A Bayesian hierarchical spatial point process model for multi-type neuroimaging meta-analysis

• Published in: arXiv.org
• Main Authors: Kang, Jian, Nichols, Thomas E, Wager, Tor D, Johnson, Timothy D
• Format: Paper Journal Article
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 04.12.2014
• Subjects: Bayesian analysis; Brain; Computer simulation; Convolution; Correlation analysis; Fields (mathematics); Importance sampling; Inference; Mapping; Mathematical models; Medical imaging; Meta-analysis; Neurology; Null hypothesis; Parameter estimation; Performance prediction; Statistics - Applications; Studies
• ISSN: 2331-8422
• DOI: 10.48550/arxiv.1412.1670

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.