Deep Ring-Wise Block Network for Joint Association Analysis and Alzheimer's Disease Diagnosis With Interpretability

In the brain imaging genomic tasks, it is challenging to provide accurate prior knowledge for estimating the association between quantitative traits (QTs) extracted from neuroimaging and genetic markers like single-nucleotide polymorphisms (SNPs). The hidden structural patterns in data limit the discovery of disease-related biomarkers. To this end, we present a deep ring-wise block network (RB-Net) for association analysis and brain disease diagnosis. Specifically, we first construct a new hidden structural pattern, namely, ring-wise block pattern, that satisfies both block and ring properties within the data before the association analysis. Subsequently, a RB-Net is developed via using an auto-encoder (AE) to represent imaging genomic data. Furthermore, we design the approach for joint association learning and automated brain disease diagnosis. Additionally, the optimization scheme based on alternating update is presented for solve the built ring-wise block-perception layer model. The performance of the designed method has been experimentally assessed on the brain imaging genomic data from the Alzheimer's Disease Neuroimaging Initiative (ADNI). The results validate that the proposed approach outperforms some competing approaches, establishes strong associations, and identifies crucial regions of interest (ROIs) across different imaging phenotypes associated with genetic risk biomarkers, thereby guiding disease interpretation and diagnosis prediction.