A novel fMRI group data analysis method based on data-driven reference extracting from group subjects

• Published in: Computer methods and programs in biomedicine Vol. 122; no. 3; pp. 362 - 371
• Main Authors: Shi, Yuhu, Zeng, Weiming, Wang, Nizhuan, Chen, Dongtailang
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier Ireland Ltd 01.12.2015
• Subjects: A priori information; Brain - physiology; Brain Mapping - methods; FastICA; Female; Functional magnetic resonance imaging; Group-independent component analysis; Humans; Image Interpretation, Computer-Assisted - methods; Internal Medicine; Intrinsic reference; Magnetic Resonance Imaging - methods; Male; Other; Intrinsic reference; Group-independent component analysis; Functional magnetic resonance imaging; A priori information; FastICA
• DOI: 10.1016/j.cmpb.2015.09.002
• ISSN: 0169-2607

•We presented a novel method to extract group intrinsic reference from all subjects in a group.•A new group ICA model with intrinsic reference (GICA-IR) was further proposed for fMRI data analysis.•GICA-IR was shown to better reflect the commonness of subjects in the group. Group-independent component analysis (GICA) is a well-established blind source separation technique that has been widely applied to study multi-subject functional magnetic resonance imaging (fMRI) data. The group-independent components (GICs) represent the commonness of all of the subjects in the group. Similar to independent component analysis on the single-subject level, the performance of GICA can be improved for multi-subject fMRI data analysis by incorporating a priori information; however, a priori information is not always considered while looking for GICs in existing GICA methods, especially when no obvious or specific knowledge about an unknown group is available. In this paper, we present a novel method to extract the group intrinsic reference from all of the subjects of the group and then incorporate it into the GICA extraction procedure. Comparison experiments between FastICA and GICA with intrinsic reference (GICA-IR) are implemented on the group level with regard to the simulated, hybrid and real fMRI data. The experimental results show that the GICs computed by GICA-IR have a higher correlation with the corresponding independent component of each subject in the group, and the accuracy of activation regions detected by GICA-IR was also improved. These results have demonstrated the advantages of the GICA-IR method, which can better reflect the commonness of the subjects in the group.