A PDE-regularized smoothing method for space-time data over manifolds with application to medical data

We propose an innovative statistical-numerical method to model spatio- temporal data, observed over a generic two-dimensional Riemanian manifold. The proposed approach consists of a regression model completed with a regu- larizing term based on the heat equation. The model is discretized through a finite element scheme set on the manifold, and solved by resorting to a fixed point-based iterative algorithm. This choice leads to a procedure which is highly efficient when compared with a monolithic approach, and which allows us to deal with massive datasets. After a preliminary assessment on simulation study cases, we investigate the performance of the new estimation tool in prac- tical contexts, by dealing with neuroimaging and hemodynamic data.