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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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WRAP-powerful-efficient-multivariate-voxel-level-studies-Rolls-2019.pdf - Accepted Version - Requires a PDF viewer. Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0. Download (2399Kb) | Preview |
Official URL: https://doi.org/10.1016/j.neuroimage.2018.12.032
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 | |||||||||||||||||||||
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| 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: |
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| 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: |
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