Structured Sparse Low-Rank Regression Model for Brain-Wide and Genome-Wide Associations

Medical Image Computing and Computer-assisted Intervention : MICCAI
Xiaofeng ZhuDinggang Shen

Abstract

With the advances of neuroimaging techniques and genome sequences understanding, the phenotype and genotype data have been utilized to study the brain diseases (known as imaging genetics). One of the most important topics in image genetics is to discover the genetic basis of phenotypic markers and their associations. In such studies, the linear regression models have been playing an important role by providing interpretable results. However, due to their modeling characteristics, it is limited to effectively utilize inherent information among the phenotypes and genotypes, which are helpful for better understanding their associations. In this work, we propose a structured sparse low-rank regression method to explicitly consider the correlations within the imaging phenotypes and the genotypes simultaneously for Brain-Wide and Genome-Wide Association (BW-GWA) study. Specifically, we impose the low-rank constraint as well as the structured sparse constraint on both phenotypes and phenotypes. By using the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset, we conducted experiments of predicting the phenotype data from genotype data and achieved performance improvement by 12.75 % on average in terms of the root-mean-square er...Continue Reading

Citations

Oct 27, 2017·Scientific Reports·Lei DuUNKNOWN Alzheimer’s Disease Neuroimaging Initiative

❮ Previous
Next ❯

Related Concepts

Related Feeds

Alzheimer's Disease: Neuroimaging

Neuroimaging can help identify pathological hallmarks of Alzheimer's disease (AD). Here is the latest research on neuroimaging modalities, including magnetic resonance imaging and positron emission tomography, in AD.