Open Access
September 2014 A Bayesian hierarchical spatial point process model for multi-type neuroimaging meta-analysis
Jian Kang, Thomas E. Nichols, Tor D. Wager, Timothy D. Johnson
Ann. Appl. Stat. 8(3): 1800-1824 (September 2014). DOI: 10.1214/14-AOAS757


Neuroimaging meta-analysis is an important tool for finding consistent effects over studies that each usually have 20 or fewer subjects. Interest in meta-analysis in brain mapping is also driven by a recent focus on so-called “reverse inference”: where as traditional “forward inference” identifies the regions of the brain involved in a task, a reverse inference identifies the cognitive processes that a task engages. Such reverse inferences, however, require a set of meta-analysis, one for each possible cognitive domain. However, existing methods for neuroimaging meta-analysis have significant limitations. Commonly used methods for neuroimaging meta-analysis are not model based, do not provide interpretable parameter estimates, and only produce null hypothesis inferences; further, they are generally designed for a single group of studies and cannot produce reverse inferences. In this work we address these limitations by adopting a nonparametric Bayesian approach for meta-analysis data from multiple classes or types of studies. In particular, foci from each type of study are modeled as a cluster process driven by a random intensity function that is modeled as a kernel convolution of a gamma random field. The type-specific gamma random fields are linked and modeled as a realization of a common gamma random field, shared by all types, that induces correlation between study types and mimics the behavior of a univariate mixed effects model. We illustrate our model on simulation studies and a meta-analysis of five emotions from 219 studies and check model fit by a posterior predictive assessment. In addition, we implement reverse inference by using the model to predict study type from a newly presented study. We evaluate this predictive performance via leave-one-out cross-validation that is efficiently implemented using importance sampling techniques.


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Jian Kang. Thomas E. Nichols. Tor D. Wager. Timothy D. Johnson. "A Bayesian hierarchical spatial point process model for multi-type neuroimaging meta-analysis." Ann. Appl. Stat. 8 (3) 1800 - 1824, September 2014.


Published: September 2014
First available in Project Euclid: 23 October 2014

zbMATH: 1304.62133
MathSciNet: MR3271354
Digital Object Identifier: 10.1214/14-AOAS757

Keywords: Bayesian spatial point processes , ‎classification‎ , hierarchical model , neuorimage meta-analysis , random intensity measure

Rights: Copyright © 2014 Institute of Mathematical Statistics

Vol.8 • No. 3 • September 2014
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