Fixed Versus Random Effects Models for fMRI Meta-Analysis

• Activity Number: 604
• Type: Contributed
• Date/Time: Wednesday, August 12, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Statistics in Imaging
• Author(s): Han Bossier* and Beatrijs Moerkerke and Ruth Seurinck
• Companies: and Ghent University and Ghent University
• Keywords: fMRI ; meta-analysis ; group analysis ; false positive rate ; power ; accuracy

Abstract

Meta-analyses for brain imaging are gaining attention given the increasing amount of fMRI studies and the need for synthesis and integration of data across studies. Standard meta-analyses are not well adapted to summarize peaks of brain activation. In this paper we look at the performance of current meta-analysis methods and investigate the effect of pooling subjects at the study level on the outcome. We do this by combining a fixed, ordinary least squares versus mixed effects pooling method with (1) a vote-counting procedure, (2) a fixed- and (3) a random effects meta-analysis. The fMRI data consists of 300 subjects. We split the group in 2, using a group analysis of 150 subjects as a benchmark. The other group of 150 subjects is divided into 10 smaller studies. Each result of a meta-analysis is overlaid on the group-analysis to calculate the false positive rate, power and overlap (i.e. spatial accuracy). Results show the most beneficial effects when pooling subjects through a mixed effects analysis regardless of the meta-analysis. Unfortunately, the highest observed power of any meta-analysis does not exceed 50%, which indicates challenges in reproducing fMRI results.