A Multi-Atlas Labeling Approach for Identifying Subject-Specific Functional Regions of Interest

Lijie Huang, Guangfu Zhou, Zhaoguo Liu, Xiaobin Dang, Zetian Yang, Xiang-Zhen Kong, Xu Wang, Yiying Song, Zonglei Zhen and Jia Liu

PLOS ONE, 2016, vol. 11, issue 1, 1-17

Abstract: The functional region of interest (fROI) approach has increasingly become a favored methodology in functional magnetic resonance imaging (fMRI) because it can circumvent inter-subject anatomical and functional variability, and thus increase the sensitivity and functional resolution of fMRI analyses. The standard fROI method requires human experts to meticulously examine and identify subject-specific fROIs within activation clusters. This process is time-consuming and heavily dependent on experts’ knowledge. Several algorithmic approaches have been proposed for identifying subject-specific fROIs; however, these approaches cannot easily incorporate prior knowledge of inter-subject variability. In the present study, we improved the multi-atlas labeling approach for defining subject-specific fROIs. In particular, we used a classifier-based atlas-encoding scheme and an atlas selection procedure to account for the large spatial variability across subjects. Using a functional atlas database for face recognition, we showed that with these two features, our approach efficiently circumvented inter-subject anatomical and functional variability and thus improved labeling accuracy. Moreover, in comparison with a single-atlas approach, our multi-atlas labeling approach showed better performance in identifying subject-specific fROIs.

Date: 2016
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0146868

DOI: 10.1371/journal.pone.0146868

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