Exploring brain imaging and genetic risk factors for Alzheimer's disease by using a SnetNMF-based approach

Min Gao; Wei Kong

doi:10.1117/12.3036746

13 July 2024 Exploring brain imaging and genetic risk factors for Alzheimer's disease by using a SnetNMF-based approach

Min Gao, Wei Kong

Proceedings Volume 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024); 132080S (2024) https://doi.org/10.1117/12.3036746
Event: 3rd International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 2024, Nanchang, China

Abstract

Multimodal imaging genetics can integrate imaging data and genetic data to help understand the pathogenesis of Alzheimer's disease (AD) and provide insights for early diagnosis. Non-negative matrix factorization (NMF) has been widely use d in the field of multimodal analysis as an effective joint feature extraction and dimensionality reduction method that can help to reveal underlying structures and patterns among data. However, these methods ignore the importance of the coefficient matrix. Based on this, this paper proposes a sparse network nonnegative matrix factorization (SnetNMF) algorithm based on the sparse constraints of netNMF. By establishing a linear relationship between structural magnetic resonance imaging (sMRI) and corresponding gene expression data under sparse constraint, the SnetNMF algorithm can effectively integrate biological information, thereby better exploring the correlations among data. By optimizing the similarity between modules, SnetNMF also can accurately identify and analyze the modules and structures within the network, thus provi ding a deeper understanding for AD research. Experiments show that the SnetNMF algorithm can accurately identify risk regions (ROIs) and key genes simultaneously for AD, genes such as MMP9, GATA1, ITGA2B, SNCA, HIF1A, etc. RO Is such as l4thVen/r4thVen, rLatVen, etc. Meanwhile, comparison experiments show that the algorithm has better reconstruction performance as well as faster running speed.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Min Gao and Wei Kong "Exploring brain imaging and genetic risk factors for Alzheimer's disease by using a SnetNMF-based approach", Proc. SPIE 13208, Third International Conference on Biomedical and Intelligent Systems (IC-BIS 2024), 132080S (13 July 2024); https://doi.org/10.1117/12.3036746

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KEYWORDS

Matrices

Brain

Genetics

Reconstruction algorithms

Biological research

Alzheimer disease

Genetic algorithms

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