A Nonparametric Bayesian Spatial Point Process Approach to Neuroimaging Meta Analysis

  • Date: 04/05/2012
  • Time: 15:30
Timothy D. Johnson (U. Michigan)

University of Victoria


A Nonparametric Bayesian Spatial Point Process Approach to Neuroimaging Meta Analysis




As the discipline of functional neuroimaging grows there is an
increasing interest in meta analysis of brain imaging studies. A typical
neuroimaging meta analysis collects peak activation coordinates (foci)
from several studies and identifies areas of consistent activation. In
our illustrative example, our colleagues collected 219 emotion studies
consisting of five distinct emotion types: sad, happy, anger, fear,
disgust. One psychological theory states that all emotions utilize the
same functional brain regions, to varying degrees. Thus, the expected
number of foci in these regions, across emotion types, should be
correlated. To date, all imaging meta analysis methods have been
developed to analyze a single population of studies. Furthermore, most
imaging meta analysis methods do not provide an interpretable fitted
model. To overcome these limitations, we propose a nonparametric
Bayesian spatial point process model that generalizes the Poisson/gamma
random field model (Wolpert and Ickstadt, 1998) in a hierarchical
fashion. Our model simultaneously fits multi-type point pattern data,
accounts for the (positive) correlation across the various types of
point patterns and results in an easily interpretable posterior
intensity function. Furthermore, our model can be used to accurately
predict the emotion type from a new study---something of great interest
to our collaborators.

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Location: SSM A104


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