Journal
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
Volume 122, Issue 3, Pages 362-371Publisher
ELSEVIER IRELAND LTD
DOI: 10.1016/j.cmpb.2015.09.002
Keywords
Group-independent component analysis; Functional magnetic resonance imaging; FastICA; A priori information; Intrinsic reference
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Funding
- National Natural Science Foundation of China [31170952, 31470954]
- Research Fund for the Doctoral Program of Higher Education of China [20113121120004]
- Shanghai Science and Technology project [14590501700]
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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. (C) 2015 Elsevier Ireland Ltd. All rights reserved.
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