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Enhanced neuroimaging genetics using multi-view non-negative matrix factorization with sparsity and prior knowledge

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Title
Enhanced neuroimaging genetics using multi-view non-negative matrix factorization with sparsity and prior knowledge
Author(s)
Ji Hye Won; Youn, Jinyoung; Hyunjin Park
Publication Date
2022-04
Journal
Medical Image Analysis, v.77
Publisher
Elsevier B.V.
Abstract
© 2022 Elsevier B.V.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.
URI
https://pr.ibs.re.kr/handle/8788114/11176
DOI
10.1016/j.media.2022.102378
ISSN
1361-8415
Appears in Collections:
Center for Neuroscience Imaging Research (뇌과학 이미징 연구단) > 1. Journal Papers (저널논문)
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