Hierarchical Spatial Bayesian Model for Functional MRI
Speaker: Thomas Nichols , University of Warwick, UK
Date: May 6 2010
Time: 3:00PM to 4:00PM
Location: 32-D507
Host: Polina Golland, CSAIL
Contact: Polina Golland, x38005, polina@csail.mit.edu
Relevant URL: There are a rich collection of tools available for making inference
for fMRI data, but most are based on the 'Mass Univariate' approach
where a univariate models are individually fit at each voxel. A
critical shortcoming of these methods is that they cannot explicitly
model the spatial structure of fMRI signals. For multi-subject fMRI
analyses, I argue this is particularly crucial, since even after atlas
warping there is considerable spatial variability in activation
location over subjects. I will present a hierarchical spatial model
for multi-subject fMRI analyses, where latent population- and
individual centres fit the anticipated focal signals. The model uses
priors for identifiability and full posterior sampling to provide
inference on a variety of measures of interest unavailable in a
mass-univariate framework, including population prevalence of
activation and inter-subject spread of activation about population
centres. I show evaluations of the model with simulations and
demonstrate it with real data. Time permitting, I will also show how
this framework generalizes to other settings, including 'spatial
meta-analyses' and lesion modelling in Multiple Sclerosis.
Joint work with Tim Johnson, University of Michigan, and Lei Xu, Vanderbilt University.
See other events that are part of Biomedical Imaging and Analysis 2009/2010
See other events happening in May 2010
