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.

The Coordinate-Based Meta-Analysis of Neuroimaging Data

“…5, top and middle row). This finding is consistent with previous analyses of the same dataset (Kober et al, 2008; Kang et al, 2011; Yue, Lindquist and Loh, 2012; Kang et al, 2014) as well as results of previous studies (Phelps and LeDoux, 2005; Costafreda et al, 2008). Other regions with moderately high values are the right and left cerebral cortex (Fig.…”

“…Early works mainly utilised exploratory data analysis and visualisation techniques to blend the results from different studies (Fox, Parsons and Lancaster, 1998) and it was not until the early 2000’s that the first methods for CBMA were proposed (Fox et al, 1997; Turkeltaub et al, 2002; Nielsen and Hansen, 2002; Wager et al, 2003). Since then, many new methods and modifications appeared in the neuroimaging (Laird et al, 2005; Wager, Lindquist and Kaplan, 2007; Radua and Mataix-Cols, 2009; Turkeltaub et al, 2012; Caspers et al, 2014, to name a few) as well as the statistics (Kang et al, 2011; Yue, Lindquist and Loh, 2012; Kang et al, 2014; Montagna et al, 2017) literature.…”

“…Unfortunately the literature on model based methods is still very limited thus our review will be almost exhaustive. In particular, we will outline the Bayesian hierarchical cluster process model of Kang et al (2011, BHICP), the spatial binary regression model of Yue, Lindquist and Loh (2012), the hierarchical Poisson/Gamma random field model of Kang et al (2014), and the spatial Bayesian latent factor regression model of Montagna et al (2017). In all cases analyses are performed under the Bayesian paradigm and thus inferences are based on posterior distributions for each model’s parameters.…”

“…Yet, the methods described previously are for a single homogeneous group of studies. Kang et al (2014) propose a model that models each type of foci separately, allowing simultaneously for dependence between the J different types.…”

“…The dataset we consider here was previously analysed by Kober et al (2008), Kang et al (2011), Yue, Lindquist and Loh (2012) and Kang et al (2014), and is now publicly available 6 . Our goal here is not to extend the analyses in these papers, but rather to facilitate a comparison among the methods described in Section 3 and, more generally, show how CBMA can be used to identify areas of consistent brain activation.…”