A powerful and efficient multivariate approach for voxel-level connectome-wide association studies
文献类型：期刊论文
| 作者 | Gong, Weikang1,6,7; Rolls, Edmund T.4; Feng, Jianfeng1,4,5; Cheng, Fan5; Cheng, Fan1; Lo, Chun-Yi Zac1; Huang, Chu-Chung2; Yang, Albert C.2; Lin, Ching-Po2; Tsai, Shih-Jen3 |
| 刊名 | NEUROIMAGE
 |
| 出版日期 | 2019 |
| 卷号 | 188期号:-页码:628-641 |
| 关键词 | Multivariate analysis Structured kernel principal component regression Association study Functional connectivity |
| ISSN号 | 1053-8119 |
| DOI | 10.1016/j.neuroimage.2018.12.032 |
| 文献子类 | Article |
| 英文摘要 | We describe an approach to multivariate analysis, termed structured kernel principal component regression (sKPCR), to identify associations in voxel-level connectomes using resting-state functional magnetic resonance imaging (rsfMRI) data. This powerful and computationally efficient multivariate method can identify voxel-phenotype associations based on the whole-brain connectivity pattern of voxels, and it can detect linear and nonlinear signals in both volume-based and surface-based rsfMRI data. For each voxel, sKPCR first extracts low-dimensional signals from the spatially smoothed connectivities by structured kernel principal component analysis, and then tests the voxel-phenotype associations by an adaptive regression model. The method's power is derived from appropriately modelling the spatial structure of the data when performing dimension reduction, and then adaptively choosing an optimal dimension for association testing using the adaptive regression strategy. Simulations based on real connectome data have shown that sKPCR can accurately control the false-positive rate and that it is more powerful than many state-of-the-art approaches, such as the connectivity-wise generalized linear model (GLM) approach, multivariate distance matrix regression (MDMR), adaptive sum of powered score (aSPU) test, and least-square kernel machine (LSKM). Moreover, since sKPCR can reduce the computational cost of non-parametric permutation tests, its computation speed is much faster. To demonstrate the utility of sKPCR for real data analysis, we have also compared sKPCR with the above methods based on the identification of voxel-wise differences between schizophrenic patients and healthy controls in four independent rsfMRI datasets. The results showed that sKPCR had better between-sites reproducibility and a larger proportion of overlap with existing schizophrenia meta-analysis findings. Code for our approach can be downloaded from https://github.com/weikanggong/sKPCR. |
| 学科主题 | Neurosciences & Neurology ; Radiology, Nuclear Medicine & Medical Imaging |
| WOS关键词 | INDEPENDENT COMPONENT ANALYSIS ; RESTING-STATE FMRI ; KERNEL MACHINES ; GENE-EXPRESSION ; BRAIN ; NUMBER ; TESTS ; CONNECTIVITY ; FRAMEWORK ; INFERENCE |
| 语种 | 英语 |
| WOS记录号 | WOS:000460064700055 |
| 出版者 | ACADEMIC PRESS INC ELSEVIER SCIENCE |
| 版本 | 出版稿 |
| 源URL | [http://202.127.25.144/handle/331004/493]  |
| 专题 | 中国科学院上海生命科学研究院营养科学研究所 |
| 作者单位 | 1.Fudan Univ, Inst Sci & Technol Brain Inspired Intelligence, Shanghai 200433, Peoples R China; 2.Natl Yang Ming Univ, Inst Neurosci, Taipei, Taiwan; 3.Taipei Vet Gen Hosp, Dept Psychiat, Taipei, Taiwan, 4.Univ Warwick, Dept Comp Sci, Coventry CV4 7AL, W Midlands, England; 5.Fudan Univ, Shanghai Ctr Math Sci, Shanghai 200433, Peoples R China; 6.Chinese Acad Sci, CAS MPG Partner Inst Computat Biol, Shanghai Inst Biol Sci, Key Lab Computat Biol, Shanghai 200031, Peoples R China; 7.Univ Chinese Acad Sci, Beijing 100049, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Gong, Weikang,Rolls, Edmund T.,Feng, Jianfeng,et al. A powerful and efficient multivariate approach for voxel-level connectome-wide association studies[J]. NEUROIMAGE,2019,188(-):628-641. |
| APA | Gong, Weikang.,Rolls, Edmund T..,Feng, Jianfeng.,Cheng, Fan.,Cheng, Fan.,...&,.(2019).A powerful and efficient multivariate approach for voxel-level connectome-wide association studies.NEUROIMAGE,188(-),628-641. |
| MLA | Gong, Weikang,et al."A powerful and efficient multivariate approach for voxel-level connectome-wide association studies".NEUROIMAGE 188.-(2019):628-641. |
入库方式： OAI收割
来源：上海营养与健康研究所
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