Feature-reduction and semi-simulated data in functional connectivity-based cortical parcellation

	Feature-reduction and semi-simulated data in functional connectivity-based cortical parcellation
	Tian, Xiaoguang 2; Liu, Cirong 2,5; Jiang, Tianzi4,5 ; Rizak, Joshua 2; Ma, Yuanye 1,2,3,6; Hu, Xintian 1,2,3,6
刊名	NEUROSCIENCE BULLETIN
	2013-06-01
卷号	29 期号:3 页码:333-347
关键词	cortical parcellation resting-state fMRI functional connectivity feature reduction stimulated data AP algorithm
英文摘要	Recently, resting-state functional magnetic resonance imaging has been used to parcellate the brain into functionally distinct regions based on the information available in functional connectivity maps. However, brain voxels are not independent units and adjacent voxels are always highly correlated, so functional connectivity maps contain redundant information, which not only impairs the computational efficiency during clustering, but also reduces the accuracy of clustering results. The aim of this study was to propose feature-reduction approaches to reduce the redundancy and to develop semi-simulated data with defined ground truth to evaluate these approaches. We proposed a feature-reduction approach based on the Affinity Propagation Algorithm (APA) and compared it with the classic featurereduction approach based on Principal Component Analysis (PCA). We tested the two approaches to the parcellation of both semi-simulated and real seed regions using the K-means algorithm and designed two experiments to evaluate their noiseresistance. We found that all functional connectivity maps (with/without feature reduction) provided correct information for the parcellation of the semisimulated seed region and the computational efficiency was greatly improved by both featurereduction approaches. Meanwhile, the APA-based feature-reduction approach outperformed the PCAbased approach in noise-resistance. The results suggested that functional connectivity maps can provide correct information for cortical parcellation, and feature-reduction does not significantly change the information. Considering the improvement in computational efficiency and the noise-resistance, feature-reduction of functional connectivity maps before cortical parcellation is both feasible and necessary.
WOS标题词	Science & Technology ; Life Sciences & Biomedicine
类目[WOS]	Neurosciences
研究领域[WOS]	Neurosciences & Neurology
关键词[WOS]	HUMAN CEREBRAL-CORTEX ; RESTING-STATE FMRI ; HUMAN BRAIN ; MRI ; VISUALIZATION ; ORGANIZATION ; ARCHITECTURE ; PRECUNEUS ; COCOMAC
收录类别	SCI
语种	英语
WOS记录号	WOS:000319359400009
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/3215]
专题	自动化研究所_脑网络组研究中心
作者单位	1.Yunnan Key Lab Primate Biomed Res, Kunming, Peoples R China 2.Chinese Acad Sci, Kunming Inst Zool, Kunming, Peoples R China 3.Chinese Acad Sci, Inst Biophys, State Key Lab Brain & Cognit Sci, Beijing 100080, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, LIAMA Ctr Computat Med, Beijing, Peoples R China 5.Univ Queensland, Queensland Brain Inst, Brisbane, Qld 4072, Australia 6.Kunming Bioint, Kunming, Peoples R China
推荐引用方式 GB/T 7714	Tian, Xiaoguang,Liu, Cirong,Jiang, Tianzi,et al. Feature-reduction and semi-simulated data in functional connectivity-based cortical parcellation[J]. NEUROSCIENCE BULLETIN,2013,29(3):333-347.
APA	Tian, Xiaoguang,Liu, Cirong,Jiang, Tianzi,Rizak, Joshua,Ma, Yuanye,&Hu, Xintian.(2013).Feature-reduction and semi-simulated data in functional connectivity-based cortical parcellation.NEUROSCIENCE BULLETIN,29(3),333-347.
MLA	Tian, Xiaoguang,et al."Feature-reduction and semi-simulated data in functional connectivity-based cortical parcellation".NEUROSCIENCE BULLETIN 29.3(2013):333-347.