Speaker: Assistant Professor Franco Pestilli, Department of Psychological & Brain Sciences, Indiana University
Title: A sparse tensor decomposition method for approximation of linear models of diffusion-weighted MRI and tractography
Abstract: Recently, linear formulations and convex optimization methods have been proposed to predict diffusion-weighted Magnetic Resonance Imaging (dMRI) data given estimates of brain connections generated using tractography algorithms. The size of the linear models comprising such methods grows with both, dMRI data and connectome resolution, and can become very large for application to modern data. In this paper, we introduce a method to predict dMRI signals for potentially very large connectomes, i.e. composed by hundred of thousand to millions of fascicles (bundles of neuronal axons), by using a sparse tensor decomposition. We show that this tensor decomposition accurately approximates the Linear Fascicle Evaluation (LiFE) model, one of the recently developed linear models. We provide a theoretical analysis of the accuracy of the sparse decomposed model, LiFESD, and demonstrate that it can reduce the size of the model significantly. Also, we develop algorithms to implement the optimization solver using the tensor representation in an efficient way.