한빛사논문
- 조회525
Ji Hye Wona,b, Jinyoung Younc,d, Hyunjin Parkb,e,*
aDepartment of Electrical and Computer Engineering, Sungkyunkwan University, Suwon, Korea
bCenter for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea
cDepartment of Neurology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea
dNeuroscience Center, Samsung Medical Center, Seoul, Korea
eSchool of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea
*Corresponding author.
Abstract
Neuroimaging genetics is a powerful approach to jointly explore genetic features with rich brain imaging phenotypes for neurodegenerative diseases. Conventional imaging genetics approaches based on canonical correlation analysis cannot accommodate multimodal inputs effectively and have limited interpretability. We propose a novel imaging genetics approach based on non-negative matrix factorization (NMF). By leveraging the parsimonious property known as topic modeling in multi-view NMF, we add sparsity constraints and prior information to identify a sparse set of biologically related features across modalities. Thus, our approach incorporates prior knowledge and improves multimodal integration capabilities and interpretability. We applied our algorithm to simulated and real imaging genetics datasets of Parkinson's disease (PD) for performance evaluation. Our algorithm could identify important associated features mapped to interpretable distinct topics more robustly than other methods. It revealed promising features of single-nucleotide polymorphisms and brain regions related to a subset of PD-related clinical scores in a few topics using a real imaging genetic dataset. The proposed imaging genetics approach can reveal novel associations between genetic and neuroimaging features to improve understanding of various neurodegenerative diseases.
논문정보
- Research article
- 2022년 04월 (BRIC 등록일 2022-04-12)
- 국내 연구진
- 의약학 > 응용의학
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