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Updated: Oct 5, 2025
Basics of Multivariate Analysis in Neuroimaging Data
Published on: July 24, 2010
Detecting Biomarkers of Alzheimer's Disease Based on Multi-constrained Uncertainty-Aware Adaptive Sparse Multi-view
Wenbo Wang1, Wei Kong2, Shuaiqun Wang1
1College of Information Engineering, Shanghai Maritime University, 1550 Haigang Ave., Shanghai, 201306, People's Republic of China.
Abstract:
Image genetics mainly explores the pathogenesis of Alzheimer's disease (AD) by studying the relationship between genetic data (such as SNP, gene expression data, and DNA methylation) and imaging data (such as structural MRI (sMRI), fMRI, and PET). Most of the existing research on brain imaging genomics uses two-way or three-way bi-multivariate methods to explore the correlation analysis between genes and brain imaging. However, many of these methods are still affected by the gradient domination or cannot take into account the effect of feature redundancy on the results, so that the typical correlation coefficient and program running speed are not significantly improved. In order to solve the above problems, this paper proposes a multi-constrained uncertainty-aware adaptive sparse multi-view canonical correlation analysis method (MC-unAdaSMCCA) to explore associations among SNPs, gene expression data, and sMRI; that is, based on traditional unAdaSMCCA, orthogonal constraints are imposed on the weights of the three data features through linear programming, which can reduce the redundancy of feature weights to improve the correlation between the data and reduce the complexity of the algorithm to significantly speed up the running speed of the program. Three adaptive sparse multi-view canonical correlation analysis methods are used as benchmarks to evaluate the difference between real neuroimaging data and synthetic data. Compared with the other three methods, our proposed method has obtained better or comparable typical correlation coefficients and typical weights. Moreover, the following experimental results show that the MC-unAdaSMCCA method cannot only identify biomarkers related to AD and mild cognitive impairment (MCI), but also has a strong ability to resist noise and process high-dimensional data. Therefore, our proposed method provides a reliable approach to multi-modal imaging genetic researches.
