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A powerful and efficient multivariate approach for voxel-level connectome-wide association studies

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Gong, Weikang, Cheng, Fan, Rolls, Edmund T., Lo, Chun-Yi Zac, Huang, Chu-Chung, Tsai, Shih-Jen, Yang, Albert C, Lin, Ching-Po and Feng, Jianfeng (2019) A powerful and efficient multivariate approach for voxel-level connectome-wide association studies. NeuroImage, 188 . pp. 628-641. doi:10.1016/j.neuroimage.2018.12.032

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Official URL: https://doi.org/10.1016/j.neuroimage.2018.12.032

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Abstract

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 non-linear 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. [Abstract copyright: Copyright © 2018 Elsevier Inc. All rights reserved.]

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
Divisions: Faculty of Science > Computer Science
SWORD Depositor: Library Publications Router
Library of Congress Subject Headings (LCSH): Multivariate analysis, Phenotype, Magnetic resonance imaging, Regression analysis
Journal or Publication Title: NeuroImage
Publisher: Elsevier
ISSN: 1053-8119
Official Date: March 2019
Dates:
DateEvent
March 2019Published
18 December 2018Available
14 December 2018Accepted
Volume: 188
Page Range: pp. 628-641
DOI: 10.1016/j.neuroimage.2018.12.032
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
2015AA020507[MSTPRC] Ministry of Science and Technology of the People's Republic of Chinahttp://dx.doi.org/10.13039/501100002855
91230201[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
1661167002[NSFC] National Natural Science Foundation of Chinahttp://dx.doi.org/10.13039/501100001809
15JC1400101Science and Technology Commission of Shanghai Municipalityhttp://dx.doi.org/10.13039/501100003399
15692106604Science and Technology Commission of Shanghai Municipalityhttp://dx.doi.org/10.13039/501100003399
NCMIS [CAS] Chinese Academy of Scienceshttp://dx.doi.org/10.13039/501100002367

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