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

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 GI...

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Published in:Medical & Biological Engineering & Computing Vol. 56; no. 4; pp. 683 - 695
Main Authors: Shi, Yuhu, Zeng, Weiming, Tang, Xiaoyan, Kong, Wei, Yin, Jun
Format: equations & formulas pictorial research tables/charts Journal Article
Published: Springer Nature Apr2018
Online Access:View this record in EBSCOhost
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      jtl: Medical & Biological Engineering & Computing
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      dt: Apr2018
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      pub: Springer Nature
      place: New York, New York
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        10.1007/s11517-017-1716-9
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        atl: An improved multi-objective optimization-based CICA method with data-driver temporal reference for group fMRI data analysis.
      aug:
        au:
          Shi, Yuhu
          Zeng, Weiming
          Tang, Xiaoyan
          Kong, Wei
          Yin, Jun
        affil: Laboratory of Digital Image and Intelligent Computation, Shanghai Maritime University, 1550 Harbor Avenue, Pudong, 201306, Shanghai, China
      sug:
        subj:
          Image Processing, Computer Assisted Methods
          Brain
          Magnetic Resonance Imaging Methods
          Factor Analysis
          Algorithms
          Computer Simulation
          Funding Source
      ab: 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.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        pictorial
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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