Meta-analysis of functional neuroimaging data: current and future directions

Abstract

Meta-analysis is an increasingly popular and valuable tool for summarizing results across many neuroimaging studies. It can be used to establish consensus on the locations of functional regions, test hypotheses developed from patient and animal studies and develop new hypotheses on structure–function correspondence. It is particularly valuable in neuroimaging because most studies do not adequately correct for multiple comparisons; based on statistical thresholds used, we estimate that roughly 10–20% of reported activations in published studies are false positives. In this article, we briefly summarize some of the most popular meta-analytic approaches and their limitations, and we outline a revised multilevel approach with increased validity for establishing consistency across studies. We also discuss multivariate methods by which meta-analysis can be used to develop and test hypotheses about co-activity of brain regions. Finally, we argue that meta-analyses can make a uniquely valuable contribution to predicting psychological states from patterns of brain activity, and we briefly discuss some methods for making such predictions.