title: A BAYESIAN HIERARCHICAL SPATIAL POINT PROCESS MODEL FOR MULTI-TYPE NEUROIMAGING META-ANALYSIS.
abstract: 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, requires 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 non-parametric 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.
authors.affiliation: Department of Biostatistics and Bioinformatics, Department of Radiology and Imaging Sciences, Emory University, Atlanta, GA, 30322, jkang30@emory.edu.
authors.forename: Jian
authors.initials: J
authors.lastname: Kang
authors.affiliation: Department of Statistics & Warwick Manufacturing Group, University of Warwick, Coventry CV47 7AL, UK, t.e.nichols@warwick.ac.uk.
authors.forename: Thomas E
authors.initials: TE
authors.lastname: Nichols
authors.affiliation: Department of Psychology and Neuroscience, University of Colorado, Boulder, CO 80309, tor.wager@colorado.edu.
authors.forename: Tor D
authors.initials: TD
authors.lastname: Wager
authors.affiliation: Department of Biostatistics, University of Michigan, Ann Arbor, MI 48104, tdjtdj@umich.edu.
authors.forename: Timothy D
authors.initials: TD
authors.lastname: Johnson
keywords: Bayesian Spatial Point Processes; Classification; Hierarchical model; Neuorimage meta-analysis; Random Intensity Measure