Meta Analysis of Functional Neuroimaging Data via Bayesian Spatial Point Processes

Abstract

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. Most imaging meta analysis methods only produce null hypothesis inferences and do not provide an interpretable fitted model. To overcome these limitations, we propose a Bayesian spatial hierarchical model using a marked independent cluster process. We model the foci as offspring of a latent study center process, and the study centers are in turn offspring of a latent population center process. The posterior intensity function of the population center process provides inference on the location of population centers, as well as the inter-study variability of foci about the population centers. We illustrate our model with a meta analysis consisting of 437 studies from 164 publications, show how two subpopulations of studies can be compared and assess our model via sensitivity analyses and simulation studies. Supplemental materials are available online.

Cluster-extent based thresholding in fMRI analyses: Pitfalls and recommendations

“…Such confidence intervals could be visualized and reported along with cluster-extent based results. Tests of differences in spatial location among two or more conditions can be performed using multivariate analysis of variance (MANOVA) with 3-D coordinate locations as a multivariate dependent variable and condition labels as a predictor (e.g., Johnson and Wichern, 2007; Wager et al, 2004), or with other explicitly spatial models (e.g., Kang et al 2011). …”

Multiple testing corrections, nonparametric methods, and random field theory

“…But in practice investigators wish to determine which brain regions are responsible for the predictive power, and thus we return to a spatial mapping exercise (Kriegeskorte et al, 2006). And perhaps the most promising direction, is the application of explicit spatial models to brain image data, for both original fMRI data (Keller et al, 2008;Xu et al, 2009;Weeda et al, 2009;Thirion et al, 2010;Kim et al, 2010;Gershman et al, 2011) and meta-analysis data (Neumann et al, 2008;Kang et al, 2011). These methods can provide spatial confidence intervals on effects of interest and more flexible and interpretable model fits.…”

Brain Imaging Analysis

“…As one strategy, meta-analyses are important to determine both the consistency in task-related changes in brain activity across studies and the consistency of simultaneously activated pairs or networks of brain regions (23, 33, 52, 72). Conducting studies with larger sample sizes would also help to yield more precise estimates and more powerful tests of statistical hypotheses, and such larger studies including hundreds or even thousands of subjects are beginning to emerge in the field.…”