Created on 13th June 2017
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.Show more
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|Krzysztof Jacek Go...||Completed||4 Jul 2017||View review|