A functional data approach to identifying Alzheimer's disease from multimodal cortical surface data
In this talk, we introduce a novel statistical framework for the classification of multimodal cortical surface data. The motivating application is the identification of subjects with Alzheimer's disease from their cortical surface geometry and associated cortical thickness map. The model proposed is based upon a reformulation of the image classification problem as a regularized multivariate functional linear regression model. This allows us to adopt a direct approach to the estimation of the most discriminant direction while controlling for its complexity with an appropriate geometric regularizer.
We apply the proposed method to a pooled dataset from the Alzheimer's Disease Neuroimaging Initiative and the Parkinson's Progression Markers Initiative, and are able to estimate discriminant directions that capture both cortical geometric and thickness predictive features of Alzheimer's disease, which are consistent with the existing neuroscience literature.