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

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.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-5A60-E Version Permalink: http://hdl.handle.net/11858/00-001M-0000-002B-5A75-F
Genre: Journal Article

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© 2016 Huang et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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 Creators:
Huang, Lijie1, Author
Zhou, Guangfu1, Author
Liu, Zhaoguo1, Author
Dang, Xiaobin1, Author
Yang, Zetian1, Author
Kong, Xiangzhen1, Author              
Wang, Xu1, Author
Song, Yiying1, Author
Zhen, Zonglei1, Author
Liu, Jia2, Author
Affiliations:
1State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, ou_persistent22              
2Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, ou_persistent22              

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 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.

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Language(s): eng - English
 Dates: 2016
 Publication Status: Published in print
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 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1371/journal.pone.0146868
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Title: PLoS One
Source Genre: Journal
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Publ. Info: San Francisco, CA : Public Library of Science
Pages: - Volume / Issue: 11 (1) Sequence Number: e0146868 Start / End Page: - Identifier: ISSN: 1932-6203
CoNE: https://pure.mpg.de/cone/journals/resource/1000000000277850