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Modeling Inter‐Subject Variability in fMRI Activation Location: A Bayesian Hierarchical Spatial Model
Author(s) -
Xu Lei,
Johnson Timothy D.,
Nichols Thomas E.,
Nee Derek E.
Publication year - 2009
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2008.01190.x
Subject(s) - computer science , univariate , markov chain monte carlo , bayesian probability , hierarchical database model , voxel , mixture model , bayesian hierarchical modeling , artificial intelligence , multivariate statistics , bayesian inference , population , gaussian , posterior probability , pattern recognition (psychology) , machine learning , data mining , physics , demography , quantum mechanics , sociology
Summary 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.