Assessing gene effects on the brain and risk for disease using machine learning

The advent of neuroimaging has provided an invaluable tool for investigating brain disorders with quantitative measurements. Neuroimaging-derived measures can not only serve as biomarkers to track the progression of brain disorders like Alzheimer's disease, but also provide quantitative, intermediate phenotypes or endophenotypes close to the biology of disease, which can be utilized for finding new genes in association with brain pathology. The latter has led to the creation of a new and expanding field called neuroimaging genetics. Most methodologies for the application of neuroimaging and other biomarkers to disease diagnosis and clinical trial design utilize only single biomarkers. Similarly, in neuroimaging genetics studies, genetic variants are typically considered one by one, in association with neuroimaging phenotypes. My dissertation introduces new automated, machine learning and multivariate approaches, which potentially offer more power to biomarker-based diagnosis and clinical trial design as well as discovery and risk prediction in neuroimaging genetics.