Exploring Complex Genetic Mechanisms in Brain Imaging Genetics via a New Multi-Task Learning Method.

Brain imaging genetics generally combines genotype data with brain structure and functional measures to investigate the genetic basis of neurological disorders. Multimodal brain imaging data carry different but complementary information, which can clearly measure the same brain. Multi-task sparse canonical correlation analysis (MTSCCA) is often used to analyze the bi-multivariate correlation between genotype data and multimodal imaging features; however, it lacks the capacity to identify class-specific biomarkers and to deeply capture complex genetic mechanisms. In this paper, a linear discrimination and decomposition method based on MTSCCA (LDMTSCCA) is proposed to solve above problems. Specifically, LDMTSCCA first extracts disease-related genetic information using sparse linear discriminant analysis, and then employs parameter decomposition to learn multi-level expression patterns of genetic loci. To better explore intricate genetic mechanisms, LDMTSCCA jointly incorporates disease states, parameter decomposition, and network connectivity constraints into the association analysis between multilocus genetic information and multimodal neuroimaging phenotypes. The proposed method is compared with the traditional single task, multi-task, multi-view, and deep learning-based canonical correlation analysis methods in Alzheimer's Disease Neuroimaging Initiative, which shows that LDMTSCC not only achieves the highest canonical correlation coefficients but also identifies multi-level biomarkers.