Application of orthogonal sparse joint non-negative matrix factorization based on connectivity in Alzheimer's disease research

Wei Kong; Feifan Xu; Shuaiqun Wang; Kai Wei; Gen Wen; Yaling Yu; Wei Kong; Feifan Xu; Shuaiqun Wang; Kai Wei; Gen Wen; Yaling Yu

doi:10.3934/mbe.2023435

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 6: 9923-9947. doi: 10.3934/mbe.2023435

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Application of orthogonal sparse joint non-negative matrix factorization based on connectivity in Alzheimer's disease research

1.
College of Information Engineering, Shanghai Maritime University, Shanghai 201306, China
2.
Bio-Med Big Data Center, CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai, China
3.
Department of Orthopedic Surgery, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China
4.
Institute of Microsurgery on Extremities, Shanghai Jiao Tong University Affiliated Sixth People's Hospital, Shanghai 200233, China

† These two authors contributed equally.
Academic Editor: Shangce Gao

Received: 27 December 2022 Revised: 19 February 2023 Accepted: 21 February 2023 Published: 27 March 2023

Based on the mining of micro- and macro-relationships of genetic variation and brain imaging data, imaging genetics has been widely applied in the early diagnosis of Alzheimer's disease (AD). However, effective integration of prior knowledge remains a barrier to determining the biological mechanism of AD. This paper proposes a new connectivity-based orthogonal sparse joint non-negative matrix factorization (OSJNMF-C) method based on integrating the structural magnetic resonance image, single nucleotide polymorphism and gene expression data of AD patients; the correlation information, sparseness, orthogonal constraint and brain connectivity information between the brain image data and genetic data are designed as constraints in the proposed algorithm, which efficiently improved the accuracy and convergence through multiple iterative experiments. Compared with the competitive algorithm, OSJNMF-C has significantly smaller related errors and objective function values than the competitive algorithm, showing its good anti-noise performance. From the biological point of view, we have identified some biomarkers and statistically significant relationship pairs of AD/mild cognitive impairment (MCI), such as rs75277622 and BCL7A, which may affect the function and structure of multiple brain regions. These findings will promote the prediction of AD/MCI.
- image genetics,
- Alzheimer's disease,
- structural magnetic resonance imaging,
- non-negative matrix factorization
Citation: Wei Kong, Feifan Xu, Shuaiqun Wang, Kai Wei, Gen Wen, Yaling Yu. Application of orthogonal sparse joint non-negative matrix factorization based on connectivity in Alzheimer's disease research[J]. Mathematical Biosciences and Engineering, 2023, 20(6): 9923-9947. doi: 10.3934/mbe.2023435

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Abstract

Based on the mining of micro- and macro-relationships of genetic variation and brain imaging data, imaging genetics has been widely applied in the early diagnosis of Alzheimer's disease (AD). However, effective integration of prior knowledge remains a barrier to determining the biological mechanism of AD. This paper proposes a new connectivity-based orthogonal sparse joint non-negative matrix factorization (OSJNMF-C) method based on integrating the structural magnetic resonance image, single nucleotide polymorphism and gene expression data of AD patients; the correlation information, sparseness, orthogonal constraint and brain connectivity information between the brain image data and genetic data are designed as constraints in the proposed algorithm, which efficiently improved the accuracy and convergence through multiple iterative experiments. Compared with the competitive algorithm, OSJNMF-C has significantly smaller related errors and objective function values than the competitive algorithm, showing its good anti-noise performance. From the biological point of view, we have identified some biomarkers and statistically significant relationship pairs of AD/mild cognitive impairment (MCI), such as rs75277622 and BCL7A, which may affect the function and structure of multiple brain regions. These findings will promote the prediction of AD/MCI.

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