Co-activation Probability Estimation (CoPE): An approach for modeling functional co-activation architecture based on neuroimaging coordinates

	Co-activation Probability Estimation (CoPE): An approach for modeling functional co-activation architecture based on neuroimaging coordinates
	Chu, Congying1,2 ; Fan, Lingzhong1,2 ; Eickhoff, Claudia R.3,5; Liu, Yong1,2 ; Yang, Yong1,2 ; Eickhoff, Simon B.3,4; Jiang, Tianzi1,2,6,7 ; Tianzi Jiang
发表期刊	NEUROIMAGE
	2015-08-15
卷号	117 期号:69 页码:397-407
文章类型	Article
摘要	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. (C) 2015 Elsevier Inc. All rights reserved.
关键词	Cope Coordinate-based Meta-analysis Functional Co-activation Working Memory
WOS标题词	Science & Technology ; Life Sciences & Biomedicine
关键词[WOS]	SPATIAL WORKING-MEMORY ; DENSITY-FUNCTION ; HUMAN BRAIN ; METAANALYSIS ; ACTIVATION ; CONNECTIVITY ; SYSTEM ; NEUROANATOMY ; SELECTION ; NETWORKS
收录类别	SCI
语种	英语
WOS研究方向	Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Neurosciences ; Neuroimaging ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:000358045100037
引用统计	被引频次：7[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/8874
专题	脑图谱与类脑智能实验室_脑网络组研究
通讯作者	Tianzi Jiang
作者单位	1.Chinese Acad Sci, Inst Automat, Brainnetome Ctr, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 3.Res Ctr Juelich, Inst Neurosci & Med INM 1, D-52425 Julich, Germany 4.Univ Dusseldorf, Inst Clin Neurosci & Med Psychol, D-40225 Dusseldorf, Germany 5.Rhein Westfal TH Aachen, Dept Psychiat Psychotherapy & Psychosomat, D-52074 Aachen, Germany 6.Univ Queensland, Queensland Brain Inst, St Lucia, Qld 4072, Australia 7.Chinese Acad Sci, Inst Automat, CAS Ctr Excellence Brain Sci, Beijing 100190, Peoples R China
第一作者单位	中国科学院自动化研究所; 模式识别国家重点实验室
推荐引用方式 GB/T 7714	Chu, Congying,Fan, Lingzhong,Eickhoff, Claudia R.,et al. Co-activation Probability Estimation (CoPE): An approach for modeling functional co-activation architecture based on neuroimaging coordinates[J]. NEUROIMAGE,2015,117(69):397-407.
APA	Chu, Congying.,Fan, Lingzhong.,Eickhoff, Claudia R..,Liu, Yong.,Yang, Yong.,...&Tianzi Jiang.(2015).Co-activation Probability Estimation (CoPE): An approach for modeling functional co-activation architecture based on neuroimaging coordinates.NEUROIMAGE,117(69),397-407.
MLA	Chu, Congying,et al."Co-activation Probability Estimation (CoPE): An approach for modeling functional co-activation architecture based on neuroimaging coordinates".NEUROIMAGE 117.69(2015):397-407.

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