi-subject fMRI data analysis. 
group independent component analysis (GICA)
As a kind of blind source separation technique, group independent component analysis (GICA) has been widely applied for the multi-subject fMRI data analysis.  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. 
multi-subject fMRI data analysis
However, spatial concatenated GICA is rarely used compared with temporal concatenated GICA due to its disadvantages.



temporal concatenated GICA
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.



principal component analysis
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.



anti-reconstruction