title: Modeling inter-subject variability in FMRI activation location: a Bayesian hierarchical spatial model.
abstract: The aim of this article is to develop a spatial model for multi-subject fMRI data. There has been extensive work on univariate modeling of each voxel for single and multi-subject data, some work on spatial modeling of single-subject data, and some recent work on spatial modeling of multi-subject data. However, there has been no work on spatial models that explicitly account for inter-subject variability in activation locations. In this article, we use the idea of activation centers and model the inter-subject variability in activation locations directly. Our model is specified in a Bayesian hierarchical framework which allows us to draw inferences at all levels: the population level, the individual level, and the voxel level. We use Gaussian mixtures for the probability that an individual has a particular activation. This helps answer an important question that is not addressed by any of the previous methods: What proportion of subjects had a significant activity in a given region. Our approach incorporates the unknown number of mixture components into the model as a parameter whose posterior distribution is estimated by reversible jump Markov chain Monte Carlo. We demonstrate our method with a fMRI study of resolving proactive interference and show dramatically better precision of localization with our method relative to the standard mass-univariate method. Although we are motivated by fMRI data, this model could easily be modified to handle other types of imaging data.
authors.affiliation: Department of Biostatistics, Vanderbilt University, Nashville, Tennessee 37232, USA. lei.xu@vanderbilt.edu
authors.forename: Lei
authors.initials: L
authors.lastname: Xu
authors.forename: Timothy D
authors.initials: TD
authors.lastname: Johnson
authors.forename: Thomas E
authors.initials: TE
authors.lastname: Nichols
authors.forename: Derek E
authors.initials: DE
authors.lastname: Nee
mesh_terms: D001499:Bayes Theorem; D001699:Biometry / Q000379:methods*; D001931:Brain Mapping / Q000706:statistics & numerical data; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D008390:Markov Chains; D015233:Models, Statistical; D009010:Monte Carlo Method