This project aims to leverage the vast UK Biobank (UKB) dataset by integrating Artificial Intelligence (AI), multimodal brain imaging (fMRI, DTI, sMRI), clinical indicators, and Genome-Wide Association Study (GWAS) data. Our central research question is: How do genetic variations mediate the development and progression of complex diseases/traits by influencing brain structure and function? We will investigate a broad spectrum of conditions including cancers, various tumors, chronic pain, anxiety-depression disorders, aging-related traits, neurological disorders (e.g., Alzheimer’s, Parkinson’s), psychiatric disorders (e.g., schizophrenia, bipolar disorder), and cardiovascular and cerebrovascular diseases, along with their associated risk factors.
Our primary objectives are: 1) To identify novel genetic associations and their corresponding brain imaging phenotypes for these complex conditions. 2) To elucidate the causal pathways through which genetic variants influence brain structure and function, thereby contributing to disease risk and progression. 3) To develop robust AI-driven predictive models for disease incidence and patient subtyping based on integrated genetic, imaging, and clinical data. This research is scientifically rational as it addresses the persistent challenge of “missing heritability” in complex diseases, providing a comprehensive, data-driven approach to unraveling intricate gene-brain-disease relationships. By leveraging UKB’s unprecedented scale and depth, we aim to uncover previously unrecognized biological insights.
