title: A novel fMRI group data analysis method based on data-driven reference extracting from group subjects.
abstract: 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.
authors.affiliation: Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China.
authors.forename: Yuhu
authors.initials: Y
authors.lastname: Shi
authors.affiliation: Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China. Electronic address: zwmcd@yahoo.com.
authors.forename: Weiming
authors.initials: W
authors.lastname: Zeng
authors.affiliation: Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China.
authors.forename: Nizhuan
authors.initials: N
authors.lastname: Wang
authors.affiliation: Lab of Digital Image and Intelligent Computation, Shanghai Maritime University, Shanghai 201306, China.
authors.forename: Dongtailang
authors.initials: D
authors.lastname: Chen
mesh_terms: D001921:Brain / Q000502:physiology; D001931:Brain Mapping / Q000379:methods*; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male
keywords: A priori information; FastICA; Functional magnetic resonance imaging; Group-independent component analysis; Intrinsic reference