University of Edinburgh
School of Mathematics and BioSS
Date: Friday 6th October, 15:05 Location: JCMB 5323
Speaker: Per Siden, Department of Computer and Information Science, Linkopings Universitet, Sweden
Title: Fast and Scalable Bayesian Spatial 3D Priors for Brain Imaging
Abstract: Spatial whole-brain Bayesian modeling of task-related functional magnetic resonance imaging (fMRI) is a serious computational challenge. The data are naturally 4D, consisting of 3D brain images measured over time, each having hundreds of thousands of data points.
We consider Gaussian Markov random field (GMRF) priors which have been successfully
used in many large scale spatial problems, thanks to the sparsity induced in the precision
matrices. However, most commonly used inference methods rely on the sparse Cholesky
factorization, which is not feasible for problems of very large size. Here, we instead develop
fast and scalable inference algorithms utilizing among other preconditioned conjugate gradient
methods, which are used both for sampling (MCMC) and stochastic optimization (VB
and MAP). The methods are applied to fMRI data using a model which also has a non-trivial
temporal component, and show to be both faster and more accurate than previous methods.
This seminar is a part of Maxwell Institute seminar series.
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