Co-activation probability estimation (CoPE): an approach for modeling functional co-activation architecture based on neuroimaging coordinates

Chu, Congying, Fan, Lingzhong, Eickhoff, Claudia R., Liu, Yong, Yang, Yong, Eickhoff, Simon B. and Jiang, Tianzi (2015) Co-activation probability estimation (CoPE): an approach for modeling functional co-activation architecture based on neuroimaging coordinates. NeuroImage, 117 397-407. doi:10.1016/j.neuroimage.2015.05.069


Author Chu, Congying
Fan, Lingzhong
Eickhoff, Claudia R.
Liu, Yong
Yang, Yong
Eickhoff, Simon B.
Jiang, Tianzi
Title Co-activation probability estimation (CoPE): an approach for modeling functional co-activation architecture based on neuroimaging coordinates
Journal name NeuroImage   Check publisher's open access policy
ISSN 1095-9572
1053-8119
Publication date 2015-08-15
Sub-type Article (original research)
DOI 10.1016/j.neuroimage.2015.05.069
Volume 117
Start page 397
End page 407
Total pages 11
Place of publication Amsterdam, Netherlands
Publisher Elsevier
Collection year 2016
Language eng
Abstract Recent progress in functional neuroimaging has prompted studies of brain activation during various cognitive tasks. Coordinate-based meta-analysis has been utilized to discover the brain regions that are consistently activated across experiments. However, within-experiment co-activation relationships, which can reflect the underlying functional relationships between different brain regions, have not been widely studied. In particular, voxel-wise co-activation, which may be able to provide a detailed configuration of the co-activation network, still needs to be modeled. To estimate the voxel-wise co-activation pattern and deduce the co-activation network, a Co-activation Probability Estimation (CoPE) method was proposed to model within-experiment activations for the purpose of defining the co-activations. A permutation test was adopted as a significance test. Moreover, the co-activations were automatically separated into local and long-range ones, based on distance. The two types of co-activations describe distinct features: the first reflects convergent activations; the second represents co-activations between different brain regions. The validation of CoPE was based on five simulation tests and one real dataset derived from studies of working memory. Both the simulated and the real data demonstrated that CoPE was not only able to find local convergence but also significant long-range co-activation. In particular, CoPE was able to identify a ‘core’ co-activation network in the working memory dataset. As a data-driven method, the CoPE method can be used to mine underlying co-activation relationships across experiments in future studies.
Keyword Coordinate-based meta-analysis
CoPE
Functional co-activation
Working memory
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

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