Fused multi-modal similarity network as prior in guiding brain imaging genetic association - He, Bing; Xie, Linhui; Varathan, Pradeep; Nho, Kwangsik; Risacher, Shannon L.; Saykin, Andrew J.; Yan, Jingwen; , The Alzheimer's Disease Neuroimaging Initiative (Frontiers in Big Data, 2023)
Brain imaging genetics aims to explore the genetic architecture underlying brain structure and functions. Recent studies showed that incorporation of prior knowledge, such as subject diagnosis information and brain regional correlation, can help identify significantly stronger imaging genetic associations. However, sometimes such information may be incomplete or even unavailable. In this project, we explore a new data-driven prior knowledge that captures the subject-level similarity by fusin multi-modal similarity networks. It was incorporated into the sparse canonical correlation analysis (SCCA) model, aimed to identify a small set of brain imaging and genetic markers that explain the similarity matrix supported by both modalities. It was applied to Amyloid and Tau imaging data of the ADNI cohort respectively. Fused similarity matrix across imaging and genetic data was found to improve the association performance better or similarly well as diagnosis information, and therefore would be a potential substitute prior when diagnosis information is not available (i.e., studies focused on healthy controls).