Abstract

Background: With the development of brain imaging technology and genotyping technology, the brain imaging genetic method has become a powerful means to investigate the pathogenesis of Alzheimer's disease (AD). However, AD generally exhibits progression, multiplicity, and intricacy, and different diagnostic groups may carry different biomarkers. At the same time, traditional models often ignore the nonlinear relationship and inherent topological characteristics of brain imaging genetic data.

Objective: Therefore, developing a more reliable method to identify diagnosis-specific genotypes and phenotypes is indispensable for exploring the pathogenesis of AD. In this paper, a novel deep self-reconstruction multitask association analysis (DS-MTAA) method is proposed for AD-related biomarkers extraction and AD classification.

Methods: First, a deep neural network is designed to learn the nonlinear relationships between samples. Also, the self-expression idea based on hypergraph regularization is utilized to perform subspace clustering on the output of the network. Then, a multitask model consisting of sparse canonical correlation analysis and regular logistic regression is constructed, in which each task is responsible for learning a diagnosis-specific genotype-phenotype pattern.

Results: Finally, the RobustBoost classifier is employed to perform the classification experiments under 5-fold cross-validations. The experimental results show that DS-MTAA can achieve better classification performance than other advanced comparison methods and identify more effective brain biomarkers and genetic markers that are strongly associated with diseases.

Conclusion: Therefore, it can be concluded that a novel multitask association analysis model with deep self-reconstruction for the diagnosis of Alzheimer’s Disease can further understand the pathogenesis of AD.

Keywords: Alzheimer’s disease, brain imaging genetics, deep self-reconstruction, hypergraph regularization, sparse canonical correlation analysis