Feature-reduction and semi-simulated data in functional connectivity-based cortical parcellation
文献类型：期刊论文
| 作者 | Tian XG2; Liu CR2,5; Hu XT[*]1,2,3,6; Jiang TZ4,5; Rizak JH2; Ma YY[*]1,2,3,6 |
| 刊名 | NEUROSCIENCE BULLETIN
 |
| 出版日期 | 2013 |
| 卷号 | 29期号:3页码:333-347 |
| 关键词 | cortical parcellation resting-state fMRI functional connectivity feature reduction AP algorithm |
| 通讯作者 | yuanma0716@vip.sina.com ; xthu@mail.kiz.ac.cn |
| 合作状况 | 其它 |
| 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. |
| 收录类别 | SCI |
| 资助信息 | This work was supported by the National Basic Research Development Program (973 Program) of China (2012CBA01304, 2011CB707800), the National High Technology Research and Development Program (863 Program) of China (2012AA020701), the National Natural Science Foundation of China (31271167, 31271168, 81271495, 31070963, 31070965), the Strategic Priority Research Program of the Chinese Academy of Science, China (XDB02020500), and the Development and Reform Project of Yunnan Province, China. |
| 语种 | 英语 |
| 源URL | [http://159.226.149.26:8080/handle/152453/9572]  |
| 专题 | 昆明动物研究所_认知障碍病理学 昆明动物研究所_神经系统编码 |
| 作者单位 | 1.Yunnan Key Lab of Primate Biomedical Research, China 2.Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, China 3.State Key Laboratory of Brain and Cognitive Science, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China 4.LIAMA Center for Computational Medicine, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China 5.The University of Queensland, Queensland Brain Institute, QLD 4072, Australia 6.Kunming Bio-International |
| 推荐引用方式 GB/T 7714 | Tian XG,Liu CR,Hu XT[*],et al. Feature-reduction and semi-simulated data in functional connectivity-based cortical parcellation[J]. NEUROSCIENCE BULLETIN,2013,29(3):333-347. |
| APA | Tian XG,Liu CR,Hu XT[*],Jiang TZ,Rizak JH,&Ma YY[*].(2013).Feature-reduction and semi-simulated data in functional connectivity-based cortical parcellation.NEUROSCIENCE BULLETIN,29(3),333-347. |
| MLA | Tian XG,et al."Feature-reduction and semi-simulated data in functional connectivity-based cortical parcellation".NEUROSCIENCE BULLETIN 29.3(2013):333-347. |
入库方式： OAI收割
来源：昆明动物研究所
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