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000863645 1001_ $$0P:(DE-HGF)0$$aNgo, Gia H.$$b0
000863645 245__ $$aBeyond consensus: Embracing heterogeneity in curated neuroimaging meta-analysis
000863645 260__ $$aOrlando, Fla.$$bAcademic Press$$c2019
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000863645 520__ $$aCoordinate-based meta-analysis can provide important insights into mind-brain relationships. A popular approach for curated small-scale meta-analysis 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 cognitive components fractionating the default network. In the second application, the author-topic model revealed that the left inferior frontal junction (IFJ) participated in multiple task-dependent co-activation patterns (N = 323). Furthermore, the author-topic model estimates compared favorably with spatial independent component analysis in both simulation and real data. 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 sub-domains, mental disorder subtypes or task-dependent co-activation patterns. Code for this study is publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/meta-analysis/Ngo2019_AuthorTopic).
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000863645 7001_ $$0P:(DE-Juel1)131678$$aEickhoff, Simon B.$$b1$$ufzj
000863645 7001_ $$0P:(DE-HGF)0$$aNguyen, Minh$$b2
000863645 7001_ $$0P:(DE-HGF)0$$aSevinc, Gunes$$b3
000863645 7001_ $$0P:(DE-HGF)0$$aFox, Peter T.$$b4
000863645 7001_ $$0P:(DE-HGF)0$$aSpreng, R. Nathan$$b5
000863645 7001_ $$0P:(DE-HGF)0$$aYeo, B. T. Thomas$$b6$$eCorresponding author
000863645 773__ $$0PERI:(DE-600)1471418-8$$a10.1016/j.neuroimage.2019.06.037$$gVol. 200, p. 142 - 158$$p142 - 158$$tNeuroImage$$v200$$x1053-8119$$y2019
000863645 8564_ $$uhttps://juser.fz-juelich.de/record/863645/files/Ngo19.pdf$$yPublished on 2019-06-20. Available in OpenAccess from 2020-06-20.
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