Integrating Imaging and Genetics Data for Improved Understanding and Detection of Alzheimer's Disease

Abstract

Alzheimer’s disease (AD) is a progressive and irreversible brain disorder characterized by a slow and intricate progression, in which the initial pathological changes occur long before noticeable symptoms. AD is highly heritable and genetic factors play an essential role in AD development. Large scale genome-wide association studies have identified numerous SNPs related to AD. However, our understanding of the connections between genetics findings and altered brain phenotype is still limited. Brain imaging genetics, an emerging approach, aims to investigate the relationship between genetic variations and brain structure or function. It has great potential to provide insights into the underlying biological mechanisms and to enable the early detection of AD. Our study aimed to develop and apply novel computational approaches for more robust discovery of imaging genetics associations and for improved detection of AD in early stage. Specifically, we focused on addressing the heterogeneity problem inherent in integrating imaging and genetics data. In aim 1, we applied a novel biclustering method to associate genetic variations with functional brain connectivity altered in AD patients. In aim 2, we proposed novel strategy to integrate imaging and genetic data to serve as a new type of prior knowledge and investigated their role in guiding imaging genetics association. Finally, in aim 3, we proposed a multi-factorial pseudotime approach to integrate heterogeneous genotype and amyloid imaging data and examined its potential for staging and early detection of AD. Collectively, results from these objectives aimed to enhance our understanding and detection of AD, providing valuable information to inform therapeutic strategies to slow or halt disease progression.