Tao, Chenyang, Nichols, Thomas E., Hua, Xue, Ching, Christopher R. K., Rolls, Edmund T., Thompson, Paul M. and Feng, Jianfeng (2016) Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications. NeuroImage, 144 (Part A). pp. 35-57. doi:10.1016/j.neuroimage.2016.08.027 ISSN 1053-8119.

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Abstract

We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging-genetic studies to identify genetic variants that are associated with brain imaging phenotypes, often in the form of high dimensional tensor fields. GRRLF identifies from the structure in the data the effective dimensionality of the data, and then jointly performs dimension reduction of the covariates, dynamic identification of latent factors, and nonparametric estimation of both covariate and latent response fields. After accounting for the latent and covariate effects, GRLLF performs a nonparametric test on the remaining factor of interest. GRRLF provides a better factorization of the signals compared with common solutions, and is less susceptible to overfitting because it exploits the effective dimensionality. The generality and the flexibility of GRRLF also allow various statistical models to be handled in a unified framework and solutions can be efficiently computed. Within the field of neuroimaging, it improves the sensitivity for weak signals and is a promising alternative to existing approaches. The operation of the framework is demonstrated with both synthetic datasets and a real-world neuroimaging example in which the effects of a set of genes on the structure of the brain at the voxel level were measured, and the results compared favorably with those from existing approaches.

Item Type:	Journal Article
Divisions:	Faculty of Science, Engineering and Medicine > Science > Computer Science Faculty of Science, Engineering and Medicine > Science > Statistics
Journal or Publication Title:	NeuroImage
Publisher:	Elsevier
ISSN:	1053-8119
Official Date:	2016
Dates:	Date Event 2016 Published 22 September 2016 Available 14 August 2016 Accepted 26 December 2015 Submitted
Volume:	144
Number:	Part A
Page Range:	pp. 35-57
DOI:	10.1016/j.neuroimage.2016.08.027
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Open Access (Creative Commons)
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