Statistical Learning from Multimodal Genetic and Neuroimaging data for prediction of Alzheimer's Disease

Alzheimer's Disease (AD) is nowadays the main cause of dementia in the world. It provokes memory and behavioural troubles in elderly people. The early diagnosis of Alzheimer's Disease is an active topic of research. Three different types of data play a major role when it comes to its diagnosis: clinical tests, neuroimaging and genetics. The two first data bring informations concerning the patient's current state. On the contrary, genetic data help to identify whether a patient could develop AD in the future. Furthermore, during the past decade, researchers have created longitudinal dataset on A and important advances for processing and analyse of complex and high-dimensional data have been made. The first contribution of this thesis will be to study how to combine different modalities in order to increase their predictive power in the context of classification. We will focus on hierarchical models that capture potential interactions between modalities. Moreover, we will adequately modelled the structure of each modality (genomic structure, spatial structure for brain images), through the use of adapted penalties such as the ridge penalty for images and the group lasso penalty for genetic data. The second contribution of this thesis will be to explore models for predict the conversion date to Alzheimer's Disease for mild cognitive impairment subjects. Such problematic has been enhanced by the TADPOLE challenge. We will use the framework provided by survival analysis. Starting from basic models such as the Cox proportional hasard model, the additive Aalen model, and the log-logistic model, we will develop other survival models for combining different modalities, such as a multilevel log-logistic model or a multilevel Cox model.