Loading [a11y]/accessibility-menu.js
Identify Complex Imaging Genetic Patterns via Fusion Self-Expressive Network Analysis | IEEE Journals & Magazine | IEEE Xplore

Identify Complex Imaging Genetic Patterns via Fusion Self-Expressive Network Analysis


Abstract:

In the brain imaging genetic studies, it is a challenging task to estimate the association between quantitative traits (QTs) extracted from neuroimaging data and genetic ...Show More

Abstract:

In the brain imaging genetic studies, it is a challenging task to estimate the association between quantitative traits (QTs) extracted from neuroimaging data and genetic markers such as single-nucleotide polymorphisms (SNPs). Most of the existing association studies are based on the extensions of sparse canonical correlation analysis (SCCA) for the identification of complex bi-multivariate associations, which can take the specific structure and group information into consideration. However, they often take the original data as input without considering its underlying complex multi-subspace structure, which will deteriorate the performance of the following integrative analysis. Accordingly, in this paper, the self-expressive property is exploited for the reconstruction of the original data before the association analysis, which can well describe the similarity structure. Specifically, we first apply the within-class similarity information to construct self-expressive networks by sparse representation. Then, we use the fusion method to iteratively fuse the self-expressive networks from multi-modality brain phenotypes into one network. Finally, we calculate the imaging genetic association based on the fused self-expressive network. We conduct the experiments on both single-modality and multi-modality phenotype data. Related experimental results validate that our method can not only better estimate the potential association between genetic markers and quantitative traits but also identify consistent multi-modality imaging genetic biomarkers to guide the interpretation of Alzheimer’s disease.
Published in: IEEE Transactions on Medical Imaging ( Volume: 40, Issue: 6, June 2021)
Page(s): 1673 - 1686
Date of Publication: 04 March 2021

ISSN Information:

PubMed ID: 33661732

Funding Agency:


References

References is not available for this document.