Identifying functional subdivisions in the human brain using meta-analytic activation modeling-based parcellation

Yang, Yong, Fan, Lingzhong, Chu, Congying, Zhuo, Junjie, Wang, Jiaojian, Fox, Peter T., Eickhoff, Simon B. and Jiang, Tianzi (2016) Identifying functional subdivisions in the human brain using meta-analytic activation modeling-based parcellation. NeuroImage, 124 Part A: 300-309. doi:10.1016/j.neuroimage.2015.08.027

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Author Yang, Yong
Fan, Lingzhong
Chu, Congying
Zhuo, Junjie
Wang, Jiaojian
Fox, Peter T.
Eickhoff, Simon B.
Jiang, Tianzi
Title Identifying functional subdivisions in the human brain using meta-analytic activation modeling-based parcellation
Journal name NeuroImage   Check publisher's open access policy
ISSN 1053-8119
1095-9572
Publication date 2016-01-01
Year available 2015
Sub-type Article (original research)
DOI 10.1016/j.neuroimage.2015.08.027
Open Access Status File (Author Post-print)
Volume 124
Issue Part A
Start page 300
End page 309
Total pages 10
Place of publication Amsterdam, The Netherlands
Publisher Elsevier
Collection year 2016
Language eng
Formatted abstract
Parcellation of the human brain into fine-grained units by grouping voxels into distinct clusters has been an effective approach for delineating specific brain regions and their subregions. Published neuroimaging studies employing coordinate-based meta-analyses have shown that the activation foci and their corresponding behavioral categories may contain useful information about the anatomical–functional organization of brain regions. Inspired by these developments, we proposed a new parcellation scheme called meta-analytic activation modeling-based parcellation (MAMP) that uses meta-analytically obtained information. The raw meta data, including the experiments and the reported activation coordinates related to a brain region of interest, were acquired from the Brainmap database. Using this data, we first obtained the “modeled activation” pattern by modeling the voxel-wise activation probability given spatial uncertainty for each experiment that featured at least one focus within the region of interest. Then, we processed these “modeled activation” patterns across the experiments with a K-means clustering algorithm to group the voxels into different subregions. In order to verify the reliability of the method, we employed our method to parcellate the amygdala and the left Brodmann area 44 (BA44). The parcellation results were quite consistent with previous cytoarchitectonic and in vivo neuroimaging findings. Therefore, the MAMP proposed in the current study could be a useful complement to other methods for uncovering the functional organization of the human brain.
Keyword Activation modeling
Behavior domain
Meta analysis
Neuroimaging
Parcellation
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ
Additional Notes Published online 18 August 2015

Document type: Journal Article
Sub-type: Article (original research)
Collections: Queensland Brain Institute Publications
Official 2016 Collection
 
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