RT Journal Article
SR Electronic
T1 Beyond Consensus: Embracing Heterogeneity in Neuroimaging Meta-Analysis
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 149567
DO 10.1101/149567
A1 Gia H. Ngo
A1 Simon B. Eickhoff
A1 Peter T. Fox
A1 R. Nathan Spreng
A1 B.T. Thomas Yeo
YR 2017
UL http://biorxiv.org/content/early/2017/06/13/149567.abstract
AB Coordinate-based meta-analysis can provide important insights into mind-brain relationships. A popular meta-analytic approach is activation likelihood estimation (ALE), which identifies brain regions consistently activated across a selected set of experiments, such as within a functional domain or mental disorder. ALE can also be utilized in meta-analytic co-activation modeling (MACM) to identify brain regions consistently co-activated with a seed region. Therefore ALE aims to find consensus across experiments, treating heterogeneity across experiments as noise. However, heterogeneity within an ALE analysis of a functional domain might indicate the presence of functional sub-domains. Similarly, heterogeneity within a MACM analysis might indicate the involvement of a seed region in multiple co-activation patterns that are dependent on task contexts. Here we demonstrate the use of the author-topic model to automatically determine if heterogeneities within ALE-type meta-analyses can be robustly explained by a small number of latent patterns. In the first application, the author-topic modeling of experiments involving self-generated thought (N = 179) revealed two cognitive components fractionating the default network. In the second application, the author-topic model revealed that the inferior frontal junction (IFJ) participated in three co-activation patterns (N = 323), which are differentially expressed depending on cognitive demands of different tasks. Overall the results suggest that the author-topic model is a flexible tool for exploring heterogeneity in ALE-type meta-analyses that might arise from functional subdomains, mental disorder subtypes or task-dependent co-activation patterns. Code and data for this study are publicly available at GITHUB_LINK_TO_BE_ADDED.