Coordinate based meta-analysis of networks in neuroimaging studies

Abstract

Meta-analysis of published neuroimaging results is commonly performed using coordinate based meta-analysis (CBMA). Most commonly CBMA algorithms detect spatial clustering of reported coordinates across multiple studies by assuming that results relating to the common hypothesis fall in similar anatomical locations. The null hypothesis is that studies report uncorrelated results, which is simulated by random coordinates. It is assumed that multiple clusters are independent yet it is likely that multiple results reported per study are not, and in fact represent a network effect. Here the multiple reported effect sizes (reported peak Z scores) are assumed multivariate normal, and maximum likelihood used to estimate the parameters of the covariance matrix. The hypothesis is that the effect sizes are correlated. The parameters are covariance of effect size, considered as edges of a network, while clusters are considered as nodes. In this way coordinate based meta-analysis of networks (CBMAN) estimates a network of reported meta-effects, rather than multiple independent effects (clusters).\n\nCBMAN uses only the same data as CBMA, yet produces extra information in terms of the correlation between clusters. Here it is validated on numerically simulated data, and demonstrated on real data used previously to demonstrate CBMA. The CBMA and CBMAN clusters are similar, despite the very different hypothesis.