Exploring Imaging Genetic Markers of Alzheimer ’s Disease Based on a Novel Nonlinear Correlation Analysis Algorithm

AbstractAlzheimer ’s disease (AD) is an irreversible neurological disorder characterized by insidious onset. Identifying potential markers in its emergence and progression is crucial for early diagnosis and treatment. Imaging genetics typically merges genetic variables with multiple imaging parameters, employing va rious association analysis algorithms to investigate the links between pathological phenotypes and genetic variations, and to unearth molecular-level insights from brain images. However, most existing imaging genetics algorithms based on sparse learning assume a linear relationship between genetic f actors and brain functions, limiting their ability to discern complex nonlinear correlation patterns and resulting in reduced accuracy. To address these issues, we propose a novel nonlinear imaging genetic association analysis method, Deep Self-Reconstruction-based Adaptive Sparse Multi-view Deep Ge neralized Canonical Correlation Analysis (DSR-AdaSMDGCCA). This approach facilitates joint learning of the nonlinear relationships between pathological phenotypes and genetic variations by integrating three different types of data: structural magnetic resonance imaging (sMRI), single-nucleotide poly morphism (SNP), and gene expression data. By incorporating nonlinear transformations in DGCCA, our model effectively uncovers nonlinear associations across multiple data types. Additionally, the DSR algorithm clusters samples with identical labels, incorporating label informati...
Source: Journal of Molecular Neuroscience - Category: Neuroscience Source Type: research