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

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Citation

Huang, L., Zhou, G., Liu, Z., Dang, X., Yang, Z., Kong, X., et al. (2016). A Multi-Atlas Labeling Approach for Identifying Subject-Specific Functional Regions of Interest. PLoS One, 11(1): e0146868. doi:10.1371/journal.pone.0146868.


Cite as: http://hdl.handle.net/11858/00-001M-0000-002B-5A60-E
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.